Python tool for Amazon sales rank monitoring

Building a Python Tool for Amazon Sales Rank Monitoring

By John Smith | July 10, 2025

Python Web Scraping Amazon Ecommerce

Building a Python Tool for Amazon Sales Rank Monitoring

For Amazon businesses and market analysts, understanding sales rank is crucial for competitive analysis, inventory management, and market research. Tracking these ranks manually can be time-consuming and inefficient. This blog post guides you through building a Python tool that automates Amazon sales rank monitoring using the SellerMagnet API, an enterprise-grade Amazon data API.

Why Monitor Amazon Sales Rank?

  • Competitive Analysis: Identify top-performing products in your niche.
  • Inventory Management: Predict demand and optimize stock levels.
  • Market Research: Spot emerging trends and market opportunities.

Prerequisites

Before you start, make sure you have:

Step-by-Step Guide

1. Setting up the Environment

Create a new Python file (e.g., amazon_rank_monitor.py) and import the necessary libraries:


import requests
import json

API_KEY = "YOUR_API_KEY" # Replace with your actual API key

2. Fetching Product Statistics with SellerMagnet API

Use the Amazon Product Statistics endpoint to retrieve the sales rank for a specific product.

Endpoint: /amazon-product-statistics

Method: GET

Parameters:

  • asin (required): Product ASIN (e.g., "B08N5WRWNW")
  • marketplaceId (required): Marketplace ID (e.g., "A1PA6795UKMFR9" for Amazon.de)
  • api_key (required): Your API key

Here’s a Python function to fetch product statistics:


def get_product_stats(asin, marketplace_id, api_key):
    url = f"https://sellermagnet-api.com/amazon-product-statistics?asin={asin}&marketplaceId={marketplace_id}&api_key={api_key}"
    response = requests.get(url)
    response.raise_for_status()  # Raise HTTPError for bad responses (4xx or 5xx)
    return response.json()

Example Usage:


asin = "B08N5WRWNW"
marketplace_id = "A1PA6795UKMFR9"  # Amazon.de

product_data = get_product_stats(asin, marketplace_id, API_KEY)

if product_data["success"]:
    sales_rank = product_data["data"]["bestSellerRank"]
    print(f"Sales Rank for {asin}: {sales_rank}")
else:
    print(f"Error: {product_data}")

Example Response:


{
  "success": true,
  "data": {
    "asin": "B08N5WRWNW",
    "amazonPrice": 2999,
    "bestSellerRank": 15,
    "buyBoxPrice": 2999,
    "buyBoxFulfillment": "FBA",
    "buyBoxSellerIdHistory": [
      [
        "2024-07-24T12:00:00Z",
        "A1234567890123"
      ]
    ],
    "salesRankHistory": [
      [
        "2024-07-24T12:00:00Z",
        15
      ]
    ],
    "trackingSince": "2023-01-01"
  }
}

3. Building the Monitoring Tool

To create a basic monitoring tool, schedule the function to run at regular intervals. For simplicity, we'll use a loop with a delay. For more robust scheduling consider using DataPipeline tools.


import time

def monitor_sales_rank(asin, marketplace_id, api_key, interval_seconds=3600):
    while True:
        product_data = get_product_stats(asin, marketplace_id, api_key)
        if product_data["success"]:
            sales_rank = product_data["data"]["bestSellerRank"]
            print(f"{time.ctime()}: Sales Rank for {asin}: {sales_rank}")
        else:
            print(f"{time.ctime()}: Error: {product_data}")
        time.sleep(interval_seconds)

# Example Usage:
monitor_sales_rank(asin, marketplace_id, API_KEY)

4. Storing the Data

For trend analysis, store the sales rank data in a database (e.g., SQLite, PostgreSQL) or a CSV file. Here’s how to append data to a CSV file:


import csv
import time

def monitor_sales_rank(asin, marketplace_id, api_key):
    filename = f"{asin}_sales_rank.csv"
    file_exists = os.path.isfile(filename)
    with open(filename, mode='a', newline='') as csvfile:
        csv_writer = csv.writer(csvfile)
        if not file_exists:
            csv_writer.writerow(['Timestamp', 'Sales Rank'])
        while True:
            product_data = get_product_stats(asin, marketplace_id, api_key)
            if product_data["success"]:
                sales_rank = product_data["data"]["bestSellerRank"]
                timestamp = time.ctime()
                csv_writer.writerow([timestamp, sales_rank])
                print(f"{timestamp}: Sales Rank for {asin}: {sales_rank}")
            else:
                print(f"{time.ctime()}: Error: {product_data}")
            time.sleep(3600)


import os
monitor_sales_rank(asin, marketplace_id, API_KEY)

5. Visualizing the Data

Use libraries like matplotlib or seaborn to visualize sales rank trends. You can also request graphs directly from the Amazon Product Statistics endpoint by using graphs=true.

