3 Ways Popular Price Comparison Websites Gather Data

A few years back, a typical online buyer looking to buy apparel or an electronic gadget would scour the web to compare prices across websites manually. Prices of the same commodity would vary vastly, which prompted the rise of sites designed to monitor price changes in real-time.

In today’s hyper-competitive e-commerce scenario, retail players grab every opportunity to attract potential customers. A price comparison website is one of the biggest opportunities e-retailers leverage to drive traffic.

These platforms are a win-win for both business owners and customers. Customers benefit by finding best shopping price comparison deals, a convenient experience, and better coverage over major e-commerce sites. On the other hand, business owners benefit by gaining more leads, better conversion rates, and enhanced visibility on a competitor price comparison engine.

The Business Model: How Do They Make Money?

Understanding the ecosystem behind the data.

Before diving into the data collection, it is vital to understand why these platforms exist. They aren't just helpful tools; they are profitable businesses. Most price comparison search engines operate on one of three models:

1. Affiliate Marketing (CPA/CPC): The most common model. When a user clicks "Buy Now" and purchases an item from Amazon or eBay via the comparison site, the platform earns a commission (Cost Per Acquisition) or a fee for the click (Cost Per Click).

2. Sponsored Listings: Retailers pay a premium to have their products appear at the top of the search results for keywords like "best running shoes."

3. Data Monetization: Aggregated data on consumer search habits is incredibly valuable. Some platforms analyze this traffic data and sell market insights to brands.

How do Price Comparison Websites Work?

Building a competitor price comparison platform isn't just about a website; it's about the backend technology.

• Web Crawlers (Spiders): Scripts written in languages like Python (using libraries like Scrapy or BeautifulSoup) that navigate the web to find product pages.

• Headless Browsers: Tools like Puppeteer or Selenium are used to scrape sites that rely heavily on JavaScript to load prices.

• Proxy Networks: To scan millions of pages without getting blocked, engines use rotating residential proxies to simulate users from different locations (e.g., checking how a price differs in New York vs. London).

Challenges Faced by Price Comparison Websites

While trending price comparison websites constantly update their algorithms to cope with dynamic web changes, they face significant hurdles in building and retaining a dynamic tool.

The challenges in getting and using data for a price comparison web crawler include:

1. Comparison Technology & Structure

One major issue is the difference in the structure of pricing data from different retailers. One site might list a price as "$19.99" while another lists it as "USD 19.99 (VAT incl)". This makes it tough to build a competitor price comparison platform that standardizes all this data. Such challenges often lead to a discrepancy between a product’s price on the website and what we see in a comparison shopping engine.

2. The Volume of Data

If the price comparison site is targeting a large product range (like Google Shopping), they are looking at a humongous amount of data—often millions of SKUs daily. This makes the task complex and requires massive server infrastructure.

3. Data Latency & Dynamic Pricing

As data updates may take too long in some engines, it results in inaccurate information, frustrating users trying to compare prices online. If a flight ticket price changes 5 times an hour, a scraper running once a day is useless.

4. Anti-Bot Measures

Modern e-commerce sites deploy sophisticated "anti-scraping" shields (like CAPTCHAs, IP bans, and Cloudflare challenges) to block web scraping price comparison bots. Overcoming these requires constant maintenance and advanced "human-mimicking" AI.

Also Read: Pricing parity: Excelling Across Both Online and Offline Channels

How Price Comparison Websites Grab Data

A lot of startups work with Datahut to obtain data for their price comparison engines at a large scale. If you are wondering how to build a price comparison website, here are the three main ways data is collected:

1. Feeds from Merchants

Price comparison websites often enter into a collaboration with stores to gain data directly from a retailer’s web feeds. Traffic from comparison shopping websites is a major source of revenue for internet retailers.

If you can build a partnership with them, they can give you access to their API. However, you will often need to pay a premium to use the API at a higher volume. Merchants usually set up either an API or use FTP to deliver the price comparison data feed in formats like CSV or XML.

