Retention and churn analysis to order
Find why users stay, and why they leave
Retention and churn analysis to order finds why your users stay and why they leave, with cohort tables, churn drivers backed by statistics, and a written readout of what to do next. I build it in SQL and Python, separate signal from noise, and hand over actions, not just charts. Day to day I run retention, churn, and NPS analysis on a product team, so this is live practice, not textbook.
For whom
- Product: growth stalls and no one knows why
- Subscriptions: churn eats the new sign-ups
- Teams: retention is a number, not an action
Included
- Cohort retention and churn curves
- Churn drivers found with statistics
- Segments that retain vs. that leave
- A written readout with the next actions
How we work
- 01
Scope
We fix the event data and the key questions.
- 02
Analyze
I build cohorts and find the churn drivers.
- 03
Readout
I write what to do next and brief the team.
Timeline: from 1 week·Price: on request, quote after the scope
Stack and integrations
- SQLPythonpandasStatisticsBigQueryPostgreSQLMixpanel
Related work
Questions
›Do you need event tracking set up first?
Ideally yes. If it is missing, I can start from what you have and outline what to instrument next.
›How do you find why users churn?
I compare cohorts and segments with proper statistics, not gut calls, and I am clear about what is a signal and what is noise.
Find why users stay, and why they leave
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