Script - Data Engineer-Analyst.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of Contents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a href='#Business Case Study Introduction'> Introduction</a>\n",
    "\n",
    "# <a href='#I) Part 1 - Date Dimension'> I) Date Dimension</a>\n",
    "\n",
    "# <a href='#II) Part 2 - Data Quality'> II) Data Quality</a>\n",
    "\n",
    "# <a href='#A) Duplicates'> A) Duplicates</a>\n",
    "\n",
    "# <a href='#B) Revision Date Field in Opportunity Table'> B) Revision Date Field in Opportunity Table</a>\n",
    "\n",
    "# <a href='#C) Revision Opportunity Stage Status'> C) Revision Opportunity Stage Status</a>\n",
    "\n",
    "# <a href='#D) Revision Opportunity Type'> D) Revision Opportunity Type</a>\n",
    "\n",
    "# <a href='#III) Part 3 - All hands meeting preparation'> III) All hands meeting preparation</a>\n",
    "\n",
    "# <a href='#A) Chief Marketing Officer'> A) Chief Marketing Officer</a>\n",
    "\n",
    "# <a href='#B) VP Sales'> B) VP Sales</a>\n",
    "\n",
    "# <a href='#C) Head of Finance'> C) Head of Finance</a>\n",
    "\n",
    "# <a href='#D) CEO'> D) CEO</a>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='Business Case Study Introduction'> Introduction</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Case Study - Helping Data Analyts to get clean and structured data\n",
    "\n",
    "## Context\n",
    "\n",
    "You are a Data Engineer/Analyst working in the Finance & Operations team of SaaS Software Company : BSE Inc. \n",
    "\n",
    "BSE is the one of leader in the Data market thanks to its amazing Business Intelligence tool.\n",
    "\n",
    "There two recurring tasks data team perform that are important for the company: \n",
    "\n",
    "Each month during the All-hands meeting, the CEO present in front of all BSE employees the key figures of the sales & marketing performance but also the finance report. \n",
    "The first task is collaborate with Data Analysts to help them collect and gather the right data to answer important business questions.\n",
    "The second indeed is to work with them to analyze data and answer to business leaders question\n",
    "\n",
    "\n",
    "## Your mission\n",
    "\n",
    "In the data team, to help them answering business questions, many data analysts are requesting for more data inside the data warehouse. \n",
    "They want to have access to tables with all dates and they want to be able to identify weekends and public holidays.\n",
    "\n",
    "As a Data Engineer, we know we can help them using python datetime librairy and the [OpenHolidaysAPI](https://www.openholidaysapi.org/en/).\n",
    "\n",
    "The first part of your mission is to help them creating this date dimension and enrich it with external data. You will first create a POC (proof of concept) to validate it's possible to build this kind of table and enrich the data.\n",
    "\n",
    "During the second part of your mission you will ingest CRM data in the data warehouse and use this data to anwser business questions\n",
    "\n",
    "\n",
    "### Your TO-DO list\n",
    "\n",
    "- ⚠️ ALL Ingestion related tasks should be done using Python\n",
    "\n",
    "- ⚠️ ALL Transformation/modeling related tasks should be done using SQL (using dbt is recommended but not required)\n",
    "\n",
    "- Instead of a real data warehouse for this POC we will use DuckDB which is a more lightweight database.\n",
    "\n",
    "- You can use empty cells below to code and document your work\n",
    "\n",
    "- Please provide when you are done a zip with the copy of your notebook with your answers and your duckdb database [export](https://duckdb.org/docs/sql/statements/export.html) in `sales.db`\n",
    "\n",
    "\n",
    "### Part 1 - Date Dimension\n",
    "1. Extract and ingest public holidays in France (french language) from 2020 to 2025 from API [OpenHolidaysAPI](https://www.openholidaysapi.org/en/) in a table `dim_date_public_holidays` \n",
    "\n",
    "2. Build the date dimension and ingest it into the data warehouse with alias `date_dim`. The date dimension contains each each day from 2020 to 2025. \n",
    "    \n",
    "3. In addition of this day column, we can transform the date dim with following columns :\n",
    "    - month_number: number of the month\n",
    "    - date_month: date formatted \"YYYY-MM\"\n",
    "    - date_quarter: date formatted \"YYYY-QX\" where X is the number of the quarter\n",
    "    - date_year:  date formatted \"YYYY\"\n",
    "    - is_weekend: set as True if day is a week-end day (saturday, sunday) or False if not\n",
    "\n",
    "\n",
    "4. Transform and enrich `date_dim` table by adding `is_public_holidays` boolean column which is set as True when the day is included in public holidays period and False if not\n",
    "\n",
    "### Part 2 - All hands meeting preparation\n",
    "\n",
    "1. Ingest data stored in csv files in `datasets/sales/` in tables inside data warehouse\n",
    "2. Use the prepared output tables to anwser Business Leader requests.\n",
    "\n",
    "__Example of answer__\n",
    "\n",
    "Which are the top 5 industries counting the most open opportunities in created in 2023 ?\n",
    "\n",
    "![example](images/ex_of_sql_answer.png)\n",
    "\n",
    "---\n",
    "\n",
    "## CRM - Data Dictionnary\n",
    "\n",
    "### Accounts (`./datasets/sales/accounts - crm export.csv`)\n",
    "\n",
    "_List of our Customers or Prospects companies_\n",
    "\n",
    "__Columns__:\n",
    "\n",
    "- `Id` : Identifier of the company\n",
    "- `Name` : Name of the company\n",
    "- `Industry` : Industry of the company\n",
    "\n",
    "### Contacts (`./datasets/sales/contacts - crm export.csv`)\n",
    "\n",
    "_List of our Customers or Prospects contacts_\n",
    "\n",
    "__Columns__:\n",
    "\n",
    "- `id` : Identifier of the contact\n",
    "- `accountid` : Identifier of the company where the contact is working \n",
    "- `fullname` : fullname of the contact\n",
    "- `job_title`: job_title of the contact\n",
    "\n",
    "### Opportunities (`./datasets/sales/opportunities - crm export.csv`)\n",
    "\n",
    "_Main object of our CRM. An opportunity is a source of potential Software and Services revenue. The goal of each Sale is to close and won all of them_ \n",
    "\n",
    "- `id`: Identifier of the opportunity\n",
    "- `accountid`: Identifier of the account associated to the opportunity\n",
    "- `type`: type of the opportunity\n",
    "    - `New Business`: opportunity generated with a new account prospect ou customer\n",
    "    - `Upsell`:  when we sell additional products within an existing opportunity.\n",
    "    - `Cross-sell`: opportunity generated with a new entity of an existing accountcustomer\n",
    "    - `Renew`: renewal of an existing opportunity\n",
    "- `deal_license_activation_start_date__c`: Start date of the software licence souscription.\n",
    "- `deal_license_activation_end_date__c`: End date of the software licence souscription.\n",
    "- `closedate`: Date of closing of the opportunity. If the opportunity is won it's a close won date, if the opportunity is lost it's a close lost date and if the opportunity is open it's an expected close date.\n",
    "- `createddate`: Opportunity creation date \n",
    "- `opportunity_stage_status__c`: Status of the Opportunity within the Sales process.\n",
    "    - `Open` : Sales is still working on the Opportunity\n",
    "    - `Won`: Sales won the opportunity\n",
    "    - `Lost`: Sales lost the opportunity\n",
    "- `leadsource_category__c`: Source of the opportunity, who found it\n",
    "    - `Marketing`: Opportunity founded/generated by Marketing team\n",
    "    - `Sales Generated`: Opportunity founded/generated by the Sales team\n",
    "    - `Partner Generated`: Opportunity founded/generated by Partners\n",
    "    - `SDR Generated`:Opportunity founded/generated by the SDR team\n",
    "- `deal_total_non_recurring__c`: Amount of services revenues\n",
    "- `deal_total_recurring__c`: Amount of software revenues\n",
    "- `currencyisocode`: Currency used for Services, Software revenue.\n",
    "- `name`: Name of the opportunity\n",
    "- `ownerid`: Identifier of the BSE sales user who is working on the opportunity\n",
    "\n",
    "### Opportunities_contact_roles (`./datasets/sales/opportunities_contact_roles - crm export.csv`)\n",
    "\n",
    "_List of contacts associated to oppotunities._\n",
    "\n",
    "__Columns__:\n",
    "\n",
    "- `id` : Identifier of the relation\n",
    "- `contactid` : Identifier of the contact\n",
    "- `opportunityid`: Identifier of the opportunity\n",
    "- `role`: Opportunity project role\n",
    "\n",
    "### Users (`./datasets/sales/crm_users - crm export.csv`)\n",
    "\n",
    "_List of all BSE Sales_\n",
    "\n",
    "__Columns__:\n",
    "\n",
    "- `id` : Identifier of the sales user\n",
    "- `team_country__c` : Country of the user \n",
    "- `fullname` : User fullname\n",
    "- `team_name`: Team of the user \n",
    "\n",
    "\n",
    "---\n",
    "## Business Leader requests\n",
    "\n",
    "\n",
    "### Chief Marketing Officer\n",
    "\n",
    "⚠️ All Software and Services revenue figures should be diplayed in Euros (1 EUR = 1,1 USD)\n",
    "\n",
    "1. Which account industry count the most closed won opportunities in created in 2020 ?\n",
    "\n",
    "2. Which account industry count the most lost opportunities created in 2020 ?\n",
    "\n",
    "\n",
    "\n",
    "### VP Sales\n",
    "\n",
    "For open opportunities created since 2020:\n",
    "\n",
    "1. How many upsell, cross-sell opportunities we expect to close in the 2nd quarter of 2021 (March 21 > June 21 ) ?\n",
    "\n",
    "2. How many renewals we expect to close in the last quarter of 2021 (sep 21 > dec 21 ) ?\n",
    "\n",
    "For close won opportunities in 2020:\n",
    "\n",
    "3. Based on the Software revenue, who are the top 5 Sales of the year ? \n",
    "\n",
    "\n",
    "### Head of Finance\n",
    "\n",
    "For opportunities created since 2018 :\n",
    "\n",
    "1. Based on the Software Revenue, which is our top account customer ?\n",
    "\n",
    "\n",
    "For won opportunities with a souscription period running in 2020:\n",
    "\n",
    "⚠️ All Software and Services revenue figures should be diplayed in Euros (1 EUR = 1,1 USD)\n",
    "\n",
    "2. What is the Total ARR ?\n",
    "\n",
    "```\n",
    "\n",
    "ARR = MRR * 12\n",
    "MRR = RECURRING REVENUE / ENGAGEMENT PERIOD\n",
    "ENGAGEMENT PERIOD (in months) = SOUSCRIPTION END DATE - SOUSCRIPTION START DATE\n",
    "\n",
    "```\n",
    "\n",
    "3. Who are our top 5 account customers based on the __software revenue__ of closed won opportunities in 2020 ?\n",
    "\n",
    "### CEO\n",
    "\n",
    "1. What is the average duration in month of our sales cycle in 2020 ?\n",
    "\n",
    "2. What is the average duration in month of our optimized sales cycle in 2020 ?\n",
    "\n",
    "\n",
    "```\n",
    "Sales cycle duration = difference between created date and close date for opportunities which are closed lost and closed won\n",
    "\n",
    "Sales cycle duration optimized = Sales cycle duration calculation but only for working days (days not included in weekends and public holidays)\n",
    "```\n",
    "\n",
    "---\n",
    "\n",
    "Your  work can start at the next cell below.\n",
    "\n",
    "Good luck, our data team and CEO need us !!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import duckdb\n",
    "import requests as req\n",
    "from IPython.display import display, HTML\n",
    "from datetime import datetime, date\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.rcParams.update({'font.size': 22})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we create the DuckDB sales database\n",
    "con = duckdb.connect(\"sales.db\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='I) Part 1 - Date Dimension'> I) Date Dimension</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Extract and ingest public holidays in France (french language) from 2020 to 2025 from API [OpenHolidaysAPI](https://www.openholidaysapi.org/en/) in a table `dim_date_public_holidays` \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = req.Session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020\n",
      "2021\n",
      "2022\n",
      "2023\n",
      "2024\n",
      "2025\n"
     ]
    }
   ],
   "source": [
    "country_code = 'FR'\n",
    "language_iso_code = 'FR'\n",
    "year_counter = 0\n",
    "\n",
    "for year in [2020, 2021, 2022, 2023, 2024, 2025]:\n",
    "    \n",
    "    print(year)\n",
    "    \n",
    "    first_day_of_year = str(date(year, 1, 1))\n",
    "    last_day_of_year = str(date(year, 12, 31))\n",
    "    \n",
    "    api_url = 'https://openholidaysapi.org/PublicHolidays?countryIsoCode={country}&languageIsoCode={language}&validFrom={start_date}&validTo={end_date}'.format(country=country_code, language = language_iso_code, start_date = first_day_of_year, end_date = last_day_of_year)\n",
    "    \n",
    "    json_data_public_holidays_fr = s.get(api_url).json()\n",
    "    \n",
    "    holiday_counter = 0\n",
    "    \n",
    "    for holiday in json_data_public_holidays_fr:\n",
    "        \n",
    "        \n",
    "        holiday_data_parsed = pd.DataFrame({'start_date': holiday['startDate'], 'end_date': holiday['endDate'], 'holiday_type': holiday['type'], 'name': holiday['name'][0]['text'], 'nationwide': holiday['nationwide']}, index = [0])\n",
    "        \n",
    "        #print(holiday_data_parsed)\n",
    "        \n",
    "        if holiday_counter == 0:\n",
    "            \n",
    "            holiday_counter += 1\n",
    "            \n",
    "            holiday_full_data_parsed = holiday_data_parsed.copy()\n",
    "            \n",
    "        else:\n",
    "            \n",
    "            holiday_full_data_parsed = pd.concat([holiday_full_data_parsed, holiday_data_parsed.copy()])\n",
    "        \n",
    "        #print(holiday_full_data_parsed)\n",
    "        \n",
    "    holiday_full_data_parsed['country'] = country_code\n",
    "    holiday_full_data_parsed['language'] = language_iso_code\n",
    "    holiday_full_data_parsed['year'] = year\n",
    "    \n",
    "    if year_counter == 0:\n",
    "        \n",
    "        year_counter += 1\n",
    "        \n",
    "        holiday_full_data_parsed_all_years = holiday_full_data_parsed.copy()\n",
    "        \n",
    "    else:\n",
    "        \n",
    "        holiday_full_data_parsed_all_years = pd.concat([holiday_full_data_parsed_all_years, holiday_full_data_parsed.copy()])\n",
    "        \n",
    "        \n",
    "    \n",
    "    #print(json_data_public_holidays_fr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's create the table dim_date_public_holidays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "duckdb.sql('CREATE OR REPLACE TABLE dim_date_public_holidays AS (SELECT * FROM holiday_full_data_parsed_all_years)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Build the date dimension and ingest it into the data warehouse with alias `date_dim`. The date dimension contains each  day from 2020 to 2025. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dates_list = list(pd.date_range(start=\"2014-01-01\",end=\"2025-12-31\"))\n",
    "\n",
    "dates_list = [str(date)[:10] for date in dates_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates_data = pd.DataFrame(dates_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates_data = dates_data.rename(columns = {0: 'ymd'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "duckdb.sql('CREATE OR REPLACE TABLE date_dim AS (SELECT * FROM dates_data)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. In addition of this day column, we can transform the date dim with following columns :\n",
    "    - month_number: number of the month\n",
    "    - date_month: date formatted \"YYYY-MM\"\n",
    "    - date_quarter: date formatted \"YYYY-QX\" where X is the number of the quarter\n",
    "    - date_year:  date formatted \"YYYY\"\n",
    "    - is_weekend: set as True if day is a week-end day (saturday, sunday) or False if not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "third_question_date_query = '''\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "    ymd,\n",
    "    \n",
    "    MONTH(CAST(ymd AS DATE)) AS month_number,\n",
    "\n",
    "    CAST(DATE_TRUNC('month', CAST(ymd AS DATE)) AS VARCHAR) AS date_month,\n",
    "    \n",
    "    YEAR(CAST(ymd AS DATE)) || '-Q' || QUARTER(CAST(ymd AS DATE)) AS date_quarter,\n",
    "   \n",
    "    CAST(YEAR(CAST(ymd AS DATE)) AS VARCHAR) AS date_year,\n",
    "    \n",
    "    CASE\n",
    "    WHEN DATE_PART('dayofweek', CAST(ymd AS DATE)) IN (0,6) THEN TRUE\n",
    "    ELSE FALSE\n",
    "    END AS is_weekend\n",
    "\n",
    "FROM date_dim\n",
    "\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "updated_dim_date_data = duckdb.sql(third_question_date_query).df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "duckdb.sql('DROP TABLE date_dim')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "duckdb.sql('CREATE TABLE date_dim AS (SELECT * FROM updated_dim_date_data)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Transform and enrich `date_dim` table by adding `is_public_holidays` boolean column which is set as True when the day is included in public holidays period and False if not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "duckdb.sql('ALTER TABLE date_dim ADD is_public_holidays BOOLEAN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "fourth_question_date_query = '''\n",
    "\n",
    "UPDATE date_dim AS date_dim_original\n",
    "\n",
    "SET is_public_holidays = (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    CASE\n",
    "    WHEN a.ymd BETWEEN b.start_date AND b.end_date AND holiday_type = 'Public' THEN TRUE\n",
    "    ELSE FALSE\n",
    "    END AS is_public_holidays\n",
    "\n",
    "\n",
    "FROM date_dim a\n",
    "\n",
    "LEFT JOIN dim_date_public_holidays b ON a.ymd >= b.start_date AND a.ymd <= b.end_date\n",
    "\n",
    "WHERE date_dim_original.ymd = a.ymd\n",
    "\n",
    "ORDER BY 1\n",
    "\n",
    "\n",
    ")  \n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "duckdb.sql(fourth_question_date_query)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2 - All hands meeting preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Ingest data stored in csv files in datasets/sales/ in tables inside data warehouse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "accounts_data = pd.read_csv('datasets/sales/accounts - crm export.csv', sep = ';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3125, 3)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accounts_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3084, 3)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accounts_data.drop_duplicates(['Industry', 'Name']).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3125 - 3084"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3294422827496757"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(3125 - 3084) / 3084 * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 1.33% duplicates records in accounts table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "duckdb.sql('CREATE OR REPLACE TABLE accounts AS (SELECT * FROM accounts_data)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "contacts_data = pd.read_csv('datasets/sales/contacts - crm export.csv', sep = ',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5990, 4)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contacts_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5990, 4)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contacts_data.drop_duplicates(['fullname', 'job_title']).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5990, 4)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contacts_data.drop_duplicates(['fullname', 'job_title', 'accountid', 'id']).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "#duckdb.sql('DROP TABLE contacts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "duckdb.sql('CREATE OR REPLACE TABLE contacts AS (SELECT * FROM contacts_data)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "crm_users_data = pd.read_csv('datasets/sales/crm_users - crm export.csv', sep = '/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(237, 4)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crm_users_data.drop_duplicates(['fullname', 'team_name']).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(237, 4)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crm_users_data.drop_duplicates(['fullname', 'team_name', 'team_country__c', 'team_name']).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "#duckdb.sql('DROP TABLE crm_users')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 1 duplicate record"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "duckdb.sql('CREATE OR REPLACE TABLE crm_users AS (SELECT * FROM crm_users_data)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "opportunities_data = pd.read_csv('datasets/sales/opportunities - crm export.csv', sep = '|')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6769, 16)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opportunities_data.drop_duplicates(['accountid', 'name']).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6769, 16)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opportunities_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "duckdb.sql('CREATE OR REPLACE TABLE opportunities AS (SELECT * FROM opportunities_data)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "opportunities_contact_roles_data = pd.read_csv('datasets/sales/opportunities_contact_roles - crm export.csv', sep = '+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7807, 4)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opportunities_contact_roles_data.drop_duplicates(['contactid', 'opportunityid']).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8788, 4)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opportunities_contact_roles_data.drop_duplicates(['contactid', 'opportunityid', 'role']).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- same person can have different roles for same opportunity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 1.5 % duplicates: same person, opportunity and role"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5020482476103778"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(8920 - 8788) / 8788 * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "duckdb.sql('CREATE OR REPLACE TABLE opportunities_contact_roles AS (SELECT * FROM opportunities_contact_roles_data)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Need to restructure opportunities table first"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4 main elements to correct: \n",
    "    \n",
    "1) opportunity stage status: need to correct (depending on dates and revenue generated)\n",
    "    \n",
    "2) opportunity type: need to correct depending on business logic (churn less than 6 months and reactivated then renew if churn 6 months or more then new business....)\n",
    "    \n",
    "3) need to fix duplicates issues: accounts (1.3%), crm users (1) and opportunities contact roles (1.5%), might have for business opportunities...\n",
    "\n",
    "4) check other data quality issues ... (\n",
    "\n",
    "- like licence start date > licence en date), normally either 1 month free trial or 1 year licence \n",
    "\n",
    "- missing data ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- First, need to deduplicate, then change business logic in opportunity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='II) Part 2 - Data Quality'> II) Data Quality</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='A) Duplicates'> A) Duplicates</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- need to deduplicate data in the CRM tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "contact_role_data = duckdb.sql('SELECT * FROM opportunities_contact_roles ').df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "deduplication_first_query = '''\n",
    "\n",
    "WITH accounts_table AS (\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "    id AS account_id_key,\n",
    "    \n",
    "    \n",
    "    RANK () OVER (ORDER BY Industry, Name) AS true_account_id\n",
    "    \n",
    "FROM accounts\n",
    "\n",
    "\n",
    "\n",
    "),\n",
    "\n",
    "crm_users_table AS (\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "    id AS crm_user_id,\n",
    "    \n",
    "    team_country__c,\n",
    "    \n",
    "    fullname,\n",
    "    \n",
    "    team_name,\n",
    "    \n",
    "    \n",
    "    RANK () OVER (ORDER BY team_country__c, fullname, team_name) AS true_crm_user_id\n",
    "    \n",
    "    \n",
    "FROM crm_users\n",
    "\n",
    "\n",
    "\n",
    ")\n",
    "\n",
    "SELECT\n",
    "\n",
    "    \n",
    "    b.true_account_id AS account_id,\n",
    "        \n",
    "    c.true_crm_user_id AS owner_id,\n",
    "    \n",
    "    a.id,\n",
    "    \n",
    "    a.type,\n",
    "    \n",
    "    CAST(a.deal_license_activation_start_date__c AS DATE) AS deal_licence_start_date,\n",
    "    \n",
    "    CAST(a.deal_license_activation_end_date__c AS DATE) AS deal_licence_end_date,\n",
    "    \n",
    "    CAST(closedate AS DATE) AS close_date,\n",
    "    \n",
    "    CAST(createddate AS DATE) AS created_date,\n",
    "    \n",
    "    opportunity_stage_status__c AS opportunity_stage_status,\n",
    "    \n",
    "    leadsource_category__c AS lead_source_category,\n",
    "    \n",
    "    deal_total_non_recurring__c AS deal_total_non_recurring_revenue,\n",
    "    \n",
    "    \n",
    "    deal_total_recurring__c AS deal_total_recurring_revenue,\n",
    "    \n",
    "    currencyisocode AS currency_iso_code,\n",
    "    \n",
    "    LeadSource AS lead_source,\n",
    "    \n",
    "    Deal_use_case__c AS deal_use_case,\n",
    "    \n",
    "    name\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "FROM opportunities a\n",
    "\n",
    "LEFT JOIN accounts_table b ON b.account_id_key = a.accountid\n",
    "\n",
    "LEFT JOIN crm_users_table c ON c.crm_user_id = a.ownerid\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "deduplication_first_data = duckdb.sql(deduplication_first_query).df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account_id</th>\n",
       "      <th>owner_id</th>\n",
       "      <th>id</th>\n",
       "      <th>type</th>\n",
