Tag Archives: Amazon

40% of Amazon sellers make between $1,000 and $25,000 per month, while super sellers earn over $100,000 per month. The key difference? Targeting the best-selling items, which Amazon publicly shares on its “Best Seller” lists. These pages are a goldmine of information, revealing consumer trends, hot products, and market opportunities. Let’s dive into “how to scrape Amazon Best Sellers” and leverage this data for better decision-making.

What is the Amazon Best Seller Rank?

Amazon ranks its best-selling products on its Best Seller pages, known as the Amazon Best Sellers Rank (BSR). While Amazon does not disclose the exact formula for ranking, it is widely accepted that sales volume is the primary factor, with recent sales carrying more weight than older ones. The list updates hourly, ensuring the most up-to-date snapshot of consumer demand.

Amazon categorizes these rankings into over 30 main categories, each with multiple subcategories—totaling around 25,000 categories. The algorithm also includes predictive factors to analyze potential future sales. These lists provide valuable insights, such as:

• Trending Products: Identify emerging trends before they peak.
• Competitive Analysis: See what your competitors are selling and how they’re performing.
• Market Research: Understand consumer preferences and demand shifts.
• Product Development: Spot market gaps and discover potential new product opportunities.

With about 25,000 categories and rankings that vary across Amazon’s 20+ domains (amazon.com, amazon.co.uk, amazon.ca, etc.), manually collecting this data is impractical. Additionally, since the rankings update hourly, staying on top of these trends requires automation—this is where scraping APIs come in.

Benefits of Scraping Amazon Best Sellers Pages

• Efficiency: Automate the extraction of large datasets quickly.
• Real-Time Data: Access up-to-the-minute information on rankings and sales.
• Customizable Data: Extract specific details such as product titles, prices, reviews, and rankings.
• Scalability: Monitor multiple categories and products simultaneously.
• Data-Driven Decisions: Use the insights for market analysis, pricing strategies, and competitive positioning.

What is an Amazon Best Sellers Scraper?

An Amazon Best Sellers Scraper  is a tool or script designed to automatically extract data from Amazon’s Best Sellers pages. These scrapers can be custom-built or utilize pre-built APIs, like Traject Data’s Amazon Rainforest API, to streamline the process.

The Legality and Challenges of Scraping Amazon

Now, let’s address the elephant in the room: is it legal to scrape Amazon? The answer is nuanced.

• However, the legality of web scraping is a complex legal area and depends on how the extracted data is used.
• Amazon employs various anti-scraping measures to detect and block automated scraping activities.

How Does Amazon Prevent Scraping?

Amazon implements several techniques to block scraping, including:

• IP Address Blocking: Blocking IPs that send too many requests.
• CAPTCHAs: Requiring users to verify they are human.
• Rate Limiting: Restricting the number of requests per IP address.
• User-Agent Analysis: Identifying bots through user-agent strings.
• Honeypot Traps: Inserting hidden elements that only bots can see.

Due to these challenges, maintaining a manual scraping script is difficult and costly.

How to Scrape Amazon Best Sellers Effectively and Responsibly

Okay, so how do you actually scrape Amazon Best Sellers while minimizing the risks? 

Use a Reliable API

For a seamless and ethical approach, consider using a dedicated API like Traject Data’s Amazon Rainforest API. APIs handle the complexities of scraping, ensuring you get accurate data without triggering Amazon’s defenses.

1. Sign up for a free API key at TrajectData.com.
2. Visit Traject Data’s documentation for integration details.
3. Watch a getting started video to learn how to use the API efficiently.
4. Leverage the Rainforest API Bestsellers Endpoint, type=bestsellers 

When making a request with type=bestsellers, the Rainforest API returns Best Seller results from an Amazon Best Sellers page. The page can be specified using the category_id and amazon_domain parameters (where category_id is returned from the Categories API) or by using the url parameter.

The Rainforest API supports all types of Amazon Bestseller pages, including:

• Bestsellers
• New Releases
• Movers & Shakers
• Most Wished For
• Gift Ideas

Leveraging the Traject Data Amazon Rainforest API

The Traject Data Amazon Rainforest API simplifies the process of scraping Amazon Best Sellers. 

• Easy Integration: Easily integrate with your BI tool of choice. Data is delivered how and when you need it.
• Comprehensive Documentation & Support: Well-documented endpoints and libraries for various programming languages.
• Structured Data: Clean, JSON-formatted results, parsed and ready for analysis.
• Comprehensive Data: Retrieve rankings, prices, reviews, and more.
• Bestsellers Endpoint: Fetch Best Seller lists from any Amazon category.

