Assemble Retention Cohort Data and Artefacts
Extract, clean and format user usage, subscription renewal and churn data grouped by acquisition timeframes into clear cohort tables. Consolidate supporting qualitative evidence, including exit survey responses, customer success logs and feature adoption records for each distinct cohort. Organise these data points into a centralised repository ready for comparative analysis.
Completing this action establishes a single source of truth for historical customer retention across distinct signup windows. It prevents selective bias during evaluation and ensures the subsequent review rests upon verifiable, complete dataset foundations.
The founder must present a comprehensive cohort matrix detailing user retention percentages, net revenue retention (NRR) and churn rates over consistent weekly or monthly intervals. This must be accompanied by raw export files, data definition notes and mapped qualitative feedback tied directly to specific cohort identifiers.
Five questions an expert would ask when reviewing your output
Use these to challenge assumptions, pressure-test your logic, and check the quality of this action's output in the context of the parent task and wider venture development.
- 1
What data sources were integrated to build this cohort matrix, and how did you verify that user identifiers match across usage and billing systems?
- 2
How have you accounted for anomalies or one-off marketing spikes when defining the boundaries of these specific cohorts?
- 3
What proportion of the logged cohort data relies on manual data entry versus automated telemetry, and what is the error margin?
- 4
Why were these specific time intervals selected for cohort grouping, and do they align with your venture's actual sales and onboarding cycles?
- 5
How does this dataset distinguish between voluntary customer churn and involuntary churn caused by payment processing failures?