How Price Parsers Work: Types, Tools, and Best Practices for Competitor Pricing

E-commerce parsers help online stores analyze their competitors by collecting data on product prices, availability, credit terms, and delivery conditions.

While there are many parsers on the market, most modern online stores have protections against parsing, so not all of these tools deliver high-quality results. If you need accurate and up-to-date data, you have two options: carefully search for a commercial parsing service and test how well it works, or develop a parser in-house (which is almost always unprofitable considering the costs of infrastructure and, most importantly, maintenance).

In this article, we explain the different types of parsers and what to pay attention to when choosing a parser for your own competitor analysis.

Contents:

• How Parsers Are Used in Price Analytics
• What Data Parsers Collect
• Types of Parsers
• File Parsers
• Link Parsers
• Category Parsers
• E-commerce Mobile App Parsers
• In-house vs. Outsourcing: Which Approach to Choose

How Parsers Are Used in Price Analytics

Parsers efficiently collect information about competitors’ assortments and product prices. Data volumes that a person would spend 10–20 hours collecting can be gathered by a parser in just minutes.

Although parsing is not a new technology, achieving a high level of quality when collecting data from online stores is quite difficult. Off-the-shelf solutions can capture 70–80% of the required data. To increase accuracy, a parser needs to be created or refined for each specific case, which requires time and money.

Price analytics platforms combine parsing (as a data collection technology) with analytical tools. This means you’re not just collecting raw data — you get dashboards, trends, and insights that turn numbers into actionable decisions for pricing and assortment strategies.

What Data Parsers Collect

E-commerce parsers can collect various types of data, including:

1. Basic product information

• Product code (SKU)
• Product name
• Manufacturer/brand name
• Product page URL
• Images (links to product photos)

2. Price and availability data

• Regular price
• Promotional price
• Stock status
• Quantity available

Most parsers collect only this basic data. More advanced tools offer broader capabilities and gather additional data that can influence sales and product positioning.

3. Extended data

• Number of product reviews
• Product rating
• Seller information (relevant for analyzing competitors on marketplaces)
• Purchase terms: credit, installment options
• Delivery terms: free shipping, delivery time
• Product position in the catalog
• Active promotions (e.g., campaign names: “Black Friday,” “Sale”)
• Labels: “New,” “Best Seller,” “Special Price”

Pricer24 collects all of this data. By default, we gather basic product information along with price and availability data, as this is necessary for high-quality price analytics. Additionally, we can customize the collection of extended data to meet specific client needs.

Important: Legal Data Collection and Limitations

We do not collect data that is protected by copyright or constitutes the intellectual property of online stores, such as product descriptions and specifications.

Types of Parsers

Parsers can be classified in different ways, but the most practical classification is based on the data source:

• File parsers
• Link parsers (product page parsers)
• Category parsers (+ search results parsers)
• Mobile app parsers

Let’s look at each type, starting with the simplest.

File Parsers

File parsers collect data from files provided by clients. Examples include supplier price lists or a client’s product catalog. File formats can be CSV, XLS(X), XML, or JSON, all of which contain structured information about products: SKU, price, availability, name, brand, and so on.

File parsers allow you to process large volumes of information quickly. The data comes in a structured, analysis-ready format, which significantly simplifies processing.

How it works

1. The client provides access to a file or API. This could be a regularly updated price list or a one-time file with product data. For example, you can provide standardized product feeds (Google Shopping, Hotline, Prom.ua, etc.) or partner access to a portal via an authorization key. In this case, the API allows the parser to retrieve data directly from the system with minimal delay.
2. The parser scans the file and recognizes the data structure: columns with SKUs, prices, availability, names, brands, and so on.
3. The parser standardizes the data into a unified format. For example, it can unify availability labels (In stock → Available).

Link Parsers

Link parsers (product page parsers) collect data directly from the HTML code of a specific product on a competitor’s website. With a single request, the parser retrieves information about the product: price, availability, and more.

How it works

Most price analytics services collect competitor product data in the following way:

1. At the start of the collaboration, they analyze the competitor’s website to find all product links.
2. They match pairs: a link to your product with the corresponding link to the same product on the competitor’s site.
3. Afterwards, they scan only those links repeatedly and do not search for new products.

The main limitation of link parsing is that by repeatedly scanning old links, you don’t account for new products in competitors’ catalogs.

Let’s imagine this scenario: You set up data collection via links for 100 products in a specific category across three competitors. During the first month, Competitor A adds 50 new products, Competitor B adds 30, and Competitor C adds 20. You have no information about these new products — in other words, you are effectively missing a portion of the market that could impact your sales. Now scale these numbers to your entire assortment. The worst part is that you will likely learn about your competitors’ new products only when you analyze why your sales are declining.

To identify new products in competitors’ catalogs, you need to:

• Regularly visit each competitor’s website
• Check the number of products in relevant categories (ideally, categories will match yours)
• Compare competitors’ products with your own assortment to spot items you don’t carry
• Identify new products
• Add links for new products to your monitoring system

The second problem is that some stores may create duplicate product pages in their catalog and lower the price on the newly created page. This is done to protect against parsing: Your parser continues to collect data from the old page (where the price is higher), while real customers see the new page with the lower price.