Unlocking Insights with Amazon Best Seller Data

“How to scrape Amazon Best Sellers” is a question many data-driven businesses and researchers ask. By understanding the benefits, challenges, and best practices, you can unlock valuable insights hidden within Amazon’s Best Sellers pages. With the right approach—leveraging a robust API like Traject Data’s Amazon Rainforest API—you can harness real-time eCommerce intelligence without the headaches of manual scraping.

If you sell on Amazon, you’ve probably wondered how to win the Amazon Buy Box (now called the Featured Offer). Approximately 82% of all Amazon sales come through the Buy Box, making it a crucial factor for success. Losing Buy Box eligibility can cause a dramatic drop in sales. In this article, we’ll provide a step-by-step guide on how to master the Amazon Buy Box in 2025.

How Does the Amazon Buy Box Work?

The Amazon Buy Box (now Featured Offer) determines which seller gets the sale when multiple sellers list the same product. When a customer clicks “Add to Cart” or “Buy Now,” the seller chosen by Amazon’s algorithm gets the order\sell.

With over 9.7 million sellers on Amazon, competition is fierce. Amazon rotates the Buy Box among eligible sellers, but some sellers get more turns in the rotation than others based on performance. If you lose eligibility due to poor seller metrics, low sales volume, or inventory issues, your sales can suffer significantly.

How Does Amazon Decide Who Wins the Buy Box?

Amazon uses a machine learning algorithm to determine the Buy Box winner. While the exact details of the algorithm are not disclosed, Amazon provides guidance on key factors influencing Buy Box success.

Factors Influencing Buy Box Eligibility and Success

How the Amazon Buy Box Algorithm Evolves

Amazon’s Buy Box algorithm constantly changes, leaving sellers struggling to adapt. Here are some recent trends:

• Faster shipping and competitive pricing matter more than just using FBA or Prime.
• Buy Box rotation frequency varies, shifting multiple times per hour for some products but staying static for others.
• Amazon enforces price consistency across marketplaces, suppressing the Buy Box if prices are lower on external sites.
• Regional differences impact strategy — in Europe, some categories display multiple Buy Box winners.

To stay competitive, sellers must track Buy Box win rates, analyze historical trends, and make real-time strategy adjustments.

Tools to Facilitate Your Amazon Buy Box Strategy

To improve Buy Box performance, sellers can use tools to track pricing, shipping speeds, and competitor behavior. While Amazon offers its own API, third-party APIs like Traject Data’s Rainforest API provide deeper insights and better tracking.

Why Amazon’s API Alone Isn’t Enough

Amazon’s API provides only a limited snapshot of Buy Box data, leaving sellers with gaps in historical trends, competitor movements, and fulfillment insights. Relying solely on Amazon’s API makes it harder to react to Buy Box changes effectively.

Traject Data fills these gaps, giving sellers real-time and historical data to make smarter decisions before competitors do.

How to Use Traject Data to Win the Amazon Buy Box

1. Track Buy Box Trends & Adjust Strategy

• Use Traject Data’s Rainforest API to monitor Buy Box winners, pricing, shipping costs, and stock levels.
• Identify shifts in Buy Box winners and adjust fulfillment strategies accordingly.

Action: Adapt inventory allocation based on real Buy Box trends.

2. Analyze Pricing Strategies of Buy Box Winners

• Compare real-time pricing across sellers.
• Avoid Buy Box suppression due to external price mismatches.

Action: Keep pricing competitive while maintaining profit margins.

3. Optimize Fulfillment Strategy

• Assess how shipping speed and cost impact Buy Box prioritization.
• Retrieve estimated delivery times and compare winners vs. non-winners.

Action: Invest in faster logistics or cost-efficient shipping.

4. Monitor Competitor Stock Levels

• Use stock estimation to track competitor inventory.
• Adjust pricing when competitors are low on stock.

Action: Take advantage of stock shortages by adjusting pricing accordingly.

5. Use Sales Velocity as a Competitive Edge

• Retrieve detailed pricing, fulfillment, and sales data.
• Analyze which sellers consistently win the Buy Box despite price differences.

Action: Increase sales momentum by offering discounts, bundling products, or optimizing fulfillment speed.

6. Adapt to Regional Buy Box Trends

• In Europe, track multiple Buy Box winners per product.
• In North America, monitor real-time Buy Box rotation.
• In Asia, focus on Prime eligibility and localized fulfillment solutions.