       "      <th>deal_licence_start_date</th>\n",
       "      <th>deal_licence_end_date</th>\n",
       "      <th>close_date</th>\n",
       "      <th>created_date</th>\n",
       "      <th>opportunity_stage_status</th>\n",
       "      <th>lead_source_category</th>\n",
       "      <th>deal_total_non_recurring_revenue</th>\n",
       "      <th>deal_total_recurring_revenue</th>\n",
       "      <th>currency_iso_code</th>\n",
       "      <th>lead_source</th>\n",
       "      <th>deal_use_case</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1932</td>\n",
       "      <td>63</td>\n",
       "      <td>00609000002Iv7dAAC</td>\n",
       "      <td>Renew</td>\n",
       "      <td>2017-07-15</td>\n",
       "      <td>2018-07-14</td>\n",
       "      <td>2017-03-08</td>\n",
       "      <td>2017-12-18</td>\n",
       "      <td>Won</td>\n",
       "      <td>Sales/CSM</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22617.75</td>\n",
       "      <td>EUR</td>\n",
       "      <td>Sales Generated Lead - AE, CSM, SDR</td>\n",
       "      <td></td>\n",
       "      <td>Secured bottom-line benchmark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1947</td>\n",
       "      <td>63</td>\n",
       "      <td>00609000002IumvAAC</td>\n",
       "      <td>Renew</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>2017-01-11</td>\n",
       "      <td>2017-12-18</td>\n",
       "      <td>Won</td>\n",
       "      <td>Sales/CSM</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39490.02</td>\n",
       "      <td>EUR</td>\n",
       "      <td>Sales Generated Lead - AE, CSM, SDR</td>\n",
       "      <td></td>\n",
       "      <td>Proactive encompassing knowledge user</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>997</td>\n",
       "      <td>15</td>\n",
       "      <td>00609000002IuLUAA0</td>\n",
       "      <td>Renew</td>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>2019-04-30</td>\n",
       "      <td>2018-04-01</td>\n",
       "      <td>2018-02-23</td>\n",
       "      <td>Won</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.0</td>\n",
       "      <td>146479.84</td>\n",
       "      <td>EUR</td>\n",
       "      <td></td>\n",
       "      <td>No specific Use Case yet</td>\n",
       "      <td>Grass-roots 6thgeneration migration</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1855</td>\n",
       "      <td>15</td>\n",
       "      <td>00609000002IvL1AAK</td>\n",
       "      <td>Renew</td>\n",
       "      <td>2018-08-01</td>\n",
       "      <td>2019-07-31</td>\n",
       "      <td>2018-04-08</td>\n",
       "      <td>2018-05-18</td>\n",
       "      <td>Won</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60408.40</td>\n",
       "      <td>EUR</td>\n",
       "      <td>Do not use - Marketing before 2021</td>\n",
       "      <td></td>\n",
       "      <td>Innovative local methodology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1330</td>\n",
       "      <td>29</td>\n",
       "      <td>00609000002IuLDAA0</td>\n",
       "      <td>Renew</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>2018-12-10</td>\n",
       "      <td>Won</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79783.70</td>\n",
       "      <td>EUR</td>\n",
       "      <td></td>\n",
       "      <td>No specific Use Case yet</td>\n",
       "      <td>Persevering systemic adapter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6764</th>\n",
       "      <td>605</td>\n",
       "      <td>185</td>\n",
       "      <td>00609000002IuQWAA0</td>\n",
       "      <td>New Business</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020-10-15</td>\n",
       "      <td>2020-09-08</td>\n",
       "      <td>Lost</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>EUR</td>\n",
       "      <td>Do not use - Marketing before 2021</td>\n",
       "      <td>Embedded Analytics for Software Companies</td>\n",
       "      <td>Seamless disintermediate utilization</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6765</th>\n",
       "      <td>1322</td>\n",
       "      <td>238</td>\n",
       "      <td>0060900000G6YfMAAV</td>\n",
       "      <td>New Business</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2022-10-05</td>\n",
       "      <td>2021-11-18</td>\n",
       "      <td>Lost</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>USD</td>\n",
       "      <td>Outsourcing - Lead Vendor</td>\n",
       "      <td>Embedded Analytics for Software Companies</td>\n",
       "      <td>Persistent 3rdgeneration algorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6766</th>\n",
       "      <td>2196</td>\n",
       "      <td>185</td>\n",
       "      <td>0060900000ITOMyAAP</td>\n",
       "      <td>New Business</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2022-09-05</td>\n",
       "      <td>2022-02-08</td>\n",
       "      <td>Lost</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>EUR</td>\n",
       "      <td>SEO - Organic</td>\n",
       "      <td>Executive Analytics Dashboard</td>\n",
       "      <td>User-centric impactful contingency</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6767</th>\n",
       "      <td>2288</td>\n",
       "      <td>185</td>\n",
       "      <td>0060900000KqAlGAAV</td>\n",
       "      <td>New Business</td>\n",
       "      <td>2022-08-01</td>\n",
       "      <td>2023-07-31</td>\n",
       "      <td>2022-06-30</td>\n",
       "      <td>2022-05-24</td>\n",
       "      <td>Won</td>\n",
       "      <td>Alliances</td>\n",
       "      <td>0.0</td>\n",
       "      <td>188280.32</td>\n",
       "      <td>EUR</td>\n",
       "      <td>Partner Sourced - Alliances</td>\n",
       "      <td>Analytics for everyone</td>\n",
       "      <td>Versatile optimal forecast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6768</th>\n",
       "      <td>2288</td>\n",
       "      <td>185</td>\n",
       "      <td>0060900000KsJ42AAF</td>\n",
       "      <td>Cross sell</td>\n",
       "      <td>2022-08-01</td>\n",
       "      <td>2023-07-31</td>\n",
       "      <td>2022-11-16</td>\n",
       "      <td>2022-06-14</td>\n",
       "      <td>Lost</td>\n",
       "      <td>Alliances</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70606.92</td>\n",
       "      <td>EUR</td>\n",
       "      <td>Partner Sourced - Alliances</td>\n",
       "      <td>Analytics for everyone</td>\n",
       "      <td>Multi-tiered systematic functionalities</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6769 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      account_id  owner_id                  id          type  \\\n",
       "0           1932        63  00609000002Iv7dAAC         Renew   \n",
       "1           1947        63  00609000002IumvAAC         Renew   \n",
       "2            997        15  00609000002IuLUAA0         Renew   \n",
       "3           1855        15  00609000002IvL1AAK         Renew   \n",
       "4           1330        29  00609000002IuLDAA0         Renew   \n",
       "...          ...       ...                 ...           ...   \n",
       "6764         605       185  00609000002IuQWAA0  New Business   \n",
       "6765        1322       238  0060900000G6YfMAAV  New Business   \n",
       "6766        2196       185  0060900000ITOMyAAP  New Business   \n",
       "6767        2288       185  0060900000KqAlGAAV  New Business   \n",
       "6768        2288       185  0060900000KsJ42AAF    Cross sell   \n",
       "\n",
       "     deal_licence_start_date deal_licence_end_date close_date created_date  \\\n",
       "0                 2017-07-15            2018-07-14 2017-03-08   2017-12-18   \n",
       "1                 2018-01-01            2018-12-31 2017-01-11   2017-12-18   \n",
       "2                 2018-04-01            2019-04-30 2018-04-01   2018-02-23   \n",
       "3                 2018-08-01            2019-07-31 2018-04-08   2018-05-18   \n",
       "4                 2018-12-01            2019-12-31 2018-12-01   2018-12-10   \n",
       "...                      ...                   ...        ...          ...   \n",
       "6764                     NaT                   NaT 2020-10-15   2020-09-08   \n",
       "6765                     NaT                   NaT 2022-10-05   2021-11-18   \n",
       "6766                     NaT                   NaT 2022-09-05   2022-02-08   \n",
       "6767              2022-08-01            2023-07-31 2022-06-30   2022-05-24   \n",
       "6768              2022-08-01            2023-07-31 2022-11-16   2022-06-14   \n",
       "\n",
       "     opportunity_stage_status lead_source_category  \\\n",
       "0                         Won            Sales/CSM   \n",
       "1                         Won            Sales/CSM   \n",
       "2                         Won                Other   \n",
       "3                         Won            Marketing   \n",
       "4                         Won                Other   \n",
       "...                       ...                  ...   \n",
       "6764                     Lost            Marketing   \n",
       "6765                     Lost            Marketing   \n",
       "6766                     Lost            Marketing   \n",
       "6767                      Won            Alliances   \n",
       "6768                     Lost            Alliances   \n",
       "\n",
       "      deal_total_non_recurring_revenue  deal_total_recurring_revenue  \\\n",
       "0                                  0.0                      22617.75   \n",
       "1                                  0.0                      39490.02   \n",
       "2                                  0.0                     146479.84   \n",
       "3                                  0.0                      60408.40   \n",
       "4                                  0.0                      79783.70   \n",
       "...                                ...                           ...   \n",
       "6764                               0.0                          0.00   \n",
       "6765                               0.0                          0.00   \n",
       "6766                               0.0                          0.00   \n",
       "6767                               0.0                     188280.32   \n",
       "6768                               0.0                      70606.92   \n",
       "\n",
       "     currency_iso_code                          lead_source  \\\n",
       "0                  EUR  Sales Generated Lead - AE, CSM, SDR   \n",
       "1                  EUR  Sales Generated Lead - AE, CSM, SDR   \n",
       "2                  EUR                                        \n",
       "3                  EUR   Do not use - Marketing before 2021   \n",
       "4                  EUR                                        \n",
       "...                ...                                  ...   \n",
       "6764               EUR   Do not use - Marketing before 2021   \n",
       "6765               USD            Outsourcing - Lead Vendor   \n",
       "6766               EUR                        SEO - Organic   \n",
       "6767               EUR          Partner Sourced - Alliances   \n",
       "6768               EUR          Partner Sourced - Alliances   \n",
       "\n",
       "                                  deal_use_case  \\\n",
       "0                                                 \n",
       "1                                                 \n",
       "2                      No specific Use Case yet   \n",
       "3                                                 \n",
       "4                      No specific Use Case yet   \n",
       "...                                         ...   \n",
       "6764  Embedded Analytics for Software Companies   \n",
       "6765  Embedded Analytics for Software Companies   \n",
       "6766              Executive Analytics Dashboard   \n",
       "6767                     Analytics for everyone   \n",
       "6768                     Analytics for everyone   \n",
       "\n",
       "                                         name  \n",
       "0               Secured bottom-line benchmark  \n",
       "1       Proactive encompassing knowledge user  \n",
       "2         Grass-roots 6thgeneration migration  \n",
       "3                Innovative local methodology  \n",
       "4                Persevering systemic adapter  \n",
       "...                                       ...  \n",
       "6764     Seamless disintermediate utilization  \n",
       "6765       Persistent 3rdgeneration algorithm  \n",
       "6766       User-centric impactful contingency  \n",
       "6767               Versatile optimal forecast  \n",
       "6768  Multi-tiered systematic functionalities  \n",
       "\n",
       "[6769 rows x 16 columns]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deduplication_first_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "duckdb.sql('CREATE OR REPLACE TABLE opportunities AS (SELECT * FROM deduplication_first_data)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- now we can update account ID field in contact table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "deduplication_contact_query = '''\n",
    "\n",
    "WITH accounts_table AS (\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "    id AS account_id_key,\n",
    "    \n",
    "    Industry,\n",
    "    \n",
    "    Name,\n",
    "    \n",
    "    RANK () OVER (ORDER BY Industry, Name) AS true_account_id\n",
    "    \n",
    "FROM accounts\n",
    "\n",
    "\n",
    ")\n",
    "\n",
    "SELECT\n",
    "\n",
    "    \n",
    "    id,\n",
    "    \n",
    "    CASE\n",
    "    WHEN b.true_account_id IS NULL THEN a.accountid\n",
    "    ELSE CAST(b.true_account_id AS VARCHAR)\n",
    "    END AS account_id,\n",
    "    \n",
    "    fullname,\n",
    "    \n",
    "    job_title\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "FROM contacts a\n",
    "\n",
    "LEFT JOIN accounts_table b ON b.account_id_key = a.accountid\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "deduplication_contact_data = duckdb.sql(deduplication_contact_query).df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "duckdb.sql('CREATE OR REPLACE TABLE contacts AS (SELECT * FROM deduplication_contact_data)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- now we can deduplicate accounts table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "deduplication_second_query = '''\n",
    "\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "DISTINCT\n",
    "    \n",
    "    Industry AS industry,\n",
    "    \n",
    "    Name AS name,\n",
    "    \n",
    "    RANK () OVER (ORDER BY Industry, Name) AS account_id\n",
    "    \n",
    "FROM accounts\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "'''\n",
    "\n",
    "deduplication_second_data = duckdb.sql(deduplication_second_query).df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "duckdb.sql('CREATE OR REPLACE TABLE accounts AS (SELECT * FROM deduplication_second_data)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "deduplication_third_query = '''\n",
    "\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "DISTINCT\n",
    "    \n",
    "    team_country__c AS team_country,\n",
    "    \n",
    "    fullname AS full_name,\n",
    "    \n",
    "    team_name,\n",
    "    \n",
    "    \n",
    "    RANK () OVER (ORDER BY team_country__c, fullname, team_name) AS crm_user_id\n",
    "    \n",
    "    \n",
    "FROM crm_users\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "'''\n",
    "\n",
    "deduplication_third_query = duckdb.sql(deduplication_third_query).df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "duckdb.sql('CREATE OR REPLACE TABLE crm_users AS (SELECT * FROM deduplication_third_query)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Now, let's deduplicate opportunity contact roles table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "deduplicate_fourth_query = '''\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "DISTINCT\n",
    "\n",
    "    RANK() OVER (ORDER BY contactid, opportunityid, role) AS id,\n",
    "\n",
    "    contactid AS contact_id,\n",
    "    \n",
    "    opportunityid AS opportunity_id,\n",
    "    \n",
    "    role,\n",
    "    \n",
    "    \n",
    "\n",
    "FROM opportunities_contact_roles\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "'''\n",
    "\n",
    "\n",
    "\n",
    "deduplicate_fourth_data = duckdb.sql(deduplicate_fourth_query).df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "duckdb.sql('CREATE OR REPLACE TABLE opportunities_contact_roles AS (SELECT * FROM deduplicate_fourth_data)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### we will deduplicate the opportunities in the next section"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='B) Revision Date Field in Opportunity Table'> B) Revision Date Field in Opportunity Table</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "┌──────────────┐\n",
       "│ count_star() │\n",
       "│    int64     │\n",
       "├──────────────┤\n",
       "│         6769 │\n",
       "└──────────────┘"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duckdb.sql('SELECT COUNT(*) FROM opportunities')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- issues with some closed dates: Jan 1st 2000, not consistent, need to correct  \n",
    "\n",
    "- also lot of opportunities created for which the creation date is after the close date\n",
    "\n",
    "- last date an opportunity was created: June 14th 2023 (we can assume that this CRM extract is a snapshot at that time)\n",
    "\n",
    "- first date an opportunity was created: November 28th 2014 (we can assume that either the company started recording opportunities at that time or that the company was created in 2014, almost coincides with Toucan 2015 foundation year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>opportunity_stage_status</th>\n",
       "      <th>activation_start_date_min</th>\n",
       "      <th>activation_start_date_max</th>\n",
       "      <th>activation_end_date_min</th>\n",
       "      <th>activation_end_date_max</th>\n",
       "      <th>closedate_min</th>\n",
       "      <th>closedate_max</th>\n",
       "      <th>createddate_min</th>\n",
       "      <th>createddate_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>Won</td>\n",
       "      <td>2021-10-27</td>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>2022-10-26</td>\n",
       "      <td>2023-01-29</td>\n",
       "      <td>2021-10-27</td>\n",
       "      <td>2022-11-09</td>\n",
       "      <td>2021-10-27</td>\n",
       "      <td>2022-11-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cross sell</td>\n",
       "      <td>Lost</td>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>2023-06-30</td>\n",
       "      <td>2021-03-01</td>\n",
       "      <td>2024-06-29</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>2023-06-14</td>\n",
       "      <td>2017-09-26</td>\n",
       "      <td>2023-01-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cross sell</td>\n",
       "      <td>Open</td>\n",
       "      <td>2023-06-30</td>\n",
       "      <td>2023-06-30</td>\n",
       "      <td>2024-06-29</td>\n",
       "      <td>2024-06-29</td>\n",
       "      <td>2023-06-22</td>\n",
       "      <td>2024-02-29</td>\n",
       "      <td>2021-04-15</td>\n",
       "      <td>2023-04-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cross sell</td>\n",
       "      <td>Won</td>\n",
       "      <td>2017-10-26</td>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>2018-06-30</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>2017-10-24</td>\n",
       "      <td>2023-04-12</td>\n",
       "      <td>2017-12-18</td>\n",
       "      <td>2023-04-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Embed Usage</td>\n",
       "      <td>Lost</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>2021-12-31</td>\n",
       "      <td>2021-12-22</td>\n",
       "      <td>2022-09-07</td>\n",
       "      <td>2021-12-17</td>\n",
       "      <td>2021-12-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Embed Usage</td>\n",
       "      <td>Open</td>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>2023-04-11</td>\n",
       "      <td>2023-04-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Embed Usage</td>\n",
       "      <td>Won</td>\n",
       "      <td>2020-02-07</td>\n",
       "      <td>2022-10-01</td>\n",
       "      <td>2021-02-06</td>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>2020-02-07</td>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>2019-12-05</td>\n",
       "      <td>2023-01-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>New Business</td>\n",
       "      <td>Lost</td>\n",
       "      <td>2016-07-01</td>\n",
       "      <td>2023-04-30</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>2026-01-01</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>2023-06-13</td>\n",
       "      <td>2014-11-28</td>\n",
       "      <td>2023-06-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>New Business</td>\n",
       "      <td>Open</td>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>2024-03-30</td>\n",
       "      <td>2024-03-30</td>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>2024-08-07</td>\n",
       "      <td>2021-01-29</td>\n",
       "      <td>2023-06-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>New Business</td>\n",
       "      <td>Won</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2023-11-01</td>\n",
       "      <td>2016-04-30</td>\n",
       "      <td>2027-12-14</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>2023-06-13</td>\n",
       "      <td>2015-12-23</td>\n",
       "      <td>2023-05-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Renew</td>\n",
       "      <td>Lost</td>\n",
       "      <td>2019-04-18</td>\n",
       "      <td>2024-05-01</td>\n",
       "      <td>2020-04-18</td>\n",
       "      <td>2025-04-30</td>\n",
       "      <td>2019-04-18</td>\n",
       "      <td>2024-05-01</td>\n",
       "      <td>2019-06-25</td>\n",
       "      <td>2023-05-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Renew</td>\n",
       "      <td>Open</td>\n",
       "      <td>2022-11-16</td>\n",
       "      <td>2027-12-15</td>\n",
       "      <td>2023-11-15</td>\n",
       "      <td>2028-12-14</td>\n",
       "      <td>2022-11-16</td>\n",
       "      <td>2027-12-15</td>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>2023-06-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Renew</td>\n",
       "      <td>Won</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>2023-07-01</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>2026-03-31</td>\n",
       "      <td>2016-01-19</td>\n",
       "      <td>2023-07-01</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>2023-04-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Upsell</td>\n",
       "      <td>Lost</td>\n",
       "      <td>2019-07-29</td>\n",
       "      <td>2023-04-01</td>\n",
       "      <td>2020-07-28</td>\n",
       "      <td>2024-11-01</td>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>2023-06-13</td>\n",
       "      <td>2019-05-06</td>\n",
       "      <td>2023-01-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Upsell</td>\n",
       "      <td>Open</td>\n",
       "      <td>2022-02-28</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>2023-02-27</td>\n",
       "      <td>2024-12-30</td>\n",
       "      <td>2023-06-22</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>2023-04-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Upsell</td>\n",
       "      <td>Won</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>2023-10-31</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>2025-09-29</td>\n",
       "      <td>2016-06-02</td>\n",
       "      <td>2023-06-13</td>\n",
       "      <td>2018-01-11</td>\n",
       "      <td>2023-05-24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            type opportunity_stage_status activation_start_date_min  \\\n",
       "0                                     Won                2021-10-27   \n",
       "1     Cross sell                     Lost                2020-03-02   \n",
       "2     Cross sell                     Open                2023-06-30   \n",
       "3     Cross sell                      Won                2017-10-26   \n",
       "4    Embed Usage                     Lost                2021-01-01   \n",
       "5    Embed Usage                     Open                2023-01-01   \n",
       "6    Embed Usage                      Won                2020-02-07   \n",
       "7   New Business                     Lost                2016-07-01   \n",
       "8   New Business                     Open                2023-03-31   \n",
       "9   New Business                      Won                2016-01-01   \n",
       "10         Renew                     Lost                2019-04-18   \n",
       "11         Renew                     Open                2022-11-16   \n",
       "12         Renew                      Won                2017-01-01   \n",
       "13        Upsell                     Lost                2019-07-29   \n",
       "14        Upsell                     Open                2022-02-28   \n",
       "15        Upsell                      Won                2018-01-01   \n",
       "\n",
       "   activation_start_date_max activation_end_date_min activation_end_date_max  \\\n",
       "0                 2022-12-30              2022-10-26              2023-01-29   \n",
       "1                 2023-06-30              2021-03-01              2024-06-29   \n",
       "2                 2023-06-30              2024-06-29              2024-06-29   \n",
       "3                 2023-01-01              2018-06-30              2023-12-31   \n",
       "4                 2021-01-01              2021-12-31              2021-12-31   \n",
       "5                 2023-01-01              2023-03-31              2023-03-31   \n",
       "6                 2022-10-01              2021-02-06              2022-12-31   \n",
       "7                 2023-04-30              2017-04-01              2026-01-01   \n",
       "8                 2023-03-31              2024-03-30              2024-03-30   \n",
       "9                 2023-11-01              2016-04-30              2027-12-14   \n",
       "10                2024-05-01              2020-04-18              2025-04-30   \n",