Sometimes, competitors also massively change the URL structure and may not set up redirects. Example:

Before: site.com/product/iphone-16-128gb
After: site.com/products/smartphones/iphone-16-128gb

In this case, all old links stop working at once. You need to detect the issue, find the new URLs, and update them in your system.

Another common scenario is when you add a product link, but the competitor has removed the product page. When the parser visits the page and encounters a 404 error, it reports that there’s no price available. This might lead you to think the competitor no longer sells the product, although the product could still be available under a different link.

When This Method Makes Sense

Link parsing is justified when you need to track specific products. For example, if you have only three products in a category while your competitor has 3,000, collecting data for the entire category is impractical. You would be paying to gather information on 2,997 unnecessary products. In this case, it’s much more efficient to provide links only for the three products you’re interested in.

Category Parsers

Category parsers collect data from all products within a specific category on a competitor’s website or marketplace.

Search results parsers are a subtype of category parser. They work similarly to category parsers, but instead of parsing an entire category, they parse pages returned by a specific search query.

How it works

The bot is given a link to the category’s starting page. For example, the refrigerators category: https://website.ua/refrigerators/.

The bot automatically goes through all product pages in the category and collects all available information about each product on every page.

A search query parser is convenient if you only need to track a single brand.

Example query: https://website.ua/ua/search/?text=ecoflow

In our example, the parser will collect everything found on the competitor’s site when searching for the keyword “Ecoflow.”

This approach is quite convenient but has certain limitations:

• If the competitor’s search or filters are implemented incorrectly, the parsing results will contain a lot of noise. For example, you might search for a phone but get results that include headphones, chargers, cases, and anything except phones.
• If the brand you need to track has a non-unique name, you will also get noise in the results. An example from our experience is the brand Grey. Most sites return search results for gray-colored products (“grey” as a color), products with the word “grey” in their name, and somewhere among all that actual products from the Grey brand.

Advantages of Category Parsers

1. Automatic detection of new products

This is the main advantage of a category parser. Let’s say that yesterday, your competitor did not have a product that you offer, but today they added it and set a price 15% lower than yours. As mentioned earlier, if you track only specific links, you simply won’t notice the appearance of this product. Parsing the entire category allows you to quickly detect new products and respond to market changes.

2. Analysis of the entire market, not just assortment overlap

Category parsers allow you to see products you don’t yet carry. This opens up opportunities for:

• Monitoring unique offers. For example, say a competitor has an exclusive product. If they sell it at a very low price, it affects the entire segment. You may need to adjust prices on your similar products to remain competitive.
• Identifying gaps in your assortment. Category parsing lets you see which products are actively sold by competitors but are missing from your assortment. This is a valuable signal for assortment expansion and helps identify specific products you might add to boost sales.
• Detecting MAP violations. This is especially relevant for brands and distributors. Sometimes, partners manipulate pricing by creating two product pages for the same item: one with the correct name and price (for brand display) and another with an obscure name and lower price (for actual sales). A category parser finds all pages (even those hidden from obvious search), giving you a true picture of your partner’s pricing.

Pitfalls of Category Parsing and How to Do It Professionally

1. Collecting excess data

For example, say you have 10 refrigerators of a certain brand in your category, while a competitor has 1,000 from various brands. You want to compare only your assortment, but the category parsing system collects 100 times more information than you need. Or consider another example from our practice: On the Amazon marketplace, іf you’re only interested in products sold by Amazon itself, there’s no need to collect data for the entire category on the Amazon marketplace.

How we solve this: We use additional filters by brand or by seller. This allows us to control the volume of data and optimize the budget for the client. We can limit the amount of data collected based on the client’s specific needs and requirements.

2. Chaotic categorization across different sites

This refers to situations where the same product is placed in different categories on different websites.

For example, vacuum cleaners:

• On Site A, they are only in the category Vacuum Cleaners.
• On Site B, they are spread across four categories: Robot Vacuums, Handheld Vacuums, Upright Vacuums, and Window Cleaners.

Another challenging segment is accessories. In one store, 10 accessories may be in the Laptop Accessories category, while in another store the same products could be spread across Laptop Batteries, Bags, Cases, Peripherals, and Chargers.

From our experience, niches like Pet Products and Auto Products also have fairly chaotic categorization.

How we solve this: During setup, we manually check for products that don’t show up in the expected product category on a competitor’s website. If a client’s product is not found on a competitor’s site, it signals that a category may have been missed. This approach ensures maximum coverage.

3. Dynamic sorting

Some websites dynamically sort products within categories based on popularity. As a result, a product that was on the first page can suddenly move to the second page. This creates a risk of collecting the product twice or missing it entirely.

How we solve this: Pricer24 keeps track of products that were previously in stock. If a product is no longer visible in its category, the system checks the direct link to see whether it is currently available. In other words, the system either verifies individual products based on previous runs — checking which products were in stock before and stopping repeated checks if they remain unavailable over a certain period — or collects data by brand.

Link Parser or Category Parser: Which to Choose?

