Repeatability
High
Cohort analysis follows well-established statistical patterns — segmentation by signup period, feature adoption sequences, conversion funnels — that are structurally identical across runs. The same pipeline can be re-executed as new data arrives.
Ambiguity Tolerance
Medium
The analytical goals are reasonably specific (conversion to paid, long-term retention, onboarding flows), but 'correlates most strongly' and 'prioritize roadmap improvements' leave room for interpretation. The agent can produce rigorous outputs but cannot fully determine which findings are actionable without founder context.
Data & Tool Availability
High
The CSV is self-contained with all necessary fields: user ID, feature, timestamp, tier, and churn status. No external API access or live system integration is required — the agent just needs the file and a Python/pandas environment.
Error Cost
Low
The output is an analytical report, not an irreversible action. A flawed correlation or miscoded cohort is discoverable and correctable before any product decision is made. The founder retains final judgment on roadmap changes.
Human Judgment Required
Medium
Statistical analysis is fully automatable, but interpreting which patterns are causally meaningful versus coincidental — and which roadmap bets are worth making given business constraints — requires the founder's product intuition and strategic context.