# Amazon Buy Box & Seller Change Audit (`practical_ai_lab_jp/pal-amazon-seller-buybox-change-monitor`) Actor

Analyze two owned Amazon seller-offer exports for Buy Box winner changes, offer price/availability shifts and missing seller observations without scraping Amazon.

- **URL**: https://apify.com/practical_ai_lab_jp/pal-amazon-seller-buybox-change-monitor.md
- **Developed by:** [Practical AI Lab](https://apify.com/practical_ai_lab_jp) (community)
- **Categories:** E-commerce, Developer tools
- **Security:** Limited permissions
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $5.00 / 1,000 buyer change reports

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Amazon Buy Box & Seller Change Audit

**Buyer task:** Amazon retail teams compare dated Buy Box and seller exports to triage repeated competitive changes.

Analyze two owned Amazon seller-offer exports for Buy Box winner changes, offer price/availability shifts and missing seller observations without scraping Amazon.

### Who this is for

Teams managing repeated vendor, catalog, branch or listing data exports who need an audit trail of historical changes and data-quality anomalies. Treat this output as decision support for business operations, not a guarantee of rankings, marketing conversions or revenue.

### How to use

1. Start with the fully fictional demonstration inputs already provided in the Actor input form.
2. To analyze your business, replace both JSON array fields with two compatible dated exports that you already own or are permitted to analyze. The two inputs are called "previous" and "current".
3. Keep business identifiers stable (ASIN and seller ID, store and SKU, or business location ID as appropriate). If the original sources have inconsistent identity keys, normalize them before comparing.
4. Execute a bounded run, then download the findings from the default dataset as JSON/CSV. See the SUMMARY key in the default key-value store for the total number of findings and high-priority records.
5. Investigate high-severity issues first, but confirm changes against your authoritative system before any commercial or storefront modification.

### Why compare over time

Amazon retail teams compare dated Buy Box and seller exports to triage repeated competitive changes. A weekly or daily analyst can use fresh exports with an unchanged schema to monitor recurring risks. This Actor is deliberately stateless: it does not fetch new platform data, automatically schedule data collection, or retain previous snapshots for a future run. Customer-supplied rows are not mixed with other accounts.

### Output fields

recordType, entity, issue, severity, before, after, details, sampleWarning. Each row explains what changed or why comparison is not reliable. Inputs are capped at 1,000 records in each snapshot; repeated or invalid identifiers are rejected instead of silently mixed.

### Actual billing and safety

A report pay-per-event charge occurs only after a successful dataset and summary save. Consult the live Pricing tab for the current USD event price and possible actor-start charge. Viewing the public fictional example does not require running this Actor. No paid external API, browser bypass, private login, proxy, user contact information or third-party scraping is used.

### Evidence limitations

Only fields provided in imported data are analyzed. Missing observations are NOT proof of actual product deletion, shop closure or seller delisting. Cross-currency prices are never converted without trustworthy declared FX evidence. Review scores are aggregate indicators and not causal evidence; never send personal reviewer names or contact information. Listed prices are not sales, and product offer changes do not show actual stock counts or transactions.

# Actor input Schema

## `previous` (type: `array`):

Previously collected authorized records; these are not retrieved by the Actor.

## `current` (type: `array`):

Recent compatible records from your authorized dataset.

## Actor input object example

```json
{
  "previous": [
    {
      "asin": "DEMO-001",
      "sellerId": "A",
      "price": 20,
      "currency": "USD",
      "buyBox": true,
      "available": true
    },
    {
      "asin": "DEMO-001",
      "sellerId": "B",
      "price": 21,
      "currency": "USD",
      "buyBox": false,
      "available": true
    }
  ],
  "current": [
    {
      "asin": "DEMO-001",
      "sellerId": "A",
      "price": 22,
      "currency": "USD",
      "buyBox": false,
      "available": true
    },
    {
      "asin": "DEMO-001",
      "sellerId": "B",
      "price": 21,
      "currency": "USD",
      "buyBox": true,
      "available": true
    }
  ]
}
```

# Actor output Schema

## `results` (type: `string`):

Download rows from the latest run in JSON/CSV.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "previous": [
        {
            "asin": "DEMO-001",
            "sellerId": "A",
            "price": 20,
            "currency": "USD",
            "buyBox": true,
            "available": true
        },
        {
            "asin": "DEMO-001",
            "sellerId": "B",
            "price": 21,
            "currency": "USD",
            "buyBox": false,
            "available": true
        }
    ],
    "current": [
        {
            "asin": "DEMO-001",
            "sellerId": "A",
            "price": 22,
            "currency": "USD",
            "buyBox": false,
            "available": true
        },
        {
            "asin": "DEMO-001",
            "sellerId": "B",
            "price": 21,
            "currency": "USD",
            "buyBox": true,
            "available": true
        }
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("practical_ai_lab_jp/pal-amazon-seller-buybox-change-monitor").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "previous": [
        {
            "asin": "DEMO-001",
            "sellerId": "A",
            "price": 20,
            "currency": "USD",
            "buyBox": True,
            "available": True,
        },
        {
            "asin": "DEMO-001",
            "sellerId": "B",
            "price": 21,
            "currency": "USD",
            "buyBox": False,
            "available": True,
        },
    ],
    "current": [
        {
            "asin": "DEMO-001",
            "sellerId": "A",
            "price": 22,
            "currency": "USD",
            "buyBox": False,
            "available": True,
        },
        {
            "asin": "DEMO-001",
            "sellerId": "B",
            "price": 21,
            "currency": "USD",
            "buyBox": True,
            "available": True,
        },
    ],
}

# Run the Actor and wait for it to finish
run = client.actor("practical_ai_lab_jp/pal-amazon-seller-buybox-change-monitor").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "previous": [
    {
      "asin": "DEMO-001",
      "sellerId": "A",
      "price": 20,
      "currency": "USD",
      "buyBox": true,
      "available": true
    },
    {
      "asin": "DEMO-001",
      "sellerId": "B",
      "price": 21,
      "currency": "USD",
      "buyBox": false,
      "available": true
    }
  ],
  "current": [
    {
      "asin": "DEMO-001",
      "sellerId": "A",
      "price": 22,
      "currency": "USD",
      "buyBox": false,
      "available": true
    },
    {
      "asin": "DEMO-001",
      "sellerId": "B",
      "price": 21,
      "currency": "USD",
      "buyBox": true,
      "available": true
    }
  ]
}' |
apify call practical_ai_lab_jp/pal-amazon-seller-buybox-change-monitor --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,practical_ai_lab_jp/pal-amazon-seller-buybox-change-monitor"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/hw9tRLYrSZnG30lDe/builds/pJm4LeafIW6DGZhiV/openapi.json