Repeatability
High
Cohort analysis follows a well-defined analytical pattern: group by signup month and tier, compute retention curves, estimate LTV, and correlate feature flags with outcomes. This structure is the same every time and maps directly to standard pandas/SQL workflows.
Ambiguity Tolerance
Medium
The three deliverables (churn breakdown, LTV estimates, feature-retention predictors) are clearly named, but success criteria like 'which features predict retention' require choosing a modeling approach and significance threshold. A human will need to validate whether the agent's analytical choices match business intent.
Data & Tool Availability
High
The user has a single CSV with all required fields (churn, ARPU, feature usage, signup month, tier). No external APIs or live system access are needed — the agent just needs the file and a Python or data-analysis environment.
Error Cost
Medium
A miscalculated LTV or a spurious feature correlation could misdirect roadmap investment, which is a real but recoverable cost. The outputs are analytical artifacts, not irreversible actions, so a human review pass before acting on results limits downside.
Human Judgment Required
Medium
The statistical work is automatable, but deciding which feature-adoption signals are actionable given engineering constraints, competitive positioning, and customer segment strategy requires human business context the agent doesn't have.