{"uid":"cap_yxj7t9F-bhYTNMQS-4jrY","slug":"promotion-observations-string-normalisation-two-measure-correlation-d682d47c","name":"Promotion Observations — String Normalisation + Two-Measure Correlation","description":"Promotion observations — String normalisation + Two-measure correlation: String normalisation + Two-measure correlation 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.","url":"https://market.datapackvibe.com/x402/assistant-ib26-retail-05-033?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","store_group","date","discount_amount","units_sold"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"units_sold":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"store_group":{"type":"string","default":"","maxLength":80},"discount_amount":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","units_sold":1,"store_group":"group-a","discount_amount":10},{"date":"2026-10-02","row_id":"row-2","units_sold":2,"store_group":"group-b","discount_amount":20},{"date":"2026-10-03","row_id":"row-3","units_sold":3,"store_group":"group-a","discount_amount":15},{"date":"2026-10-04","row_id":"row-4","units_sold":4,"store_group":"group-b","discount_amount":30}],"maxItems":200,"minItems":1}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_9BDv51do4SbAD7bUM_TBB","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Normalises strings and computes correlation between discount_amount and units_sold across up to 200 promotion observation rows, returning two analytical reports.","exampleAgentPrompt":"Can you normalise the store group labels and run a two-measure correlation between discount_amount and units_sold for my promotion records — I have 4 rows: row-1 on 2026-10-01 group-a discount 10 units 1, row-2 on 2026-10-02 group-b discount 20 units 2, row-3 on 2026-10-03 group-a discount 15 units 3, row-4 on 2026-10-04 group-b discount 30 units 4?","exampleUseCases":[{"title":"Discount-to-sales correlation for campaign review","prompt":"I have 50 rows of promotion data with store group, date, discount amount, and units sold — can you normalise the store group strings and tell me how strongly discount amount correlates with units sold across all those rows?"},{"title":"Multi-store promotion effectiveness analysis","prompt":"We ran promotions across groups A and B over the last quarter; here are 120 rows with row IDs, dates, discount amounts, and units sold — run string normalisation and compute the two-measure correlation between discount and units for me."},{"title":"Small batch promotion data quality and stats check","prompt":"I've got a handful of promotion observation records — about 8 rows covering two store groups from last month — can you clean up the store group names and work out the correlation between the discount amounts and units sold?"}],"resultDescription":"Two independent analytical reports: (1) a string normalisation report cleaning and standardising the store_group and other string fields across submitted rows, and (2) a two-measure correlation report quantifying the statistical relationship between discount_amount and units_sold across all supplied rows. No external data is fetched; all analysis is performed locally on the provided dataset.","failureModes":["Fewer than 1 row submitted — validation error","More than 200 rows submitted — request rejected with row limit error","Missing required fields (row_id, store_group, date, discount_amount, units_sold) — schema validation failure","Invalid date format (not YYYY-MM-DD) — pattern validation error","Null values in both numeric fields across all rows — correlation cannot be computed","Payment of 0.01 USDC not provided — 402 payment required response"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation of categorical fields and a statistical correlation between two numeric promotion measures (discount_amount and units_sold) in a single call on up to 200 rows. Prefer it over general-purpose analytics tools when working with retail promotion observation data in a structured row format and you want two bundled analytical reports for a flat $0.01 USDC fee without any live lookup or prediction.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-26T18:50:36.173Z","isFirstParty":false,"canonicalSlug":"promotion-observations-string-normalisation-two-measure-correlation-d682d47c"}