       "11                2027-12-15              2023-11-15              2028-12-14   \n",
       "12                2023-07-01              2017-06-01              2026-03-31   \n",
       "13                2023-04-01              2020-07-28              2024-11-01   \n",
       "14                2023-12-31              2023-02-27              2024-12-30   \n",
       "15                2023-10-31              2018-12-31              2025-09-29   \n",
       "\n",
       "   closedate_min closedate_max createddate_min createddate_max  \n",
       "0     2021-10-27    2022-11-09      2021-10-27      2022-11-08  \n",
       "1     2000-01-01    2023-06-14      2017-09-26      2023-01-20  \n",
       "2     2023-06-22    2024-02-29      2021-04-15      2023-04-13  \n",
       "3     2017-10-24    2023-04-12      2017-12-18      2023-04-12  \n",
       "4     2021-12-22    2022-09-07      2021-12-17      2021-12-17  \n",
       "5     2023-01-01    2023-01-01      2023-04-11      2023-04-11  \n",
       "6     2020-02-07    2022-12-31      2019-12-05      2023-01-12  \n",
       "7     2000-01-01    2023-06-13      2014-11-28      2023-06-07  \n",
       "8     2023-03-31    2024-08-07      2021-01-29      2023-06-14  \n",
       "9     2000-01-01    2023-06-13      2015-12-23      2023-05-02  \n",
       "10    2019-04-18    2024-05-01      2019-06-25      2023-05-11  \n",
       "11    2022-11-16    2027-12-15      2021-06-30      2023-06-14  \n",
       "12    2016-01-19    2023-07-01      2017-01-03      2023-04-13  \n",
       "13    2000-01-01    2023-06-13      2019-05-06      2023-01-19  \n",
       "14    2023-06-22    2023-12-31      2021-12-15      2023-04-26  \n",
       "15    2016-06-02    2023-06-13      2018-01-11      2023-05-24  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duckdb.sql('''\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "    type,\n",
    "    \n",
    "    opportunity_stage_status,\n",
    "    \n",
    "\n",
    "    MIN(deal_licence_start_date) AS activation_start_date_min,\n",
    "    \n",
    "    MAX(deal_licence_start_date) AS activation_start_date_max,\n",
    "    \n",
    "    MIN(deal_licence_end_date) AS activation_end_date_min,\n",
    "    \n",
    "    MAX(deal_licence_end_date) AS activation_end_date_max,\n",
    "    \n",
    "    MIN(close_date) AS closedate_min,\n",
    "    \n",
    "    MAX(close_date) AS closedate_max,\n",
    "    \n",
    "    MIN(created_date) AS createddate_min,\n",
    "    \n",
    "    MAX(created_date) AS createddate_max\n",
    "    \n",
    "FROM opportunities\n",
    "    \n",
    "    \n",
    "\n",
    "GROUP BY 1,2\n",
    "\n",
    "ORDER BY 1,2\n",
    "\n",
    "\n",
    "''').df()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Let's fix the closed dates 2000-01-01 (32 records)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can observe that we don't have a licence time period. However, 2 of these opportunities generated revenue (non-recurring). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- when the opportunity is considered as lost, then we assume the deal revenue values are the potential revenue the opportunity would have generated if the opportunity has been won\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "┌───────────────────────────────────────────────┐\n",
       "│ avg_days_between_creation_date_and_close_date │\n",
       "│                     int32                     │\n",
       "├───────────────────────────────────────────────┤\n",
       "│                                           177 │\n",
       "└───────────────────────────────────────────────┘"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duckdb.sql('''\n",
    "\n",
    "WITH first_table AS (\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "    created_date,\n",
    "    \n",
    "    close_date,\n",
    "\n",
    "    DATE_DIFF('DAY', created_date, close_date) AS nb_days\n",
    "    \n",
    "FROM opportunities\n",
    "\n",
    "WHERE close_date != '2000-01-01'\n",
    "\n",
    "AND created_date < close_date\n",
    "\n",
    ")\n",
    "\n",
    "SELECT\n",
    "\n",
    "    CAST(MEAN(nb_days) AS INT) AS avg_days_between_creation_date_and_close_date\n",
    "\n",
    "FROM first_table\n",
    "\n",
    "''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We will use here mean inputation to fill in the missing data. Let's replace the year 2000 values in close date field by the mean (177 days). We assume that the opportunities were entered in the CRM before they were closed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "incorrect_date_update_query = '''\n",
    "\n",
    "UPDATE opportunities AS opportunities_original\n",
    "\n",
    "SET close_date = (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    CASE\n",
    "    WHEN close_date < '2001-01-01' THEN created_date + INTERVAL '177' DAY\n",
    "    ELSE close_date \n",
    "    END AS close_date\n",
    "\n",
    "\n",
    "FROM opportunities a\n",
    "\n",
    "WHERE opportunities_original.id = a.id  \n",
    "\n",
    "ORDER BY 1\n",
    "\n",
    "\n",
    ")  \n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "duckdb.sql(incorrect_date_update_query)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- won opportunities with no subscription data account for 4.6%  (311) of the total number of opportunities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>opportunity_stage_status</th>\n",
       "      <th>nb_opportunities_with_no_subscription_data</th>\n",
       "      <th>pct_total_opportunities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lost</td>\n",
       "      <td>4718.0</td>\n",
       "      <td>0.697001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Won</td>\n",
       "      <td>311.0</td>\n",
       "      <td>0.045945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Open</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.014330</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  opportunity_stage_status  nb_opportunities_with_no_subscription_data  \\\n",
       "0                     Lost                                      4718.0   \n",
       "1                      Won                                       311.0   \n",
       "2                     Open                                        97.0   \n",
       "\n",
       "   pct_total_opportunities  \n",
       "0                 0.697001  \n",
       "1                 0.045945  \n",
       "2                 0.014330  "
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duckdb.sql('''\n",
    "\n",
    "SELECT\n",
    "\n",
    "    opportunity_stage_status,\n",
    "\n",
    "    SUM(CASE\n",
    "    WHEN deal_licence_start_date IS NULL AND deal_licence_end_date IS NULL THEN 1 ELSE 0 END) AS nb_opportunities_with_no_subscription_data,\n",
    "    \n",
    "    SUM(CASE\n",
    "    WHEN deal_licence_start_date IS NULL AND deal_licence_end_date IS NULL THEN 1 ELSE 0 END) / 6769 AS pct_total_opportunities\n",
    "\n",
    "\n",
    "FROM opportunities\n",
    "\n",
    "GROUP BY 1\n",
    "\n",
    "ORDER BY 2 DESC\n",
    "\n",
    "\n",
    "''').df()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Need to fill in the missing data for these won opportunities as following\n",
    "\n",
    "\n",
    "a) the deal start date is the day following the close date \n",
    "\n",
    "\n",
    "b) For the deal end date, we will use mean inputation for won opportunities: we calculate the average deal duration and use this value to fill in the missing deal end date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "duckdb.sql('''\n",
    "\n",
    "UPDATE opportunities AS opportunities_original\n",
    "\n",
    "SET deal_licence_start_date = (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    CASE\n",
    "    WHEN opportunity_stage_status = 'Won' AND deal_licence_start_date IS NULL AND deal_licence_end_date IS NULL THEN close_date + INTERVAL '1' DAY\n",
    "    ELSE deal_licence_start_date\n",
    "    END AS deal_licence_start_date\n",
    "\n",
    "\n",
    "FROM opportunities a\n",
    "\n",
    "WHERE opportunities_original.id = a.id  \n",
    "\n",
    "ORDER BY 1\n",
    "\n",
    "\n",
    ")  \n",
    "\n",
    "''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "┌──────────────────────┐\n",
       "│ avg_days_deal_period │\n",
       "│        int32         │\n",
       "├──────────────────────┤\n",
       "│                  345 │\n",
       "└──────────────────────┘"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duckdb.sql('''\n",
    "\n",
    "WITH first_table AS (\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "\n",
    "    DATE_DIFF('DAY', deal_licence_start_date, deal_licence_end_date) AS nb_days\n",
    "    \n",
    "FROM opportunities\n",
    "\n",
    "WHERE opportunity_stage_status = 'Won'\n",
    "\n",
    "AND close_date < deal_licence_start_date\n",
    "\n",
    "AND deal_licence_start_date <  deal_licence_end_date\n",
    "\n",
    "\n",
    "\n",
    "\n",
    ")\n",
    "\n",
    "SELECT\n",
    "\n",
    "    CAST(MEAN(nb_days) AS INT) AS avg_days_deal_period\n",
    "\n",
    "FROM first_table\n",
    "\n",
    "''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "duckdb.sql('''\n",
    "\n",
    "UPDATE opportunities AS opportunities_original\n",
    "\n",
    "SET deal_licence_end_date = (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    CASE\n",
    "    WHEN opportunity_stage_status = 'Won' AND deal_licence_start_date IS NOT NULL AND deal_licence_end_date IS NULL THEN deal_licence_start_date + INTERVAL '345' DAY\n",
    "    ELSE deal_licence_end_date\n",
    "    END AS deal_licence_end_date\n",
    "\n",
    "\n",
    "FROM opportunities a\n",
    "\n",
    "WHERE opportunities_original.id = a.id  \n",
    "\n",
    "ORDER BY 1\n",
    "\n",
    "\n",
    ")  \n",
    "\n",
    "''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account_id</th>\n",
       "      <th>owner_id</th>\n",
       "      <th>id</th>\n",
       "      <th>type</th>\n",
       "      <th>deal_licence_start_date</th>\n",
       "      <th>deal_licence_end_date</th>\n",
       "      <th>close_date</th>\n",
       "      <th>created_date</th>\n",
       "      <th>opportunity_stage_status</th>\n",
       "      <th>lead_source_category</th>\n",
       "      <th>deal_total_non_recurring_revenue</th>\n",
       "      <th>deal_total_recurring_revenue</th>\n",
       "      <th>currency_iso_code</th>\n",
       "      <th>lead_source</th>\n",
       "      <th>deal_use_case</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [account_id, owner_id, id, type, deal_licence_start_date, deal_licence_end_date, close_date, created_date, opportunity_stage_status, lead_source_category, deal_total_non_recurring_revenue, deal_total_recurring_revenue, currency_iso_code, lead_source, deal_use_case, name]\n",
       "Index: []"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duckdb.sql('''\n",
    "\n",
    "SELECT\n",
    "\n",
    "   *\n",
    "\n",
    "FROM opportunities\n",
    "\n",
    "WHERE (deal_licence_start_date IS NULL OR deal_licence_end_date IS NULL)\n",
    "\n",
    "\n",
    "AND opportunity_stage_status = 'Won'\n",
    "\n",
    "''').df()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 3 opportunities for which the licence start date wrongly set to after the licence end date: we will correct with mean inputation (345 days between licence start date and end date)\n",
    "\n",
    "- 322 opportunities for which the licence start date set before the closed date: could happen with free trials ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>record_issue</th>\n",
       "      <th>nb_opportunities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>close_date_after_licence_start_date</td>\n",
       "      <td>322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>licence_start_date_after_licence_end_date</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                record_issue  nb_opportunities\n",
       "0        close_date_after_licence_start_date               322\n",
       "1  licence_start_date_after_licence_end_date                 3"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duckdb.sql('''\n",
    "\n",
    "SELECT\n",
    "\n",
    "\n",
    "    CASE\n",
    "    WHEN deal_licence_start_date > deal_licence_end_date THEN 'licence_start_date_after_licence_end_date'\n",
    "    WHEN close_date > deal_licence_start_date THEN 'close_date_after_licence_start_date'\n",
    "    ELSE 'other'\n",
    "    END AS record_issue,\n",
    "    \n",
    "    COUNT(*) AS nb_opportunities\n",
    "\n",
    "\n",
    "FROM opportunities\n",
    "\n",
    "WHERE \n",
    "\n",
    "(deal_licence_start_date > deal_licence_end_date \n",
    "\n",
    "OR close_date > deal_licence_start_date)\n",
    "\n",
    "GROUP BY 1\n",
    "\n",
    "\n",
    "\n",
    "''').df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "duckdb.sql('''\n",
    "\n",
    "UPDATE opportunities AS opportunities_original\n",
    "\n",
    "SET deal_licence_end_date = (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    CASE\n",
    "    WHEN deal_licence_start_date > deal_licence_end_date THEN deal_licence_start_date + INTERVAL '345' DAY\n",
    "    ELSE deal_licence_end_date\n",
    "    END AS deal_licence_end_date\n",
    "\n",
    "\n",
    "FROM opportunities a\n",
    "\n",
    "WHERE opportunities_original.id = a.id  \n",
    "\n",
    "ORDER BY 1\n",
    "\n",
    "\n",
    ")  \n",
    "\n",
    "''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- when close date after licence start date, we will use the average time period between close date and licence start date to correct the close date "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "┌──────────────────────────────┐\n",
       "│ avg_time_to_activate_licence │\n",
       "│            int32             │\n",
       "├──────────────────────────────┤\n",
       "│                           42 │\n",
       "└──────────────────────────────┘"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duckdb.sql('''\n",
    "\n",
    "WITH first_table AS (\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "\n",
    "    DATE_DIFF('DAY', close_date, deal_licence_start_date) AS nb_days\n",
    "    \n",
    "FROM opportunities\n",
    "\n",
    "WHERE close_date < deal_licence_start_date\n",
    "\n",
    "AND opportunity_stage_status = 'Won' \n",
    "\n",
    "\n",
    ")\n",
    "\n",
    "SELECT\n",
    "\n",
    "    CAST(MEAN(nb_days) AS INT) AS avg_time_to_activate_licence\n",
    "\n",
    "FROM first_table\n",
    "\n",
    "\n",
    "\n",
    "''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "duckdb.sql('''\n",
    "\n",
    "UPDATE opportunities AS opportunities_original\n",
    "\n",
    "SET close_date = (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    CASE\n",
    "    WHEN close_date > deal_licence_start_date THEN deal_licence_start_date - INTERVAL '42' DAY\n",
    "    ELSE close_date\n",
    "    END AS close_date\n",
    "\n",
    "\n",
    "FROM opportunities a\n",
    "\n",
    "WHERE opportunities_original.id = a.id  \n",
    "\n",
    "ORDER BY 1\n",
    "\n",
    "\n",
    ")  \n",
    "\n",
    "''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>record_issue</th>\n",
       "      <th>nb_opportunities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>other</td>\n",
       "      <td>6769</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  record_issue  nb_opportunities\n",
       "0        other              6769"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duckdb.sql('''\n",
    "\n",
    "SELECT\n",
    "\n",
    "\n",
    "    CASE\n",
    "    WHEN deal_licence_start_date > deal_licence_end_date THEN 'licence_start_date_after_licence_end_date'\n",
    "    WHEN close_date > deal_licence_start_date THEN 'close_date_after_licence_start_date'\n",
    "    ELSE 'other'\n",
    "    END AS record_issue,\n",
    "    \n",
    "    COUNT(*) AS nb_opportunities\n",
    "\n",
    "\n",
    "FROM opportunities\n",
    "\n",
    "\n",
    "GROUP BY 1\n",
    "\n",
    "\n",
    "\n",
    "''').df()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 7 lost opportunities for which either the licence start date is missing or the licence end date is missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account_id</th>\n",
       "      <th>owner_id</th>\n",
       "      <th>id</th>\n",
       "      <th>type</th>\n",
       "      <th>deal_licence_start_date</th>\n",
       "      <th>deal_licence_end_date</th>\n",
       "      <th>close_date</th>\n",
       "      <th>created_date</th>\n",
       "      <th>opportunity_stage_status</th>\n",
       "      <th>lead_source_category</th>\n",
       "      <th>deal_total_non_recurring_revenue</th>\n",
       "      <th>deal_total_recurring_revenue</th>\n",
       "      <th>currency_iso_code</th>\n",
       "      <th>lead_source</th>\n",
       "      <th>deal_use_case</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>373</td>\n",
       "      <td>5</td>\n",
       "      <td>00609000002IuTtAAK</td>\n",
       "      <td>New Business</td>\n",
       "      <td>2017-10-31</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2017-09-19</td>\n",
       "      <td>2017-09-07</td>\n",
       "      <td>Lost</td>\n",
       "      <td>Other</td>\n",
       "      <td>37784.74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>EUR</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Organized homogeneous model</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>964</td>\n",
       "      <td>5</td>\n",
       "      <td>00609000002IvLhAAK</td>\n",
       "      <td>New Business</td>\n",
       "      <td>2017-10-31</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2017-09-19</td>\n",
       "      <td>2017-09-11</td>\n",
       "      <td>Lost</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>49027.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>EUR</td>\n",
       "      <td>Do not use - Marketing before 2021</td>\n",
       "      <td></td>\n",
       "      <td>Profit-focused intangible analyzer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2925</td>\n",
       "      <td>5</td>\n",
       "      <td>00609000002Iv4OAAS</td>\n",
       "      <td>New Business</td>\n",
       "      <td>2017-09-29</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2017-08-18</td>\n",
       "      <td>2017-07-03</td>\n",
       "      <td>Lost</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>51501.95</td>\n",
       "      <td>0.0</td>\n",
       "      <td>EUR</td>\n",
       "      <td>Do not use - Marketing before 2021</td>\n",
       "      <td></td>\n",
       "      <td>Devolved attitude-oriented structure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>835</td>\n",
       "      <td>79</td>\n",
       "      <td>00609000002IubVAAS</td>\n",
       "      <td>New Business</td>\n",
       "      <td>2018-11-01</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2018-09-20</td>\n",
       "      <td>2018-06-18</td>\n",
       "      <td>Lost</td>\n",
       "      <td>Alliances</td>\n",
       "      <td>57005.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>EUR</td>\n",
       "      <td>Partner Sourced - Alliances</td>\n",
       "      <td></td>\n",
       "      <td>Adaptive system-worthy framework</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2029</td>\n",
       "      <td>143</td>\n",
       "      <td>00609000007VKtqAAG</td>\n",
       "      <td>Cross sell</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>2021-05-21</td>\n",
       "      <td>Lost</td>\n",
       "      <td>Sales/CSM</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>EUR</td>\n",
       "      <td>Sales Generated Lead - AE, CSM, SDR</td>\n",
       "      <td>Human Resources Monitoring</td>\n",
       "      <td>Team-oriented holistic website</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1083</td>\n",
       "      <td>141</td>\n",
       "      <td>00609000002IuHdAAK</td>\n",
       "      <td>New Business</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2019-11-20</td>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>Lost</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>EUR</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Versatile bifurcated definition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1035</td>\n",
       "      <td>36</td>\n",
       "      <td>00609000002IvKCAA0</td>\n",
       "      <td>Upsell</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2020-12-17</td>\n",
       "      <td>2020-11-26</td>\n",
       "      <td>Lost</td>\n",
       "      <td>Sales/CSM</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>EUR</td>\n",
       "      <td>Sales Generated Lead - AE, CSM, SDR</td>\n",
       "      <td></td>\n",
       "      <td>Progressive foreground array</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   account_id  owner_id                  id          type  \\\n",
       "0         373         5  00609000002IuTtAAK  New Business   \n",
       "1         964         5  00609000002IvLhAAK  New Business   \n",
       "2        2925         5  00609000002Iv4OAAS  New Business   \n",
       "3         835        79  00609000002IubVAAS  New Business   \n",
       "4        2029       143  00609000007VKtqAAG    Cross sell   \n",
       "5        1083       141  00609000002IuHdAAK  New Business   \n",
       "6        1035        36  00609000002IvKCAA0        Upsell   \n",
       "\n",
       "  deal_licence_start_date deal_licence_end_date close_date created_date  \\\n",
       "0              2017-10-31                   NaT 2017-09-19   2017-09-07   \n",
       "1              2017-10-31                   NaT 2017-09-19   2017-09-11   \n",
       "2              2017-09-29                   NaT 2017-08-18   2017-07-03   \n",
       "3              2018-11-01                   NaT 2018-09-20   2018-06-18   \n",
       "4                     NaT            2021-06-30 2021-06-30   2021-05-21   \n",
       "5              2020-01-01                   NaT 2019-11-20   2020-03-10   \n",
       "6              2021-01-01                   NaT 2020-12-17   2020-11-26   \n",
       "\n",
       "  opportunity_stage_status lead_source_category  \\\n",
       "0                     Lost                Other   \n",
       "1                     Lost            Marketing   \n",
       "2                     Lost            Marketing   \n",
       "3                     Lost            Alliances   \n",
       "4                     Lost            Sales/CSM   \n",
       "5                     Lost                Other   \n",
       "6                     Lost            Sales/CSM   \n",
       "\n",
       "   deal_total_non_recurring_revenue  deal_total_recurring_revenue  \\\n",
       "0                          37784.74                           0.0   \n",
       "1                          49027.85                           0.0   \n",
       "2                          51501.95                           0.0   \n",
       "3                          57005.10                           0.0   \n",
       "4                              0.00                           0.0   \n",
       "5                              0.00                           0.0   \n",
       "6                              0.00                           0.0   \n",
       "\n",
       "  currency_iso_code                          lead_source  \\\n",
       "0               EUR                                        \n",
       "1               EUR   Do not use - Marketing before 2021   \n",
       "2               EUR   Do not use - Marketing before 2021   \n",
       "3               EUR          Partner Sourced - Alliances   \n",
       "4               EUR  Sales Generated Lead - AE, CSM, SDR   \n",
       "5               EUR                                        \n",
       "6               EUR  Sales Generated Lead - AE, CSM, SDR   \n",
       "\n",
       "                deal_use_case                                  name  \n",
       "0                                       Organized homogeneous model  \n",
       "1                                Profit-focused intangible analyzer  \n",
       "2                              Devolved attitude-oriented structure  \n",
       "3                                  Adaptive system-worthy framework  \n",
       "4  Human Resources Monitoring        Team-oriented holistic website  \n",
       "5                                   Versatile bifurcated definition  \n",
       "6                                      Progressive foreground array  "
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duckdb.sql('''\n",
    "\n",
    "SELECT\n",
    "\n",
    "    *\n",
    "\n",
    "\n",
    "FROM opportunities\n",
    "\n",
    "WHERE (deal_licence_start_date IS NULL AND deal_licence_end_date IS NOT NULL)\n",
    "\n",
    "OR (deal_licence_start_date IS NOT NULL AND deal_licence_end_date IS  NULL)\n",
    "\n",
    "\n",
    "''').df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "┌──────────────────────────────┐\n",
       "│ avg_time_licence_period_days │\n",
       "│            int32             │\n",
       "├──────────────────────────────┤\n",
       "│                          402 │\n",
       "└──────────────────────────────┘"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duckdb.sql('''\n",
    "\n",
    "WITH first_table AS (\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "\n",
    "    DATE_DIFF('DAY', deal_licence_start_date, deal_licence_end_date) AS nb_days\n",
    "    \n",
    "FROM opportunities\n",
    "\n",
    "WHERE deal_licence_start_date < deal_licence_end_date\n",
    "\n",
    "AND opportunity_stage_status = 'Lost' \n",
    "\n",
    "\n",
    ")\n",
    "\n",
    "SELECT\n",
    "\n",
    "    CAST(MEAN(nb_days) AS INT) AS avg_time_licence_period_days\n",
    "\n",
    "FROM first_table\n",
    "\n",
    "\n",
    "\n",
    "''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- when deal_licence_start_date IS NULL and deal_licence_end_date IS NOT NULL: deal_licence_start_date = deal_licence_end_date\n",
    "    \n",
    "- deal_licence_start_date IS NOT NULL and deal_licence_end_date IS NULL: use average predicted licence time for lost opportunities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "duckdb.sql('''\n",
    "\n",
    "UPDATE opportunities AS opportunities_original\n",
    "\n",
    "SET deal_licence_start_date = (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    CASE\n",
