Repeatability
High
The task is structurally identical each time new data arrives — load analytics, cluster by engagement, correlate with retention outcomes. This is a standard analytical pipeline that can be templated and re-run monthly with minimal adjustment.
Ambiguity Tolerance
Medium
The core outputs (segments, feature correlations, churn predictors) are well-defined, but 'engagement level' and 'most strongly correlate' require methodological choices (e.g., k-means vs. RFM, Pearson vs. logistic regression) that the agent must make or be instructed on. Success is largely verifiable but not fully crisp without a defined methodology spec.
Data & Tool Availability
High
The task explicitly states the data exists (8 months, 450 customers, named fields). Assuming the agent receives a structured export (CSV, SQL, or similar), it has everything needed. No live API access or external context is required.
Error Cost
Low
This is an analytical output, not an action — the agent produces a report or notebook, not a decision. Errors are catchable in human review before any business action is taken, and no customer data is modified or exposed externally.
Human Judgment Required
Medium
Statistical analysis is fully automatable, but translating findings into product or sales strategy requires domain knowledge about restaurant SaaS and customer context that the agent lacks. A human pass on the 'so what' layer is genuinely valuable, though not required for the analysis itself.