{"uid":"cap_Dcz25dDqed7t5upEu_Wsx","slug":"customer-segment-observations-daily-sum-series-iqr-outlier-flags-f0eac8f7","name":"Customer Segment Observations — Daily Sum Series + IQR Outlier Flags","description":"Customer segment observations — Daily sum series + IQR outlier flags: Daily sum series + IQR outlier flags 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-16-140?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_E69mjkc8XhgCGBmhxSi8u","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":"Computes daily sum time series and IQR-based outlier flags for customer segment observation data (account_count or revenue_amount), returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"I have customer segment observations for multiple company groups with dates, account counts, and revenue amounts — can you compute the daily sum series and flag any IQR outliers for the account_count field across my rows?","exampleUseCases":[{"title":"Revenue anomaly detection for segment report","prompt":"I have 150 rows of daily revenue observations across several company groups — can you run a daily sum series and IQR outlier detection on the revenue_amount field so I can spot any unusual days?"},{"title":"Account count trend aggregation","prompt":"Take these customer segment records with account counts per company group and date, and give me the daily sum series plus any IQR-based outlier flags for the account_count field — up to 200 rows."},{"title":"Segmented business performance audit","prompt":"I need to audit our customer segment data across group-a and group-b for the past quarter — compute daily sums and flag any IQR outliers on revenue_amount so I can identify which days had abnormal performance."}],"resultDescription":"Two analytical reports: (1) a daily sum time series aggregating the selected field (account_count or revenue_amount) by date across supplied customer segment rows, and (2) IQR outlier flags identifying which rows or days fall outside the interquartile range bounds — all computed locally without external lookups.","failureModes":["Input array exceeds 200 rows — request will be rejected or truncated","Missing required fields (row_id, company_group, date, account_count, revenue_amount) cause validation errors","Invalid date format (not YYYY-MM-DD) triggers schema validation failure","Numeric values outside allowed range (-1e12 to 1e12) rejected","Insufficient data points to compute meaningful IQR (too few rows) may yield degenerate outlier flags","Invalid field enum value (not account_count or revenue_amount) causes error"],"whenToPreferThis":"Choose this endpoint when you have up to 200 customer segment observation records with account counts and/or revenue amounts across company groups and dates, and you need both a daily aggregation (sum series) and statistical outlier detection (IQR method) in a single low-cost call ($0.01 USDC for 2 reports). Prefer it over generic analytics tools when your data is already structured with row_id, company_group, date, and numeric metrics, and you want local computation without any external data lookup or prediction.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T12:48:45.785Z","isFirstParty":false,"canonicalSlug":"customer-segment-observations-daily-sum-series-iqr-outlier-flags-f0eac8f7"}