Repeatability
High
The analytical steps — join channel fees to orders, compute margin, group by SKU/source/geography, flag statistical outliers — are structurally identical every time this runs. It can be templated and re-run monthly with minimal modification.
Ambiguity Tolerance
Medium
The three objectives (profitability segmentation, repeat-purchase rate by product/source, geographic return anomalies) are concrete, but 'anomalously high' requires a threshold definition and 'repeat-purchase' requires a time-window decision that the user hasn't specified. These are resolvable with a short clarification pass or reasonable defaults.
Data & Tool Availability
High
The user has the transaction logs in hand. The agent needs the COGS table and per-channel fee schedules (Amazon referral %, Shopify subscription/transaction fees) to be provided — these are standard business inputs the user almost certainly has. With those supplied, no external API access is required.
Error Cost
Low
The outputs are analytical findings and flags, not executed transactions or published decisions. A miscalculation produces a wrong insight that a human reviews before acting on — easily caught and corrected before any real-world consequence.
Human Judgment Required
Low
The work is arithmetic, aggregation, and statistical flagging — no taste, ethics, or relationship context is needed. A human should review the findings before making strategic decisions, but the analysis itself does not require human intuition.