{"uid":"cap_MQ7OLOIv12WdP49bntUX0","slug":"customer-segment-observations-string-normalisation-adjacent-row-changes-3da945e7","name":"Customer Segment Observations — String Normalisation + Adjacent Row Changes","description":"Customer segment observations — String normalisation + Adjacent row changes: String normalisation + Adjacent row changes 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-16-029?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_PSVMbXxrYOrYrVLJMWm3o","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 company_group strings and computes adjacent-row changes for customer segment observation data (account_count, revenue_amount) across up to 200 rows.","exampleAgentPrompt":"Can you clean up the company_group strings and show me the adjacent row-over-row changes for account_count in this customer segment data? I have 4 rows: row-1 on 2026-10-01 with group-a and account_count 10, row-2 on 2026-10-02 with group-b and 20, row-3 on 2026-10-03 with group-a and 15, row-4 on 2026-10-04 with group-b and 30.","exampleUseCases":[{"title":"Monthly segment revenue trend review","prompt":"I have monthly customer segment records for Q3 — each row has a company_group, date, account_count, and revenue_amount. Can you normalise the group name strings and calculate the adjacent row-over-row changes in revenue_amount for me? Here are 6 rows: row-1 2026-07-01 group-A 100 5000, row-2 2026-07-02 Group-A 110 5200, row-3 2026-07-03 group-b 90 4800, row-4 2026-07-04 GROUP-B 95 4900, row-5 2026-07-05 group-a 105 5100, row-6 2026-07-06 group-b 88 4750."},{"title":"Account count change detection across segments","prompt":"My data pipeline outputs customer segment observations with messy company_group labels and I need two things: standardised group strings and the consecutive row differences in account_count. Please run string normalisation and adjacent row changes on these 5 rows with field account_count: row-1 2026-09-01 EnterpriseA 200, row-2 2026-09-02 enterprise-a 215, row-3 2026-09-03 SMB 80, row-4 2026-09-04 smb 78, row-5 2026-09-05 EnterpriseA 220."},{"title":"Segment data quality and delta audit","prompt":"Before I load this customer segment dataset into our warehouse I want to normalise the company_group names and also see what the adjacent row changes look like for revenue_amount so I can spot anomalies. Can you process these 3 rows: row-1 2026-11-01 Retail-East 500 12000, row-2 2026-11-02 retail_east 480 11500, row-3 2026-11-03 Retail East 520 13000?"}],"resultDescription":"Returns two analytical reports: (1) normalised company_group strings correcting casing and formatting inconsistencies across the supplied rows, and (2) adjacent row changes (deltas) for the selected numeric field (account_count or revenue_amount) between consecutive rows, based entirely on the supplied input data with no external lookups.","failureModes":["Fewer than 1 row supplied — request rejected","More than 200 rows supplied — exceeds limit, request rejected","Invalid date format (not YYYY-MM-DD) — schema validation error","Missing required fields (row_id, company_group, date) — validation failure","Numeric values outside ±1,000,000,000,000 — schema rejection","Invalid field enum value (must be account_count or revenue_amount) — error"],"whenToPreferThis":"Choose this endpoint when you have customer segment observation data that needs both string normalisation on the company_group field AND sequential (adjacent row) numeric change analysis in a single API call for up to 200 rows. Ideal for data-cleaning pipelines before warehouse ingestion, or for quick delta analysis on segment time-series without any external data enrichment or prediction.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-25T06:57:29.783Z","isFirstParty":false,"canonicalSlug":"customer-segment-observations-string-normalisation-adjacent-row-changes-3da945e7"}