# Customer Segment Observations — Category Frequencies + Trailing Mean Series

> Customer Segment Observations — Category Frequencies + 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 2026-10-16).

Analyzes up to 200 customer segment observation rows to produce two reports: category frequency distribution and trailing mean time series for account_count or revenue_amount.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-company-16-103?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-16
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/customer-segment-observations-category-frequencies-trailing-mean-series-83c37cba
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_Tjt0ttcf7ZAKNLb7qX-Kn

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-category-frequencies-trailing-mean-series-83c37cba -d '<json body>'
```

Example prompt: Run category frequency and trailing mean analysis on my customer segment data — I have rows with company_group, date, account_count, and revenue_amount fields. Use account_count as the metric and a window of 3. Here's the data: [{"row_id":"r1","company_group":"enterprise","date":"2025-01-01","account_count":50,"revenue_amount":5000},{"row_id":"r2","company_group":"smb","date":"2025-01-02","account_count":120,"revenue_amount":2000}].

## When to prefer this

Choose this endpoint when you need two complementary statistical views — category frequency counts and a rolling average trend — on customer segment data (account_count or revenue_amount) in a single low-cost call. Ideal for business analysts or agents that have already collected segment observation records and want quick descriptive statistics without building custom analytics. Best suited for datasets up to 200 rows where no external data enrichment or predictive modeling is needed.

## Known failure modes

- Fewer than 1 row supplied — validation error
- More than 200 rows supplied — exceeds limit, request rejected
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Window size smaller than 2 or larger than 200 — validation error
- Field not set to 'account_count' or 'revenue_amount' — enum validation failure
- Numeric values outside ±1,000,000,000,000 range — validation error
- Missing required fields (row_id, company_group, date) in any row — request rejected

## How this service works

Customer segment observations — Category frequencies + Trailing mean series: Category frequencies + Trailing mean series 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 derived from the supplied rows: (1) a category frequency report showing how often each company_group appears across observations, and (2) a trailing mean time series for the chosen numeric field (account_count or revenue_amount) using the specified rolling window size. Both reports are computed locally from the supplied data with no 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"
  },
  "window": {
   "type": "integer",
   "default": 2,
   "maximum": 200,
   "minimum": 2
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/customer-segment-observations-category-frequencies-trailing-mean-series-83c37cba/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)