# Product Photography Analytics — String Normalisation + Two-Measure Correlation

> Product Photography Analytics — String Normalisation + Two-Measure Correlation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-30).

Runs string normalisation and two-measure correlation analysis on product photography records (required_images vs completed_images) for up to 200 rows, returning two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-retail-09-033?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-30
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/product-photography-analytics-string-normalisation-two-measure-376ac647
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_IOoAJo4w0VgTgQG_JqyHv

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 product-photography-analytics-string-normalisation-two-measure-376ac647 -d '<json body>'
```

Example prompt: Analyse the correlation between required and completed product images for these store groups — I have 4 rows of data: group-a on 2026-10-01 needed 10 images and completed 1, group-b on 2026-10-02 needed 20 and completed 2, group-a on 2026-10-03 needed 15 and completed 3, group-b on 2026-10-04 needed 30 and completed 4. Give me the string normalisation and two-measure correlation reports.

## When to prefer this

Choose this endpoint when you have product photography tracking data (required vs completed image counts by store group and date) and need both string normalisation of text fields and a two-measure statistical correlation report in a single call, at low cost ($0.01 USDC for up to 200 rows). Prefer it over generic analytics tools when your dataset fits within 200 rows and you want two bundled reports without external data lookups or ML predictions.

## Known failure modes

- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, required_images, completed_images) causes row rejection
- Malformed date strings not matching YYYY-MM-DD pattern fail schema validation
- Numeric values outside ±1,000,000,000,000 are rejected
- Empty rows array returns an error
- Payment failure (insufficient USDC) blocks execution

## How this service works

Product photography — String normalisation + Two-measure correlation: String normalisation + Two-measure correlation for supplied product photography records (required_images, completed_images). 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: (1) a string normalisation report covering the text fields (row_id, store_group, date), and (2) a two-measure correlation report quantifying the statistical relationship between required_images and completed_images across the submitted rows. No external lookups or predictions are performed; analysis is fully local on the supplied data.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "store_group",
     "date",
     "required_images",
     "completed_images"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "store_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "required_images": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "completed_images": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "store_group": "group-a",
     "required_images": 10,
     "completed_images": 1
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "store_group": "group-b",
     "required_images": 20,
     "completed_images": 2
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "store_group": "group-a",
     "required_images": 15,
     "completed_images": 3
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "store_group": "group-b",
     "required_images": 30,
     "completed_images": 4
    }
   ],
   "maxItems": 200,
   "minItems": 1
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/product-photography-analytics-string-normalisation-two-measure-376ac647/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)