Repeatability
High
Cohort analysis follows a well-defined statistical playbook: segment by signup date, compute retention curves, correlate feature usage with survival. This structure is the same every time the data is refreshed, making it highly automatable.
Ambiguity Tolerance
Medium
The core outputs — retention-correlated features and early churn segments — are reasonably crisp, but 'early churn signals' and 'prioritize onboarding improvements' involve judgment calls about thresholds and business priorities that the agent will need to make explicit assumptions about.
Data & Tool Availability
High
The user has 14 months of structured event-level data with the exact fields needed (login date, feature, session duration, plan tier). A data agent with Python/pandas/SQL access and the CSV or database connection can execute this end-to-end without external dependencies.
Error Cost
Low
The output is an analytical report used to inform onboarding decisions, not to trigger irreversible actions. Errors surface as flawed recommendations that a founder can sanity-check before acting — low stakes for a first pass.
Human Judgment Required
Medium
Statistical correlation is fully automatable, but deciding which churn signals are causally meaningful versus coincidental, and which onboarding changes are feasible, requires product intuition the agent lacks. A human review of the flagged segments is necessary before acting.