# Customer Segment Observations — Record Quality Audit + Exact Row Deduplication + Trailing Mean Series

> Customer Segment Observations — Record Quality Audit + Exact Row Deduplication + Trailing Mean Series 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).

Runs three analytical reports (record quality audit, exact row deduplication, and trailing mean series) on customer segment observations data containing account counts and revenue amounts, for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-company-16-216?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-25
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/customer-segment-observations-record-quality-audit-exact-row-f12ddef6
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_jKfqJoPTvIbz1xafD3LA9

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-record-quality-audit-exact-row-f12ddef6 -d '<json body>'
```

Example prompt: Can you run a record quality audit, exact row deduplication check, and trailing mean series (window size 3) on my customer segment observations data — I have account_count and revenue_amount fields across company groups and dates, and I want the trailing mean computed over account_count?

## When to prefer this

Use this endpoint when you need three bundled local analytics — quality audit, deduplication, and rolling mean — on customer segment time series data (account_count and revenue_amount) in a single low-cost call. It is ideal for pre-processing pipelines, data cleaning checks before reporting, or exploratory EDA on small-to-medium segment datasets (up to 200 rows). It does not make external lookups or predictions, so it's appropriate when you want deterministic, offline computation on data you already have.

## Known failure modes

- More than 200 rows submitted — request rejected or truncated
- Missing required fields (row_id, company_group, date, account_count, revenue_amount) cause validation errors
- Invalid date format (not YYYY-MM-DD) causes parsing failure
- Window size larger than number of rows results in insufficient data for trailing mean
- Numeric values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000) rejected
- Empty rows array returns error

## How this service works

Customer segment observations — Record quality audit + Exact row deduplication + Trailing mean series: Record quality audit + Exact row deduplication + Trailing mean series for supplied customer segment observations records (account_count, revenue_amount). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.

## Output

Three independent analytical reports: (1) a record quality audit flagging missing values, anomalies, or format issues in the supplied rows; (2) an exact row deduplication report identifying fully identical records; and (3) a trailing (rolling) mean series computed over the selected numeric field (account_count or revenue_amount) using the specified window size — all derived locally from the supplied data with no external calls.

## 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"
  },
  "window": {
   "type": "integer",
   "default": 2,
   "maximum": 200,
   "minimum": 2
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/customer-segment-observations-record-quality-audit-exact-row-f12ddef6/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)