# Customer Segment Observations — Daily Sum Series + Date by Category Pivot

> Customer Segment Observations — Daily Sum Series + Date by Category Pivot 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).

Generates two analytical reports from customer segment data: a daily sum time series and a date-by-category pivot table, for up to 200 rows of account_count or revenue_amount data.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-company-16-143?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-daily-sum-series-date-by-category-pivot-5fe10f40
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_8n9w0c8e9-zyyrl0z0VZx

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-daily-sum-series-date-by-category-pivot-5fe10f40 -d '<json body>'
```

Example prompt: I have customer segment data with dates, company groups, account counts, and revenue amounts — can you run both a daily sum series and a date-by-category pivot on the account_count field? Here are the rows: group-a on 2026-10-01 had 10 accounts and $1 revenue, group-b on 2026-10-02 had 20 accounts and $2 revenue, group-a on 2026-10-03 had 15 accounts and $3 revenue, group-b on 2026-10-04 had 30 accounts and $4 revenue.

## When to prefer this

Use this endpoint when you have up to 200 rows of customer segment observation data and need both a daily sum time series AND a date-by-category pivot in a single low-cost call ($0.01 USDC). It is ideal when your data is already structured with date, company_group, and one of account_count or revenue_amount, and you want local analytical computation with no external dependencies, live lookups, or predictions. Prefer this over building your own aggregation when you want packaged dual-report output for business intelligence or reporting workflows.

## Known failure modes

- Input array is empty or missing (minItems: 1 not met)
- More than 200 rows supplied (maxItems exceeded)
- Invalid date format — must match YYYY-MM-DD pattern
- Missing required fields: row_id, company_group, date, account_count, or revenue_amount
- Invalid field enum value — must be 'account_count' or 'revenue_amount'
- Numeric values out of allowed range (±1,000,000,000,000)
- row_id exceeds 80 characters or is empty

## How this service works

Customer segment observations — Daily sum series + Date by category pivot: Daily sum series + Date by category pivot 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 returned together: (1) a daily sum time series showing the selected metric (account_count or revenue_amount) aggregated by date, and (2) a date-by-category pivot table cross-tabulating dates against company_group values. Both reports are computed locally from the supplied rows with no external data lookup.

## 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"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/customer-segment-observations-daily-sum-series-date-by-category-pivot-5fe10f40/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)