Building a Python Script for Amazon Price Sensitivity Analysis with SellerMagnetAPI

By Emma Johnson | July 23, 2025

Building a Python Script for Amazon Price Sensitivity Analysis with SellerMagnetAPI

In today's competitive e-commerce landscape, understanding price sensitivity is crucial for Amazon businesses. Knowing how changes in price impact sales volume allows for strategic pricing decisions, optimized inventory management, and effective competitive analysis. This post will guide you through building a Python script using the SellerMagnetAPI to perform comprehensive price sensitivity analysis on Amazon products.

SellerMagnet provides an enterprise-grade Amazon data API, offering access to real-time and historical product data. By leveraging this API, businesses and market analysts can gain valuable insights to inform their strategies. Forget Amazon Web Scraping complexities and ensure you are legally compliant with up-to-date data.

Why Price Sensitivity Analysis Matters

Competitive Analysis: Monitor competitor pricing strategies and react accordingly.
Inventory Management: Adjust pricing to optimize inventory turnover.
Market Research: Identify price thresholds that impact demand.
Profit Maximization: Find the price point that maximizes revenue and profitability.

Getting Started with SellerMagnetAPI

Before diving into the code, ensure you have a SellerMagnetAPI account and a valid API key. SellerMagnet offers flexible pricing plans suitable for various business needs. To get started you can Try Free.

Prerequisites

Python 3.6+
requests library (install via pip install requests)

Step-by-Step Guide to Building the Script

1. Setting Up the Environment

First, import the necessary libraries and set up your API key and the target Amazon marketplace.


import requests
import json

API_KEY = "YOUR_API_KEY"  # Replace with your actual API key
MARKETPLACE_ID = "ATVPDKIKX0DER"  # Amazon.com Marketplace ID
ASIN = "B08N5WRWNW"  # Example ASIN (replace with the product you want to analyze)

2. Retrieving Product Statistics

Use the Amazon Product Statistics endpoint to fetch historical sales rank and pricing data for a specific ASIN. This is core to price sensitivity analysis. The Documentation will help you understand the schema.


def get_product_statistics(asin, marketplace_id, api_key):
    url = "https://sellermagnet-api.com/amazon-product-statistics"
    params = {
        "asin": asin,
        "marketplaceId": marketplace_id,
        "api_key": api_key,
        "graphs": "true" #Enables graph data for trends
    }
    response = requests.get(url, params=params)
    response.raise_for_status()  # Raise an exception for HTTP errors
    return response.json()

product_data = get_product_statistics(ASIN, MARKETPLACE_ID, API_KEY)

if product_data["success"]:
    print("Successfully retrieved product statistics.")
else:
    print("Error retrieving product statistics:", product_data.get("errors", "Unknown error"))

Example Response:


{
  "success": true,
  "data": {
    "asin": "B0CLTBHXWQ",
    "amazonPrice": 45000,
    "bestSellerRank": 15,
    "buyBoxPrice": 41800,
    "buyBoxFulfillment": "FBM",
    "buyBoxSellerIdHistory": [
      [
        "2025-06-14 17:08:00",
        "A2I59UVTUWUFH0"
      ]
    ],
    "salesRankHistory": [
      [
        "2025-06-14 01:58:00",
        15
      ]
    ],
    "trackingSince": "2023-12-30",
    "graphs": {
      "priceTrend": [
        {
          "timestamp": "2025-06-14T17:08:00",
          "price": 41800
        }
      ],
      "rankTrend": [
        {
          "timestamp": "2025-06-14T01:58:00",
          "rank": 15
        }
      ]
    }
  }
}

3. Analyzing Price and Sales Rank Data

Extract the price and sales rank history from the API response and analyze the relationship. This involves calculating correlation or identifying price thresholds where significant changes in sales rank occur.


def analyze_price_sensitivity(product_data):
    if not product_data["success"] or not product_data["data"]["graphs"]:
        print("Insufficient data for analysis.")
        return

    price_trend = product_data["data"]["graphs"]["priceTrend"]
    rank_trend = product_data["data"]["graphs"]["rankTrend"]

    if not price_trend or not rank_trend:
      print("No price or rank trend data available.")
      return

    # Basic analysis: Calculate the average price and rank
    total_price = sum(entry["price"] for entry in price_trend)
    total_rank = sum(entry["rank"] for entry in rank_trend)
    
    avg_price = total_price / len(price_trend) if price_trend else 0
    avg_rank = total_rank / len(rank_trend) if rank_trend else 0

    print(f"Average Price: {avg_price / 100:.2f}") #Convert cents to dollars
    print(f"Average Sales Rank: {avg_rank}")