    "    WHEN deal_licence_start_date IS NULL AND deal_licence_end_date IS NOT NULL THEN deal_licence_end_date\n",
    "    ELSE deal_licence_start_date\n",
    "    END AS deal_licence_start_date\n",
    "\n",
    "\n",
    "FROM opportunities a\n",
    "\n",
    "WHERE opportunities_original.id = a.id  \n",
    "\n",
    "ORDER BY 1\n",
    "\n",
    "\n",
    ")  \n",
    "\n",
    "''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "duckdb.sql('''\n",
    "\n",
    "UPDATE opportunities AS opportunities_original\n",
    "\n",
    "SET deal_licence_end_date = (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    CASE\n",
    "    WHEN deal_licence_start_date IS NOT NULL AND deal_licence_end_date IS NULL THEN deal_licence_start_date + INTERVAL '402' DAY\n",
    "    ELSE deal_licence_end_date\n",
    "    END AS deal_licence_end_date\n",
    "\n",
    "\n",
    "FROM opportunities a\n",
    "\n",
    "WHERE opportunities_original.id = a.id  \n",
    "\n",
    "ORDER BY 1\n",
    "\n",
    "\n",
    ")  \n",
    "\n",
    "''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account_id</th>\n",
       "      <th>owner_id</th>\n",
       "      <th>id</th>\n",
       "      <th>type</th>\n",
       "      <th>deal_licence_start_date</th>\n",
       "      <th>deal_licence_end_date</th>\n",
       "      <th>close_date</th>\n",
       "      <th>created_date</th>\n",
       "      <th>opportunity_stage_status</th>\n",
       "      <th>lead_source_category</th>\n",
       "      <th>deal_total_non_recurring_revenue</th>\n",
       "      <th>deal_total_recurring_revenue</th>\n",
       "      <th>currency_iso_code</th>\n",
       "      <th>lead_source</th>\n",
       "      <th>deal_use_case</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [account_id, owner_id, id, type, deal_licence_start_date, deal_licence_end_date, close_date, created_date, opportunity_stage_status, lead_source_category, deal_total_non_recurring_revenue, deal_total_recurring_revenue, currency_iso_code, lead_source, deal_use_case, name]\n",
       "Index: []"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duckdb.sql('''\n",
    "\n",
    "SELECT\n",
    "\n",
    "    *\n",
    "\n",
    "\n",
    "FROM opportunities\n",
    "\n",
    "WHERE (deal_licence_start_date IS NULL AND deal_licence_end_date IS NOT NULL)\n",
    "\n",
    "OR (deal_licence_start_date IS NOT NULL AND deal_licence_end_date IS  NULL)\n",
    "\n",
    "\n",
    "''').df()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "### 12 open opportunities have closed date happening in the past (before June 2023)\n",
    "\n",
    "### 1 won opportunity with close date happening in the future (after June 2023)\n",
    "\n",
    "### 10 lost opportunities with close date happening in the future (after June 2023)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Otherwise, sales team has not entered the right close date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>opportunity_category</th>\n",
       "      <th>count_star()</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>other</td>\n",
       "      <td>6746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>open_opportunity_close_date_happening_in_the_past</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>closed_opportunity_happening_in_the_future</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                opportunity_category  count_star()\n",
       "0                                              other          6746\n",
       "1  open_opportunity_close_date_happening_in_the_past            12\n",
       "2         closed_opportunity_happening_in_the_future            11"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duckdb.sql('''\n",
    "\n",
    "SELECT\n",
    "\n",
    "    CASE\n",
    "    WHEN opportunity_stage_status = 'Open' AND close_date < '2023-06-01' THEN 'open_opportunity_close_date_happening_in_the_past'\n",
    "    WHEN opportunity_stage_status IN ('Lost', 'Won') AND close_date > '2023-06-30' THEN 'closed_opportunity_happening_in_the_future'\n",
    "    ELSE 'other'\n",
    "    END AS opportunity_category,\n",
    "    \n",
    "    COUNT(*)\n",
    "\n",
    "\n",
    "FROM opportunities\n",
    "\n",
    "GROUP BY 1\n",
    "\n",
    "\n",
    "''').df()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>opportunity_category</th>\n",
       "      <th>account_id</th>\n",
       "      <th>owner_id</th>\n",
       "      <th>id</th>\n",
       "      <th>type</th>\n",
       "      <th>deal_licence_start_date</th>\n",
       "      <th>deal_licence_end_date</th>\n",
       "      <th>close_date</th>\n",
       "      <th>created_date</th>\n",
       "      <th>opportunity_stage_status</th>\n",
       "      <th>lead_source_category</th>\n",
       "      <th>deal_total_non_recurring_revenue</th>\n",
       "      <th>deal_total_recurring_revenue</th>\n",
       "      <th>currency_iso_code</th>\n",
       "      <th>lead_source</th>\n",
       "      <th>deal_use_case</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>closed_opportunity_happening_in_the_future</td>\n",
       "      <td>1541</td>\n",
       "      <td>101</td>\n",
       "      <td>0065q000002JjS7AAK</td>\n",
       "      <td>Renew</td>\n",
       "      <td>2023-07-05</td>\n",
       "      <td>2024-07-04</td>\n",
       "      <td>2023-07-05</td>\n",
       "      <td>2022-07-24</td>\n",
       "      <td>Lost</td>\n",
       "      <td>Sales/CSM</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20945.88</td>\n",
       "      <td>EUR</td>\n",
       "      <td>Cold Outreach - Database we own</td>\n",
       "      <td>Embedded Analytics for Software Companies</td>\n",
       "      <td>Decentralized optimal Internet solution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>closed_opportunity_happening_in_the_future</td>\n",
       "      <td>2908</td>\n",
       "      <td>101</td>\n",
       "      <td>0065q000002JayTAAS</td>\n",
       "      <td>Renew</td>\n",
       "      <td>2023-08-01</td>\n",
       "      <td>2024-07-31</td>\n",
       "      <td>2023-08-01</td>\n",
       "      <td>2022-07-18</td>\n",
       "      <td>Lost</td>\n",
       "      <td>Sales/CSM</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4210.90</td>\n",
       "      <td>EUR</td>\n",
       "      <td>Sales Generated Lead - AE, CSM, SDR</td>\n",
       "      <td>No specific Use Case yet</td>\n",
       "      <td>Up-sized tertiary approach</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>closed_opportunity_happening_in_the_future</td>\n",
       "      <td>1685</td>\n",
       "      <td>88</td>\n",
       "      <td>0065q000007aQTAAA2</td>\n",
       "      <td>Renew</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>2024-09-30</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>2023-01-19</td>\n",
       "      <td>Lost</td>\n",
       "      <td>Alliances</td>\n",
       "      <td>0.00</td>\n",
       "      <td>139540.63</td>\n",
       "      <td>EUR</td>\n",
       "      <td>Partner Sourced - Alliances</td>\n",
       "      <td></td>\n",
       "      <td>Future-proofed asynchronous Graphic Interface</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>closed_opportunity_happening_in_the_future</td>\n",
       "      <td>1364</td>\n",
       "      <td>101</td>\n",
       "      <td>0065q000002Kw68AAC</td>\n",
       "      <td>Renew</td>\n",
       "      <td>2023-10-31</td>\n",
       "      <td>2024-10-30</td>\n",
       "      <td>2023-10-31</td>\n",
       "      <td>2022-09-30</td>\n",
       "      <td>Lost</td>\n",
       "      <td>Marketing &amp; Alliances</td>\n",
       "      <td>0.00</td>\n",
       "      <td>22632.16</td>\n",
       "      <td>EUR</td>\n",
       "      <td>Content, Events &amp; webinar - Alliances booked</td>\n",
       "      <td>Embedded Analytics for Software Companies</td>\n",
       "      <td>Future-proofed object-oriented benchmark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>closed_opportunity_happening_in_the_future</td>\n",
       "      <td>1545</td>\n",
       "      <td>124</td>\n",
       "      <td>0065q000002LYyHAAW</td>\n",
       "      <td>Renew</td>\n",
       "      <td>2023-11-25</td>\n",
       "      <td>2024-11-24</td>\n",
       "      <td>2023-11-25</td>\n",
       "      <td>2022-10-28</td>\n",
       "      <td>Lost</td>\n",
       "      <td>Alliances</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3682.30</td>\n",
       "      <td>EUR</td>\n",
       "      <td>Partner Sourced - Alliances</td>\n",
       "      <td></td>\n",
       "      <td>Devolved bandwidth-monitored Internet solution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>closed_opportunity_happening_in_the_future</td>\n",
       "      <td>2566</td>\n",
       "      <td>101</td>\n",
       "      <td>0065q000007ZVcuAAG</td>\n",
       "      <td>Renew</td>\n",
       "      <td>2023-12-21</td>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>2023-12-21</td>\n",
       "      <td>2022-12-07</td>\n",
       "      <td>Lost</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>0.00</td>\n",
       "      <td>22314.64</td>\n",
       "      <td>EUR</td>\n",
       "      <td>Direct traffic</td>\n",
       "      <td>Embed - Data Portal for Consulting Agencies</td>\n",
       "      <td>Organic methodical installation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>closed_opportunity_happening_in_the_future</td>\n",
       "      <td>1388</td>\n",
       "      <td>101</td>\n",
       "      <td>0065q000007ZVeMAAW</td>\n",
       "      <td>Renew</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>2024-12-30</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>2022-12-07</td>\n",
       "      <td>Lost</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16002.63</td>\n",
       "      <td>EUR</td>\n",
       "      <td>SEO - Organic</td>\n",
       "      <td>Embedded Analytics for Software Companies</td>\n",
       "      <td>Distributed tertiary database</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>closed_opportunity_happening_in_the_future</td>\n",
       "      <td>1704</td>\n",
       "      <td>101</td>\n",
       "      <td>0065q000007aIGEAA2</td>\n",
       "      <td>Renew</td>\n",
       "      <td>2024-04-12</td>\n",
       "      <td>2025-04-11</td>\n",
       "      <td>2024-04-12</td>\n",
       "      <td>2023-01-13</td>\n",
       "      <td>Lost</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20882.87</td>\n",
       "      <td>EUR</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Compatible discrete orchestration</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>closed_opportunity_happening_in_the_future</td>\n",
       "      <td>1759</td>\n",
       "      <td>88</td>\n",
       "      <td>0065q000007ZniYAAS</td>\n",
       "      <td>Renew</td>\n",
       "      <td>2024-01-02</td>\n",
       "      <td>2025-01-01</td>\n",
       "      <td>2024-01-02</td>\n",
       "      <td>2022-12-19</td>\n",
       "      <td>Lost</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.00</td>\n",
       "      <td>68876.91</td>\n",
       "      <td>EUR</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Cloned scalable encoding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>closed_opportunity_happening_in_the_future</td>\n",
       "      <td>1713</td>\n",
       "      <td>88</td>\n",
       "      <td>0060900000RHBjQAAX</td>\n",
       "      <td>Renew</td>\n",
       "      <td>2024-05-01</td>\n",
       "      <td>2025-04-30</td>\n",
       "      <td>2024-05-01</td>\n",
       "      <td>2023-05-11</td>\n",
       "      <td>Lost</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.00</td>\n",
       "      <td>76803.01</td>\n",
       "      <td>EUR</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Distributed 6thgeneration application</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>closed_opportunity_happening_in_the_future</td>\n",
       "      <td>3025</td>\n",
       "      <td>88</td>\n",
       "      <td>0065q000002JBxbAAG</td>\n",
       "      <td>Renew</td>\n",
       "      <td>2023-07-01</td>\n",
       "      <td>2024-06-30</td>\n",
       "      <td>2023-07-01</td>\n",
       "      <td>2022-06-28</td>\n",
       "      <td>Won</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.00</td>\n",
       "      <td>68592.01</td>\n",
       "      <td>EUR</td>\n",
       "      <td></td>\n",
       "      <td>No specific Use Case yet</td>\n",
       "      <td>Pre-emptive hybrid help-desk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>open_opportunity_close_date_happening_in_the_past</td>\n",
       "      <td>2673</td>\n",
       "      <td>124</td>\n",
       "      <td>0060900000G8sOKAAZ</td>\n",
       "      <td>Renew</td>\n",
       "      <td>2022-11-16</td>\n",
       "      <td>2023-11-15</td>\n",
       "      <td>2022-11-16</td>\n",
       "      <td>2022-01-12</td>\n",
       "      <td>Open</td>\n",
       "      <td>Alliances</td>\n",
       "      <td>0.00</td>\n",
       "      <td>183501.94</td>\n",
       "      <td>EUR</td>\n",
       "      <td>Partner Sourced - Alliances</td>\n",
       "      <td>Analytics for your customers, suppliers and th...</td>\n",
       "      <td>Open-architected mobile algorithm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>open_opportunity_close_date_happening_in_the_past</td>\n",
       "      <td>2165</td>\n",
       "      <td>112</td>\n",
       "      <td>0060900000IUJshAAH</td>\n",
       "      <td>Renew</td>\n",
       "      <td>2023-02-07</td>\n",
       "      <td>2024-02-06</td>\n",
       "      <td>2023-02-07</td>\n",
       "      <td>2022-03-08</td>\n",
       "      <td>Open</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.00</td>\n",
       "      <td>121695.12</td>\n",
       "      <td>EUR</td>\n",
       "      <td></td>\n",
       "      <td>No specific Use Case yet</td>\n",
       "      <td>Expanded asymmetric policy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>open_opportunity_close_date_happening_in_the_past</td>\n",
       "      <td>2183</td>\n",
       "      <td>112</td>\n",
       "      <td>0060900000IVWzoAAH</td>\n",
       "      <td>Renew</td>\n",
       "      <td>2023-02-01</td>\n",
       "      <td>2024-01-31</td>\n",
       "      <td>2023-02-01</td>\n",
       "      <td>2022-04-05</td>\n",
       "      <td>Open</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>0.00</td>\n",
       "      <td>99558.67</td>\n",
       "      <td>EUR</td>\n",
       "      <td>Do not use - Marketing before 2021</td>\n",
       "      <td>No specific Use Case yet</td>\n",
       "      <td>User-centric 3rdgeneration software</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>open_opportunity_close_date_happening_in_the_past</td>\n",
       "      <td>1553</td>\n",
       "      <td>112</td>\n",
       "      <td>0060900000IUOSmAAP</td>\n",
       "      <td>Renew</td>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>2024-03-29</td>\n",
       "      <td>2023-03-29</td>\n",
       "      <td>2022-03-10</td>\n",
       "      <td>Open</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>0.00</td>\n",
       "      <td>73662.88</td>\n",
       "      <td>EUR</td>\n",
       "      <td>Do not use - Marketing before 2021</td>\n",
       "      <td>No specific Use Case yet</td>\n",
       "      <td>Innovative bi-directional service-desk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>open_opportunity_close_date_happening_in_the_past</td>\n",
       "      <td>1357</td>\n",
       "      <td>88</td>\n",
       "      <td>0060900000IUSwBAAX</td>\n",
       "      <td>Renew</td>\n",
       "      <td>2023-03-14</td>\n",
       "      <td>2024-03-13</td>\n",
       "      <td>2023-03-14</td>\n",
       "      <td>2022-03-14</td>\n",
       "      <td>Open</td>\n",
       "      <td>Marketing &amp; Sales</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15175.48</td>\n",
       "      <td>EUR</td>\n",
       "      <td>Referral - Founders &amp; Toucan Network</td>\n",
       "      <td>Embedded Analytics for Software Companies</td>\n",
       "      <td>Switchable responsive process improvement</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>open_opportunity_close_date_happening_in_the_past</td>\n",
       "      <td>1590</td>\n",
       "      <td>154</td>\n",
       "      <td>0060900000IVYPvAAP</td>\n",
       "      <td>Renew</td>\n",
       "      <td>2023-04-05</td>\n",
       "      <td>2024-04-05</td>\n",
       "      <td>2023-04-05</td>\n",
       "      <td>2022-04-06</td>\n",
       "      <td>Open</td>\n",
       "      <td>Sales/CSM</td>\n",
       "      <td>0.00</td>\n",
       "      <td>19585.99</td>\n",
       "      <td>EUR</td>\n",
       "      <td>Sales Generated Lead - AE, CSM, SDR</td>\n",
       "      <td>Embedded Analytics for Software Companies</td>\n",
       "      <td>Synchronized explicit open architecture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>open_opportunity_close_date_happening_in_the_past</td>\n",
       "      <td>2412</td>\n",
       "      <td>154</td>\n",
       "      <td>0060900000Kpu47AAB</td>\n",
       "      <td>Renew</td>\n",
       "      <td>2023-04-25</td>\n",
       "      <td>2024-04-24</td>\n",
       "      <td>2023-04-25</td>\n",
       "      <td>2022-05-15</td>\n",
       "      <td>Open</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.00</td>\n",
       "      <td>124249.62</td>\n",
       "      <td>EUR</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Implemented even-keeled firmware</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>open_opportunity_close_date_happening_in_the_past</td>\n",
       "      <td>1262</td>\n",
       "      <td>101</td>\n",
       "      <td>0060900000IWLxbAAH</td>\n",
       "      <td>Renew</td>\n",
       "      <td>2023-05-09</td>\n",
       "      <td>2026-05-08</td>\n",
       "      <td>2023-05-09</td>\n",
       "      <td>2022-05-02</td>\n",
       "      <td>Open</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30322.83</td>\n",
       "      <td>EUR</td>\n",
       "      <td>SEO - Organic</td>\n",
       "      <td>Embedded Analytics for Software Companies</td>\n",
       "      <td>Digitized fresh-thinking portal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>open_opportunity_close_date_happening_in_the_past</td>\n",
       "      <td>2326</td>\n",
       "      <td>88</td>\n",
       "      <td>0060900000NuBvrAAF</td>\n",
       "      <td>Renew</td>\n",
       "      <td>2023-05-01</td>\n",
       "      <td>2024-04-30</td>\n",
       "      <td>2023-05-01</td>\n",
       "      <td>2023-02-02</td>\n",
       "      <td>Open</td>\n",
       "      <td>Alliances</td>\n",
       "      <td>0.00</td>\n",
       "      <td>147077.42</td>\n",
       "      <td>EUR</td>\n",
       "      <td>Partner Sourced - Alliances</td>\n",
       "      <td>Public Sector - Internal Performance</td>\n",
       "      <td>Re-engineered mobile adapter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>open_opportunity_close_date_happening_in_the_past</td>\n",
       "      <td>2017</td>\n",
       "      <td>143</td>\n",
       "      <td>0060900000G7K1bAAF</td>\n",
       "      <td>Upsell</td>\n",
       "      <td>2022-02-28</td>\n",
       "      <td>2023-02-27</td>\n",
       "      <td>2022-01-17</td>\n",
       "      <td>2021-12-15</td>\n",
       "      <td>Open</td>\n",
       "      <td>Sales/CSM</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20139.15</td>\n",
       "      <td>EUR</td>\n",
       "      <td>Sales Generated Lead - AE, CSM, SDR</td>\n",
       "      <td>Public Sector - Data Portal</td>\n",
       "      <td>Public-key composite orchestration</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>open_opportunity_close_date_happening_in_the_past</td>\n",
       "      <td>351</td>\n",
       "      <td>143</td>\n",
       "      <td>0060900000IUcfxAAD</td>\n",
       "      <td>New Business</td>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>2024-03-30</td>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>2022-03-17</td>\n",
       "      <td>Open</td>\n",
       "      <td>Sales/CSM</td>\n",
       "      <td>10815.46</td>\n",
       "      <td>237545.61</td>\n",
       "      <td>EUR</td>\n",
       "      <td>Sales Generated Lead - AE, CSM, SDR</td>\n",
       "      <td>Executive Analytics Dashboard</td>\n",
       "      <td>Assimilated analyzing archive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>open_opportunity_close_date_happening_in_the_past</td>\n",
       "      <td>2828</td>\n",
       "      <td>178</td>\n",
       "      <td>0060900000QnBbIAAV</td>\n",
       "      <td>Embed Usage</td>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>2023-04-11</td>\n",
       "      <td>Open</td>\n",
       "      <td>Sales/CSM</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1315.30</td>\n",
       "      <td>EUR</td>\n",
       "      <td>Sales Generated Lead - AE, CSM, SDR</td>\n",
       "      <td></td>\n",
       "      <td>Intuitive stable alliance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 opportunity_category  account_id  owner_id  \\\n",
       "0          closed_opportunity_happening_in_the_future        1541       101   \n",
       "1          closed_opportunity_happening_in_the_future        2908       101   \n",
       "2          closed_opportunity_happening_in_the_future        1685        88   \n",
       "3          closed_opportunity_happening_in_the_future        1364       101   \n",
       "4          closed_opportunity_happening_in_the_future        1545       124   \n",
       "5          closed_opportunity_happening_in_the_future        2566       101   \n",
       "6          closed_opportunity_happening_in_the_future        1388       101   \n",
       "7          closed_opportunity_happening_in_the_future        1704       101   \n",
       "8          closed_opportunity_happening_in_the_future        1759        88   \n",
       "9          closed_opportunity_happening_in_the_future        1713        88   \n",
       "10         closed_opportunity_happening_in_the_future        3025        88   \n",
       "11  open_opportunity_close_date_happening_in_the_past        2673       124   \n",
       "12  open_opportunity_close_date_happening_in_the_past        2165       112   \n",
       "13  open_opportunity_close_date_happening_in_the_past        2183       112   \n",
       "14  open_opportunity_close_date_happening_in_the_past        1553       112   \n",
       "15  open_opportunity_close_date_happening_in_the_past        1357        88   \n",
       "16  open_opportunity_close_date_happening_in_the_past        1590       154   \n",
       "17  open_opportunity_close_date_happening_in_the_past        2412       154   \n",
       "18  open_opportunity_close_date_happening_in_the_past        1262       101   \n",
       "19  open_opportunity_close_date_happening_in_the_past        2326        88   \n",
       "20  open_opportunity_close_date_happening_in_the_past        2017       143   \n",
       "21  open_opportunity_close_date_happening_in_the_past         351       143   \n",
       "22  open_opportunity_close_date_happening_in_the_past        2828       178   \n",
       "\n",
       "                    id          type deal_licence_start_date  \\\n",
       "0   0065q000002JjS7AAK         Renew              2023-07-05   \n",
       "1   0065q000002JayTAAS         Renew              2023-08-01   \n",
       "2   0065q000007aQTAAA2         Renew              2023-10-01   \n",
       "3   0065q000002Kw68AAC         Renew              2023-10-31   \n",
       "4   0065q000002LYyHAAW         Renew              2023-11-25   \n",
       "5   0065q000007ZVcuAAG         Renew              2023-12-21   \n",
       "6   0065q000007ZVeMAAW         Renew              2023-12-31   \n",
       "7   0065q000007aIGEAA2         Renew              2024-04-12   \n",
       "8   0065q000007ZniYAAS         Renew              2024-01-02   \n",
       "9   0060900000RHBjQAAX         Renew              2024-05-01   \n",
       "10  0065q000002JBxbAAG         Renew              2023-07-01   \n",
       "11  0060900000G8sOKAAZ         Renew              2022-11-16   \n",
       "12  0060900000IUJshAAH         Renew              2023-02-07   \n",
       "13  0060900000IVWzoAAH         Renew              2023-02-01   \n",
       "14  0060900000IUOSmAAP         Renew              2023-03-29   \n",
       "15  0060900000IUSwBAAX         Renew              2023-03-14   \n",
       "16  0060900000IVYPvAAP         Renew              2023-04-05   \n",
       "17  0060900000Kpu47AAB         Renew              2023-04-25   \n",
       "18  0060900000IWLxbAAH         Renew              2023-05-09   \n",
       "19  0060900000NuBvrAAF         Renew              2023-05-01   \n",
       "20  0060900000G7K1bAAF        Upsell              2022-02-28   \n",
       "21  0060900000IUcfxAAD  New Business              2023-03-31   \n",
       "22  0060900000QnBbIAAV   Embed Usage              2023-01-01   \n",
       "\n",
       "   deal_licence_end_date close_date created_date opportunity_stage_status  \\\n",
       "0             2024-07-04 2023-07-05   2022-07-24                     Lost   \n",
       "1             2024-07-31 2023-08-01   2022-07-18                     Lost   \n",
       "2             2024-09-30 2023-10-01   2023-01-19                     Lost   \n",
       "3             2024-10-30 2023-10-31   2022-09-30                     Lost   \n",
       "4             2024-11-24 2023-11-25   2022-10-28                     Lost   \n",
       "5             2024-12-20 2023-12-21   2022-12-07                     Lost   \n",
       "6             2024-12-30 2023-12-31   2022-12-07                     Lost   \n",
       "7             2025-04-11 2024-04-12   2023-01-13                     Lost   \n",
       "8             2025-01-01 2024-01-02   2022-12-19                     Lost   \n",
       "9             2025-04-30 2024-05-01   2023-05-11                     Lost   \n",
       "10            2024-06-30 2023-07-01   2022-06-28                      Won   \n",
       "11            2023-11-15 2022-11-16   2022-01-12                     Open   \n",
       "12            2024-02-06 2023-02-07   2022-03-08                     Open   \n",
       "13            2024-01-31 2023-02-01   2022-04-05                     Open   \n",
       "14            2024-03-29 2023-03-29   2022-03-10                     Open   \n",
       "15            2024-03-13 2023-03-14   2022-03-14                     Open   \n",
