In today’s dynamic real estate landscape, traditional data sources often lag behind the rapid shifts in buyer behavior and market trends. Enter the power of real estate data with SERP API—a transformative approach that leverages real-time search engine insights to stay ahead of the curve.
The Current Challenge: Navigating a Frozen Market
With many homeowners locked into sub-3% mortgages, inventory remains tight, and affordability is a pressing concern. Conventional reports frequently arrive too late to inform timely decisions. Real estate professionals require proactive tools to anticipate demand and adapt strategies accordingly.
Harnessing SERP Data for Real Estate Intelligence
Traject Data offers access to raw, real-time SERP (Search Engine Results Page) data, unveiling what potential buyers are searching for and where market shifts are emerging. Here’s how this data can be utilized:
1. Market Analysis
Detect spikes in searches like “townhouses in Tempe” or “condos in Jersey City,” signaling rising interest before listings catch up.
Monitor demographic trends, such as the growing popularity of “single-level homes near parks” among baby boomers.
Track evolving preferences, with queries shifting from “homes with home office” to “homes near transit” as remote work dynamics change.
2. Competitor Intelligence
Identify which brokerages consistently rank high in target neighborhoods.
Observe how competitors adapt listings with affordability-focused language like “low down payment” or “first-time buyer eligible.”
3. SEO Optimization
Incorporate trending descriptors such as “walkable,” “starter home,” or “ADU-ready” based on rising search interest.
Adjust strategies as attention shifts between urban, suburban, and exurban areas.
4. Investment Opportunities
Use rising search interest in overlooked suburbs or fast-growing secondary cities to guide acquisitions.
Monitor demand for models like build-to-rent, co-living, and rent-to-own, especially in price-sensitive regions.
Real-World Applications
Brokerages utilize SERP API to pinpoint underserved markets and enhance local SEO. Investment firms track demand signals to inform land or portfolio decisions. Marketing agencies craft campaigns aligned with trending keywords and consumer intent.
Stay Ahead with Real-Time Insights
By integrating real estate data with SERP API, professionals can uncover early buyer interest, identify emerging neighborhoods, and act before the competition. Embrace this innovative approach to transform challenges into opportunities in the ever-evolving real estate market.
For more information on leveraging SERP data for real estate intelligence, consider exploring Traject Data’s offerings.
Ready to See What Traject Data Can Help You Do?
We’re your premier partner in web scraping for SERP data. Get started with one of our APIs for free and see the data possibilities that you can start to collect.
Rogue sellers don’t just hurt your margins—they erode customer trust, violate your pricing policies, and undermine your brand’s reputation across marketplaces. In this post, we’ll explore how an unauthorized seller scraping API can help you automate detection and take back control.
Why Unauthorized Seller Monitoring Matters More Than Ever
When unauthorized sellers undercut your pricing, the damage ripples outward:
Customers lose trust when they receive products that are misrepresented or poorly packaged.
Your legitimate partners are frustrated, asking why they’re upholding Minimum Advertised Price (MAP) guidelines while others aren’t.
Your brand reputation suffers.
You face legal and compliance headaches from policy violations.
Manual monitoring simply can’t keep up. Unauthorized sellers change tactics constantly—switching listings, altering prices in-cart, or using bundles to disguise violations.
Brands that effectively fight back don’t wait to react. They implement scalable, proactive monitoring strategies across all sales channels—turning unauthorized seller monitoring into a competitive advantage.
The High Cost of Doing Nothing
By the time you detect rogue sellers manually, the damage may already be done. Here’s how these unauthorized listings hurt your business:
Price undercutting that devalues your product in the eyes of consumers.
Misinformation that leads to customer confusion or negative reviews.
Exploited policy gaps that create compliance risk and revenue loss.
Marketplace chaos, with thousands of listings appearing (and reappearing) in real time.
If you’re still relying on spreadsheets and manual checks, it’s time to upgrade. There are low cost, automated solutions that can do the heavy lifting for you.
Why You Need an Unauthorized Seller Monitoring Scraping API
An unauthorized seller monitoring scraping API enables automated, real-time detection of rogue resellers across ecommerce marketplaces like Google Shopping, Amazon, Walmart, Target, and eBay.
Here’s how it works:
The API continuously scans listings to identify unauthorized sellers offering your products.
It extracts key data—seller info, pricing, product titles, and URLs.
This data powers your MAP (Minimum Advertised Price) detection, compliance efforts, and enforcement workflows.
Instead of reacting days later, you can act the moment a policy violation appears.
How Traject Data Helps You Stop Rogue Resellers
Traject Data gives you clean, fast, reliable data to power your unauthorized seller monitoring scraping API strategy. We provide the raw intelligence your compliance or brand protection team needs to stay one step ahead.
This broad visibility helps ensure no unauthorized listing goes unnoticed.
Power Your MAP Strategy with Real-Time Raw Data
We don’t identify violations for you—we give you the raw data to identify them yourself:
Product prices
Seller names
Listing URLs
In-cart pricing details
Secondary buy box monitoring
You get the insights to detect shady tactics like pricing games or misrepresented bundles—fast.
