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Customer Segment Observations — String Normalisation + Adjacent Row Changes
x402
Customer Segment Observations — String Normalisation + Adjacent Row Changes is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-25).
Normalises company_group strings and computes adjacent-row changes for customer segment observation data (account_count, revenue_amount) across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
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.
Output
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.
Example use cases
Monthly segment revenue trend review
“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.”
Account count change detection across segments
“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.”
Segment data quality and delta audit
“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?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-16-029?utm_source=zero.xyzWhen to prefer
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.
Side effects
- 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
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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