Action: Align strategies with marketplace-specific rules and trends.

Take Control of Your Amazon Buy Box Strategy

✅ Pull Buy Box data daily for high-competition categories and weekly for lower-volume products.

✅ Analyze pricing and shipping trends to determine key Buy Box factors.

✅ Monitor competitor behavior to adapt before they do.

✅ Use historical trends to predict future Buy Box patterns and adjust fulfillment accordingly.

✅ Compare non-Buy Box offers to identify pricing opportunities.

✅ Track regional Buy Box trends to stay compliant with country-specific rules.

Get Ahead, Stay Ahead

Waiting to see how Amazon Buy Box changes affect your business means losing to competitors who are already adapting. Success requires constant monitoring, strategic agility, and real-time insights.

With Traject Data’s Rainforest API, businesses can adjust pricing, fulfillment, and inventory strategies proactively. Unlike Amazon’s API, which only provides real-time snapshots, Traject Data enables historical tracking, competitor insights, and fulfillment impact analysis.

Don’t let Amazon’s shifting rules dictate your success. Contact us today to see how Traject Data can transform your Buy Box strategy.

Ecommerce businesses frequently encounter inventory challenges. In fact, 56% of retailers report difficulties with accurate inventory tracking. Keeping track of competitor inventory levels is a game-changer for online retailers. Whether you’re looking to maximize sales, anticipate demand, or make smarter pricing decisions, scraping competitor inventory data provides valuable insights. This guide will walk you through the benefits, legal considerations, challenges, and best tools for scraping competitor inventory levels effectively.

Benefits of Scraping Competitor Inventory Data

Monitoring competitor stock levels allows online retailers to make data-driven decisions and stay ahead in a competitive market. Here are some key benefits:

• Identify When Competitors Run Out of Stock: If a competitor runs out of a high-demand product, you can capitalize on the opportunity by adjusting your pricing, boosting promotions, or ensuring your stock is well-maintained.
• Efficient Inventory Management: Poor inventory management causes businesses to lose up to 11% of their annual revenue, mainly due to stockouts and overstocking.
• Improved Omnichannel Management: For companies selling across multiple platforms, scraping ensures accurate inventory representation across all channels.
• Better Supplier Monitoring: Scraping can extend to supplier websites, helping businesses plan inventory more effectively.
• Monitor the velocity of competitor sales: When scraping inventory levels over time, you can begin to track sales volumes. 

How to Scrape Competitor Inventory Data Legally and Effectively

Scraping competitor inventory data can be incredibly useful, but it’s essential to follow legal and ethical best practices to avoid violating website terms of service or data privacy laws.

Best Practices for Legal Scraping

1. Use APIs When Available: Leveraging an API ensures compliant data collection while reducing maintenance costs. 
2. Scrape Only Publicly Available Data: Avoid accessing password-protected, user-restricted, or proprietary information.
3. Implement Rate Limiting: Prevent excessive requests that could overwhelm the target website and lead to IP bans.
4. Focus on Factual Data: Stick to publicly available product codes, prices, and stock levels, which are typically not protected by copyright.
5. Comply with Data Privacy Regulations: Ensure compliance with laws like GDPR and CCPA by avoiding the collection of personal data.

Steps to Scrape Competitor Inventory Data

1. Identify Competitors: Determine which retailers you want to track and which specific inventory data is most relevant to your business.
2. Select a Scraping Tool or API: You can either develop a custom scraper using Python or use a dedicated web scraper API.
3. Set Up Your Scraper: Configure it to collect essential data such as product names, SKUs, pricing, and stock levels.
4. Parse and Structure the Data: Organize the collected data in a structured format like CSV or JSON. A dedicated web scraper API can automate the process of structuring and parsing the data for quicker analysis.
5. Schedule Regular Monitoring: Inventory levels fluctuate frequently, so set up automated scraping at regular intervals to maintain accuracy.

Main Challenges of Scraping Inventory Data

Extracting value from inventory data presents unique challenges compared to other web scraping applications, such as price monitoring.

• Location-Specific Data: Many retailers require a zip code input to display inventory, meaning you must ensure your scraper accounts for regional availability.
• In-Stock Validation vs. Inventory Levels: Some sites provide data on whether an item is in stock, while others provide a count of in-stock items.
• Frequent Website Changes: Retailers often update their website structures, requiring scrapers to be regularly maintained.
• IP Blocking & Anti-Scraping Measures: Some retailers implement security measures to prevent automated scraping, making API-based solutions a more reliable option.