Lightning-Fast Data Delivery
Speed matters. Traject Data delivers real-time scraping results in under 6 seconds on average, with a P90 of 10 seconds. That means your enforcement team can respond to violations before they cause harm.
Flexible, Developer-Friendly API Integrations
Our APIs fit into your existing systems and workflows—whether you’re feeding data into a MAP dashboard, building alerts, or automating takedown notices. Traject Data helps you move fast, without having to manage scraping infrastructure yourself.
Deep Product and Seller Insights
With Traject Data, your team gets granular, real-time visibility:
Product and pricing info
Seller listings
SKU-level detail
Secondary buy box pricing
All the raw data you need to investigate and act.
Why the Amazon Secondary Buy Box Matters
Did you know that rogue sellers often avoid MAP enforcement by targeting the secondary buy box?
Traject Data’s unauthorized seller monitoring scraping API gives you visibility into both the primary and secondary buy boxes—critical for true pricing enforcement.
Here’s why it matters:
Pricing integrity: Secondary buy box listings often appear cheaper, hurting your brand value.
Customer influence: Buyers still consider these listings—sometimes preferring them over the primary.
Faster enforcement: With real-time data, you can take swift action to protect your margins and reputation.
Proven ROI: A Case Study with Sigil
Sigil, a leading brand protection company, used Traject Data to dramatically improve enforcement efficiency and ROI:
$4M in revenue recovered by identifying violations faster on Amazon
2+ engineers freed up by eliminating the need to maintain their own scraping tools
Effortless expansion into new marketplaces—without additional overhead
With Traject Data, Sigil scaled their enforcement without scaling their headcount.
Ready to Stop the Race to the Bottom?
If you’re still relying on spreadsheets and manual checks, you’re already behind. Traject Data gives you the speed, scale, and insight you need to stop rogue resellers in their tracks.
How Traject Data Powers Your Unauthorized Seller Monitoring Scraping API
Here’s what it looks like in action:
1. Track Core SKUs and Marketplaces
Monitor your most critical product listings across Amazon, Google Shopping, Walmart, and more.
2. Set Violation Thresholds
Define your own MAP enforcement rules—by product, price delta, or seller behavior.
3. Get Real-Time Pricing and Seller Data
Use our fast, flexible APIs to ingest pricing and seller data in real time.
4. Automate Alerts and Enforcement
Trigger alerts and actions automatically—whether you’re reporting sellers or issuing takedowns.
Want help configuring your unauthorized seller monitoring strategy? Book a demo with our team today and learn how Traject Data can power your enforcement efforts—at scale.
Ready to See What Traject Data Can Help You Do?
We’re your premier partner in web scraping for SERP data. Get started with one of our APIs for free and see the data possibilities that you can start to collect.
If you’re searching for the best SERP APIs, you already know how valuable real-time search engine data is for SEO, competitive analysis, and market insights. But with so many SERP API providers on the market, how do you choose the right one?
In this post, we’ll break down what makes a SERP API great, explore the top five SERP APIs available today, and explain why Traject Data stands out as the best option for businesses looking for scalable, reliable, and future-ready SERP data solutions.
What Is a SERP API?
A SERP API (Search Engine Results Page API) allows developers and marketers to programmatically collect data from search engine results pages—like Google, Bing, Yahoo, and others. Whether you’re tracking keywords, monitoring competitors, or analyzing trends, a SERP API gives you the data you need without manual scraping or unreliable tools.
Why You Need a SERP API
Automating your SERP tracking can drive up to 3x better results compared to manual methods by:
Offering a comprehensive view of your market and competitors
Enabling faster pattern recognition
Providing real-time data for agile decision-making
Supporting data-driven SEO and marketing strategies
How to Choose the Best SERP API
Not all SERP APIs are built the same. When evaluating your options, keep these critical factors in mind:
1. Search Engine Coverage
You need an API that covers more than just Google. Look for one that supports Bing, Yahoo, Amazon, eBay, and even regional search engines like Baidu, Yandex, and Naver—especially if you’re targeting international markets.
2. Speed and Reliability
Timely, structured data is non-negotiable. The best SERP APIs deliver accurate, structured results consistently—no matter how frequently you make requests.
3. Structured Output
Some APIs require extensive post-processing. Opt for providers that give you ready-to-analyze, structured outputs, saving your team hours of manual work.
4. Integration & Automation
Your SERP API should integrate easily into your current tools or workflow. Bonus points if it supports automation, batch requests, and flexible data delivery options.
5. Scalability
As your data needs grow, your SERP provider must scale with you. Make sure your API can handle thousands (or millions) of requests without breaking a sweat.
6. Documentation & Support
Great documentation means your dev team spends less time troubleshooting. Responsive support is a must when you need quick answers.
7. Resilience
Google and other engines update their algorithms constantly. Choose an API provider that can adapt quickly to changes and minimize downtime.
8. Free Trial
Test before you commit. A free trial lets you evaluate performance, integration ease, and data quality firsthand.
When it comes to the best SERP APIs, Traject Data leads the way for reliability, performance, and ease of use. Here’s why:
✅ Automated Data Collection
Gather Google organic SERP data at scale—no manual scraping, no downtime. Monitor your competitors and SERP placements in real time.