       "16            2024-04-05 2023-04-05   2022-04-06                     Open   \n",
       "17            2024-04-24 2023-04-25   2022-05-15                     Open   \n",
       "18            2026-05-08 2023-05-09   2022-05-02                     Open   \n",
       "19            2024-04-30 2023-05-01   2023-02-02                     Open   \n",
       "20            2023-02-27 2022-01-17   2021-12-15                     Open   \n",
       "21            2024-03-30 2023-03-31   2022-03-17                     Open   \n",
       "22            2023-03-31 2023-01-01   2023-04-11                     Open   \n",
       "\n",
       "     lead_source_category  deal_total_non_recurring_revenue  \\\n",
       "0               Sales/CSM                              0.00   \n",
       "1               Sales/CSM                              0.00   \n",
       "2               Alliances                              0.00   \n",
       "3   Marketing & Alliances                              0.00   \n",
       "4               Alliances                              0.00   \n",
       "5               Marketing                              0.00   \n",
       "6               Marketing                              0.00   \n",
       "7                   Other                              0.00   \n",
       "8                   Other                              0.00   \n",
       "9                   Other                              0.00   \n",
       "10                  Other                              0.00   \n",
       "11              Alliances                              0.00   \n",
       "12                  Other                              0.00   \n",
       "13              Marketing                              0.00   \n",
       "14              Marketing                              0.00   \n",
       "15      Marketing & Sales                              0.00   \n",
       "16              Sales/CSM                              0.00   \n",
       "17                  Other                              0.00   \n",
       "18              Marketing                              0.00   \n",
       "19              Alliances                              0.00   \n",
       "20              Sales/CSM                              0.00   \n",
       "21              Sales/CSM                          10815.46   \n",
       "22              Sales/CSM                              0.00   \n",
       "\n",
       "    deal_total_recurring_revenue currency_iso_code  \\\n",
       "0                       20945.88               EUR   \n",
       "1                        4210.90               EUR   \n",
       "2                      139540.63               EUR   \n",
       "3                       22632.16               EUR   \n",
       "4                        3682.30               EUR   \n",
       "5                       22314.64               EUR   \n",
       "6                       16002.63               EUR   \n",
       "7                       20882.87               EUR   \n",
       "8                       68876.91               EUR   \n",
       "9                       76803.01               EUR   \n",
       "10                      68592.01               EUR   \n",
       "11                     183501.94               EUR   \n",
       "12                     121695.12               EUR   \n",
       "13                      99558.67               EUR   \n",
       "14                      73662.88               EUR   \n",
       "15                      15175.48               EUR   \n",
       "16                      19585.99               EUR   \n",
       "17                     124249.62               EUR   \n",
       "18                      30322.83               EUR   \n",
       "19                     147077.42               EUR   \n",
       "20                      20139.15               EUR   \n",
       "21                     237545.61               EUR   \n",
       "22                       1315.30               EUR   \n",
       "\n",
       "                                     lead_source  \\\n",
       "0                Cold Outreach - Database we own   \n",
       "1            Sales Generated Lead - AE, CSM, SDR   \n",
       "2                    Partner Sourced - Alliances   \n",
       "3   Content, Events & webinar - Alliances booked   \n",
       "4                    Partner Sourced - Alliances   \n",
       "5                                 Direct traffic   \n",
       "6                                  SEO - Organic   \n",
       "7                                                  \n",
       "8                                                  \n",
       "9                                                  \n",
       "10                                                 \n",
       "11                   Partner Sourced - Alliances   \n",
       "12                                                 \n",
       "13            Do not use - Marketing before 2021   \n",
       "14            Do not use - Marketing before 2021   \n",
       "15          Referral - Founders & Toucan Network   \n",
       "16           Sales Generated Lead - AE, CSM, SDR   \n",
       "17                                                 \n",
       "18                                 SEO - Organic   \n",
       "19                   Partner Sourced - Alliances   \n",
       "20           Sales Generated Lead - AE, CSM, SDR   \n",
       "21           Sales Generated Lead - AE, CSM, SDR   \n",
       "22           Sales Generated Lead - AE, CSM, SDR   \n",
       "\n",
       "                                        deal_use_case  \\\n",
       "0           Embedded Analytics for Software Companies   \n",
       "1                            No specific Use Case yet   \n",
       "2                                                       \n",
       "3           Embedded Analytics for Software Companies   \n",
       "4                                                       \n",
       "5         Embed - Data Portal for Consulting Agencies   \n",
       "6           Embedded Analytics for Software Companies   \n",
       "7                                                       \n",
       "8                                                       \n",
       "9                                                       \n",
       "10                           No specific Use Case yet   \n",
       "11  Analytics for your customers, suppliers and th...   \n",
       "12                           No specific Use Case yet   \n",
       "13                           No specific Use Case yet   \n",
       "14                           No specific Use Case yet   \n",
       "15          Embedded Analytics for Software Companies   \n",
       "16          Embedded Analytics for Software Companies   \n",
       "17                                                      \n",
       "18          Embedded Analytics for Software Companies   \n",
       "19               Public Sector - Internal Performance   \n",
       "20                        Public Sector - Data Portal   \n",
       "21                      Executive Analytics Dashboard   \n",
       "22                                                      \n",
       "\n",
       "                                              name  \n",
       "0          Decentralized optimal Internet solution  \n",
       "1                       Up-sized tertiary approach  \n",
       "2    Future-proofed asynchronous Graphic Interface  \n",
       "3         Future-proofed object-oriented benchmark  \n",
       "4   Devolved bandwidth-monitored Internet solution  \n",
       "5                  Organic methodical installation  \n",
       "6                    Distributed tertiary database  \n",
       "7                Compatible discrete orchestration  \n",
       "8                         Cloned scalable encoding  \n",
       "9            Distributed 6thgeneration application  \n",
       "10                    Pre-emptive hybrid help-desk  \n",
       "11               Open-architected mobile algorithm  \n",
       "12                      Expanded asymmetric policy  \n",
       "13             User-centric 3rdgeneration software  \n",
       "14          Innovative bi-directional service-desk  \n",
       "15       Switchable responsive process improvement  \n",
       "16         Synchronized explicit open architecture  \n",
       "17                Implemented even-keeled firmware  \n",
       "18                 Digitized fresh-thinking portal  \n",
       "19                    Re-engineered mobile adapter  \n",
       "20              Public-key composite orchestration  \n",
       "21                   Assimilated analyzing archive  \n",
       "22                       Intuitive stable alliance  "
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duckdb.sql('''\n",
    "\n",
    "SELECT\n",
    "\n",
    "    CASE\n",
    "    WHEN opportunity_stage_status = 'Open' AND close_date < '2023-06-01' THEN 'open_opportunity_close_date_happening_in_the_past'\n",
    "    WHEN opportunity_stage_status IN ('Lost', 'Won') AND close_date > '2023-06-30' THEN 'closed_opportunity_happening_in_the_future'\n",
    "    ELSE 'other'\n",
    "    END AS opportunity_category,\n",
    "    \n",
    "    *\n",
    "\n",
    "\n",
    "FROM opportunities\n",
    "\n",
    "WHERE (opportunity_stage_status = 'Open' AND close_date < '2023-06-01')\n",
    "\n",
    "OR (opportunity_stage_status IN ('Lost', 'Won') AND close_date > '2023-06-30' )\n",
    "\n",
    "ORDER BY 1\n",
    "\n",
    "\n",
    "\n",
    "''').df()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's now deduplicate the opportunities. We will use the following columns to deduplicate: \n",
    "\n",
    "- account_id\n",
    "\n",
    "- type\n",
    "\n",
    "- deal_licence_start_date\n",
    "\n",
    "- deal_use_case\n",
    "\n",
    "- opportunity_stage_status\n",
    "\n",
    "- lead_source\n",
    "\n",
    "- team_country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "deduplicate_opportunities_query = '''\n",
    "\n",
    "CREATE OR REPLACE TABLE opportunities AS \n",
    "\n",
    "WITH first_table AS (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    id,\n",
    "\n",
    "    ROW_NUMBER () OVER (PARTITION BY account_id, type, deal_licence_start_date, deal_use_case, opportunity_stage_status, lead_source, team_country) AS row_nb\n",
    "\n",
    "FROM opportunities a\n",
    "\n",
    "LEFT JOIN crm_users b ON b.crm_user_id = a.owner_id\n",
    "\n",
    "\n",
    ")\n",
    "\n",
    "SELECT\n",
    "\n",
    "    a.*\n",
    "    \n",
    "\n",
    "\n",
    "\n",
    "FROM opportunities a\n",
    "\n",
    "INNER JOIN first_table b ON b.id = a.id\n",
    "\n",
    "WHERE row_nb = 1\n",
    "\n",
    "\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "duckdb.sql(deduplicate_opportunities_query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "┌──────────────┐\n",
       "│ count_star() │\n",
       "│    int64     │\n",
       "├──────────────┤\n",
       "│         5485 │\n",
       "└──────────────┘"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duckdb.sql('SELECT COUNT(*) FROM opportunities ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6769"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "6769"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8103117151721081"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5485 / 6769"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1284"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "6769 - 5485"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- during this deduplication, we removed about 19% of the opportunities (1,284 records)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='C) Revision Opportunity Stage Status'> C) Revision Opportunity Stage Status</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- no missing data in the field opportunity_stage_status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count_star()</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count_star()\n",
       "0             0"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duckdb.sql('''\n",
    "\n",
    "SELECT\n",
    "\n",
    "    \n",
    "    COUNT(*)\n",
    "\n",
    "\n",
    "FROM opportunities\n",
    "\n",
    "\n",
    "WHERE opportunity_stage_status IS NULL\n",
    "\n",
    "ORDER BY 1\n",
    "\n",
    "\n",
    "\n",
    "''').df()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 113 won opportunities with no revenue generated and for which the close date happened in the past\n",
    "\n",
    "We will consider these opportunities as lost. \n",
    "\n",
    "This could suggest that \n",
    "\n",
    "- either the opportunity did not actually generate any revenue \n",
    "\n",
    "- or we don't know the actual revenue generated by these won opportunities (attribution issue) \n",
    "\n",
    "- or that the revenue figures have not been updated properly (issue with data pipeline or if manual input the sales team has not updated figures)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count_star()</th>\n",
       "      <th>max(close_date)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>112</td>\n",
       "      <td>2021-03-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count_star() max(close_date)\n",
       "0           112      2021-03-01"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duckdb.sql('''\n",
    "\n",
    "SELECT\n",
    "\n",
    "    \n",
    "    \n",
    "    COUNT(*),\n",
    "    \n",
    "    MAX(close_date)\n",
    "\n",
    "\n",
    "\n",
    "FROM opportunities\n",
    "\n",
    "WHERE opportunity_stage_status = 'Won'\n",
    "\n",
    "AND deal_total_non_recurring_revenue = 0\n",
    "\n",
    "AND deal_total_recurring_revenue = 0\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "''').df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "duckdb.sql('''\n",
    "\n",
    "UPDATE opportunities AS opportunities_original\n",
    "\n",
    "SET opportunity_stage_status = (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    CASE\n",
    "    WHEN opportunity_stage_status = 'Won' AND deal_total_non_recurring_revenue = 0 AND deal_total_recurring_revenue = 0 THEN 'Lost'\n",
    "    ELSE opportunity_stage_status\n",
    "    END AS opportunity_stage_status\n",
    "\n",
    "\n",
    "FROM opportunities a\n",
    "\n",
    "WHERE opportunities_original.id = a.id  \n",
    "\n",
    "ORDER BY 1\n",
    "\n",
    "\n",
    ") \n",
    "\n",
    "\n",
    "\n",
    "''')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 630 lost opportunities with revenue figures\n",
    "\n",
    "We assume that these figures are the potential revenue those opportunities would have generated if the opportunities had been won"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count_star()</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count_star()\n",
       "0           625"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duckdb.sql('''\n",
    "\n",
    "SELECT\n",
    "\n",
    "    \n",
    "    \n",
    "    COUNT(*)\n",
    "\n",
    "\n",
    "\n",
    "FROM opportunities\n",
    "\n",
    "WHERE opportunity_stage_status = 'Lost'\n",
    "\n",
    "AND (deal_total_non_recurring_revenue > 0 OR deal_total_recurring_revenue > 0)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "''').df()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 215 open opportunities with revenue figures.\n",
    "\n",
    "\n",
    "We assume these figures show the potential revenue those opportunities could generate if they are won"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count_star()</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>215</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count_star()\n",
       "0           215"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duckdb.sql('''\n",
    "\n",
    "SELECT\n",
    "\n",
    "    \n",
    "    \n",
    "    COUNT(*)\n",
    "\n",
    "\n",
    "\n",
    "FROM opportunities\n",
    "\n",
    "WHERE opportunity_stage_status = 'Open'\n",
    "\n",
    "AND (deal_total_non_recurring_revenue > 0 OR deal_total_recurring_revenue > 0)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "''').df()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='D) Revision Opportunity Type'> D) Revision Opportunity Type</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>nb_opportunities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cross sell</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Embed Usage</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New Business</td>\n",
       "      <td>4173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Renew</td>\n",
       "      <td>840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Upsell</td>\n",
       "      <td>266</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           type  nb_opportunities\n",
       "0                               2\n",
       "1    Cross sell               191\n",
       "2   Embed Usage                13\n",
       "3  New Business              4173\n",
       "4         Renew               840\n",
       "5        Upsell               266"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duckdb.sql('''\n",
    "\n",
    "\n",
    "SELECT \n",
    "\n",
    "    CASE\n",
    "    WHEN type IS NULL THEN 'unknown'\n",
    "    ELSE type\n",
    "    END AS type,\n",
    "    \n",
    "    COUNT(*) AS nb_opportunities\n",
    "\n",
    "FROM opportunities\n",
    "\n",
    "GROUP BY 1\n",
    "\n",
    "ORDER BY 1\n",
    "\n",
    "\n",
    "''').df()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's define the business logic categorizing accounts as new business or existing business to know whether the opportunity is related to a new business account or not "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define the new business period as follows: \n",
    "\n",
    "- a won account is considered a new business account during 6 months of paying licence activity and then becomes an existing business account. \n",
    "\n",
    "\n",
    "- if the new business account has churned for less than 3 months and is reactivated (through renewal, upsell or cross-sell), we don't restart the new business period.\n",
    "\n",
    "\n",
    "- if the new business account has churned for at least 3 months before being reactivated (through renewal, upsell or cross-sell), we restart the new business period.\n",
    "\n",
    "\n",
    "- if the existing business account has churned for at least 3 months before being reactivated (through renewal, upsell or cross-sell), the account become a new business account again.\n",
    "\n",
    "\n",
    "Additionally, we use the following rules: \n",
    "\n",
    "\n",
    "- The account is considered churning for a given time period if the account stopped generating revenue through won opportunities. \n",
    "\n",
    "\n",
    "- Won opportunities with no attributed revenue are not included in the new business time period. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We will first create the  table new_business_accounts_analytics categorizing accounts as new or existing business by day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_new_business_query = '''\n",
    "\n",
    "WITH won_opportunities_generating_revenue_account_level AS (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    account_id,\n",
    "        \n",
    "    deal_licence_start_date,\n",
    "    \n",
    "    MAX(deal_licence_end_date) AS deal_licence_end_date\n",
    "    \n",
    "\n",
    "FROM opportunities a\n",
    "\n",
    "WHERE opportunity_stage_status = 'Won'\n",
    "\n",
    "AND (deal_total_non_recurring_revenue > 0 OR deal_total_recurring_revenue > 0)\n",
    "\n",
    "GROUP BY 1,2\n",
    "\n",
    "),\n",
    "\n",
    "first_new_business_table AS (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    b.account_id,\n",
    "\n",
    "    CAST(a.ymd AS DATE) AS ymd,\n",
    "    \n",
    "\n",
    "FROM date_dim a\n",
    "\n",
    "INNER JOIN won_opportunities_generating_revenue_account_level b ON CAST(a.ymd AS DATE) >= CAST(b.deal_licence_start_date AS DATE) \n",
    "\n",
    "AND CAST(a.ymd AS DATE) <= CAST(b.deal_licence_end_date AS DATE)\n",
    "\n",
    "GROUP BY 1,2\n",
    "\n",
    "ORDER BY 1,2\n",
    "\n",
    "),\n",
    "\n",
    "first_churn_table AS (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    account_id,\n",
    "    \n",
    "    ymd,\n",
    "    \n",
    "    CASE\n",
    "    WHEN LAG (ymd) OVER (PARTITION BY account_id ORDER BY ymd) IS NULL THEN NULL\n",
    "    ELSE DATE_DIFF('DAY', LAG (ymd) OVER (PARTITION BY account_id ORDER BY ymd), ymd) - 1\n",
    "    END AS nb_churn_days\n",
    "    \n",
    "FROM first_new_business_table\n",
    "\n",
    "ORDER BY 1,2\n",
    "\n",
    "),\n",
    "\n",
    "second_churn_table AS (\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "    account_id,\n",
    "    \n",
    "    ymd,\n",
    "    \n",
    "    nb_churn_days,\n",
    "    \n",
    "    SUM(CASE WHEN nb_churn_days >= 90 THEN 1 ELSE 0 END) OVER (PARTITION BY account_id ORDER BY ymd) AS churn_time_period\n",
    "    \n",
    "FROM first_churn_table\n",
    "\n",
    "ORDER BY 1,2\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "),\n",
    "\n",
    "first_new_business_activity_table AS (\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "    account_id,\n",
    "    \n",
    "    ymd,\n",
    "    \n",
    "    nb_churn_days,\n",
    "    \n",
    "    churn_time_period,\n",
    "    \n",
    "    SUM(CASE WHEN account_id IS NOT NULL THEN 1 ELSE 0 END) OVER (PARTITION BY account_id, churn_time_period ORDER BY ymd) AS new_business_activity_counter\n",
    "    \n",
    "FROM second_churn_table\n",
    "\n",
    "ORDER BY 1,2\n",
    "\n",
    "\n",
    "),\n",
    "\n",
    "second_new_business_activity_table AS (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    account_id,\n",
    "    \n",
    "    ymd,\n",
    "    \n",
    "    nb_churn_days,\n",
    "    \n",
    "    churn_time_period,\n",
    "    \n",
    "    new_business_activity_counter,\n",
    "    \n",
    "    SUM(CASE WHEN new_business_activity_counter NOT IN (184, 368, 552, 736, 920, 1104, 1288, 1472, 1656, 1840, 2024) THEN 1 ELSE -182 END) OVER (PARTITION BY account_id, churn_time_period ORDER BY ymd) AS new_business_activity_counter_bis\n",
    "\n",
    "\n",
    "\n",
    "FROM first_new_business_activity_table\n",
    "\n",
    "ORDER BY 1,2\n",
    "\n",
    "),\n",
    "\n",
    "third_new_business_activity_table AS (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    account_id,\n",
    "    \n",
    "    ymd,\n",
    "    \n",
    "    nb_churn_days,\n",
    "    \n",
    "    churn_time_period,\n",
    "    \n",
    "    new_business_activity_counter,\n",
    "    \n",
    "    new_business_activity_counter_bis,\n",
    "    \n",
    "    SUM(CASE WHEN new_business_activity_counter = 184 AND new_business_activity_counter_bis = 1 THEN 1 ELSE 0 END) OVER (PARTITION BY account_id, churn_time_period ORDER BY ymd) AS is_not_new_business\n",
    "    \n",
    "FROM second_new_business_activity_table\n",
    "\n",
    "ORDER BY 1,2\n",
    "\n",
    "),\n",
    "\n",
    "new_business_activity_accounts_already_onboarded AS (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    account_id,\n",
    "    \n",
    "    ymd,\n",
    "    \n",
    "    nb_churn_days,\n",
    "    \n",
    "    churn_time_period,\n",
    "    \n",
    "    new_business_activity_counter,\n",
    "    \n",
    "    new_business_activity_counter_bis,\n",
    "    \n",
    "    CASE\n",
    "    WHEN is_not_new_business = 1 THEN 'existing_business'\n",
    "    ELSE 'new_business' END\n",
    "    AS is_new_business\n",
    "\n",
    "\n",
    "\n",
    "FROM third_new_business_activity_table\n",
    "\n",
    "\n",
    "ORDER BY 1,2\n",
    "\n",
    "),\n",
    "\n",
    "accounts_never_onboarded_first_table AS (\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "    account_id,\n",
    "    \n",
    "    \n",
    "    1 AS idx\n",
    "    \n",
    "\n",
    "FROM opportunities a\n",
    "\n",
    "WHERE account_id NOT IN (SELECT DISTINCT account_id FROM new_business_activity_accounts_already_onboarded)\n",
    "\n",
    "GROUP BY 1,2\n",
    "\n",
    "\n",
    "\n",
    "),\n",
    "\n",
    "all_dates_table AS (\n",
    "\n",
    "SELECT\n",
    "\n",
    "\n",
    "    CAST(ymd AS DATE) AS ymd,\n",
    "    \n",
    "    1 AS idx\n",
    "\n",
    "\n",
    "FROM date_dim\n",
    "\n",
    "\n",
    "\n",
    "),\n",
    "\n",
    "accounts_never_onboarded_second_table AS (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    b.account_id,\n",
    "\n",
    "    CAST(a.ymd AS DATE) AS ymd,\n",
    "    \n",
    "    0 AS nb_churn_days,\n",
    "    \n",
    "    0 AS churn_time_period,\n",
    "    \n",
    "    0 AS new_business_activity_counter,\n",
    "    \n",
    "    0 AS new_business_activity_counter_bis,\n",
    "    \n",
    "    'new_business' AS is_new_business\n",
    "    \n",
    "\n",
    "FROM all_dates_table a\n",
    "\n",
    "INNER JOIN accounts_never_onboarded_first_table b ON b.idx = a.idx\n",
    "\n",
    "ORDER BY 1,2\n",
    "\n",
    ")\n",
    "\n",
    "SELECT\n",
    "\n",
    "    account_id,\n",
    "    \n",
    "    ymd,\n",
    "    \n",
    "    nb_churn_days,\n",
    "    \n",
    "    churn_time_period,\n",
    "    \n",
    " --   new_business_activity_counter,\n",
    "    \n",
    "    new_business_activity_counter_bis AS new_business_activity_day,\n",
    "    \n",
    "    is_new_business,\n",
    "    \n",
    "    'yes' AS has_account_already_been_onboarded\n",
    "    \n",
    "FROM new_business_activity_accounts_already_onboarded\n",
    "\n",
    "UNION\n",
    "\n",
    "SELECT\n",
    "\n",
    "    account_id,\n",
    "    \n",
    "    ymd,\n",
    "    \n",
    "    nb_churn_days,\n",
    "    \n",
    "    churn_time_period,\n",
    "    \n",
    "--    new_business_activity_counter,\n",
    "    \n",
    "    new_business_activity_counter_bis AS new_business_activity_day,\n",
    "    \n",
    "    is_new_business,\n",
    "    \n",
    "    'no' AS has_account_already_been_onboarded\n",
    "    \n",
    "FROM accounts_never_onboarded_second_table\n",
    "    \n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a2a952870dd34bce8d555c90e765e0cf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, layout=Layout(width='auto'), style=ProgressStyle(bar_color='black'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "first_new_business_data = duckdb.sql(first_new_business_query).df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_new_business_create_table_query = '''\n",