If all online stores had the same structure, the market would be much simpler, and a single solution would suffice. In the current situation, the best approach is a hybrid one.

For main categories where your assortment significantly overlaps with competitors, it’s most convenient to use a category parser. It automatically collects data on all products, allowing you to detect new products in the market and track gaps in your assortment.

This is especially relevant if:

• Assortment overlap with the competitor is ≥50%
• The market is dynamic and new products appear constantly
• You need to see the entire market, not just your own assortment
• You’re a brand that needs to monitor MAP/MSRP violations

For specific products that are difficult to classify, link parsing is a better option. It allows you to track exactly the SKUs you’re interested in without collecting unnecessary data.

This approach is especially useful if:

• You have a narrow specialization (for example, three products versus 3,000 from a competitor)
• You need precise control over specific SKUs
• Y0ur budget is limited and your assortment is stable

By combining both approaches, you get a complete picture of the market while avoiding overspending on unnecessary data.

When choosing a price intelligence solution, be sure to clarify:

• What types of parsers does the provider use?
• How do they detect new products?
• Do they support a hybrid approach?
• How frequently is data updated?
• What percentage of assortment coverage do they guarantee?

E-commerce Mobile App Parsers

Online stores often offer different prices on their web versions and in their mobile apps. Therefore, to have a complete and realistic view of the market, you need data from both sources.

Mobile app parsers in e-commerce are specialized software tools that automatically collect structured data about products, prices, availability, reviews, and other information directly from the mobile apps of stores and marketplaces.

Due to the nature of mobile apps, this type of parsing is technically more complex than collecting data from websites. The most common method is mobile device emulation (Android/iOS) with automated interface interactions. In other words, a program simulates user actions: it loads sections, scrolls through product lists, opens product pages, and saves the necessary data. To the app, it appears as if it’s running on a real device.

This approach requires high technical expertise. Modern mobile apps fight emulators by checking system properties, hardware characteristics of the “device,” and user behavior patterns. They use anti-bot protection, require authentication or verification, update regularly, and change interface structures. In addition, emulators consume significant resources, which complicates scaling when collecting large volumes of data.

Pricer24 has the capability to parse mobile apps, overcoming all these technical challenges. Our platform uses advanced emulation and automation technologies to collect data from mobile versions of marketplaces and online stores. Thanks to our proprietary infrastructure and experience with various protection systems, we provide clients with accurate and up-to-date data from competitors’ mobile channels, enabling decisions based on complete market analytics.

In-house vs. Outsourcing: Which Approach to Choose

Parsing is often mistakenly seen as a one-time technical task: write the code and it works. In reality, every parser requires ongoing maintenance and improvement.

Websites are constantly changing. Catalog structures are updated, designs and HTML markup evolve, anti-bot protections are strengthened, and new mechanics appear (for example, sections with open promo codes that immediately apply discounts, or dynamically loaded content).

Each such change requires adjustments to the parser so it can continue collecting information.

The Problem with In-house Development

Companies are rarely willing to pay a developer solely to maintain a parser, as the workload is often inconsistent. If a company has an internal specialist, they are usually assigned other high-priority development tasks. In practice, this often leads to delays of one to two weeks when the parser needs attention but the specialist is busy with other issues. During this time, the parser may function incorrectly or not at all, and you will not receive data.

Additionally, the quality of a parser’s work largely depends on the parsing strategy:

• What exactly should be parsed? (Which products, which competitors, which parameters?)
• How should the data be structured for analytics?
• How should the quality of the collected data be verified?
• How should changes in competitors’ assortments be handled?

Companies often lack the expertise to develop a proper parsing strategy and have nowhere to acquire it, which results in poor-quality data from the very first day.

Features of an Outsourcing Solution

Specialized price intelligence platforms, such as Pricer24, can offer advantages over in-house solutions due to their ready-to-use features, ongoing updates, and expertise in e-commerce data parsing. To get the most value from any platform, it’s important to clearly define your tasks, specify your preferred data format, and establish criteria to evaluate the effectiveness of data collection.

Key criteria include:

• Discovery rate: Percentage of all products — both in your catalog and in the catalog being parsed — that are successfully found
• Parsing frequency: Once a day, once a month, or ten times a day (depending on the dynamics of your category)
• Link verification frequency: Important when there are duplicate product pages in catalogs
• Acceptable error rate during matching: How accurately your product A is matched to your competitor’s product B 
• Other relevant metrics

The choice between in-house and outsourcing affects your costs. However, data quality is also a critical consideration, as it ultimately impacts your long-term profits.

Conclusion

At first glance, parsing may seem simple: run bots and get data. However, after 5+ years of working with various clients and market segments, we have seen that high-quality results require a comprehensive approach.

Each type of parser has its own advantages and limitations, and successful competitor price monitoring relies on:

• Choosing the right type of parser for your specific needs
• Skillfully combining different approaches
• Regularly verifying data quality
• Optimizing your budget for data collection

At Pricer24, we combine deep e-commerce expertise, a flexible approach to data collection, and high standards of quality and service, ensuring that each client receives exactly the data they need to make strategic decisions.