✅ Comprehensive Search Engine Coverage
Whether you’re targeting global audiences or specific regional markets, Traject Data provides coverage across all major engines, including Google, Bing, Yahoo, Amazon, Baidu, and more.
✅ Ready-to-Use Structured Data
Our API returns structured, clean, real-time data that integrates seamlessly into your SEO tools, dashboards, and analytics systems.
✅ Built to Withstand Industry Shifts
Google’s updates have caused outages across the industry—but not here. Traject Data is built to adapt. With advanced anti-blocking technology, IP rotation, and dynamic behavioral systems, we ensure your data pipeline stays open—even as algorithms change.
✅ Scalable & Developer-Friendly
From startups to enterprise-grade applications, Traject Data’s infrastructure is built for scale. Easily track millions of keywords and rankings with a flexible pricing model and robust documentation.
✅ Success Stories That Speak for Themselves
One of our e-commerce clients used Traject Data to maintain real-time SERP visibility during a major Google update—while competitors scrambled to restore service. With uninterrupted access to accurate data, they optimized their SEO strategy and maintained their market lead.
Getting Started with Traject Data’s SERP API
Ready to experience the best SERP API for yourself?
Whether you’re a retailer, marketer, or SEO analyst, Traject Data gives you the real-time SERP visibility you need to win. When you’re choosing among the best SERP APIs, go with the one that’s built for performance, resilience, and growth.
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.
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.
Ready to See What Traject Data Can Help You Do?
We’re your premier partner in web scraping for SERP data. Get started with one of our APIs for free and see the data possibilities that you can start to collect.
Ever wondered how businesses effortlessly gather all that search engine data? Well, a big part of that comes down to SERP scraper APIs. If you’re looking to dive into the world of data-driven decision-making, understanding how to use these APIs is a great starting point.
In this blog post, we’ll break down how to get started with SERP scraper APIs. We’ll cover everything from the basics to practical steps, empowering you to harness the power of search engine results page (SERP) data.
What is a SERP Scraper API?
Before we get into the “how-to,” let’s define our terms. A SERP scraper API is a tool that allows you to automatically extract data from search engine results pages.
SERP: The Search Engine Results Page, which is the page displayed by a search engine (like Google, Bing, etc.) in response to a user’s search query.
Whenever you search for something on Google, Bing, or any other search engine, you get a page full of results. This page contains a treasure trove of information, including:
Organic search results
Paid advertisements
Featured snippets
Local packs
And much more
A SERP scraper API is designed to grab this data and deliver it to you in a structured, easy-to-use format (like JSON). This automation is a game-changer because it eliminates the need for manual data collection, saving you time and resources.
Why Use a SERP Scraper API?
You might be wondering, “Why should I bother with a SERP scraper API?” Here are a few compelling reasons:
SEO Analysis: Track keyword rankings, monitor competitor strategies, and identify opportunities to improve your search engine optimization efforts.
Competitor Analysis: Keep an eye on your competitors’ online presence, ads, pricing, inventory, and marketing tactics.
Omnichannel eCommerce Monitoring: Collect data from multiple sources to build comprehensive databases and reports across all channels.
Pricing Strategies: Make informed decisions based on real-time data and gain a competitive edge.
Brand Protection: Identify unauthorized use of your brand name or logo, allowing you to take swift action against infringement.
Review Monitoring: Gather reviews from various platforms to understand customer sentiment, identify areas for improvement, and manage your online reputation.
Are SERP Scraping APIs legal?
Scraping publicly available data is legal. However, users must comply with a website’s terms of service or risk being blocked. Scraper APIs must follow these guidelines:
Scrape Only Publicly Available Data
Respect a Site’s Terms of Service
Ensure Compliance with Data Privacy Laws
How to Get Started with SERP Scraper APIs
Let’s get to the heart of the matter: how to get started with SERP scraper APIs. Here’s a step-by-step guide to help you navigate this process:
1. Choose the Right SERP Scraper API Provider
The first and most crucial step is selecting a reliable SERP scraper API provider. With so many options available, it’s essential to choose one that meets your specific needs. Here are some factors to consider:
Data Accuracy and Reliability: Ensure the provider delivers accurate and up-to-date data.
Coverage: Check if the API supports the search engines and locations you need.
Scalability: Consider whether the API can handle your current and future data needs.
Ease of Use: Look for a provider with clear documentation and a user-friendly interface.
Pricing: Evaluate the pricing structure and choose a plan that fits your budget. Many SERP providers also provide a free trial.
Customer Support: Good customer support is essential in case you run into any issues.
Integration Abilities: Whether you’re integrating the SERP API into an existing tool or analytics platform, seamless integration is vital. Evaluate the SERP API vendor’s batch automation and delivery options. Can the API provider deliver the required data to your specified destination frequently enough to keep you updated on search results?
Resilience and Reliability: As search engines make frequent updates, Google’s recent updates caused major outages for SERP providers. Choose a platform designed for adaptability to reduce downtime.