Where to Find Inventory Data

If you’re looking to scrape inventory data, here are some common sources:

• Google Shopping – Aggregates product availability across multiple retailers.
• Amazon – Provides stock levels for millions of products.
• Walmart – Offers in-store and online inventory tracking.
• Target – Displays local store stock levels.
• Home Depot – Shows inventory for both online and in-store products.

Benefits of Traject Data’s Inventory API

Traject Data’s Inventory API simplifies competitor inventory tracking by providing reliable, real-time data from multiple sources with flexible pricing options. Here’s what makes it stand out:

• Real-Time Updates: Get up-to-the-minute stock levels directly from retailer websites.
• Multiple Retailers: Access inventory data from a wide range of marketplaces to get a comprehensive view of product availability.
• Structured Data Format: The API delivers clean, structured data, reducing the need for manual parsing.
• Coverage: Traject Data provides APIs for Google Shopping, Amazon, Walmart, Home Depot, and Target.

Get Started with Traject Data’s Inventory APIs

If you’re ready to gain a competitive edge with real-time inventory insights, explore Traject Data’s APIs today and start optimizing your inventory strategy.

Sales forecasting on Amazon is a critical component of any successful Amazon business. Improving forecasting accuracy, even by a small percentage, can significantly impact a company’s production and financial planning, marketing strategies, inventory control, and supply chain management.

Without reliable sales forecasts, ecommerce businesses risk overstocking (leading to increased storage costs and potential waste) or understocking (resulting in lost sales and dissatisfied customers). By investing in sales forecasting, Amazon ecommerce businesses can optimize operations, reduce costs, and maximize profits.

But how do you forecast your Amazon sales for a new product you haven’t sold before? Let’s dive in.

What Is Amazon Sales Forecasting?

Amazon sales forecasting is the practice of predicting future customer sales and demand for products sold on Amazon. It involves analyzing historical sales data, market trends, and other relevant factors to estimate future sales volume. This allows sellers to optimize inventory management and make informed decisions about stock levels and pricing to meet customer needs effectively.

How to Forecast Ecommerce Sales using Amazon Data

Amazon sales forecasting relies on analyzing historical data and market trends to predict future demand. But where do you get this data? Fortunately, Amazon is a massive ecommerce platform with a wealth of publicly available data. As of 2025, Amazon holds a 37.8% shareshare of the US ecommerce market—and it’s still growing. Including third-party sellers, the total number of products available on Amazon exceeds 350 million, making it a rich source of historical sales data. So, no matter what product you’re interested in selling, you’ll likely be able to find relevant data for analysis.

Businesses typically use a combination of statistical models, machine learning algorithms, and domain expertise to refine their forecasts. The process often includes:

• Collecting Data – Gather historical sales data from key sources like Amazon, Walmart, and Google. Consider seasonal trends, competitor insights, and external market factors for a comprehensive analysis.
• Choosing a Forecasting Model – Utilize time-series analysis, regression models, or AI-driven machine learning techniques.
• Analyzing Customer Behavior – Incorporate customer demographics, purchase history, and engagement trends.
• Adjusting for Market Conditions – Factor in economic trends, supply chain disruptions, and promotional campaigns.
• Validating the Forecast – Compare past forecasts to actual sales to refine and improve accuracy over time.

A well-executed sales forecasting strategy helps ecommerce businesses anticipate demand fluctuations and make proactive business decisions. Not all market conditions can be predicted, so you’ll need to monitor and maintain your algorithm as new market variables are introduced.

What Is the Best Formula for Amazon Sales Forecasting?

For ecommerce demand forecasting, especially when selling a product on Amazon, several commonly used formulas can help predict the number of units sold. Here are some of the most relevant methods:

Exponential Smoothing	A forecasting technique that uses a weighted average of past data points to predict future values.
Time Series Analysis	This method analyzes historical data to identify patterns and trends in demand. It uses statistical techniques to forecast future demand based on past patterns.
Sales Rank-Based Estimation	Amazon sellers often use sales rank to estimate potential sales. For example, if a product at rank 25,000 sells 250 units per month, a product at rank 5,000 might sell approximately five times more, or 1,250 units.

Machine learning methods are becoming increasingly prevalent in demand forecasting. However, historical data is crucial—you’ll need a statistically significant dataset to make accurate predictions.

Accurate forecasting requires continuous monitoring and adjustment based on actual performance and market conditions. As the market changes, you’ll need to refine your calculations.

