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Customer Segment Observations — Base-100 Series + Explicit Missing-Value Fill
x402
Customer Segment Observations — Base-100 Series + Explicit Missing-Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-16).
Indexes customer segment time-series data to a base-100 scale and fills missing values in account_count or revenue_amount fields using zero, mean, or median imputation — up to 200 rows, two reports per call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Customer segment observations — Base-100 series + Explicit missing-value fill: Base-100 series + Explicit missing-value fill 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
Two analytical reports: one showing the base-100 indexed series for the selected field (account_count or revenue_amount) anchored at the specified base_index row, and one showing the dataset with missing values explicitly filled using the chosen method (zero, mean, or median). Both reports are computed locally from the supplied rows with no external lookups.
Example use cases
Segment growth comparison with baseline index
“I have monthly account counts for three customer groups over the past year — can you rebase all of them to 100 starting from January (row 0) and fill any null account_count values using median so I can compare growth across groups on a level scale?”
Revenue gap filling before reporting
“My customer segment table has some missing revenue_amount entries scattered across different company groups. Can you fill those gaps using mean imputation and also produce a base-100 indexed revenue series starting from row 0? There are about 120 rows total.”
Normalize segment metrics for dashboard charting
“I need to normalize account_count data for our B2B and SMB customer segments — index everything to base-100 from the first row and use zero-fill for any missing values. Here are 80 rows of segment observations across the two groups.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-company-16-169?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both base-100 series normalization and missing-value imputation applied to customer segment data (account_count or revenue_amount) in a single low-cost call. It is ideal for preprocessing up to 200 rows of time-series segment observations before charting or reporting, requiring no external data sources or live lookups. Prefer alternatives if you need string normalization, deduplication, correlation, or histogram analysis — those are handled by sibling endpoints on the same platform.
Side effects
- Fewer than 1 row supplied — request rejected
- More than 200 rows submitted — request rejected, maxItems exceeded
- base_index references a row position outside the supplied row count — indexing fails
- Invalid date format (not YYYY-MM-DD) on any row — schema validation error
- Field value outside the allowed numeric range (±1 trillion) — validation rejection
- Unsupported method enum value — schema validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string | |
| base_index | integer |
Tags
x402
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