2. Sign Up and Get Your API Key
Once you’ve chosen a provider, the next step is to sign up for an account. Most providers offer free trials or starter plans that allow you to test their services before committing to a paid subscription.
After signing up, you’ll typically receive an API key. This unique key is your credential for accessing the API. Treat it like a password and keep it secure.
3. Read the Documentation
Before you start making API requests, it’s crucial to familiarize yourself with the provider’s documentation. The documentation will provide you with essential information, such as:
API endpoints
Request parameters
Authentication methods
Response formats
Error codes
Usage limits
4. Make Your First API Request
Now that you have your API key and understand the documentation, it’s time to make your first API request. The specific request will depend on the provider and the type of data you want to extract.
Here’s a general example of how an API request might look (using a Traject Data’s SerpWOW API):
GET /search
Performing a search is as simple as making a GET HTTP request to the SerpWow /search endpoint. The only required parameters are:
api_key – Sign up for free to get an API key.
q – Your search query.
For example, to search for the phrase pizza, the SerpWow search request would be:
Once you’ve extracted the data, you’ll likely want to store it in a database or file for further analysis. You can then use various tools and techniques to gain insights from the data, such as:
Spreadsheets (e.g., Google Sheets, Microsoft Excel)
Comprehensive Coverage: Traject Data collects data across all major search engines, Google, Bing, Amazon, Walmart, and more.
User-Friendly API: Easily integrate SERP scraping into your existing marketing tools.
Built for the Future: At Traject Data, we’ve designed our scraping solutions with adaptability in mind, enabling us to respond quickly to industry shifts like Google’s latest updates.
Client Success Stories: We’ve helped numerous businesses navigate these challenges and come out stronger on the other side.
If you’re ready to gain a competitive edge with real-time insights, explore Traject Data’s APIs today and start optimizing your SEO strategy.
Ready to See What Traject Data Can Help You Do?
We’re your premier partner in web scraping for SERP data. Get started with one of our APIs for free and see the data possibilities that you can start to collect.
In the world of digital marketing, data is everything. Understanding your competitors’ Google Ads strategies can help you refine your own approach, optimize ad copy, and identify valuable keywords. But how can you gather this data efficiently? Scraping Google Ads is a powerful way to collect insights on competitor campaigns, ad placements, and keyword trends.
In this article, we’ll explore why scraping Google Ads is useful, the best methods to do it, the legal considerations, and how Traject Data can help automate the process.
Why Scrape Google Ads?
The average business wastes 76% of their PPC budget on ineffective strategies, largely due to not tracking competitors’ activities. Scraping Google Ads can unlock valuable data that helps businesses refine their pay-per-click (PPC) strategies. Here’s what you can gain:
Competitive Intelligence. By analyzing Google Ads results, you can uncover which keywords your competitors are targeting, how frequently their ads appear, and what messaging they use. This information allows you to refine your bidding strategy and gain an edge in the market.
Monitor Brand Bidding. A common practice today is “brand bidding”, where competitors bid on your brand terms. A customer searches for your company, but your competitor’s site appears first in the search results. You could be losing customers to this practice, making it essential to monitor.
Ad Copy Optimization. Seeing how competitors craft their ad copy provides insight into what works and what doesn’t. You can analyze calls to action, headline structures, and descriptions to improve your own ad performance.
PPC Strategy Enhancement. Scraping Google Ads data helps you identify which keywords are being heavily contested and which might present opportunities for lower-cost, high-converting traffic.
Market Trends & Insights. By tracking ad placements over time, businesses can spot seasonal trends, shifts in competitor focus, and emerging industry keywords.
Best Methods to Scrape Google Ads
There are several ways to collect Google Ads data, some free and some paid, each with its own advantages and limitations.
1. Manual Data Collection
The simplest method is to conduct Google searches and document the ads manually. Type in the search term, and see what appears. While this is effective for small-scale research, it’s time-consuming and doesn’t scale well.
2. Google Transparency Center
The Google Ads Transparency Center provides useful tools to monitor ads. Visit the Google Ad Transparency Center, search for the competitor’s domain or name, and explore their active and past ads, filtering by ad type, location, or date range. This tool is useful but limited and requires manual effort.
3. Google Ads API
Google provides an official Ads API, but it’s primarily designed for advertisers managing their own campaigns. It doesn’t provide competitive data unless you have direct access to an advertiser’s account.
4. Third-Party Analytics Tools
Platforms like Semrush and SpyFu can monitor competitor ads for you. However, these services can be expensive and limit the number of competitors you can track.
5. SERP APIs (Search Engine Results Page APIs)
SERP APIs automate the collection of ad data from search engine results. A SERP API like Traject Data’s SERPWow allows you to scrape thousands of result pages across all major search engines—including Google, Bing, Yahoo, Baidu, Yandex, Naver, Amazon, and eBay. By using a SERP API, you can automate data collection, gain real-time insights, and get a comprehensive view of different markets and channels.
Challenges of Scraping Google Ads
Scraping Google Ads comes with some challenges, including:
Google’s Anti-Scraping Measures: Google employs CAPTCHAs, IP blocking, and JavaScript-based rendering to prevent automated scraping. Creating manual scraping scripts requires excessive maintenance, as Google frequently updates its anti-scraping tactics.
