{"uid":"cap_E42brneMYutKT_CeSB3v0","slug":"customer-segment-observations-exact-row-deduplication-two-measure-bcbdc165","name":"Customer Segment Observations — Exact Row Deduplication + Two-Measure Correlation","description":"Customer segment observations — Exact row deduplication + Two-measure correlation: Exact row deduplication + Two-measure correlation for supplied customer segment observations records (account_count, revenue_amount). 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-company-16-050?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","company_group","date","account_count","revenue_amount"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"account_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"company_group":{"type":"string","default":"","maxLength":80},"revenue_amount":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","account_count":10,"company_group":"group-a","revenue_amount":1},{"date":"2026-10-02","row_id":"row-2","account_count":20,"company_group":"group-b","revenue_amount":2},{"date":"2026-10-03","row_id":"row-3","account_count":15,"company_group":"group-a","revenue_amount":3},{"date":"2026-10-04","row_id":"row-4","account_count":30,"company_group":"group-b","revenue_amount":4}],"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_K1fLiaPcDcSc_z8k5kF93","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":"Deduplicates customer segment observation rows exactly and computes correlation between account_count and revenue_amount across up to 200 rows.","exampleAgentPrompt":"Can you deduplicate my customer segment observation rows and then run a correlation analysis between account_count and revenue_amount? Here are my records: row-1 for group-a on 2026-10-01 with account_count=10 and revenue_amount=1, row-2 for group-b on 2026-10-02 with account_count=20 and revenue_amount=2, row-3 for group-a on 2026-10-03 with account_count=15 and revenue_amount=3.","exampleUseCases":[{"title":"Detecting duplicate segment records","prompt":"I have a list of customer segment observations with company groups, dates, account counts, and revenues — can you scan them for exact duplicate rows and give me a deduplication report? Here's the data: row-1, group-a, 2026-10-01, account_count=10, revenue_amount=500; row-2, group-a, 2026-10-01, account_count=10, revenue_amount=500; row-3, group-b, 2026-10-02, account_count=25, revenue_amount=1200."},{"title":"Measuring revenue-to-account correlation","prompt":"Can you compute the correlation between account_count and revenue_amount across my customer segment observations? I want to know how strongly these two measures move together. My data has rows like: row-1 group-a 2026-09-01 accounts=50 revenue=4000, row-2 group-b 2026-09-01 accounts=80 revenue=6500, row-3 group-a 2026-09-02 accounts=55 revenue=4400."},{"title":"Combined data quality and correlation audit","prompt":"Run both a deduplication check and a two-measure correlation report on my customer segment data — I need to know if there are any repeated rows AND how account count relates to revenue. Here are my 5 records: row-1 group-x 2026-11-01 10 accounts 1000 revenue, row-2 group-y 2026-11-01 20 accounts 2000 revenue, row-3 group-x 2026-11-02 10 accounts 1000 revenue, row-4 group-z 2026-11-02 5 accounts 450 revenue, row-5 group-y 2026-11-03 22 accounts 2100 revenue."}],"resultDescription":"Returns two analytical reports: (1) an exact row deduplication report identifying which rows are exact duplicates within the submitted dataset, and (2) a two-measure correlation report quantifying the statistical relationship between account_count and revenue_amount across the provided rows. Both reports are computed locally on the submitted data with no external lookups.","failureModes":["More than 200 rows submitted — request rejected or truncated","Missing required fields (row_id, company_group, date, account_count, revenue_amount) — validation error","Invalid date format not matching YYYY-MM-DD pattern — schema validation failure","All account_count or revenue_amount values are null — correlation cannot be computed","Payment of 0.01 USDC not provided or declined — 402 Payment Required response","Fewer than 1 row submitted — minimum row count not met"],"whenToPreferThis":"Choose this endpoint when you have a structured customer segment dataset with account_count and revenue_amount columns and need both deduplication and correlation analysis in a single $0.01 call. It is ideal for batch data quality checks on datasets of up to 200 rows where no live data lookup, prediction, or external service is required — just local analytical computation on supplied rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T12:48:51.057Z","isFirstParty":false,"canonicalSlug":"customer-segment-observations-exact-row-deduplication-two-measure-bcbdc165"}