Example API Call with Graphs:


curl "https://sellermagnet-api.com/amazon-product-statistics?asin=B08N5WRWNW&marketplaceId=A1PA6795UKMFR9&api_key=YOUR_API_KEY&graphs=true"

This will return URLs to pre-generated graphs, like:


{
  "success": true,
  "data": {
    "asin": "B0CLTBHXWQ",
    "productTitle": "Playstation 5 Console Edizione Digital Slim",
    "buyBoxPrice": 41800,
    "buyBoxFulfillment": "FBM",
    "buyBoxSellerIdHistory": [
      [
        "2025-06-14 17:08:00",
        "A2I59UVTUWUFH0"
      ]
    ],
    "categoryTree": [
      {
        "catId": 412603031,
        "name": "Videogiochi"
      },
      {
        "catId": 20904349031,
        "name": "PlayStation 5"
      },
      {
        "catId": 20904364031,
        "name": "Console"
      }
    ],
    "graphs": {
      "amazonAsSellerPriceHistory": "https://sellermagnet-api-webspace.s3.eu-central-1.amazonaws.com/amazon/api/charts/B0CLTBHXWQ/1749913774/B0CLTBHXWQ_amazon_price_1749913773.png",
      "lowestFBAPriceHistory": "https://sellermagnet-api-webspace.s3.eu-central-1.amazonaws.com/amazon/api/charts/B0CLTBHXWQ/1749913776/B0CLTBHXWQ_fba_price_1749913773.png",
      "lowestFBMPriceHistory": "https://sellermagnet-api-webspace.s3.eu-central-1.amazonaws.com/amazon/api/charts/B0CLTBHXWQ/1749913775/B0CLTBHXWQ_fbm_price_1749913773.png",
      "monthlySoldHistory": "https://sellermagnet-api-webspace.s3.eu-central-1.amazonaws.com/amazon/api/charts/B0CLTBHXWQ/1749913778/B0CLTBHXWQ_monthly_sold_1749913773.png",
      "productRatingHistory": "https://sellermagnet-api-webspace.s3.eu-central-1.amazonaws.com/amazon/api/charts/B0CLTBHXWQ/1749913777/B0CLTBHXWQ_rating_1749913773.png"
    },
    "listedSince": "2023-12-30 01:00:00",
    "lowestFBAPrice": 44999,
    "lowestFBMPrice": 41700,
    "marketplaceId": "APJ6JRA9NG5V4",
    "marketplaceNewPriceHistory": [
      [
        "2025-06-14",
        41700
      ]
    ],
    "offers": {
      "A11IL2PNWYJU7H": {
        "isFBA": true,
        "lastUpdated": "2025-06-14 17:08:00",
        "priceHistory": [
          [
            "2025-06-14 06:22:00",
            44999,
            0
          ]
        ],
        "stockHistory": [
          [
            "2025-05-29 11:32:00",
            1
          ]
        ]
      },
      "A12FLY25DT7QO0": {
        "isFBA": false,
        "lastUpdated": "2025-06-14 17:08:00",
        "priceHistory": [
          [
            "2025-06-09 15:32:00",
            41800,
            0
          ]
        ],
        "stockHistory": [
          [
            "2025-06-14 13:34:00",
            49
          ]
        ]
      },
      "A18KSDUE00UP6J": {
        "isFBA": false,
        "lastUpdated": "2025-06-14 17:08:00",
        "priceHistory": [
          [
            "2025-05-29 11:32:00",
            42890,
            0
          ]
        ],
        "stockHistory": [
          [
            "2025-05-30 18:30:00",
            3
          ]
        ]
      }
    },
    "productReviewAverage": 4.7,
    "productTotalReviews": 3129,
    "rootCategory": {
      "id": 412603031,
      "name": "Videogiochi"
    },
    "stats": {
      "amazonAsSellerPriceHistory": [
        [
          "2025-06-14",
          44999
        ]
      ],
      "buyBoxPriceHistory": [
        [
          "2025-06-13",
          41700
        ]
      ],
      "monthlySoldHistory": [
        [
          "2025-06",
          1000
        ]
      ],
      "productRatingCountHistory": [
        [
          "2025-06-14 15:28:00",
          3129
        ]
      ],
      "productRatingHistory": [
        [
          "2025-02-02 01:30:00",
          4.7
        ]
      ],
      "salesRankHistory": [
        [
          "2025-06-14 01:58:00",
          15
        ]
      ]
    }
  }
}

Practical Use Cases

  • Dynamic Pricing: Adjust prices based on sales rank changes.
  • Restock Alerts: Trigger alerts when sales rank indicates increased demand.
  • Promotional Campaigns: Evaluate campaign effectiveness by monitoring sales rank.

Advanced Tips

  • Multi-ASIN Monitoring: Scale the tool to monitor multiple products simultaneously.
  • Marketplace Expansion: Adapt the tool to different Amazon marketplaces using the appropriate marketplaceId. You can use the Get Amazon Marketplaces endpoint to list available marketplaces.
  • Integrate with Other APIs: Combine sales rank data with other SellerMagnet API endpoints like Amazon Product Offers or Amazon Product Lookup for deeper insights.

Legalities and Ethical Considerations

Always adhere to Amazon's terms of service and avoid overloading their servers with excessive requests. Implement reasonable delays between API calls to ensure compliance.

Conclusion

Building a Python tool for Amazon sales rank monitoring provides valuable insights for businesses and analysts. Using the SellerMagnet API simplifies data retrieval, allowing you to focus on analysis and decision-making. Start building your tool today and gain a competitive edge in the Amazon marketplace!

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