Evolving Ad Formats: Google Ads frequently change, making it difficult to consistently extract and structure data across different ad types and formats.
Geotargeting and Personalization: Ads can change based on user behavior, location, and other factors, making it difficult to capture consistent data.
Best Practices for Scraping Google Ads
While it is legal to view public ad data, scraping Google Ads requires a careful and ethical approach to ensure compliance with legal and technical restrictions. Follow these best practices:
Use APIs for Reliable Data Extraction: Instead of manually scraping Google search results, use a SERP API to obtain structured ad data efficiently without violating Google’s terms of services and risking an IP block.
Scrape Only Publicly Available Data: Avoid scraping personal or sensitive information. Focus on ad placements, copy, and keyword usage.
Implement Rate Limiting: Sending too many requests in a short period can trigger Google’s anti-scraping defenses. Implement rate limits to avoid detection.
Respect Google’s Terms of Service: Google actively discourages web scraping, so ensure your methods align with legal guidelines.
Ensure Compliance with Data Privacy Laws: Follow regulations like GDPR and CCPA when handling scraped data.
How Traject Data Can Help
Traject Data offers a reliable SERP API that allows businesses to collect Google Ads data without dealing with the complexities of manual scraping. With real-time data extraction and structured results, Traject Data simplifies competitor ad monitoring.
Benefits of Using Traject Data’s SERP API:
Automated Data Collection: Gather Google Ads data at scale without manual work. Monitor competitor ads as they appear in search results.
Comprehensive Coverage: Traject Data collects data across all major search engines.
User-Friendly API: Easily integrate ad scraping into your existing marketing tools.
Scraping Google Ads provides businesses with critical insights to optimize their PPC strategies and stay ahead of the competition. While there are challenges and legal considerations, using a SERP API like Traject Data’s can streamline the process and ensure compliance.
We’re your premier partner in web scraping for SERP data. Get started with one of our APIs for free and see the data possibilities that you can start to collect.
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
Traject Data Table
Factor
Definition
How to Win
Type of Seller Account
Individual vs. Professional seller accounts
Only Professional Sellers are eligible to win the Buy Box
Condition of Product
New vs. Used products
Only new products qualify for the Buy Box
Price
Total cost (product price + shipping)
Competitive pricing increases chances of winning
Shipping Speed
How fast the item ships
Faster shipping improves Buy Box chances
Shipping Price
Cost of shipping
Free or low-cost shipping is preferred
Fulfillment Method
Either FBA, FBM, or SFP
FBA and SFP sellers have an advantage over standard FBM
Stock Availability
How often a seller runs out of stock
Having the product in stock is required.
Order Defect Rate (ODR)
% of defective orders in a 60-day period
Must be under 1%
Valid Tracking Rate (VTR)
% of seller-fulfilled packages with tracking
Aim for 95%+
Late Shipment Rate (LSR)
Orders shipped late (10- & 30-day period)
Keep below 4%
Pre-Fulfillment Cancellation Rate (CR)
% of seller-canceled orders in 7 days
Aim for under 2.5%
Feedback Score
Average of customer ratings
Higher ratings improve chances
Response Time
How quickly the seller replies to customers
Aim for under 12 hours
Refund Rate
How often customers request refunds
Lower is better
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.
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.
Ready to See What Traject Data Can Help You Do?
We’re your premier partner in web scraping for SERP data. Get started with one of our APIs for free and see the data possibilities that you can start to collect.
Why would an SEO limit themselves to some keywords, when all keywords are available?
Historically, it’s been a matter of resources and capacity. The more data points you introduce, multiplied by the number of keywords you’re tracking, the faster it becomes unmanageable. When using a typical SEO platform to track keyword data, it requires prioritization and selection of a more limited keyword list that leaves off low-traffic queries and details about things like rich results.
That’s where big data, in the raw output form that can be parsed by BI tools, comes in to expand a keyword universe.
We’ll walk through the process of what raw, big keyword data looks like, how it’s given the structure that allows it to be manipulated, and the qualities your business should look for when choosing a BI tool that will allow you to turn data into insights.
Why use Big Data for SEO
SEOs or digital marketers have a finite amount of keywords for their brand that they have the capacity to track. Typically, this is determined by the keywords that are known to drive traffic to a site, with the threshold being a significant number of clicks.
What happens when you have a very niche longtail keyword that only generates a nominal amount of clicks? It’s probably not going to move the needle on your bottom line, so it gets left off the list of priority keywords to track. Then you have a few more very niche keywords that generate just another handful of clicks.
For many businesses, this list of “insignificant” keywords can grow into the thousands, where the aggregate of all those nominal clicks suddenly has a lot more power over your business than you originally expected. For example, listen to Wil Reynolds of Seer Interactive recount his experience with discovering that 300,000 previously ignored keywords with ~1 click each accounted for 92% of his client’s traffic.
The problem then becomes the ability to scale the keyword research process to track exponentially more keywords, as well as more SERP data points, like various rich results.