Pros:

• High Accuracy: Data comes directly from the source, so it is rarely wrong.

• Legal Safety: Since it is a partnership, there are no legal risks regarding scraping.

Cons:

• Slow Updates: Feeds are often updated only once every 24 hours, missing intraday flash sales.

• Formatting Nightmares: Every merchant sends data in a different format, requiring heavy engineering to standardize.

The Hidden Challenges:

• The "Chicken-and-Egg" Barrier: Relying solely on merchant feeds creates a massive barrier to entry. Large retailers often ignore requests from smaller price comparison sites until they prove substantial traffic. You are stuck in a loop: you need the data to get the traffic, but you need the traffic to gain access to the data.

• Integration Overhead: Standardization is a technical nightmare. One retailer might send a 50GB monthly XML file, another uses a real-time API with rate limits, and a third pushes a daily CSV via FTP. Your engineering team must build and maintain custom adaptors for every single partner.

• Schema Instability: If a retailer changes their data schema without warning—which happens frequently—your comparison engine breaks for that store until you manually fix the code.

• Ignoring the "Long Tail": This method excludes thousands of smaller e-commerce sellers who lack the technical capability to generate sophisticated data feeds. By relying only on direct feeds, you miss out on potentially cheaper vendors, limiting your ability to truly offer the "best price" to your users.

Ideal For: Established platforms that can demand partnerships with big retailers.

Note: One of the common ways of monetizing these sites is by becoming an affiliate partner and getting a referral commission.

2. Product Feeds from Third-Party APIs

Retail product data can also be obtained through third-party APIs. When integrated with shopping carts, some services provide e-commerce data via an API request. These companies actively crawl the most common retailer websites—like a Google Shopping price comparison API approach—and provide this data in a structured database.

This method saves development time and allows easy integration of price comparison engines with multiple shopping carts. Shopping giants like Magento, WooCommerce, and Shopify can be integrated via unified APIs to connect shopping carts simultaneously.

Pros:

• Speed to Market: You can launch your app in days rather than months.

• Less Maintenance: You don't have to worry about fixing broken crawlers or managing proxies.

• Unified Schema: You get data in a single, consistent format regardless of whether it came from Amazon or a small shop.

Cons:

• Cost Scaling: As your traffic grows, API calls become extremely expensive (often priced per 1,000 requests).

• Limited Coverage: You are restricted to the retailers the API provider supports; you cannot add a niche competitor easily.

The Hidden Challenges:

• The "Black Box" Problem: You have zero visibility into the data collection process. If an API delivers outdated pricing during Black Friday, you cannot force a refresh. You are entirely at the mercy of the provider's update schedule.

• Data Homogenization: To make data compatible across 50 retailers, API providers often strip away unique attributes (like "warranty terms" or "box contents") to fit a standard schema. You end up renting the same generic data as your competitors, making it hard to differentiate your product.

• Dependency Risk: Building your business on a third-party API ties your survival to their pricing. If they raise prices by 30% overnight, your margins vanish, and switching providers is a painful, code-intensive process.

Ideal For: Startups and MVPs testing the market before building their own proprietary infrastructure.

Also Read: How to Leverage Web Scraping to Create a Competitor Price Monitoring Strategy

3. Web Scraping for Price Comparison

In most cases, options 1 and 2 might not be available when you are just starting up and have fewer funds. In such cases, web scraping price comparison data is one of the most reliable and efficient ways of getting product data from target sites.

You can acquire pricing data by either building your own in-house price comparison web scraper or employing a Data-as-a-Service (DaaS) provider.

Pros:

• Unlimited Scale: You can scrape any website, including niche competitors that don't have APIs.

• Real-Time Control: You control how often to scrape (e.g., every hour) to capture dynamic pricing.

• Deep Data Extraction: Unlike APIs, you can grab every detail on the page—reviews, ratings, shipping times, and stock levels.

Cons:

• Anti-Bot Blocks: Websites will actively try to block you, requiring expensive rotating proxies and CAPTCHA solvers.

