{"uid":"cap_uGVW7ioRSZiH-Wuj9BO8n","slug":"customer-segment-observations-string-normalisation-category-frequencies-cdbb9fb4","name":"Customer Segment Observations — String Normalisation & Category Frequencies","description":"Customer segment observations — String normalisation + Category frequencies: String normalisation + Category frequencies 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-16-024?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_hB1POIsnGay-v-v6k4xOH","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 category frequency analysis on customer segment observation records (account_count, revenue_amount) for up to 200 rows, returning two analytical reports.","exampleAgentPrompt":"Can you run string normalisation and category frequency analysis on my customer segment observations? I have 4 rows: row-1 on 2026-10-01 for group-a with 10 accounts and $1 revenue, row-2 on 2026-10-02 for group-b with 20 accounts and $2 revenue, row-3 on 2026-10-03 for group-a with 15 accounts and $3 revenue, and row-4 on 2026-10-04 for group-b with 30 accounts and $4 revenue.","exampleUseCases":[{"title":"Segment category frequency audit","prompt":"I have 50 rows of customer segment data with company_group labels that may be inconsistently named — can you normalise those strings and tell me the frequency of each category, plus break down account_count and revenue_amount distributions?"},{"title":"Revenue distribution by company group","prompt":"Run a category frequency report on these 120 customer segment records — I want to see how often each company_group appears and how the revenue_amount values are distributed across those groups."},{"title":"Data quality check on segment observations","prompt":"Can you normalise the string fields and compute category frequencies for my 80-row customer segment dataset? I'm especially interested in spotting inconsistencies in the company_group field alongside the account_count values."}],"resultDescription":"Returns two analytical reports: (1) a string normalisation report that standardizes the company_group and other string fields across the supplied rows, and (2) a category frequency report showing the distribution and counts of categorical values (such as company_group) along with summaries of numeric fields (account_count, revenue_amount) for up to 200 input rows.","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) triggers a pattern validation error","Numeric values outside ±1,000,000,000,000 are rejected","Empty rows array returns an error","Payment failure (insufficient USDC) prevents execution"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation and category frequency analysis on customer segment data (account_count, revenue_amount) in a single low-cost call. It is ideal for small-to-medium datasets (up to 200 rows) where you want two complementary local analytical reports without requiring external lookups, predictions, or live data connections. Prefer this over general-purpose analytics tools when working specifically with company-group-segmented customer observation records and cost efficiency ($0.01 per call) is a priority.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-25T06:58:51.915Z","isFirstParty":false,"canonicalSlug":"customer-segment-observations-string-normalisation-category-frequencies-cdbb9fb4"}