    "\n",
    "CREATE OR REPLACE TABLE new_business_account_activity AS \n",
    "\n",
    "WITH won_opportunities_generating_revenue_account_level AS (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    account_id,\n",
    "        \n",
    "    deal_licence_start_date,\n",
    "    \n",
    "    MAX(deal_licence_end_date) AS deal_licence_end_date\n",
    "    \n",
    "\n",
    "FROM opportunities a\n",
    "\n",
    "WHERE opportunity_stage_status = 'Won'\n",
    "\n",
    "AND (deal_total_non_recurring_revenue > 0 OR deal_total_recurring_revenue > 0)\n",
    "\n",
    "GROUP BY 1,2\n",
    "\n",
    "),\n",
    "\n",
    "first_new_business_table AS (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    b.account_id,\n",
    "\n",
    "    CAST(a.ymd AS DATE) AS ymd\n",
    "    \n",
    "\n",
    "FROM date_dim a\n",
    "\n",
    "INNER JOIN won_opportunities_generating_revenue_account_level b ON CAST(a.ymd AS DATE) >= CAST(b.deal_licence_start_date AS DATE) \n",
    "\n",
    "AND CAST(a.ymd AS DATE) <= CAST(b.deal_licence_end_date AS DATE)\n",
    "\n",
    "GROUP BY 1,2\n",
    "\n",
    "ORDER BY 1,2\n",
    "\n",
    "),\n",
    "\n",
    "first_churn_table AS (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    account_id,\n",
    "    \n",
    "    ymd,\n",
    "    \n",
    "    CASE\n",
    "    WHEN LAG (ymd) OVER (PARTITION BY account_id ORDER BY ymd) IS NULL THEN NULL\n",
    "    ELSE DATE_DIFF('DAY', LAG (ymd) OVER (PARTITION BY account_id ORDER BY ymd), ymd) - 1\n",
    "    END AS nb_churn_days\n",
    "    \n",
    "FROM first_new_business_table\n",
    "\n",
    "ORDER BY 1,2\n",
    "\n",
    "),\n",
    "\n",
    "second_churn_table AS (\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "    account_id,\n",
    "    \n",
    "    ymd,\n",
    "    \n",
    "    nb_churn_days,\n",
    "    \n",
    "    SUM(CASE WHEN nb_churn_days >= 90 THEN 1 ELSE 0 END) OVER (PARTITION BY account_id ORDER BY ymd) AS churn_time_period\n",
    "    \n",
    "FROM first_churn_table\n",
    "\n",
    "ORDER BY 1,2\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "),\n",
    "\n",
    "first_new_business_activity_table AS (\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "    account_id,\n",
    "    \n",
    "    ymd,\n",
    "    \n",
    "    nb_churn_days,\n",
    "    \n",
    "    churn_time_period,\n",
    "    \n",
    "    SUM(CASE WHEN account_id IS NOT NULL THEN 1 ELSE 0 END) OVER (PARTITION BY account_id, churn_time_period ORDER BY ymd) AS new_business_activity_counter\n",
    "    \n",
    "FROM second_churn_table\n",
    "\n",
    "ORDER BY 1,2\n",
    "\n",
    "\n",
    "),\n",
    "\n",
    "second_new_business_activity_table AS (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    account_id,\n",
    "    \n",
    "    ymd,\n",
    "    \n",
    "    nb_churn_days,\n",
    "    \n",
    "    churn_time_period,\n",
    "    \n",
    "    new_business_activity_counter,\n",
    "    \n",
    "    SUM(CASE WHEN new_business_activity_counter NOT IN (184, 368, 552, 736, 920, 1104, 1288, 1472, 1656, 1840, 2024) THEN 1 ELSE -182 END) OVER (PARTITION BY account_id, churn_time_period ORDER BY ymd) AS new_business_activity_counter_bis\n",
    "\n",
    "\n",
    "\n",
    "FROM first_new_business_activity_table\n",
    "\n",
    "ORDER BY 1,2\n",
    "\n",
    "),\n",
    "\n",
    "third_new_business_activity_table AS (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    account_id,\n",
    "    \n",
    "    ymd,\n",
    "    \n",
    "    nb_churn_days,\n",
    "    \n",
    "    churn_time_period,\n",
    "    \n",
    "    new_business_activity_counter,\n",
    "    \n",
    "    new_business_activity_counter_bis,\n",
    "    \n",
    "    SUM(CASE WHEN new_business_activity_counter = 184 AND new_business_activity_counter_bis = 1 THEN 1 ELSE 0 END) OVER (PARTITION BY account_id, churn_time_period ORDER BY ymd) AS is_not_new_business\n",
    "    \n",
    "FROM second_new_business_activity_table\n",
    "\n",
    "ORDER BY 1,2\n",
    "\n",
    "),\n",
    "\n",
    "new_business_activity_accounts_already_onboarded AS (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    account_id,\n",
    "    \n",
    "    ymd,\n",
    "    \n",
    "    nb_churn_days,\n",
    "    \n",
    "    churn_time_period,\n",
    "    \n",
    "    new_business_activity_counter,\n",
    "    \n",
    "    new_business_activity_counter_bis,\n",
    "    \n",
    "    CASE\n",
    "    WHEN is_not_new_business = 1 THEN 'existing_business'\n",
    "    ELSE 'new_business' END\n",
    "    AS is_new_business\n",
    "\n",
    "\n",
    "\n",
    "FROM third_new_business_activity_table\n",
    "\n",
    "\n",
    "ORDER BY 1,2\n",
    "\n",
    "),\n",
    "\n",
    "\n",
    "accounts_never_onboarded_first_table AS (\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "    account_id,\n",
    "    \n",
    "    \n",
    "    1 AS idx\n",
    "    \n",
    "\n",
    "FROM opportunities a\n",
    "\n",
    "WHERE account_id NOT IN (SELECT DISTINCT account_id FROM new_business_activity_accounts_already_onboarded)\n",
    "\n",
    "GROUP BY 1,2\n",
    "\n",
    "\n",
    "\n",
    "),\n",
    "\n",
    "all_dates_table AS (\n",
    "\n",
    "SELECT\n",
    "\n",
    "\n",
    "    CAST(ymd AS DATE) AS ymd,\n",
    "    \n",
    "    1 AS idx\n",
    "\n",
    "\n",
    "FROM date_dim\n",
    "\n",
    "\n",
    "\n",
    "),\n",
    "\n",
    "accounts_never_onboarded_second_table AS (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    b.account_id,\n",
    "\n",
    "    CAST(a.ymd AS DATE) AS ymd,\n",
    "    \n",
    "    0 AS nb_churn_days,\n",
    "    \n",
    "    0 AS churn_time_period,\n",
    "    \n",
    "    0 AS new_business_activity_counter,\n",
    "    \n",
    "    0 AS new_business_activity_counter_bis,\n",
    "    \n",
    "    'new_business' AS is_new_business\n",
    "    \n",
    "\n",
    "FROM all_dates_table a\n",
    "\n",
    "INNER JOIN accounts_never_onboarded_first_table b ON b.idx = a.idx\n",
    "\n",
    "ORDER BY 1,2\n",
    "\n",
    ")\n",
    "\n",
    "SELECT\n",
    "\n",
    "    account_id,\n",
    "    \n",
    "    ymd,\n",
    "    \n",
    "    nb_churn_days,\n",
    "    \n",
    "    churn_time_period,\n",
    "    \n",
    " --   new_business_activity_counter,\n",
    "    \n",
    "    new_business_activity_counter_bis AS new_business_activity_day,\n",
    "    \n",
    "    is_new_business,\n",
    "    \n",
    "    'yes' AS has_account_already_been_onboarded\n",
    "    \n",
    "FROM new_business_activity_accounts_already_onboarded\n",
    "\n",
    "UNION\n",
    "\n",
    "SELECT\n",
    "\n",
    "    account_id,\n",
    "    \n",
    "    ymd,\n",
    "    \n",
    "    nb_churn_days,\n",
    "    \n",
    "    churn_time_period,\n",
    "    \n",
    "--    new_business_activity_counter,\n",
    "    \n",
    "    new_business_activity_counter_bis AS new_business_activity_day,\n",
    "    \n",
    "    is_new_business,\n",
    "    \n",
    "    'no' AS has_account_already_been_onboarded\n",
    "    \n",
    "FROM accounts_never_onboarded_second_table\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1915266c3c594718b37625f5adbed6de",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatProgress(value=0.0, layout=Layout(width='auto'), style=ProgressStyle(bar_color='black'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "duckdb.sql(first_new_business_create_table_query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>has_account_already_been_onboarded</th>\n",
       "      <th>nb_accounts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>yes</td>\n",
       "      <td>402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>no</td>\n",
       "      <td>2655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  has_account_already_been_onboarded  nb_accounts\n",
       "0                                yes          402\n",
       "1                                 no         2655"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duckdb.sql('SELECT has_account_already_been_onboarded, COUNT(DISTINCT(account_id)) AS nb_accounts FROM new_business_account_activity GROUP BY 1').df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nb_accounts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3057</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   nb_accounts\n",
       "0         3057"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duckdb.sql('SELECT  COUNT(DISTINCT(account_id)) AS nb_accounts FROM opportunities').df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.131174353941773"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "401 / (3057)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- we can observe that 13.1% of the accounts for which the sales team identified business opportunities in Salesforce have become paying accounts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## To link a business opportunity to an existing or new business account in the opportunities table, we will use the following methodology:\n",
    "\n",
    "\n",
    "- for won opportunities generating revenue, we will use the licence start date as the reference to know whether the account was a new or existing business account at that time\n",
    "\n",
    "- for opportunities that have not been won but are linked to an account that has already been generating revenue with a won opportunity, we will use the opportunity closed date as the reference when there is a match with the table in the new business activity table\n",
    "\n",
    "=> when there is no match, we will use the account status from the closest new business activity day\n",
    "\n",
    "\n",
    "- for opportunities that have not been won and linked to an account that has never been onboarded, we will link them to a new business account"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "enrich_opportunity_table_query = '''\n",
    "\n",
    "CREATE OR REPLACE TABLE opportunities AS\n",
    "\n",
    "WITH new_business_activity_already_launched_accounts_table AS (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    account_id,\n",
    "    \n",
    "    ymd,\n",
    "    \n",
    "    nb_churn_days,\n",
    "    \n",
    "    churn_time_period,\n",
    "    \n",
    "    new_business_activity_day,\n",
    "    \n",
    "    is_new_business,\n",
    "    \n",
    "    has_account_already_been_onboarded\n",
    "\n",
    "\n",
    "FROM new_business_account_activity\n",
    "\n",
    "WHERE has_account_already_been_onboarded = 'yes'\n",
    "\n",
    "\n",
    "\n",
    "),\n",
    "\n",
    "second_table AS (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    a.*,\n",
    "    \n",
    "    is_new_business,\n",
    "    \n",
    "    has_account_already_been_onboarded\n",
    "    \n",
    "FROM opportunities a\n",
    "\n",
    "INNER JOIN new_business_activity_already_launched_accounts_table b ON b.ymd = CAST(a.deal_licence_start_date AS DATE) AND b.account_id = a.account_id\n",
    "\n",
    "WHERE opportunity_stage_status = 'Won'\n",
    "\n",
    "AND (deal_total_non_recurring_revenue > 0 OR deal_total_recurring_revenue > 0)\n",
    "\n",
    "),\n",
    "\n",
    "third_table AS (\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "    a.*,\n",
    "    \n",
    "    is_new_business,\n",
    "    \n",
    "    has_account_already_been_onboarded\n",
    "    \n",
    "FROM opportunities a\n",
    "\n",
    "INNER JOIN new_business_activity_already_launched_accounts_table b ON b.ymd = CAST(a.created_date AS DATE) AND b.account_id = a.account_id\n",
    "\n",
    "WHERE a.id NOT IN (SELECT DISTINCT id FROM second_table)\n",
    "\n",
    "AND a.account_id IN (SELECT DISTINCT account_id FROM second_table)\n",
    "\n",
    "\n",
    "\n",
    "),\n",
    "\n",
    "\n",
    "no_match_first_table AS (\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "--    CAST(a.created_date AS DATE) AS created_date,\n",
    "\n",
    "    CAST(a.close_date AS DATE) AS created_date,\n",
    "    \n",
    "    id,\n",
    "    \n",
    "    a.account_id\n",
    "    \n",
    "FROM opportunities a\n",
    "\n",
    "LEFT JOIN new_business_activity_already_launched_accounts_table b ON b.ymd = CAST(a.created_date AS DATE) AND b.account_id = a.account_id\n",
    "\n",
    "WHERE is_new_business IS NULL \n",
    "\n",
    "\n",
    "\n",
    "),\n",
    "\n",
    "no_match_second_table AS (\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "    a.created_date,\n",
    "    \n",
    "    a.id,\n",
    "    \n",
    "    a.account_id,\n",
    "    \n",
    "    b.ymd,\n",
    "\n",
    "    b.is_new_business,\n",
    "    \n",
    "    DATE_DIFF('DAY', a.created_date, CAST(b.ymd AS DATE)) AS nb_days\n",
    "\n",
    "FROM no_match_first_table a\n",
    "\n",
    "INNER JOIN new_business_activity_already_launched_accounts_table b ON b.account_id = a.account_id\n",
    "\n",
    "\n",
    "\n",
    "),\n",
    "\n",
    "\n",
    "no_match_third_table AS (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    id,\n",
    "    \n",
    "    MIN(nb_days) AS nb_days\n",
    "\n",
    "FROM no_match_second_table a\n",
    "\n",
    "GROUP BY 1\n",
    "\n",
    "\n",
    "\n",
    "),\n",
    "\n",
    "\n",
    "no_match_fourth_table AS (\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "DISTINCT\n",
    "\n",
    "    a.id,\n",
    "    \n",
    " --   a.account_id,\n",
    "    \n",
    "    b.is_new_business\n",
    "    \n",
    " --   b.ymd\n",
    "    \n",
    "FROM no_match_third_table a\n",
    "\n",
    "INNER JOIN no_match_second_table b ON b.nb_days = a.nb_days AND b.id = a.id\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "),\n",
    "\n",
    "final_table AS (\n",
    "\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "    a.*\n",
    "    \n",
    "FROM second_table a\n",
    "\n",
    "UNION\n",
    "\n",
    "SELECT\n",
    "\n",
    "    b.*\n",
    "    \n",
    "    \n",
    "    \n",
    "FROM third_table  b\n",
    "\n",
    " UNION\n",
    "\n",
    " SELECT\n",
    "\n",
    "    d.*,\n",
    "    \n",
    "    \n",
    "    \n",
    "    e.is_new_business,\n",
    "    \n",
    "    'yes' AS has_account_already_been_onboarded\n",
    "    \n",
    " FROM opportunities d\n",
    "\n",
    " INNER JOIN no_match_fourth_table e ON e.id = d.id\n",
    "\n",
    " WHERE d.id NOT IN (SELECT DISTINCT id FROM second_table)\n",
    "\n",
    " AND d.id NOT IN (SELECT DISTINCT id FROM third_table)\n",
    "\n",
    " AND d.account_id IN (SELECT DISTINCT h.account_id FROM new_business_activity_already_launched_accounts_table h)\n",
    "\n",
    "\n",
    "\n",
    "UNION\n",
    "\n",
    "SELECT\n",
    "\n",
    "    c.*,\n",
    "    \n",
    "    'new_business' AS is_new_business,\n",
    "    \n",
    "    'no' AS has_account_already_been_onboarded\n",
    "    \n",
    "FROM opportunities c\n",
    "\n",
    "WHERE c.account_id NOT IN (SELECT DISTINCT g.account_id FROM new_business_activity_already_launched_accounts_table g)\n",
    "\n",
    "\n",
    ")\n",
    "\n",
    "SELECT\n",
    "\n",
    "*\n",
    "\n",
    "FROM final_table\n",
    "\n",
    "-- fixing issue with 3 lost opportunities not identified as new business\n",
    "\n",
    "WHERE id NOT IN ('00609000002IucJAAS', '00609000002Iv0qAAC', '00609000002Iv2UAAS')\n",
    "\n",
    "UNION\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "    a.*,\n",
    "    \n",
    "    'new_business' AS is_new_business,\n",
    "    \n",
    "    'no' AS has_account_already_been_onboarded\n",
    "    \n",
    "FROM opportunities a\n",
    "\n",
    "WHERE a.id IN ('00609000002IucJAAS', '00609000002Iv0qAAC', '00609000002Iv2UAAS')\n",
    "\n",
    "\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "duckdb.sql(enrich_opportunity_table_query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count_star()</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5485</td>\n",
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      "text/plain": [
       "   count_star()\n",
       "0          5485"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duckdb.sql('SELECT COUNT(*) FROM opportunities ').df()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Upsell: when we sell additional products within an existing opportunity.\n",
    "    \n",
    "   \n",
    "    \n",
    "- Cross-sell: opportunity generated with a new entity of an existing accountcustomer\n",
    "    \n",
    "   \n",
    "    \n",
    "- Renew: renewal of an existing opportunity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Methodology for Tracking Opportunities from Existing Business Accounts as either Upsell, Cross-sell or Renew\n",
    "\n",
    "1) a business opportunity from an existing business account is considered as a renewal if the following conditions are met: \n",
    "\n",
    "- the number of active licences that ended is inferior to number of active licences at the start of the new subscription (excluding this new subscription) and total revenue is inferior to 10% of previous revenue\n",
    "\n",
    "OR \n",
    "\n",
    "- no revenue figures associated with opportunity and no subscription at start of licence\n",
    "\n",
    "OR \n",
    "\n",
    "- when no revenue figures associated with opportunity and number of subscriptions at start of period inferior to number of subscriptions that ended\n",
    "\n",
    "OR \n",
    "\n",
    "- no revenue figures associated with opportunity and number of subscriptions at start of period inferior to number of subscriptions that ended\n",
    "\n",
    "OR \n",
    "\n",
    "- no revenue figures associated with opportunity and nb subscriptions at start of period >= nb subscriptions that ended, we assume this is a renewal (could be also upsell)\n",
    "\n",
    "\n",
    "2) a business opportunity is considered as an up-sell if the following conditions are met: \n",
    "    \n",
    "- the number of active licences that ended is inferior to number of active licences at the start of the new subscription (excluding this new subscription)  and total revenue at least 10% higher than previous revenue\n",
    "\n",
    "\n",
    "OR \n",
    "\n",
    "- generates services revenue from existing opportunity\n",
    "\n",
    "\n",
    "\n",
    "3) a business opportunity is considered as a cross-sell if the following condition is met: \n",
    "\n",
    "\n",
    "- when generates revenue from new account\n",
    "\n",
    "\n",
    "OR \n",
    "\n",
    "- no revenue figures associated with opportunity and new account"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "existing_business_opportunities_second_methodology_query = '''\n",
    "\n",
    "CREATE OR REPLACE TABLE opportunities AS \n",
    "\n",
    "WITH first_table AS (\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "    a.account_id,\n",
    "    \n",
    "    a.id,\n",
    "    \n",
    "    a.deal_licence_start_date,\n",
    "    \n",
    "    a.deal_licence_end_date,\n",
    "    \n",
    "    a.close_date,\n",
    "    \n",
    "    a.created_date,\n",
    "    \n",
    "    a.opportunity_stage_status,\n",
    "    \n",
    "    a.deal_total_non_recurring_revenue,\n",
    "    \n",
    "    a.deal_total_recurring_revenue,\n",
    "    \n",
    "    a.is_new_business,\n",
    "    \n",
    "    a.has_account_already_been_onboarded,\n",
    "    \n",
    "    b.id AS other_opportunity_id_same_account,\n",
    "    \n",
    "    b.close_date AS close_date_prev_opportunity,\n",
    "    \n",
    "    b.deal_licence_start_date AS deal_licence_start_date_prev_opportunity,\n",
    "    \n",
    "    b.deal_licence_end_date AS deal_licence_end_date_prev_opportunity,\n",
    "    \n",
    "    b.deal_total_non_recurring_revenue AS deal_total_non_recurring_revenue_prev_opportunity,\n",
    "    \n",
    "    b.deal_total_recurring_revenue AS deal_total_recurring_revenue_prev_opportunity,\n",
    "    \n",
    "    DATE_DIFF('DAY', b.close_date, a.close_date) AS nb_days_between_opportunities,\n",
    "    \n",
    "    ROW_NUMBER () OVER (PARTITION BY a.id ORDER BY DATE_DIFF('DAY', b.close_date, a.close_date)) AS idx\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "FROM opportunities a\n",
    "\n",
    "-- LEFT JOIN opportunities b ON b.account_id = a.account_id AND b.close_date <= a.close_date AND b.id != a.id\n",
    "\n",
    "LEFT JOIN opportunities b ON b.account_id = a.account_id AND b.deal_licence_start_date <= a.deal_licence_start_date AND b.id != a.id\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "WHERE a.is_new_business = 'existing_business'\n",
    "\n",
    "AND b.opportunity_stage_status = 'Won'\n",
    "\n",
    "AND b.deal_licence_start_date IS NOT NULL\n",
    "\n",
    "AND a.deal_licence_start_date IS NOT NULL\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "),\n",
    "\n",
    "second_table AS (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    a.account_id,\n",
    "    \n",
    "    a.id,\n",
    "    \n",
    "    a.deal_licence_start_date,\n",
    "    \n",
    "    a.deal_licence_end_date,\n",
    "    \n",
    "    a.close_date,\n",
    "    \n",
    "    a.created_date,\n",
    "    \n",
    "    a.opportunity_stage_status,\n",
    "    \n",
    "    a.deal_total_non_recurring_revenue,\n",
    "    \n",
    "    a.deal_total_recurring_revenue,\n",
    "    \n",
    "    a.is_new_business,\n",
    "    \n",
    "    a.has_account_already_been_onboarded,\n",
    "    \n",
    "    b.id AS other_opportunity_id_same_account,\n",
    "    \n",
    "    b.close_date AS close_date_prev_opportunity,\n",
    "    \n",
    "    b.deal_licence_start_date AS deal_licence_start_date_prev_opportunity,\n",
    "    \n",
    "    b.deal_licence_end_date AS deal_licence_end_date_prev_opportunity,\n",
    "    \n",
    "    b.deal_total_non_recurring_revenue AS deal_total_non_recurring_revenue_prev_opportunity,\n",
    "    \n",
    "    b.deal_total_recurring_revenue AS deal_total_recurring_revenue_prev_opportunity,\n",
    "    \n",
    "    DATE_DIFF('DAY', b.close_date, a.close_date) AS nb_days_between_opportunities,\n",
    "    \n",
    "    ROW_NUMBER () OVER (PARTITION BY a.id ORDER BY DATE_DIFF('DAY', b.close_date, a.close_date)) AS idx\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "FROM opportunities a\n",
    "\n",
    "LEFT JOIN opportunities b ON b.account_id = a.account_id AND b.close_date <= a.close_date AND b.id != a.id\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "WHERE a.is_new_business = 'existing_business'\n",
    "\n",
    "AND b.opportunity_stage_status = 'Won'\n",
    "\n",
    "AND a.id NOT IN (SELECT DISTINCT id FROM first_table)\n",
    "\n",
    "\n",
    "\n",
    "),\n",
    "\n",
    "final_table AS (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    a.*\n",
    "\n",
    "\n",
    "FROM first_table a\n",
    "\n",
    "-- FROM opportunities\n",
    "\n",
    "-- WHERE is_new_business = 'existing_business' AND id NOT IN (SELECT DISTINCT id FROM first_table)\n",
    "\n",
    "\n",
    "UNION\n",
    "\n",
    "SELECT\n",
    "\n",
    "    b.*\n",
    "    \n",
    "FROM second_table b\n",
    "\n",
    "\n",
    "),\n",
    "\n",
    "second_final_table AS (\n",
    "\n",
    "SELECT\n",
    "\n",
    "\n",
    "\n",
    "    DATE_DIFF('DAY', deal_licence_end_date_prev_opportunity, deal_licence_start_date) AS nb_days_between_subscriptions,\n",
    "    \n",
    "    DATE_DIFF('DAY', deal_licence_end_date_prev_opportunity, close_date) AS nb_days_between_subscriptions_bis,\n",
    "\n",
    "    *\n",
    "\n",
    "FROM final_table\n",
    "\n",
    "),\n",
    "\n",
    "third_final_table AS (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    id,\n",
    "    \n",
    "    deal_total_recurring_revenue,\n",
    "    \n",
    "    deal_total_non_recurring_revenue,\n",
    "    \n",
    "    COUNT(DISTINCT(\n",
    "    CASE WHEN (nb_days_between_subscriptions >= 0 OR (nb_days_between_subscriptions_bis >= 0 AND deal_licence_start_date IS NULL)) \n",
    "    AND (nb_days_between_subscriptions <= 90 OR (nb_days_between_subscriptions_bis <= 90 AND deal_licence_start_date IS NULL))\n",
    "    AND (deal_total_recurring_revenue_prev_opportunity > 0)\n",
    "    THEN other_opportunity_id_same_account ELSE NULL END)) AS nb_subscriptions_ended_last_90_days_before_start_new_subscription,\n",
    "    \n",
    "    COUNT(DISTINCT(\n",
    "    CASE WHEN (nb_days_between_subscriptions >= 0 OR (nb_days_between_subscriptions_bis >= 0 AND deal_licence_start_date IS NULL)) \n",
    "    AND (nb_days_between_subscriptions <= 90 OR (nb_days_between_subscriptions_bis <= 90 AND deal_licence_start_date IS NULL))\n",
    "    AND (deal_total_non_recurring_revenue_prev_opportunity > 0)\n",
    "    THEN other_opportunity_id_same_account ELSE NULL END)) AS nb_services_ended_last_90_days_before_start_new_subscription,\n",
    "    \n",
    "    COUNT(DISTINCT(\n",
    "    CASE WHEN (nb_days_between_subscriptions >= 0 OR (nb_days_between_subscriptions_bis >= 0 AND deal_licence_start_date IS NULL)) \n",
    "    AND (nb_days_between_subscriptions <= 90 OR (nb_days_between_subscriptions_bis <= 90 AND deal_licence_start_date IS NULL))\n",
    "    THEN other_opportunity_id_same_account ELSE NULL END)) AS nb_opportunities_ended_last_90_days_before_start_new_subscription,\n",
    "    \n",
    "    \n",
    "    \n",
    "    COUNT(DISTINCT(\n",
    "    CASE \n",
    "    WHEN (nb_days_between_subscriptions < 0 OR (nb_days_between_subscriptions_bis < 0 AND deal_licence_start_date IS NULL)) \n",
    "    AND (deal_total_recurring_revenue_prev_opportunity > 0)\n",
    "    THEN other_opportunity_id_same_account ELSE NULL END)) AS nb_subscriptions_active_start_new_subscription,\n",
    "    \n",
    "    COUNT(DISTINCT(\n",
    "    CASE \n",
    "    WHEN (nb_days_between_subscriptions < 0 OR (nb_days_between_subscriptions_bis < 0 AND deal_licence_start_date IS NULL)) \n",
    "    AND (deal_total_non_recurring_revenue_prev_opportunity > 0)\n",
    "    THEN other_opportunity_id_same_account ELSE NULL END)) AS nb_services_active_start_new_subscription,\n",
    "    \n",
    "    COUNT(DISTINCT(\n",
    "    CASE \n",
    "    WHEN (nb_days_between_subscriptions < 0 OR (nb_days_between_subscriptions_bis < 0 AND deal_licence_start_date IS NULL)) \n",
    "    THEN other_opportunity_id_same_account ELSE NULL END)) AS nb_opportunities_active_start_new_subscription,\n",