• Maintenance: If a website changes its HTML layout (even slightly), your scraper might break immediately.

The Hidden Challenges:

• The "Cat and Mouse" Game: It’s not just about writing code once. Modern e-commerce sites use sophisticated anti-bot shields like Cloudflare and Akamai. To bypass them, your team needs to constantly update "fingerprints," manage headless browsers, and rotate residential IPs. It is a full-time job, not a "set and forget" script.

• Silent Data Corruption: A scraper doesn't always "crash" when a site changes. Sometimes, it starts scraping the wrong data silently—for example, grabbing the price of a cheap accessory instead of the main product. Without robust Quality Assurance (QA) layers, you might display incorrect prices for weeks before noticing.

• Legal & Ethical Gray Areas: While scraping public data is generally legal, navigating "Terms of Service" violations and robots.txt compliance requires careful legal counsel. Aggressive scraping can lead to IP bans or cease-and-desist letters if not done respectfully.

For efficient results, the quality of the data has to be top-grade. Since managing a price comparison website is itself a cumbersome task, we recommend you go with a web scraping service provider for pricing data extraction. Web scraping services employ crawling bots to regularly gather information from websites. This instantly provides the price comparison engine with accurate and updated information without depending on merchants.

We recommend you read our post on How to Choose the Right Web Scraping Service for Your Organization.

 Real-World Case Study: The Cost of Bad Data

Why accuracy matters more than speed.

Imagine a leading electronics retailer using a price comparison tool to set prices for the new iPhone launch. They rely on an internal scraper that breaks down over the weekend.

• The Result: Their data showed a competitor price of $999 (old price), while the competitor had actually dropped to $949 for a flash sale.

• The Loss: The retailer kept their price high, losing thousands of potential sales within 48 hours.

The Solution: By switching to a managed provider like Datahut, the retailer ensured their feeds were monitored 24/7. The system would have alerted them to the $949 drop instantly, allowing them to match the price and save the weekend's revenue.

Partnering with a Dedicated Data Solution (Datahut)

For businesses that want to focus on building the best price comparison website rather than managing complex crawlers, partnering with a managed service is the ultimate leverage. This is where Datahut steps in. We take the technical hassle out of web scraping for price comparison by handling the entire data pipeline—from navigating complex anti-bot measures to delivering a clean, ready-to-use price comparison data feed.

By choosing a dedicated partner like Datahut, you ensure your competitor price comparison engine is powered by high-quality, 99.9% accurate data. This allows you to bypass the technical overhead and focus on growing your traffic, confident that your users are seeing the most reliable pricing available.

Datahut offers affordable data extraction services. If you need help with your web scraping projects, contact us today and we will be glad to help.

Frequently Asked Questions (FAQs)

1. How do price comparison websites collect pricing data?

Price comparison websites collect data mainly through three methods: web scraping, retailer APIs, and direct merchant feeds. These approaches help them gather real-time product prices, availability, discounts, and seller information from multiple sources.

2. Is web scraping legal for price comparison websites?

Web scraping is legal when done responsibly and ethically. Price comparison platforms typically scrape publicly available data, follow website terms, respect robots.txt rules, and avoid collecting personal or sensitive information to remain compliant with regulations.

3. What is the difference between APIs and web scraping for price data?

APIs provide structured, permission-based access to pricing data directly from retailers, while web scraping extracts data from publicly visible web pages. APIs are more stable, but scraping offers broader coverage when APIs are unavailable.

4. How often do price comparison sites update their data?

Update frequency varies by platform and data source. Some websites refresh prices multiple times a day using automated scrapers or live APIs, while others update at scheduled intervals depending on retailer limitations and data volume.

5. Why do prices differ between price comparison sites and retailer websites?

Price differences can occur due to update delays, regional pricing, exclusive discounts, taxes, shipping costs, or seller-specific offers. Real-time price synchronization is challenging, especially across thousands of retailers.