What Is an Amazon Sales Estimation API?

If you’re not a machine learning expert or mathematician, you’ll likely want to leverage an Amazon sales estimation tool. An Amazon Sales Estimation API is a software application that allows businesses to predict future sales revenue for their online store by analyzing historical sales data, market trends, and other relevant factors.

How It Works:

• Retrieves and processes historical sales data from Amazon for a product.
• Uses machine learning and statistical models to generate accurate sales forecasts.
• Provides real-time sales projections to assist with inventory management, marketing, promotion, and pricing strategies as the market evolves.

Benefits of Using an Amazon Sales Estimation API

1. Improved Inventory Management – Accurate forecasts help businesses maintain optimal stock levels, reducing overstocking and stockouts.
2. Reduced Costs – Minimizes expenses associated with overstocking and storage.
3. Enhanced Marketing Strategies – Understanding future demand enables businesses to fine-tune marketing efforts for peak seasons and targeted campaigns.
4. Optimized Pricing Strategies – Forecasting demand allows businesses to optimize pricing for maximum profitability.
5. Data-Driven Decision Making – Advanced analytics provide valuable insights that drive smarter, more strategic business choices.
6. Increased Customer Satisfaction – Ensures products are readily available when customers need them.

Traject Data’s Amazon Sales Estimation API

For businesses looking for a powerful and reliable sales forecasting solution, Traject Data’s Amazon Sales Estimation API is designed to provide accurate and actionable insights.

Key Features:

• Sales Estimates – Provides sales projections for individual products and categories.
• Real-Time Data Access – Uses up-to-date market trends and customer behavior insights so you can continually monitor while the market evolves.
• Customizable Forecasting Parameters – Adjust forecasts based on your products, industry, and geographic location.

By leveraging Traject Data’s Amazon Sales Estimation API, ecommerce businesses can make informed decisions on inventory, pricing, and marketing strategies—ultimately driving higher profitability.See how Traject Data’s Advanced Amazon Sales Estimation API can transform your sales forecasting and explore our detailed API documentation.

We’re a few weeks away from Black Friday, but for many consumers the holiday season has already been here for a while, and retailers have started capturing their (un)fair share of wallet earlier. Holiday shoppers have taken advantage of big savings with record-breaking events like Amazon’s second Prime Big Deal Days and Walmart’s first Holiday Deals event in October. In fact, 48% of holiday shoppers began shopping in August, September or October.

Still, 37% of holiday shoppers plan to do their shopping in November and will be looking to score big deals on Black Friday and Cyber Monday. To build their own effective pricing and product strategies, Amazon and Walmart sellers, and competitor marketplaces need to keep an eye on the strategies of successful brands, especially during major sales events like Black Friday and Cyber Monday.

Product pricing in particular is one of the most complex aspects of the big ecommerce marketplaces. Pricing strategies need to consider all factors, some of which are applicable across retailers or marketplaces, i.e. retail holidays or shopping events. Then there are the factors that are unique to a marketplace like Amazon – extreme competition.

Amazon sellers and similar marketplaces must continuously iterate on their pricing strategies by:

• Capturing up-to-date pricing data for products in their categories of interest on a regular cadence so they can track product price changes over time i.e. peak and lowest prices
• Constantly monitoring competitors, especially in categories with many sellers offering the same type of products
  • Track the types of offers and deals i.e. Prime exclusive and Black Friday, and discount amounts
  • Make price adjustments to stay competitive and therefore increase the chances of winning the Buy Box 

Traject Data’s Rainforest API (Amazon) and BlueCart API (Walmart) provide a real-time deal and discount data feed to keep your analytics and tools fresh. Track ASINs/Product IDs at scale without worrying about maintaining web scrapers.

• Effectively execute a dynamic pricing strategy with up-to-the-minute data
• Learn how to optimize your assortment
• Analyze competitor ad strategy

Here are some examples of data points our customers rely on us for to equip their businesses for marketplace promotion analytics:

Rainforest product results capture Prime Exclusive deals and deal badge

Rainforest API returns results with is_prime_exclusive_deal to indicate whether a product is a Prime Exclusive Deal and with deal_badge to indicate that a deal badge is present and the text presented when request type=product. View docs.

Rainforest search results return a deal object

Rainforest API returns a deal object with a type=search request. The object returns three possible results: link, text and badge_text. View docs.

Try Rainforest API free to capture Amazon data.

Try BlueCart API free to capture Walmart data.