{"uid":"cap_yCiLAjzSRraFXpqbU9Jnj","slug":"customer-segment-observations-string-normalisation-two-measure-4f97cdab","name":"Customer Segment Observations — String Normalisation + Two-Measure Correlation","description":"Customer segment observations — String normalisation + Two-measure correlation: String normalisation + 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-16-033?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_YzOpuoc10BDQuUtJ1yES_","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":"Performs string normalisation and two-measure correlation analysis (account_count vs revenue_amount) on up to 200 customer segment observation rows, returning two independent analytical reports.","exampleAgentPrompt":"Run string normalisation and a two-measure correlation analysis on these customer segment records — I have up to 50 rows with company_group, date, account_count, and revenue_amount fields — and give me two separate analytical reports.","exampleUseCases":[{"title":"Segment revenue-account correlation check","prompt":"I have a CSV of customer segment observations with company group names, dates, account counts, and revenue amounts — about 80 rows. Can you normalize the group name strings and run a correlation analysis between account count and revenue amount, and give me two reports?"},{"title":"Monthly KPI relationship analysis","prompt":"Take these 120 monthly customer segment records — each has a row ID, company group, date, account count, and revenue amount — and run string normalisation plus a two-measure correlation between account count and revenue amount. I want two independent analytical reports back."},{"title":"Segment data quality and metric correlation","prompt":"I've got messy company group names across 30 customer segment rows, each with account counts and revenue amounts. Can you normalize the strings and compute a correlation between those two measures, returning two separate local analytical reports?"}],"resultDescription":"Returns two independent analytical reports: one covering string normalisation of the company_group field across the supplied rows, and one covering the statistical correlation between account_count and revenue_amount across up to 200 customer segment observation records. No external lookups or predictions are made; all analysis uses local analytical engines on the supplied data.","failureModes":["More than 200 rows submitted — request rejected or truncated","Missing required fields (row_id, company_group, date, account_count, or revenue_amount) — validation error","Date field not matching YYYY-MM-DD pattern — schema validation failure","Null values in both numeric fields across all rows — insufficient data for correlation","Malformed JSON body — request parsing error"],"whenToPreferThis":"Choose this endpoint when you have tabular customer segment data with company group labels and two numeric KPIs (account_count and revenue_amount) and need both string normalisation of the group names and a statistical correlation between the two measures in a single call. It is ideal for small-to-medium datasets (up to 200 rows) where you want two bundled local analytical reports without any external data lookups or predictions, and at a low fixed cost of $0.01 USDC per call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-25T06:58:17.210Z","isFirstParty":false,"canonicalSlug":"customer-segment-observations-string-normalisation-two-measure-4f97cdab"}