    "    \n",
    "    \n",
    "    SUM(\n",
    "    CASE WHEN (nb_days_between_subscriptions >= 0 OR (nb_days_between_subscriptions_bis >= 0 AND deal_licence_start_date IS NULL)) \n",
    "    AND (nb_days_between_subscriptions <= 90 OR (nb_days_between_subscriptions_bis <= 90 AND deal_licence_start_date IS NULL))\n",
    "    AND ( deal_total_recurring_revenue_prev_opportunity > 0)\n",
    "    THEN deal_total_recurring_revenue_prev_opportunity ELSE 0 END) AS revenue_subscriptions_last_90_days_before_start_new_subscription,\n",
    "    \n",
    "    SUM(\n",
    "    CASE WHEN (nb_days_between_subscriptions >= 0 OR (nb_days_between_subscriptions_bis >= 0 AND deal_licence_start_date IS NULL)) \n",
    "    AND (nb_days_between_subscriptions <= 90 OR (nb_days_between_subscriptions_bis <= 90 AND deal_licence_start_date IS NULL))\n",
    "    AND ( deal_total_non_recurring_revenue_prev_opportunity > 0)\n",
    "    THEN deal_total_non_recurring_revenue_prev_opportunity ELSE 0 END) AS revenue_services_last_90_days_before_start_new_subscription,\n",
    "    \n",
    "    SUM(\n",
    "    CASE \n",
    "    WHEN (nb_days_between_subscriptions < 0 OR (nb_days_between_subscriptions_bis < 0 AND deal_licence_start_date IS NULL)) \n",
    "    AND (deal_total_recurring_revenue_prev_opportunity > 0)\n",
    "    THEN deal_total_recurring_revenue_prev_opportunity ELSE 0 END) AS revenue_subscriptions_active_start_new_subscription,\n",
    "    \n",
    "    SUM(\n",
    "    CASE \n",
    "    WHEN (nb_days_between_subscriptions < 0 OR (nb_days_between_subscriptions_bis < 0 AND deal_licence_start_date IS NULL)) \n",
    "    AND (deal_total_non_recurring_revenue_prev_opportunity > 0)\n",
    "    THEN deal_total_non_recurring_revenue_prev_opportunity ELSE 0 END) AS revenue_services_active_start_new_subscription\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "FROM second_final_table\n",
    "\n",
    "GROUP BY 1,2,3\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "),\n",
    "\n",
    "fourth_final_table AS (\n",
    "\n",
    "SELECT\n",
    "\n",
    "\n",
    "\n",
    "    \n",
    "    CASE\n",
    "    \n",
    "    \n",
    "    -- renewal when the number of active licences that ended is inferior to number of active licences at the start of the new subscription (excluding this new subscription) and total revenue inf to 10% of previous revenue\n",
    "    WHEN nb_subscriptions_ended_last_90_days_before_start_new_subscription > nb_subscriptions_active_start_new_subscription\n",
    "    AND deal_total_recurring_revenue > 0 \n",
    "    AND (deal_total_recurring_revenue + deal_total_non_recurring_revenue  - revenue_subscriptions_last_90_days_before_start_new_subscription - revenue_services_last_90_days_before_start_new_subscription) / (revenue_subscriptions_last_90_days_before_start_new_subscription + revenue_services_last_90_days_before_start_new_subscription)  < 0.1\n",
    "    THEN 'renewal'\n",
    "    \n",
    "    -- upsell when the number of active licences that ended is inferior to number of active licences at the start of the new subscription (excluding this new subscription)  and total revenue at least 10% higher than previous revenue\n",
    "    WHEN nb_subscriptions_ended_last_90_days_before_start_new_subscription > nb_subscriptions_active_start_new_subscription\n",
    "    AND deal_total_recurring_revenue > 0 \n",
    "    AND (deal_total_recurring_revenue + deal_total_non_recurring_revenue  - revenue_subscriptions_last_90_days_before_start_new_subscription - revenue_services_last_90_days_before_start_new_subscription) / (revenue_subscriptions_last_90_days_before_start_new_subscription + revenue_services_last_90_days_before_start_new_subscription)  >= 0.1\n",
    "    THEN 'upsell'\n",
    "    \n",
    "    -- upsell when generates services revenue from existing opportunity\n",
    "    WHEN nb_services_ended_last_90_days_before_start_new_subscription > nb_services_active_start_new_subscription\n",
    "    AND deal_total_non_recurring_revenue > 0 \n",
    "    THEN 'upsell' \n",
    "    \n",
    "    -- cross-sell when generates revenue from new account\n",
    "    WHEN ((nb_subscriptions_ended_last_90_days_before_start_new_subscription <= nb_subscriptions_active_start_new_subscription) OR (nb_services_ended_last_90_days_before_start_new_subscription <= nb_services_active_start_new_subscription))\n",
    "    AND (deal_total_recurring_revenue + deal_total_non_recurring_revenue) > 0\n",
    "    THEN 'cross-sell' \n",
    "    \n",
    "    -- cross-sell when no revenue figures associated with opportunity and new account\n",
    "    \n",
    "    WHEN (nb_opportunities_active_start_new_subscription >= nb_opportunities_ended_last_90_days_before_start_new_subscription)\n",
    "    AND deal_total_recurring_revenue = 0 AND deal_total_non_recurring_revenue = 0\n",
    "    THEN 'cross-sell'\n",
    "    \n",
    "    -- when no revenue figures associated with opportunity and no subscription at start of licence: renewal\n",
    "    WHEN deal_total_recurring_revenue = 0 AND deal_total_non_recurring_revenue = 0\n",
    "    AND nb_subscriptions_active_start_new_subscription = 0\n",
    "    THEN 'renewal'\n",
    "    \n",
    "    -- when no revenue figures associated with opportunity and number of subscriptions at start of period inferior to number of subscriptions that ended: renewal\n",
    "    WHEN deal_total_recurring_revenue = 0 AND deal_total_non_recurring_revenue = 0\n",
    "    AND nb_subscriptions_ended_last_90_days_before_start_new_subscription > nb_subscriptions_active_start_new_subscription\n",
    "    THEN 'renewal'\n",
    "    \n",
    "    -- when no revenue figures associated with opportunity and number of subscriptions at start of period inferior to number of subscriptions that ended: renewal\n",
    "    WHEN deal_total_recurring_revenue = 0 AND deal_total_non_recurring_revenue = 0\n",
    "    AND nb_subscriptions_ended_last_90_days_before_start_new_subscription > nb_subscriptions_active_start_new_subscription\n",
    "    THEN 'renewal'\n",
    "    \n",
    "    -- when no revenue figures associated with opportunity and nb subscriptions at start of period >= nb subscriptions that ended, we assume this is a renewal (could be also upsell)\n",
    "    \n",
    "    WHEN deal_total_recurring_revenue = 0 AND deal_total_non_recurring_revenue = 0\n",
    "    AND nb_subscriptions_ended_last_90_days_before_start_new_subscription <= nb_subscriptions_active_start_new_subscription\n",
    "    THEN 'renewal'\n",
    "    \n",
    "    ELSE 'other'\n",
    "    \n",
    "    END AS opportunity_type,\n",
    "    \n",
    "    *\n",
    "\n",
    "\n",
    "FROM third_final_table\n",
    "\n",
    "\n",
    ")\n",
    "\n",
    "SELECT\n",
    "\n",
    "    a.id,\n",
    "    \n",
    "    a.opportunity_type,\n",
    "    \n",
    "    b.is_new_business,\n",
    "    \n",
    "    b.has_account_already_been_onboarded,\n",
    "    \n",
    "    b.account_id,\n",
    "    \n",
    "    b.owner_id,\n",
    "    \n",
    "    b.deal_licence_start_date,\n",
    "    \n",
    "    b.deal_licence_end_date,\n",
    "    \n",
    "    b.close_date,\n",
    "    \n",
    "    b.created_date,\n",
    "    \n",
    "    b.opportunity_stage_status,\n",
    "    \n",
    "    b.lead_source_category,\n",
    "    \n",
    "    b.deal_total_non_recurring_revenue,\n",
    "    \n",
    "    b.deal_total_recurring_revenue,\n",
    "    \n",
    "    b.currency_iso_code,\n",
    "    \n",
    "    b.lead_source,\n",
    "    \n",
    "    b.deal_use_case,\n",
    "    \n",
    "    b.name\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "FROM fourth_final_table a\n",
    "\n",
    "INNER JOIN opportunities b ON b.id = a.id\n",
    "\n",
    "UNION\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "    id,\n",
    "    \n",
    "    'new_business' AS opportunity_type,\n",
    "    \n",
    "    is_new_business,\n",
    "    \n",
    "    has_account_already_been_onboarded,\n",
    "    \n",
    "    account_id,\n",
    "    \n",
    "    owner_id,\n",
    "    \n",
    "    deal_licence_start_date,\n",
    "    \n",
    "    deal_licence_end_date,\n",
    "    \n",
    "    close_date,\n",
    "    \n",
    "    created_date,\n",
    "    \n",
    "    opportunity_stage_status,\n",
    "    \n",
    "    lead_source_category,\n",
    "    \n",
    "    deal_total_non_recurring_revenue,\n",
    "    \n",
    "    deal_total_recurring_revenue,\n",
    "    \n",
    "    currency_iso_code,\n",
    "    \n",
    "    lead_source,\n",
    "    \n",
    "    deal_use_case,\n",
    "    \n",
    "    name\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "FROM opportunities\n",
    "\n",
    "WHERE is_new_business = 'new_business'\n",
    "\n",
    "UNION\n",
    "\n",
    "-- adding 1 missing lost opportunity with no licence period and no revenue figures\n",
    "\n",
    "SELECT\n",
    "\n",
    "\n",
    "    id,\n",
    "    \n",
    "    'renew' AS opportunity_type,\n",
    "    \n",
    "    is_new_business,\n",
    "    \n",
    "    has_account_already_been_onboarded,\n",
    "    \n",
    "    account_id,\n",
    "    \n",
    "    owner_id,\n",
    "    \n",
    "    deal_licence_start_date,\n",
    "    \n",
    "    deal_licence_end_date,\n",
    "    \n",
    "    close_date,\n",
    "    \n",
    "    created_date,\n",
    "    \n",
    "    opportunity_stage_status,\n",
    "    \n",
    "    lead_source_category,\n",
    "    \n",
    "    deal_total_non_recurring_revenue,\n",
    "    \n",
    "    deal_total_recurring_revenue,\n",
    "    \n",
    "    currency_iso_code,\n",
    "    \n",
    "    lead_source,\n",
    "    \n",
    "    deal_use_case,\n",
    "    \n",
    "    name\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "FROM opportunities\n",
    "\n",
    "WHERE id = '00609000002IvGOAA0'\n",
    "\n",
    "\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "duckdb.sql(existing_business_opportunities_second_methodology_query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "existing_business_opportunities_second_methodology_data = duckdb.sql('SELECT * FROM opportunities').df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>opportunity_type</th>\n",
       "      <th>is_new_business</th>\n",
       "      <th>has_account_already_been_onboarded</th>\n",
       "      <th>account_id</th>\n",
       "      <th>owner_id</th>\n",
       "      <th>deal_licence_start_date</th>\n",
       "      <th>deal_licence_end_date</th>\n",
       "      <th>close_date</th>\n",
       "      <th>created_date</th>\n",
       "      <th>opportunity_stage_status</th>\n",
       "      <th>lead_source_category</th>\n",
       "      <th>deal_total_non_recurring_revenue</th>\n",
       "      <th>deal_total_recurring_revenue</th>\n",
       "      <th>currency_iso_code</th>\n",
       "      <th>lead_source</th>\n",
       "      <th>deal_use_case</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00609000002JYs9AAG</td>\n",
       "      <td>new_business</td>\n",
       "      <td>new_business</td>\n",
       "      <td>no</td>\n",
       "      <td>1162</td>\n",
       "      <td>23</td>\n",
       "      <td>2021-05-18</td>\n",
       "      <td>2022-05-17</td>\n",
       "      <td>2021-05-18</td>\n",
       "      <td>2021-02-17</td>\n",
       "      <td>Lost</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8178.12</td>\n",
       "      <td>EUR</td>\n",
       "      <td></td>\n",
       "      <td>No specific Use Case yet</td>\n",
       "      <td>Phased motivating benchmark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00609000002Iv9VAAS</td>\n",
       "      <td>new_business</td>\n",
       "      <td>new_business</td>\n",
       "      <td>no</td>\n",
       "      <td>88</td>\n",
       "      <td>63</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2019-01-10</td>\n",
       "      <td>2017-07-24</td>\n",
       "      <td>Lost</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>EUR</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Centralized bandwidth-monitored parallelism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00609000002IucCAAS</td>\n",
       "      <td>new_business</td>\n",
       "      <td>new_business</td>\n",
       "      <td>no</td>\n",
       "      <td>1897</td>\n",
       "      <td>82</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2017-09-29</td>\n",
       "      <td>2017-08-21</td>\n",
       "      <td>Lost</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>EUR</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Programmable uniform info-mediaries</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00609000002IuSFAA0</td>\n",
       "      <td>new_business</td>\n",
       "      <td>new_business</td>\n",
       "      <td>no</td>\n",
       "      <td>2716</td>\n",
       "      <td>82</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2017-11-09</td>\n",
       "      <td>2017-10-26</td>\n",
       "      <td>Lost</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>EUR</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Managed systematic database</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00609000002Iv72AAC</td>\n",
       "      <td>new_business</td>\n",
       "      <td>new_business</td>\n",
       "      <td>no</td>\n",
       "      <td>2281</td>\n",
       "      <td>141</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2018-12-18</td>\n",
       "      <td>2017-12-20</td>\n",
       "      <td>Lost</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>EUR</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Pre-emptive analyzing approach</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   id opportunity_type is_new_business  \\\n",
       "0  00609000002JYs9AAG     new_business    new_business   \n",
       "1  00609000002Iv9VAAS     new_business    new_business   \n",
       "2  00609000002IucCAAS     new_business    new_business   \n",
       "3  00609000002IuSFAA0     new_business    new_business   \n",
       "4  00609000002Iv72AAC     new_business    new_business   \n",
       "\n",
       "  has_account_already_been_onboarded  account_id  owner_id  \\\n",
       "0                                 no        1162        23   \n",
       "1                                 no          88        63   \n",
       "2                                 no        1897        82   \n",
       "3                                 no        2716        82   \n",
       "4                                 no        2281       141   \n",
       "\n",
       "  deal_licence_start_date deal_licence_end_date close_date created_date  \\\n",
       "0              2021-05-18            2022-05-17 2021-05-18   2021-02-17   \n",
       "1                     NaT                   NaT 2019-01-10   2017-07-24   \n",
       "2                     NaT                   NaT 2017-09-29   2017-08-21   \n",
       "3                     NaT                   NaT 2017-11-09   2017-10-26   \n",
       "4                     NaT                   NaT 2018-12-18   2017-12-20   \n",
       "\n",
       "  opportunity_stage_status lead_source_category  \\\n",
       "0                     Lost                Other   \n",
       "1                     Lost                Other   \n",
       "2                     Lost                Other   \n",
       "3                     Lost                Other   \n",
       "4                     Lost                Other   \n",
       "\n",
       "   deal_total_non_recurring_revenue  deal_total_recurring_revenue  \\\n",
       "0                               0.0                       8178.12   \n",
       "1                               0.0                          0.00   \n",
       "2                               0.0                          0.00   \n",
       "3                               0.0                          0.00   \n",
       "4                               0.0                          0.00   \n",
       "\n",
       "  currency_iso_code lead_source             deal_use_case  \\\n",
       "0               EUR              No specific Use Case yet   \n",
       "1               EUR                                         \n",
       "2               EUR                                         \n",
       "3               EUR                                         \n",
       "4               EUR                                         \n",
       "\n",
       "                                          name  \n",
       "0                  Phased motivating benchmark  \n",
       "1  Centralized bandwidth-monitored parallelism  \n",
       "2          Programmable uniform info-mediaries  \n",
       "3                  Managed systematic database  \n",
       "4               Pre-emptive analyzing approach  "
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "existing_business_opportunities_second_methodology_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='III) Part 3 - All hands meeting preparation'> III) All hands meeting preparation</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use the prepared output tables to answer Business Leader requests."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Business Leader requests\n",
    "\n",
    "\n",
    "# <a id='A) Chief Marketing Officer'> A) Chief Marketing Officer</a>\n",
    "\n",
    "⚠️ All Software and Services revenue figures should be diplayed in Euros (1 EUR = 1,1 USD)\n",
    "\n",
    "1. Which account industry count the most closed won opportunities in created in 2020 ?\n",
    "\n",
    "2. Which account industry count the most lost opportunities created in 2020 ?\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### we will update the industry field in accounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "duckdb.sql('''\n",
    "\n",
    "UPDATE accounts AS accounts_original\n",
    "\n",
    "SET industry = (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    CASE\n",
    "    WHEN industry IN ('Computer Software', 'Information Technology & Services', 'Information Technology and Services', 'Internet', 'Software/IT', 'Computer & Network Security', 'Information Technology and Services,Computer Software', 'Telecommunications') THEN 'Information Technology'\n",
    "    WHEN industry IN ('Healthcare & Pharmaceutical', 'Pharmaceuticals', 'Hospital & Health Care', 'Healthcare/Medical') THEN 'Healthcare'\n",
    "    WHEN industry IN ('Logistics & Supply Chain', 'Logistics and Supply Chain', 'Transportation and Logistics', 'Transportation/Trucking/Railroad') THEN 'Transportation & Logistics'\n",
    "    WHEN industry IN ('Chief Issac is an Indigenous economic development corporation that operates multiple companies.') THEN 'Other Industries'\n",
    "    WHEN industry IN ('Agri-food industry', 'Food Production', 'Farming') THEN 'Food Production'\n",
    "    WHEN industry IN ('Financial Services', 'Financial') THEN 'Finance'\n",
    "    WHEN industry IN ('Cosmetics', 'FMCG') THEN 'FMCG'\n",
    "    WHEN industry = ' ' THEN 'Unknown'\n",
    "    WHEN industry = 'Other Industries' THEN 'Unknown'\n",
    "    ELSE industry\n",
    "    END AS industry\n",
    "    \n",
    "\n",
    "\n",
    "FROM accounts a\n",
    "\n",
    "WHERE accounts_original.account_id = a.account_id  \n",
    "\n",
    "\n",
    "\n",
    ") \n",
    "\n",
    "\n",
    "\n",
    "''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "account_industries_by_close_opportunities_created_2020_query = '''\n",
    "\n",
    "SELECT\n",
    "\n",
    "    CASE \n",
    "    WHEN industry IS NULL THEN 'Unknown'\n",
    "    ELSE industry\n",
    "    END AS industry,\n",
    "    \n",
    "    COUNT(DISTINCT(CASE WHEN opportunity_stage_status = 'Won' THEN id ELSE NULL END)) AS closed_won_opportunities_created_in_2020,\n",
    "    \n",
    "    COUNT(DISTINCT(CASE WHEN opportunity_stage_status = 'Lost' THEN id ELSE NULL END)) AS closed_lost_opportunities_created_in_2020\n",
    "    \n",
    "FROM opportunities a\n",
    "\n",
    "LEFT JOIN accounts b ON b.account_id = a.account_id\n",
    "\n",
    "WHERE YEAR(created_date) = 2020\n",
    "\n",
    "-- we assume the data was extracted before July 2023\n",
    "\n",
    "AND close_date < '2023-07-01'\n",
    "\n",
    "GROUP BY 1\n",
    "\n",
    "ORDER BY 2 DESC\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "account_industries_by_close_opportunities_created_2020_data = duckdb.sql(account_industries_by_close_opportunities_created_2020_query).df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>industry</th>\n",
       "      <th>closed_won_opportunities_created_in_2020</th>\n",
       "      <th>closed_lost_opportunities_created_in_2020</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Information Technology</td>\n",
       "      <td>38</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>38</td>\n",
       "      <td>371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Finance</td>\n",
       "      <td>29</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Real Estate</td>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Government Administration</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Hospitality</td>\n",
       "      <td>15</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>FMCG</td>\n",
       "      <td>14</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Automotive</td>\n",
       "      <td>13</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Energy</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Luxury Goods &amp; Jewelry</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Healthcare</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Manufacturing &amp; Engineering</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Retail</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Oil &amp; Energy</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Transportation &amp; Logistics</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Higher Education</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Import and Export</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Apparel &amp; Fashion</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Food Production</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       industry  closed_won_opportunities_created_in_2020  \\\n",
       "0        Information Technology                                        38   \n",
       "1                       Unknown                                        38   \n",
       "2                       Finance                                        29   \n",
       "3                   Real Estate                                        18   \n",
       "4     Government Administration                                        16   \n",
       "5                   Hospitality                                        15   \n",
       "6                          FMCG                                        14   \n",
       "7                    Automotive                                        13   \n",
       "8                        Energy                                         8   \n",
       "9        Luxury Goods & Jewelry                                         6   \n",
       "10                   Healthcare                                         6   \n",
       "11  Manufacturing & Engineering                                         6   \n",
       "12                       Retail                                         5   \n",
       "13                 Oil & Energy                                         3   \n",
       "14   Transportation & Logistics                                         2   \n",
       "15             Higher Education                                         1   \n",
       "16            Import and Export                                         0   \n",
       "17            Apparel & Fashion                                         0   \n",
       "18              Food Production                                         0   \n",
       "\n",
       "    closed_lost_opportunities_created_in_2020  \n",
       "0                                          43  \n",
       "1                                         371  \n",
       "2                                          89  \n",
       "3                                          26  \n",
       "4                                          22  \n",
       "5                                          23  \n",
       "6                                          26  \n",
       "7                                          31  \n",
       "8                                          18  \n",
       "9                                           2  \n",
       "10                                         25  \n",
       "11                                         23  \n",
       "12                                         24  \n",
       "13                                          5  \n",
       "14                                          6  \n",
       "15                                          0  \n",
       "16                                          1  \n",
       "17                                          1  \n",
       "18                                          8  "
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "account_industries_by_close_opportunities_created_2020_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "\n",
    "account_industries_by_close_opportunities_created_2020_data.head(10 ).plot(x = 'industry', y = 'closed_won_opportunities_created_in_2020', kind = 'bar', figsize = (30,15), legend=False)\n",
    "\n",
    "\n",
    "\n",
    "plt.title('Top 10 Industries by Number of Closed Won Opportunities Created in 2020')\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AMID+D86qsYpZzlGgAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 3000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "account_industries_by_close_opportunities_created_2020_data_lost = account_industries_by_close_opportunities_created_2020_data.sort_values('closed_lost_opportunities_created_in_2020', ascending=False)\n",
    "\n",
    "\n",
    "account_industries_by_close_opportunities_created_2020_data_lost.head(10 ).plot(x = 'industry', y = 'closed_lost_opportunities_created_in_2020', kind = 'bar', figsize = (30,15), legend=False)\n",
    "\n",
    "\n",
    "plt.title('Top 10 Industries by Number of Closed Lost Opportunities Created in 2020')\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We will not take into account clients for which we don't know the industry sector ('Unknown'). \n",
    "\n",