    # Further analysis can be added here, such as:
    # - Calculating the correlation between price changes and sales rank changes
    # - Identifying price thresholds where significant changes in sales rank occur
    # Example: Calculating price deltas and corresponding sales rank deltas
    price_deltas = [price_trend[i]["price"] - price_trend[i-1]["price"] if i > 0 else 0 for i in range(len(price_trend))]
    rank_deltas = [rank_trend[i]["rank"] - rank_trend[i-1]["rank"] if i > 0 else 0 for i in range(len(rank_trend))]

    # Printing out price and rank changes
    for i in range(1, len(price_trend)):
        price_change = price_trend[i]["price"] - price_trend[i-1]["price"]
        rank_change = rank_trend[i]["rank"] - rank_trend[i-1]["rank"]
        print(f"Time: {price_trend[i]['timestamp']}, Price Change: {price_change}, Rank Change: {rank_change}")

analyze_price_sensitivity(product_data)

4. Enhancing the Analysis with Additional Data

To refine your analysis, consider incorporating data from other SellerMagnetAPI endpoints:

Amazon Product Offers: Get real-time pricing and seller information.
Amazon Seller Review: Assess seller reputation and its impact on sales.
Amazon Product Lookup: Gather detailed product specifications.
Amazon Product Estimate Sales: Estimate monthly sales to find pricing sweet spot.

For instance, you can modify the script to fetch current offers and compare them with historical data:


def get_product_offers(asin, marketplace_id, api_key):
    url = "https://sellermagnet-api.com/amazon-product-offers"
    params = {
        "asin": asin,
        "marketplaceId": marketplace_id,
        "api_key": api_key
    }
    response = requests.get(url, params=params)
    response.raise_for_status()
    return response.json()

offers_data = get_product_offers(ASIN, MARKETPLACE_ID, API_KEY)

if offers_data["success"]:
    print("Successfully retrieved product offers.")
    # Process and analyze offers data
else:
    print("Error retrieving product offers:", offers_data.get("errors", "Unknown error"))

Example response:


{
  "data": {
    "asin": "B0CL61F39H",
    "buyBox": {
      "condition": "New",
      "deliveryDate": "2025-06-28",
      "fulfillmentType": "FBA",
      "inventory": 30,
      "positivePercentage": 0,
      "priceWithoutShipping": 499,
      "sellerId": "Amazon",
      "sellerName": "Amazon",
      "shippingPrice": 0,
      "totalPrice": 499,
      "totalReviews": 0
    },
    "currency": {
      "code": "USD",
      "name": "United States Dollar",
      "symbol": "$"
    },
    "marketplaceId": "ATVPDKIKX0DER",
    "offers": [
      {
        "condition": "New",
        "deliveryDate": "2025-06-28",
        "fulfillmentType": "FBA",
        "inventory": 30,
        "positivePercentage": 0,
        "priceWithoutShipping": 499,
        "sellerId": "Amazon",
        "sellerName": "Amazon",
        "shippingPrice": 0,
        "totalPrice": 499,
        "totalReviews": 0
      }
    ],
    "productLink": "https://www.amazon.com/dp/B0CL61F39H",
    "productMainImage": "https://m.media-amazon.com/images/I/31kTNmpm6vL.jpg",
    "productTitle": "PlayStation\u00ae5 console (slim)"
  },
  "success": true
}

5. Automating and Scaling the Analysis

To automate the price sensitivity analysis, you can schedule the script to run periodically using tools like cron or Windows Task Scheduler. For large-scale analysis, consider using SellerMagnet's DataPipeline for efficient data extraction and processing.

Advanced Techniques

Correlation Analysis: Use statistical methods to quantify the relationship between price and sales rank.
Regression Analysis: Build predictive models to forecast sales based on pricing strategies.
Time Series Analysis: Analyze historical data to identify seasonal trends and patterns.
Geo-Specific Analysis: Factor in Geolocation to identify regional price sensitivities.

By integrating these techniques with SellerMagnetAPI data, you can gain a deeper understanding of market dynamics.

Conclusion

Building a Python script for Amazon price sensitivity analysis with SellerMagnetAPI empowers businesses to make data-driven pricing decisions. By leveraging real-time and historical data, you can optimize inventory management, refine competitive strategies, and ultimately maximize profitability. Explore SellerMagnet's other offerings, such as the Amazon Bestsellers endpoint, the Amazon ASIN/ISBN/EAN Converter, and Amazon Product Estimate Sales. Refer to our Documentation and Code Examples for more details. Check the API Status anytime to be sure of uptime.

For more information, Contact us or Login to your account. Remember to Try Free to explore all the features of SellerMagnetAPI.

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