{"uid":"cap_xr6gBcTcYvkZ3ilcoApGw","slug":"customer-segment-observations-string-normalisation-date-by-category-c5a59681","name":"Customer Segment Observations — String Normalisation + Date by Category Pivot","description":"Customer segment observations — String normalisation + Date by category pivot: String normalisation + Date by category pivot 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-16-035?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},"field":{"enum":["account_count","revenue_amount"],"type":"string","default":"account_count"}}},"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_YYQdQl4pEYapibuKsruOf","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 date-by-category pivot analysis on up to 200 rows of customer segment observation records (account_count, revenue_amount), returning 2 independent analytical reports.","exampleAgentPrompt":"I have customer segment observations with account counts and revenue amounts across a few company groups — can you normalize the company group strings and pivot the account_count field by date and category? Here are my rows: row-1 group-a 2026-10-01 10 accounts $1 revenue, row-2 group-b 2026-10-02 20 accounts $2 revenue, row-3 group-a 2026-10-03 15 accounts $3 revenue.","exampleUseCases":[{"title":"Revenue pivot by company group","prompt":"I've got 80 rows of customer segment data with revenue_amount and company_group labels — can you normalize the group name strings and pivot revenue_amount by date and category so I can see trends across groups?"},{"title":"Account count analysis across segments","prompt":"Run a string normalisation and date-by-category pivot on my account_count data — I have 150 customer segment observation rows with row IDs, company groups like 'group-a' and 'group-b', dates in YYYY-MM-DD format, and account counts."},{"title":"Cleaning messy segment labels before reporting","prompt":"My segment observation export has inconsistent company group names and I need both a string normalisation report and a date pivot for account_count — can you process these 60 rows and give me both analytical reports?"}],"resultDescription":"Returns 2 independent analytical reports: (1) a string normalisation report cleaning and standardising company_group label strings across the supplied rows, and (2) a date-by-category pivot report showing the selected metric (account_count or revenue_amount) broken down by date and company group category. All computation is performed locally on the supplied data with no external lookups.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, company_group, date, account_count, revenue_amount) causes rejection","Invalid date format (non-YYYY-MM-DD) fails pattern validation","Numeric values outside ±1,000,000,000,000 are rejected","Empty rows array returns an error","Invalid field enum value (not account_count or revenue_amount) causes failure"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation and date-by-category pivot analysis on customer segment observation data (account_count or revenue_amount) in a single call for up to 200 rows. Ideal when working entirely with supplied data requiring no live lookups, predictions, or external integrations, and when cost efficiency ($0.01 for 2 reports) matters.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-25T00:52:24.398Z","isFirstParty":false,"canonicalSlug":"customer-segment-observations-string-normalisation-date-by-category-c5a59681"}