When the capacity of an SEO tool is limited, it makes sense to prioritize and limit keyword lists or the search features you track. However, moving to big data where the data is in its rawest form—and able to be pumped into any analytic environment—allows businesses to expand their keyword universe to all keywords, not just a “representative” sample size.
To start, you will have to decide what this analytic environment will be, and how you will process and manipulate it without a typical SEO platform. Traject Data can be directly connected to the BI tool that your company likely already uses to make other data-driven decisions. Next we’ll dig into the process the data undergoes so it can be parsed into useful data sets and made ready for that BI tool.
How Traject Data can be used
With more SERP data points now available for tracking, use cases for leveraging the data have increased as well. Here are a few things your business can do with Traject Data:
Inform organic SEO and content creation strategy.
Track keyword rankings across millions of keywords.
Identify industry trends and evolving SERPs.
Blend with other data sources to identify revenue-generating keywords.
Optimize advertising campaigns based on evolving search engine algorithms and search intent.
Monitor local performance across geo-location specifications.
Raw data from the Traject Data SERP API comes in the form of JSON files that describe any given SERP, including the search features, ranked websites, and anything else that appears for that particular query. Imagine you query “teriyaki and wok” and you’re shown this SERP. You’ll notice that result #1 is a standard blue link, not a local pack, a video, or any other type of search feature.
The output from the Traject Data SERP API for this query would look like this:
Underneath “SERP” in the JSON file, each ranking position will be labeled by number and list out the attributes of that ranking position. In the “teriyaki and wok” case, you can see the data for the link, the description, and which types of search features do and do not take up position #1 for that query.
If you were to keep scrolling, the same data would be listed for position #2, #3, and so on for that query’s SERP.
This more comprehensive set of SERP data points allows SEOs to increase their control over all the possible rich results at their disposal. Tracking this data will show rich results they’ve won and lost to competitors, search features that are new to a keyword after Google makes an algorithm update, and other data points that will help them home in on all of their high-return traffic opportunities.
These are some of the most common rich results that we provide data on. You can click through on each to learn more about how you can leverage them.
How is this data converted into something I can manipulate and draw insights from?
The information in the JSON files is pretty extensive to look through for just one SERP. This raw output would be almost impossible to manipulate without building logic, or using a tool that can sort through it to access what you need and create more relevant datasets.
The Traject Data SERP API JSON files are stored in Amazon S3 or similar. There, the data points from the JSON files are structured and tagged into individual fields so that they are able to be queried, sorted, and more easily analyzed. After the JSON files have been given this structure, using a product like Amazon Athena gives you the ability to query the database and pull the selection that’s interesting to you.
This staging ground and additional ability to organize the data creates a way for previously unmanageable amounts of data to be pulled into subsets that are relevant for a certain report or analysis.
Leveraging a BI tool
The dataset is now ready to be connected to your BI tool which will allow you to visualize it for easy understanding. From there, you can build reports and combine it with your business’s other data, like sales and marketing data.
BI tools are an important cornerstone to this process because the visualizations allow users to discover new insights with the time and expertise (or bias) that would typically go into creating pivot tables to investigate a hypothesis.
With the various tools that are compatible with Traject Data, users can toggle between different types of visualizations much more deftly than building out a new analysis manually each time. Expert level BI users can manipulate the data into more complex visualizations, but beginner level users can still glean a multitude of insights from the visualizations the tool suggests, once the data finishes populating.
Traject Data keyword data is able to connect to all of the most common BI tools on the market. To make a decision on which BI tool to use, consider the required skill level, pricing, and capabilities below.
Tableau connects and extracts the data stored in various places, and is touted as being able to pull data from just about any platform imaginable. It can extract “simple” databases such as excel or pdf files, to more complex databases like Oracle or AWS.
After ensuring thorough data access, Tableau emphasizes its ease of content discovery by allowing users to organize resources by project, recommending relevant data, allowing ‘go back’ with revision history, and enabling search.
Here’s an example Tableau sales dashboard. You’ll notice that showing the insights is highly visual, and that analytics can be performed almost instantaneously as users change any setting on the dashboard. This viz uses Tableau’s clustering feature to uncover insights in the sales data across the United States.
Tableau divides their product tiers by implementation and user needs. First, your business will need to decide whether you’d like to host Tableau on-premise, in the public cloud, or have Tableau host your server. Then decide how many of each of the following user type you need:
Creator: This is a data analyst role who will load and standardize data. Every Tableau account is mandated to have at least one Creator
Explorer: general business user role, can make and edit visualizations
Viewer: can view and interact with visualizations Explorers and Creators have made
Tableau’s pricing differs between users of on-premise/public cloud and Tableau hosted, and bills annually. For on-prem, select the number of licenses you need:
Creator: $70 USD/user/month
Explorer: $42 USD/user/month
Viewer: $15 USD/user/month
Pros
Quick and easy representation of big data which helps in resolving the big data issues
Can be used with no coding or development knowledge
Easy to understand, drag and drop interface
Can use pretty much any data source imaginable—Tableau integrates with over 250 applications
Has an additional platform for cleansing and prepping data before it’s loaded and analyzed
There’s a mobile app with comparable functionality
Has a heavy community presence for learning in forums
Cons
Some users criticize its collaboration capabilities: The notification functionality is simple, and only an admin can configure scheduled email subscription. Users can use custom Python to create trigger-based notifications, but that practice isn’t baked into Tableau.