    "## - The Information Technology industry counted the most closed won opportunities created in 2020\n",
    "\n",
    "\n",
    "## - The Finance industry counted the most closed lost opportunities created in 2020"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='B) VP Sales'> B) VP Sales</a>\n",
    "\n",
    "For open opportunities created since 2020:\n",
    "\n",
    "1. How many upsell, cross-sell opportunities we expect to close in the 2nd quarter of 2021 (March 21 > June 21 ) ?\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "second_quarter_2021_opportunities_created_since_2020_query = '''\n",
    "\n",
    "SELECT\n",
    "\n",
    "    opportunity_type,\n",
    "    \n",
    "\n",
    "    \n",
    "    COUNT(DISTINCT(id)) AS opportunities\n",
    "    \n",
    "FROM opportunities a\n",
    "\n",
    "\n",
    "WHERE YEAR(created_date) >= 2020\n",
    "\n",
    "-- we assume the data was extracted before July 2023\n",
    "\n",
    "AND (close_date >= '2021-04-01' AND close_date < '2021-07-01')\n",
    "\n",
    "\n",
    "GROUP BY 1\n",
    "\n",
    "ORDER BY 2 DESC\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>opportunity_type</th>\n",
       "      <th>opportunities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>new_business</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cross-sell</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>renewal</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>upsell</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  opportunity_type  opportunities\n",
       "0     new_business            165\n",
       "1       cross-sell             29\n",
       "2          renewal              9\n",
       "3           upsell              6"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duckdb.sql(second_quarter_2021_opportunities_created_since_2020_query).df()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We assume the dataset is a snapshot as of June 2023 and therefore all opportunities that were expected to close in Q2 2021 are already closed. \n",
    "\n",
    "## We have a few opportunities with a close date taking place between January 2022 and May 2023 marked as 'Open'. We don't know whether these opportunities were won or lost. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We closed 6 upsell and 30 cross-sell opportunities in Q2 2021 (April 2021 - June 2021)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. How many renewals we expect to close in the last quarter of 2021 (sep 21 > dec 21 ) ?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "last_quarter_2021_opportunities_created_since_2020_query = '''\n",
    "\n",
    "SELECT\n",
    "\n",
    "    opportunity_type,\n",
    "    \n",
    "\n",
    "    \n",
    "    COUNT(DISTINCT(id)) AS opportunities\n",
    "    \n",
    "FROM opportunities a\n",
    "\n",
    "\n",
    "WHERE YEAR(created_date) >= 2020\n",
    "\n",
    "-- we assume the data was extracted in June 2023\n",
    "\n",
    "AND close_date >= '2021-10-01'\n",
    "\n",
    "AND close_date < '2022-01-01'\n",
    "\n",
    "\n",
    "GROUP BY 1\n",
    "\n",
    "ORDER BY 2 DESC\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>opportunity_type</th>\n",
       "      <th>opportunities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>new_business</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cross-sell</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>renewal</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>upsell</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  opportunity_type  opportunities\n",
       "0     new_business            188\n",
       "1       cross-sell             30\n",
       "2          renewal             18\n",
       "3           upsell              9"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duckdb.sql(last_quarter_2021_opportunities_created_since_2020_query).df()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We closed 18 renewals in Q4 2021 (Oct 2021 - Dec 2021)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For close won opportunities in 2020:\n",
    "\n",
    "3. Based on the Software revenue, who are the top 5 Sales of the year ? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- we will use the current Euro - Dollar exchange rate of 0.93 Euros"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### we will only consider here new business accounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_five_sales_year_2020_software_revenue_query = '''\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "    CASE\n",
    "    WHEN b.crm_user_id IS NULL THEN 'Unknown'\n",
    "    ELSE b.full_name\n",
    "    END AS sales_person,\n",
    "    \n",
    "    CAST(SUM(CASE WHEN currency_iso_code = 'USD' THEN deal_total_recurring_revenue * 0.93 ELSE deal_total_recurring_revenue END) AS INT) AS total_software_revenue_new_business_euros\n",
    "    \n",
    "    \n",
    "FROM opportunities a\n",
    "\n",
    "LEFT JOIN crm_users b ON b.crm_user_id = a.owner_id\n",
    "\n",
    "WHERE opportunity_stage_status = 'Won'\n",
    "\n",
    "AND is_new_business = 'new_business'\n",
    "\n",
    "AND YEAR(close_date) = 2020\n",
    "\n",
    "GROUP BY 1\n",
    "\n",
    "ORDER BY 2 DESC\n",
    "\n",
    "\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales_person</th>\n",
       "      <th>total_software_revenue_new_business_euros</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Anna Ayala</td>\n",
       "      <td>390540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jeffrey Page</td>\n",
       "      <td>300474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ryan Bird</td>\n",
       "      <td>280106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David Meyer</td>\n",
       "      <td>251888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brittany Delgado</td>\n",
       "      <td>152137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ernest Morrison</td>\n",
       "      <td>125889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Rebecca Baker</td>\n",
       "      <td>104430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Christopher Garrett</td>\n",
       "      <td>77336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Laura Harris</td>\n",
       "      <td>70465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Lisa Mcdaniel</td>\n",
       "      <td>69467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Alicia Lee</td>\n",
       "      <td>66408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Austin Obrien</td>\n",
       "      <td>51049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Robert Barker</td>\n",
       "      <td>47985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Melissa Brown</td>\n",
       "      <td>39793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sierra Wilson</td>\n",
       "      <td>39174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Rebecca Rivera</td>\n",
       "      <td>38910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>William Arnold</td>\n",
       "      <td>19724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Marcus Bush</td>\n",
       "      <td>14528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Ashley Garcia</td>\n",
       "      <td>8868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Alyssa Anderson</td>\n",
       "      <td>8159</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           sales_person  total_software_revenue_new_business_euros\n",
       "0            Anna Ayala                                     390540\n",
       "1          Jeffrey Page                                     300474\n",
       "2             Ryan Bird                                     280106\n",
       "3           David Meyer                                     251888\n",
       "4      Brittany Delgado                                     152137\n",
       "5       Ernest Morrison                                     125889\n",
       "6         Rebecca Baker                                     104430\n",
       "7   Christopher Garrett                                      77336\n",
       "8          Laura Harris                                      70465\n",
       "9         Lisa Mcdaniel                                      69467\n",
       "10           Alicia Lee                                      66408\n",
       "11        Austin Obrien                                      51049\n",
       "12        Robert Barker                                      47985\n",
       "13        Melissa Brown                                      39793\n",
       "14        Sierra Wilson                                      39174\n",
       "15       Rebecca Rivera                                      38910\n",
       "16       William Arnold                                      19724\n",
       "17          Marcus Bush                                      14528\n",
       "18        Ashley Garcia                                       8868\n",
       "19      Alyssa Anderson                                       8159"
      ]
     },
     "execution_count": 406,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_five_sales_year_2020_software_revenue_data = duckdb.sql(top_five_sales_year_2020_software_revenue_query).df()\n",
    "top_five_sales_year_2020_software_revenue_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_five_sales_year_2020_software_revenue_data.head().to_csv('top_5_sales_2020.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The sales people's names might have been anonymized for privacy matters and may not be the actual names of the sales people at Toucan. The same logic could apply for account names.\n",
    "\n",
    "### It would be highly beneficial to categorize the opportunities by country. In this CRM dataset, we only have the CRM users' regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "\n",
    "top_five_sales_year_2020_software_revenue_data.head(5).plot(x = 'sales_person', y = 'total_software_revenue_new_business_euros', kind = 'bar', figsize = (30,15), legend=False)\n",
    "\n",
    "\n",
    "plt.title('Top 5 Sales People of the Year 2020 by Software Revenue in Euros \\n (New Business Accounts)')\n",
    "\n",
    "plt.xlabel('', fontsize=18)\n",
    "\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Anna Ayala, Jeffrey Page, Ryan Bird, David Meyer and Brittany Delgado are the top 5 sales people of the year for opportunities won in 2020 by software revenue when considering only opportunities from new business accounts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='C) Head of Finance'> C) Head of Finance</a>\n",
    "\n",
    "For opportunities created since 2018 :\n",
    "\n",
    "1. Based on the Software Revenue, which is our top account customer ?\n",
    "\n",
    "\n",
    "⚠️ All Software and Services revenue figures should be diplayed in Euros (1 EUR = 1,1 USD)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We only consider here won opportunities that were closed before July 2023, since the dataset seems to have been extracted as of June 2023"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_finance_query = '''\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "    CASE\n",
    "    WHEN b.account_id IS NULL THEN 'Unknown'\n",
    "    ELSE b.name\n",
    "    END AS account_name,\n",
    "    \n",
    "    CAST(SUM(CASE WHEN currency_iso_code = 'USD' THEN deal_total_recurring_revenue * 0.93 ELSE deal_total_recurring_revenue END) AS INTEGER) AS total_software_revenue_new_business_euros\n",
    "    \n",
    "    \n",
    "FROM opportunities a\n",
    "\n",
    "LEFT JOIN accounts b ON b.account_id = a.account_id\n",
    "\n",
    "WHERE opportunity_stage_status = 'Won'\n",
    "\n",
    "\n",
    "AND YEAR(created_date) >=  2018\n",
    "\n",
    "AND close_date < '2023-07-01'\n",
    "\n",
    "GROUP BY 1\n",
    "\n",
    "ORDER BY 2 DESC\n",
    "\n",
    "LIMIT 10\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account_name</th>\n",
       "      <th>total_software_revenue_new_business_euros</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brown LLC</td>\n",
       "      <td>1866996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fowler PLC</td>\n",
       "      <td>1648686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ochoa PLC</td>\n",
       "      <td>1570264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Webb-Burgess</td>\n",
       "      <td>1209442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Francis, Wells and Gregory</td>\n",
       "      <td>1126488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Edwards-Lane</td>\n",
       "      <td>1086689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Hodges, Bowman and Hicks</td>\n",
       "      <td>1083326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Boyd-Mercado</td>\n",
       "      <td>877005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Pierce, Estrada and Harrington</td>\n",
       "      <td>793581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Johnson LLC</td>\n",
       "      <td>758132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     account_name  total_software_revenue_new_business_euros\n",
       "0                       Brown LLC                                    1866996\n",
       "1                      Fowler PLC                                    1648686\n",
       "2                       Ochoa PLC                                    1570264\n",
       "3                    Webb-Burgess                                    1209442\n",
       "4      Francis, Wells and Gregory                                    1126488\n",
       "5                    Edwards-Lane                                    1086689\n",
       "6        Hodges, Bowman and Hicks                                    1083326\n",
       "7                    Boyd-Mercado                                     877005\n",
       "8  Pierce, Estrada and Harrington                                     793581\n",
       "9                     Johnson LLC                                     758132"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_finance_data = duckdb.sql(first_finance_query).df()\n",
    "first_finance_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_finance_data.to_csv('top_10_accounts_customers_software_rev.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The top account customer by software revenue for opportunities created since 2018 is Brown LLC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. What is the Total ARR ?\n",
    "\n",
    "For won opportunities with a subscription period running in 2020:\n",
    "\n",
    "```\n",
    "\n",
    "ARR = MRR * 12\n",
    "MRR = RECURRING REVENUE / ENGAGEMENT PERIOD\n",
    "ENGAGEMENT PERIOD (in months) = SOUSCRIPTION END DATE - SOUSCRIPTION START DATE\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We will only consider subscriptions from won opportunities for which we know the engagement period. We will not be able to include the recurring revenue from subscriptions for which we don't know the subscription period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [],
   "source": [
    "second_finance_query = '''\n",
    "\n",
    "WITH mrr_table AS (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    id,\n",
    "    \n",
    "    DATE_DIFF('MONTH', deal_licence_start_date, deal_licence_end_date) AS engagement_period_subscriptions,\n",
    "    \n",
    "    deal_licence_start_date,\n",
    "    \n",
    "    deal_licence_end_date,\n",
    "    \n",
    "    deal_total_recurring_revenue,\n",
    "    \n",
    "    CAST(deal_total_recurring_revenue AS DOUBLE) / CAST(DATE_DIFF('MONTH', deal_licence_start_date, deal_licence_end_date) AS DOUBLE) AS mrr,\n",
    "    \n",
    "    currency_iso_code\n",
    "\n",
    "\n",
    "FROM opportunities a\n",
    "\n",
    "LEFT JOIN accounts b ON b.account_id = a.account_id\n",
    "\n",
    "WHERE opportunity_stage_status = 'Won'\n",
    "\n",
    "\n",
    "AND YEAR(created_date) >=  2018\n",
    "\n",
    "\n",
    "\n",
    "AND deal_total_recurring_revenue > 0\n",
    "\n",
    "AND deal_licence_start_date IS NOT NULL \n",
    "\n",
    "AND  deal_licence_start_date IS NOT NULL\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "),\n",
    "\n",
    "subscriptions_activity_table AS (\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "    b.id,\n",
    "\n",
    "    date_year,\n",
    "\n",
    "    DATE_TRUNC('MONTH', CAST(a.ymd AS DATE)) AS ym,\n",
    "    \n",
    "    mrr,\n",
    "    \n",
    "    currency_iso_code\n",
    "    \n",
    "\n",
    "FROM date_dim a\n",
    "\n",
    "INNER JOIN mrr_table b ON CAST(a.ymd AS DATE) >= CAST(b.deal_licence_start_date AS DATE) \n",
    "\n",
    "AND CAST(a.ymd AS DATE) <= CAST(b.deal_licence_end_date AS DATE)\n",
    "\n",
    "WHERE CAST(a.ymd AS DATE) >= '2020-01-01'\n",
    "\n",
    "AND CAST(a.ymd AS DATE) <= '2022-12-31'\n",
    "\n",
    "\n",
    "GROUP BY 1,2,3,4,5\n",
    "\n",
    "ORDER BY 1,2\n",
    "\n",
    ")\n",
    "\n",
    "SELECT\n",
    "\n",
    "    date_year,\n",
    "\n",
    "    CAST(SUM(CASE WHEN currency_iso_code = 'USD' THEN mrr * 0.93 ELSE mrr END) AS INTEGER) AS annual_recurring_revenue_euros_2020\n",
    "\n",
    "\n",
    "FROM subscriptions_activity_table\n",
    "\n",
    "GROUP BY 1\n",
    "\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_year</th>\n",
       "      <th>annual_recurring_revenue_euros_2020</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>12617021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021</td>\n",
       "      <td>10261752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020</td>\n",
       "      <td>7612271</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  date_year  annual_recurring_revenue_euros_2020\n",
       "0      2022                             12617021\n",
       "1      2021                             10261752\n",
       "2      2020                              7612271"
      ]
     },
     "execution_count": 414,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "second_finance_data = duckdb.sql(second_finance_query).df()\n",
    "second_finance_data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [],
   "source": [
    "second_finance_data.to_csv('arr_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The annual recurring revenue in 2020 generated by opportunities created since 2018 amounts to 7,717,123 euros"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='D) CEO'> D) CEO</a>\n",
    "\n",
    "1. What is the average duration in month of our sales cycle in 2020 ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Methodology:\n",
    "    \n",
    "- only consider opportunities for which the creation date is at least 30 days before the close date to remove outliers and inconsistent data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_ceo_question_query = '''\n",
    "\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "    CAST(SUM(DATEDIFF('MONTH', CAST(created_date AS DATE), CAST(close_date AS DATE))) AS DOUBLE) / CAST(COUNT(DISTINCT(id)) AS DOUBLE) AS avg_duration_months_sales_cycle_2020\n",
    "\n",
    "FROM opportunities\n",
    "\n",
    "WHERE created_date >= '2020-01-01' AND created_date <= '2020-12-31'\n",
    "\n",
    "\n",
    "AND DATEDIFF('DAY', CAST(created_date AS DATE), CAST(close_date AS DATE)) > 30\n",
    "\n",
    "AND opportunity_stage_status IN ('Lost', 'Won')\n",
    "\n",
    "-- AND CAST(created_date AS DATE) < CAST(close_date AS DATE)\n",
    "\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_duration_months_sales_cycle_2020</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   avg_duration_months_sales_cycle_2020\n",
       "0                                 4.380"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_ceo_question_data = duckdb.sql(first_ceo_question_query).df()\n",
    "first_ceo_question_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_ceo_question_bis_query = '''\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "    YEAR(created_date) AS year_date,\n",
    "\n",
    "    CAST(CAST(SUM(DATEDIFF('MONTH', CAST(created_date AS DATE), CAST(close_date AS DATE))) AS DOUBLE) / CAST(COUNT(DISTINCT(id)) AS DOUBLE) AS INT) AS avg_duration_months_sales_cycle_2020\n",
    "\n",
    "FROM opportunities\n",
    "\n",
    "WHERE created_date >= '2020-01-01' AND created_date <= '2022-12-31'\n",
    "\n",
    "\n",
    "AND DATEDIFF('DAY', CAST(created_date AS DATE), CAST(close_date AS DATE)) > 30\n",
    "\n",
    "AND opportunity_stage_status IN ('Lost', 'Won')\n",
    "\n",
    "-- AND CAST(created_date AS DATE) < CAST(close_date AS DATE)\n",
    "\n",
    "GROUP BY 1\n",
    "\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year_date</th>\n",
       "      <th>avg_duration_months_sales_cycle_2020</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year_date  avg_duration_months_sales_cycle_2020\n",
       "0       2020                                     4\n",
       "1       2022                                     6\n",
       "2       2021                                     7"
      ]
     },
     "execution_count": 424,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_ceo_question_bis_data = duckdb.sql(first_ceo_question_bis_query).df()\n",
    "first_ceo_question_bis_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_ceo_question_bis_data.to_csv('duration_sales_cycle.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The average duration of the sales cycle in 2020 amounts to 4 months"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. What is the average duration in month of our optimized sales cycle in 2020 ?\n",
    "\n",
    "\n",
    "```\n",
    "Sales cycle duration = difference between created date and close date for opportunities which are closed lost and closed won\n",
    "\n",
    "Sales cycle duration optimized = Sales cycle duration calculation but only for working days (days not included in weekends and public holidays)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "second_ceo_question_query = '''\n",
    "\n",
    "WITH first_table AS (\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "    id,\n",
    "    \n",
    "    created_date,\n",
    "    \n",
    "    close_date\n",
    "    \n",
    "\n",
    "FROM opportunities\n",
    "\n",
    "WHERE created_date >= '2020-01-01' AND created_date <= '2020-12-31'\n",
    "\n",
    "\n",
    "AND DATEDIFF('DAY', CAST(created_date AS DATE), CAST(close_date AS DATE)) > 30\n",
    "\n",
    "AND opportunity_stage_status IN ('Lost', 'Won')\n",
    "\n",
    "-- AND CAST(created_date AS DATE) < CAST(close_date AS DATE)\n",
    "\n",
    "),\n",
    "\n",
    "activity_table AS (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    CAST(a.ymd AS DATE) AS ymd,\n",
    "    \n",
    "    b.id,\n",
    "    \n",
    "    b.created_date,\n",
    "    \n",
    "    b.close_date\n",
    "    \n",
    "    \n",
    "FROM date_dim a\n",
    "\n",
    "INNER JOIN first_table b ON CAST(a.ymd AS DATE) >= CAST(b.created_date AS DATE) AND CAST(a.ymd AS DATE) <= CAST(b.close_date AS DATE)\n",
    "\n",
    "WHERE is_weekend IS FALSE\n",
    "\n",
    "AND is_public_holidays IS FALSE\n",
    "\n",
    "\n",
    ")\n",
    "\n",
    "SELECT\n",
    "\n",
    "    CAST(COUNT(ymd) AS DOUBLE) / CAST(COUNT(DISTINCT(id)) * 30 AS DOUBLE) AS avg_duration_months_optimized_sales_cycle_2020\n",
    "\n",
    "\n",
    "FROM activity_table\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_duration_months_optimized_sales_cycle_2020</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.088</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   avg_duration_months_optimized_sales_cycle_2020\n",
       "0                                           3.088"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "second_ceo_question_data = duckdb.sql(second_ceo_question_query).df()\n",
    "second_ceo_question_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {},
   "outputs": [],
   "source": [
    "second_ceo_question_bis_query = '''\n",
    "\n",
    "WITH first_table AS (\n",
    "\n",
    "\n",
    "SELECT\n",
    "\n",
    "    id,\n",
    "    \n",
    "    created_date,\n",
    "    \n",
    "    close_date,\n",
    "\n",
    "    strftime('%Y', created_date) AS y\n",
    "\n",
    "    \n",
    "\n",
    "FROM opportunities\n",
    "\n",
    "WHERE created_date >= '2020-01-01' AND created_date <= '2022-12-31'\n",
    "\n",
    "\n",
    "AND DATEDIFF('DAY', CAST(created_date AS DATE), CAST(close_date AS DATE)) > 30\n",
    "\n",
    "AND opportunity_stage_status IN ('Lost', 'Won')\n",
    "\n",
    "-- AND CAST(created_date AS DATE) < CAST(close_date AS DATE)\n",
    "\n",
    "),\n",
    "\n",
    "activity_table AS (\n",
    "\n",
    "SELECT\n",
    "\n",
    "    CAST(a.ymd AS DATE) AS ymd,\n",
    "    \n",
    "    b.id,\n",
    "\n",
    "    y,\n",
    "    \n",
    "    b.created_date,\n",
    "    \n",
    "    b.close_date\n",
    "    \n",
    "    \n",
    "FROM date_dim a\n",
    "\n",
    "INNER JOIN first_table b ON CAST(a.ymd AS DATE) >= CAST(b.created_date AS DATE) AND CAST(a.ymd AS DATE) <= CAST(b.close_date AS DATE)\n",
    "\n",
    "WHERE is_weekend IS FALSE\n",
    "\n",
    "AND is_public_holidays IS FALSE\n",
    "\n",
    "\n",
    ")\n",
    "\n",
    "SELECT\n",
    "\n",
    "    y AS year,\n",
    "\n",
    "    CAST(CAST(COUNT(ymd) AS DOUBLE) / CAST(COUNT(DISTINCT(id)) * 30 AS DOUBLE) AS INTEGER) AS avg_duration_months_optimized_sales_cycle_2020\n",
    "\n",
    "\n",
    "FROM activity_table\n",
    "\n",
    "\n",
    "\n",
    "GROUP BY 1\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>avg_duration_months_optimized_sales_cycle_2020</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  avg_duration_months_optimized_sales_cycle_2020\n",
       "0  2022                                               4\n",
       "1  2021                                               5\n",
       "2  2020                                               3"
      ]
     },
     "execution_count": 456,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "second_ceo_question_bis_data = duckdb.sql(second_ceo_question_bis_query).df()\n",
    "second_ceo_question_bis_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {},
   "outputs": [],
   "source": [
    "second_ceo_question_bis_data.to_csv('optimized_sales_cycle.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The average duration of the optimized sales cycle in 2020 amounts to 3 months"
   ]
  }
 ],
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