# Customer Segment Observations — String Normalisation + Explicit Missing-Value Fill

> Customer Segment Observations — String Normalisation + 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 2026-10-25).

Normalises string fields and fills missing values (account_count, revenue_amount) in customer segment observation records using zero, mean, or median imputation, returning two analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-company-16-037?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-string-normalisation-explicit-missing-d4f26b61
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_yJ7rNKju2-37rPoaD5Rsy

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-string-normalisation-explicit-missing-d4f26b61 -d '<json body>'
```

Example prompt: Can you clean my customer segment data — normalise the company_group strings and fill missing revenue_amount values using median imputation? I have about 150 rows with fields: row_id, company_group, date, account_count, and revenue_amount.

## When to prefer this

Choose this endpoint when you need lightweight, local, deterministic data cleaning on customer segment observation records — specifically string normalisation of categorical fields (company_group) combined with missing-value imputation for numeric metrics (account_count or revenue_amount). Ideal for preprocessing pipelines where you want two bundled reports at low cost ($0.01) without any external API calls, predictions, or live data lookups, and your dataset fits within 200 rows.

## Known failure modes

- More than 200 rows submitted — exceeds maxItems limit
- Invalid date format (not YYYY-MM-DD) causes validation error
- row_id missing or empty string fails minLength constraint
- Invalid method value outside zero/mean/median enum
- Invalid field value outside account_count/revenue_amount enum
- Numeric values outside ±1,000,000,000,000 range rejected

## How this service works

Customer segment observations — String normalisation + Explicit missing-value fill: String normalisation + 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 independent analytical reports per call covering: (1) string-normalised company_group fields and (2) explicitly filled missing values for the chosen numeric field (account_count or revenue_amount) using the specified method (zero, mean, or median), applied across up to 200 input rows. No external lookups or predictions are included.

## 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-string-normalisation-explicit-missing-d4f26b61/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)