Can be a fairly expensive tool based on your business and needs
To leverage the full capabilities of Tableau, users report a much steeper learning curve that that of other BI tools.
Operates with a multitude of products that all handle a different aspect of the data analytics process. Some companies may enjoy the customized experience — it’s not for you if you’re looking for one tool that gives all users a transferable experience
The bottom line
Tableau is the top of the food chain for BI tools in terms of quality of analytics and visualization. However, for many users, the cumbersome process of piecing together multiple Tableau products and deploying on-prem may be too complex—and too expensive.
Looker is the data visualization tool owned by Google. Its main differentiator is its 100% browser-based experience, which eliminates the need for installation and maintenance at the same level as a tool like Tableau. Looker also emphasizes shareability in many of their opening statements, referring to the fact that the browser-based experience allows for link sharing, as opposed to sharing with files.
Like most other BI tools, Looker asserts that any novice can use it and that in-depth SQL knowledge can create more intricate reports, but isn’t strictly necessary. However, there does seem to be a significant investment in publishing lectures, documentation, and learning puzzles to appeal to Looker users across disciplines and learning styles.
With Looker, data analysts can use a language called LookML to create mini-applications that “add efficiency and power to data exploration.” Looker explains that the LookML syntax simplifies the development of powerful models and enhances the capabilities of SQL. According to them, “this language-based approach leads to faster query execution, and optimizes performance along the way.”
Below is an example of a Looker E-commerce dashboard that visualizes different data points from a sample company.
Looker is unique from other BI tools in that it doesn’t publicly share its pricing, reasoning that it’s customizable for small, medium and large businesses. SoftwareConnect estimates the following prices:
Generally $300-$500/user/month, up to 10 users
$50/user/month after 10 users
Pros
Emphasizes pre-built templates or blocks to speed up setup time and move more quickly to custom reports
100% browser-based experience eliminates the need for desktop client software installation and maintenance, and allows for link-based sharing of content – makes collaboration “frictionless”
Consistent platform experience for all users; for instance, they can complete tasks without shifting from one desktop tool (i.e. Tableau Desktop), to another (i.e. Tableau Prep Builder), and then to the web to complete specific tasks.
Users can leverage the ML code (that most say is fairly easy to learn) to help produce applications and optimize queries
Still works for those with no SQL knowledge, as they have a rich database of videos and learning materials
Cons
Some users report problems with load time, even calling the wait for a visualization “a run-in with the spinning wheel of death”
Users report lessened functionality and viewability when using the mobile app
If third-party estimates are correct, Looker can actually be more expensive than Tableau (which has gotten negative reviews for price) in some cases
Out-of-the-box dashboard templates can be incredibly useful, but some report difficulty attempting to customize them
Completely hosted means no options for fundamentally changing the tool besides waiting for a new release
The bottom line
If you don’t want to install and maintain an on-prem solution, Looker’s hosted platform has data visualization capabilities that rival or exceed other BI tools. You’ll be able to enjoy a report sharing experience that isn’t cumbersome, but watch out for load times and the inability to change the functions of the tool itself.
Domo Business Cloud, a low-code data app platform, refers to itself as: “the platform that allows everyone to drive action from data.” As with other top data visualization tools, users can combine data from disparate sources, and according to Domo, without needing duplication or pre-transformation to use.
Domo also includes collaboration tools that let teams communicate directly on visualizations with messages and annotations, in addition to scheduled updates and benchmarking alerts similar to those of other BI tools. There isn’t an on-prem deployment option, but Domo touts the cloud-based solution as a baked-in method to help your business scale indefinitely.
Below is an example of a Domo dashboard that uses Traject Data and shows the breakdown of all the different types of search features that appear in the chosen dataset.
Plans and Pricing
Domo’s editions are primarily divided by number of users, support level, and administrative options. The enterprise edition offers unlimited storage, data sets, personalized data permissions, and advanced admin controls.
Domo previously (until 2019) offered two plans: Professional ($175/user/month) and Enterprise ($250/user/month). They have since removed pricing from their website.
Pros
Regarded as one of the fastest-operating BI platforms
Provides templates based on business function
Many users enjoy the UI more than that of other BI tools, citing its “vibrance.” If you frequently use reports to woo stakeholders, this might be meaningful
Allows for team communication and collaboration directly on the platform
Seamlessly blends data from hundreds of sources, the reason Domo has been able to gain traction on Tableau
Offer 1-Click Apps: pre-built connectors that allow users to upload data without relying on IT
As a cloud-based solution, implementation tends to be shorter than on-prem solutions like some Tableau
Offers a full mobile app
Cons
Can be difficult to configure connectors from different sources all at once
No on-prem deployment option
If you plan to create many similar reports, some users complain that you can’t copy a report and make slight changes to the copy, as it will update the original
The bottom line
If your business favors cloud-based over on-prem and will be using the tool for extensive communication and collaboration while building visualizations, Domo is a great option.
