# Customer Segment Observations — Grouped Numeric Summary + Explicit Missing-Value Fill

> Customer Segment Observations — Grouped Numeric Summary + 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 2026-10-20).

Computes a grouped numeric summary and fills missing values for customer segment observation records (account_count, revenue_amount) across up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-company-16-099?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-20
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/customer-segment-observations-grouped-numeric-summary-explicit-bced40e5
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_f5_lrXEBZcZ8urQGrZNG0

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability customer-segment-observations-grouped-numeric-summary-explicit-bced40e5 -d '<json body>'
```

Example prompt: I have a set of customer segment records with company_group, date, account_count, and revenue_amount — some values are null. Can you group them by company and compute a numeric summary, then fill the missing account_count values using median imputation? Here are up to 200 rows.

## When to prefer this

Choose this endpoint when you need both grouped aggregate statistics and missing-value imputation for customer segment data (account_count or revenue_amount) in a single low-cost call. Ideal for pre-processing segment exports before dashboards, reports, or downstream ML pipelines when you have up to 200 rows and do not need external data enrichment or predictions.

## Known failure modes

- More than 200 rows submitted — request rejected
- Missing required fields (row_id, company_group, date) — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Invalid fill method value not in [zero, mean, median] — enum error
- Invalid field value not in [account_count, revenue_amount] — enum error
- Empty rows array — minimum item count violation

## How this service works

Customer segment observations — Grouped numeric summary + Explicit missing-value fill: Grouped numeric summary + 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

Returns two reports: (1) a grouped numeric summary (e.g. count, min, max, mean per company_group) for the specified field across all supplied rows, and (2) the same dataset with missing values in the chosen field explicitly filled using the selected method (zero, mean, or median). Both reports are computed locally without external lookups.

## Request schema (JSON Schema)

```json
{
 "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"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/customer-segment-observations-grouped-numeric-summary-explicit-bced40e5/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)