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Promotion Observations IQR Outlier Flags and Date-by-Category Pivot
x402
Promotion Observations IQR Outlier Flags and Date-by-Category Pivot is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-22).
Runs two analytical reports on supplied promotion data: IQR-based outlier detection and a date-by-category pivot table for discount_amount or units_sold across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Promotion observations — IQR outlier flags + Date by category pivot: IQR outlier flags + Date by category pivot for supplied promotion observations records (discount_amount, units_sold). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Two independent analytical reports: (1) IQR outlier flags identifying which rows have statistically extreme values for the selected field (discount_amount or units_sold), including IQR boundaries and flag status per row; (2) a date-by-store-group pivot table summarizing the selected field's values across all supplied promotion records. Both reports are computed locally on the supplied data with no external lookups.
Example use cases
Outlier detection in discount campaigns
“I have 120 rows of promotion data with row IDs, store groups, dates, discount amounts, and units sold — can you flag any discount_amount values that are IQR outliers and also give me a pivot of discount amounts by date and store group?”
Units sold anomaly check by store
“Run an IQR outlier check on units_sold across my 80 promotion observations and also pivot the units sold by date and store group so I can see patterns — here are the rows with row_id, store_group, date, discount_amount, and units_sold.”
Retail promo performance summary
“I've got quarterly promotion records for two store groups — about 150 rows — and I want to know which rows have statistically extreme discount amounts via IQR flagging, plus a date-by-category breakdown of discount_amount as a pivot table.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-retail-05-178?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 promotion observation records and need both outlier detection and a pivot summary in a single $0.01 call, without requiring any external data sources, live lookups, or predictions. It is ideal for retail analytics workflows where you already have the raw promotion data and want rapid statistical flagging and category-date summarization bundled together.
Side effects
- Input array exceeds 200 rows — request will be rejected
- Missing required fields (row_id, store_group, date, discount_amount, units_sold) on any row causes validation error
- Invalid date format (not YYYY-MM-DD) causes schema rejection
- Insufficient data variance for meaningful IQR computation (e.g. all identical values)
- Invalid field enum value (must be 'discount_amount' or 'units_sold')
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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