You can expect that Power BI, the data visualization tool owned by Microsoft, will play nicely with other Microsoft products like Azure and Office. In fact, this is one of the first points that the Power BI website confirms.
These other Microsoft products, such as Power Apps, Power Automate, and Power Virtual Agents that create an “end to end” experience give users a feeling similar to picking and choosing certain Tableau products for a more customized experience, not the singular consistent experience of Looker and Domo.
Power BI connects to most types of on-premise databases, and they have a large and growing list of cloud-based connection options as well. Users report that it also works well for companies that don’t have a data warehouse solution, because Power BI acts as a facilitator that enables data set processing.
It has around 16 different chart types, which is a good, middle-of-the-road amount if you want to represent your data in a variety of formats and visualizations.
Below is an example of a Sales and Marketing Dashboard created with Power BI.
Power BI has 2 plans. The Pro plan is the most inexpensive BI solution out of comparable tools in the market, but the Premium plan can get pricey.
Power BI Pro
Self-service and modern BI in the cloud
Collaboration, publishing, sharing, and ad-hoc analysis
Fully managed by Microsoft
$9.99/user/month
Power BI Premium
Enterprise BI, big data analytics, cloud and on-premises reporting
Advanced administration and deployment controls
Dedicated cloud compute and storage resources
Allows any user to consume Power BI content
$20/user/month or $4,995/capacity/month for dedicated cloud compute and storage resource with annual subscription
Pros
The most inexpensive tool of its caliber on the market
Like the other tools, Power BI connects to hundreds of data sources and can read data from Microsoft Excel and text files like XML and JSON
Boasts a mobile app with comparable functionality
Anyone with moderately advanced Excel skills is likely to have an intuitive experience, as users report the functionality as being similar
Microsoft releases updates to Power BI monthly and listens to the user community. If you submit suggestions for improvements, other users can rank the suggestions
Plays well with Excel and is has easy export functionality
Cons
Limits the size of datasets that you can pipe in – can try creating multiple queries to process the entire data set or shift to Power BI Premium
On-premises reporting is only included in the Power BI Premium package, which can add additional costs
Some users report the design is not intuitive and the UI/UX isn’t as pleasant as other tools
Custom visuals don’t seem to be configurable – limitations to what can actually be changed if you want to optimize a visual
No solution for data scrubbing, so data needs to be cleaned before using Power BI
The bottom line
If you don’t have a lot of budget and won’t be using datasets that exceed the size limit, Power BI will give you top-tier analytical capabilities—and may even listen to you on the updates you’d like to see.
Final Thoughts on Choosing a BI Tool for Traject Data
Because Traject Data works with all major BI tools on the market, decide on a tool that fits your business best with the above information in mind.
Once you’ve chosen a BI tool, you’re prepared to follow these 3 steps to begin visualizing the additional SEO data points our SERP API provides:
Pull the JSON files for each keyword you want to track with the Traject Data SERP API.
Use S3 and Athena (for example) to make a database from your JSON files that you can query and filter to create a more relevant dataset.
Connect your dataset to the BI tool that works best for you and your organization.
Complete these steps, and you’ll have visualizations of endless keyword data to inform your SEO strategy and business decisions as a whole.
Ready to See What Traject Data Can Help You Do?
We’re your premier partner in web scraping for SERP data. Get started with one of our APIs for free and see the data possibilities that you can start to collect.
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
Use APIs When Available: Leveraging an API ensures compliant data collection while reducing maintenance costs.
Scrape Only Publicly Available Data: Avoid accessing password-protected, user-restricted, or proprietary information.
Implement Rate Limiting: Prevent excessive requests that could overwhelm the target website and lead to IP bans.
Focus on Factual Data: Stick to publicly available product codes, prices, and stock levels, which are typically not protected by copyright.
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
Identify Competitors: Determine which retailers you want to track and which specific inventory data is most relevant to your business.
Select a Scraping Tool or API: You can either develop a custom scraper using Python or use a dedicated web scraper API.
Set Up Your Scraper: Configure it to collect essential data such as product names, SKUs, pricing, and stock levels.
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.
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.
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.
Ready to See What Traject Data Can Help You Do?
We’re your premier partner in web scraping for SERP data. Get started with one of our APIs for free and see the data possibilities that you can start to collect.
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.
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
Improved Inventory Management – Accurate forecasts help businesses maintain optimal stock levels, reducing overstocking and stockouts.
Reduced Costs – Minimizes expenses associated with overstocking and storage.
Enhanced Marketing Strategies – Understanding future demand enables businesses to fine-tune marketing efforts for peak seasons and targeted campaigns.
Optimized Pricing Strategies – Forecasting demand allows businesses to optimize pricing for maximum profitability.
Data-Driven Decision Making – Advanced analytics provide valuable insights that drive smarter, more strategic business choices.
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.
Ready to See What Traject Data Can Help You Do?
We’re your premier partner in web scraping for SERP data. Get started with one of our APIs for free and see the data possibilities that you can start to collect.
