{"uid":"cap_7w8UHlIXdRvyla8ppmPnB","slug":"newsletter-observations-string-normalisation-two-measure-correlation-1a97e8fe","name":"Newsletter Observations — String Normalisation + Two-Measure Correlation","description":"Newsletter observations — String normalisation + Two-measure correlation: String normalisation + Two-measure correlation for supplied newsletter observations records (delivered_messages, open_count). 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-news-08-033?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","channel_group","date","delivered_messages","open_count"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"open_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"channel_group":{"type":"string","default":"","maxLength":80},"delivered_messages":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","open_count":1,"channel_group":"group-a","delivered_messages":10},{"date":"2026-10-02","row_id":"row-2","open_count":2,"channel_group":"group-b","delivered_messages":20},{"date":"2026-10-03","row_id":"row-3","open_count":3,"channel_group":"group-a","delivered_messages":15},{"date":"2026-10-04","row_id":"row-4","open_count":4,"channel_group":"group-b","delivered_messages":30}],"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_WT1VKQgSmmNvy5XahYY19","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":"Runs string normalisation and two-measure correlation analysis on newsletter observation records (delivered_messages, open_count) for up to 200 rows, returning 2 independent analytical reports.","exampleAgentPrompt":"Can you run string normalisation and two-measure correlation on these newsletter records? I have up to 50 rows with fields row_id, channel_group, date, delivered_messages, and open_count — I want to see how delivered messages and open count relate to each other across channel groups.","exampleUseCases":[{"title":"Newsletter campaign open-rate correlation","prompt":"I have 120 rows of newsletter data with delivered_messages and open_count per channel group and date — can you normalize the channel group strings and compute the correlation between delivered messages and opens so I can see if higher delivery volume drives more opens?"},{"title":"Multi-channel string cleanup and stats","prompt":"My newsletter export has inconsistent channel_group labels across 80 rows. Can you normalize those strings and also run a two-measure correlation between delivered_messages and open_count so I get both a cleaned view and a statistical relationship report?"},{"title":"Weekly newsletter performance analysis","prompt":"Here are 30 rows of weekly newsletter observations with row_id, date, channel_group, delivered_messages, and open_count — please run the string normalisation and two-measure correlation reports so I can understand the data quality and metric relationships."}],"resultDescription":"Returns two independent analytical reports: (1) a string normalisation report covering the channel_group and other string fields across the supplied rows, and (2) a two-measure correlation report quantifying the statistical relationship between delivered_messages and open_count across up to 200 input rows.","failureModes":["Input array exceeds 200 rows — request rejected","Missing required fields (row_id, channel_group, date, delivered_messages, open_count) cause validation errors","Invalid date format (not YYYY-MM-DD) triggers pattern mismatch","Numeric values outside allowed range (-1e12 to 1e12) rejected","Empty rows array (minItems: 1 not met) causes error","Payment of 0.01 USDC not completed — 402 response returned"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation and correlation analysis together on newsletter data (delivered_messages vs open_count) in a single low-cost call. It is purpose-built for newsletter observation records with up to 200 rows and returns two structured analytical reports without requiring any external data sources or live lookups.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T18:34:39.162Z","isFirstParty":false,"canonicalSlug":"newsletter-observations-string-normalisation-two-measure-correlation-1a97e8fe"}