This project is a simulator for SaaS product metrics (e.g. user growth and funnel analytics). Generate user-level and session-level datasets, given high-level growth parameters for the SaaS product and examine core concepts in SaaS growth metrics, e.g.:
- Retention curves
- DAU
The aim for this project is to simulate high-level SaaS metrics and demonstrate key SaaS concepts via synthetic data generated via Python. It has no practical use for causal inference or predictive modelling, from how the high-level parameters drive the data generation.
This project is a work in progress:
src: functions for generating synthetic data are stored here (available for import).notebooks: demo analytics on the SaaS simulations.tests: unit tests for functionality.
The user metrics are generated first, given these high-level parameters:
- Date range
approx_yoy_growth_rate: e.g. +200% year-on-year user growth.start_users: e.g. 10k users at the start of date range.
By the end date, the total users would then be approx. 30k (10k + 200% growth).
User engagement (as measured by user activity) is simulated over time via a random walk with drift, e.g. % of daily active users (% DAU) might move from 25% one quarter to 20% in the next quarter (with some movement in between).
From choice of different parameters or different seeds, simulations might lead to a business with explosive growth, stagnant growth or even decline in engagement (or even a mixture of these cases over time!).
The low-level metadata are filled based on the constraints provided by the high-level metrics. Hence this is not a natural process (it's not the user / session characteristics that are driving the high-level metrics).