Prices, trends, competitors: how marketplace analytics is changing the rules of e-commerce

Imagine playing poker where only you are allowed to peek at the opponents' cards. This is exactly how data scraping/parsing turns the world of e-commerce upside down. It transforms the risky game of guesswork into business management based on facts: what competitors are selling, at what prices, and what customers really want. It is no longer just a tool for IT specialists - it is a new mandatory rule for survival and growth for anyone who wants to succeed in the market.

What is web scraping?

Web scraping (web parsing) is the process of automated data collection and extraction from various websites. In simple terms, instead of manually opening internet pages, copying and pasting text into, let's say, an Excel spreadsheet, a person runs a parser and a special program (or script) does this work for them.

How it works (basic):

1. A scraper program sends a request to the website server (just like your browser does when you open a page).
2. The server responds by sending the HTML code of the page (the same "skeleton" that the browser turns into a beautiful image).
3. The scraper analyzes this code, finds the necessary data in it (such as names, prices, descriptions) according to certain rules (for example, searches for a specific CSS class or HTML tag).
4. The data is extracted and structured into a convenient format (Excel table, CSV, JSON, or a database).

Analogy: Imagine you need to transfer the prices of 10,000 products from an online catalog into a table and also categorize them into tens or hundreds of categories. Web scraping is like hiring a super fast and tireless robot that can do this in a few minutes instead of your many hours of routine work.

Why are marketplaces considered the "golden vein" of data for businesses?

Marketplaces (such as Amazon, AliExpress, Wildberries, Ozon) are not just shopping websites, they have become digital trading platforms of the 21st century. They are the world's largest aggregators of real market data in a mode close to real-time. Marketplaces offer you a giant, constantly updated marketing research that reflects the real behavior of millions of customers. You get factual, not forecast data: what, how much, and why people are buying right now. Scraping prices and other data from them provides direct access to understanding demand, supply, and consumer behavior. The ability to automatically gather and analyze this information gives businesses a specific competitive advantage: fast decision-making and fact-based, rather than intuition-based, strategies.

• Understanding Competitors: This is your "competitive intelligence." You can monitor competitors' assortment in real-time, their pricing strategies (promotions, discounts), and how they optimize their product listings (their SEO strategy).
• Dynamic Pricing (Repricing): Marketplaces allow for instant reactions to market changes. If a competitor lowers prices, you will be notified and can automatically adjust your prices to avoid losing sales. Conversely, if demand rises, you can promptly increase your margins.
• Trend and Niche Discovery: You can track which products are gaining popularity quickly (so-called "hot items"). This allows you to enter a promising niche in a timely manner before it becomes oversaturated.
• Product and Reputation Improvement: By studying reviews of your products and those of your competitors, you receive direct feedback from the market. You learn about product weaknesses (e.g., size issues or defects) and competitors' strengths to enhance your own product.
• Testing Business Ideas: Before investing in developing or purchasing a new product, you can assess its market demand and how satisfied customers are with existing alternatives. This is the fastest way to reduce risks when launching new products.

What data can be parsed from marketplaces?

Parsing allows extracting almost all data that a user sees in a product card. First and foremost, this includes basic product information: name, SKU, brand, category, detailed description, and technical specifications, including weight and dimensions. Along with this key process is the parsing of prices - not only the current price but also the discount amount and the price change history. This data is complemented by information about the seller: their name, overall rating, and the number of reviews, which helps assess the reliability of the counterparty.

Reviews and ratings are of particular value for analysis. We collect not only the overall star rating but also review texts, their publication dates, and seller responses, providing a deep insight into the customer experience. Equally important is timely information about product availability and delivery times, which directly impacts purchasing decisions.

For marketing purposes, SEO data is critically important: titles, meta descriptions, and keywords that competitors use in the structure of their pages. Analyzing this information allows one to understand their strategy for attracting organic traffic and adjust their own. Thus, the collected array of data creates a complete picture of the market situation for making informed decisions.

Data Parsing Implementation Methods

Collecting data from marketplaces can be done in several ways, the choice of which depends on budget, technical skills, and the scale of the task. Developing a parser in Python provides maximum flexibility and control: for simple static websites, the combination of Requests library for page retrieval and BeautifulSoup for analysis is ideal, while for complex dynamic platforms that heavily use JavaScript, tools like Selenium or Playwright are necessary, as they simulate real user actions in the browser.

For large projects, the best solution would be the Scrapy framework, which offers built-in mechanisms for asynchronous operation, bypassing restrictions, and pipeline data processing.

For those who do not have resources for development, there are ready-made No-Code/Low-Code platforms, such as ParseHub or Octoparse. These services allow you to configure parsing through a visual interface by simply selecting the necessary data on the page with a mouse, which is ideal for quick, one-time tasks. For specialized purposes, like parsing and monitoring prices, there are ready-made services like Competera or Priceva, which take care of all the technical aspects, providing data through convenient dashboards.

A hybrid approach often proves to be the most effective, combining different methods. For instance, you can use the official marketplace API (such as Amazon SP-API) to obtain structured data about products, and for collecting reviews that are not always available through the API, you can apply lightweight Python parsers. This approach allows you to comply with platform rules, minimize the risks of blocking, and use the most suitable tool for each specific task.

When collecting data on a large scale, one of the main challenges is overcoming bot protection systems (anti-bot systems) and preventing IP address blocking. To address this, anti-detection browsers are used - tools that enable the creation and management of numerous unique "digital fingerprints" (browser fingerprints). They disguise the parameters through which a website can identify a user as a bot or link multiple requests to a single source.

Benefits of using anti-detection browsers for website parsing:

• Mimicking Real User: Anti-detect browsers replace parameters like User-Agent, screen resolution, font information, WebGL, and Canvas fingerprints to make each session appear as a request from a unique, real user, rather than an automated script.
• Managing Multiple Profiles: They allow efficient management of a large number of profiles, each with its own IP address (via proxy server), history, cookies, and unique fingerprint, significantly reducing the risk of total blocking.
• Behavior Simulation: In combination with browser automation tools (Selenium/Playwright), anti-detect browsers allow scripts to more convincingly simulate human behavior (random delays, cursor movements, natural scrolling), bypassing behavioral anti-bot checks.
• Resistance to Blocking: Thanks to naturally looking (human-like) digital fingerprints, anti-detect browsers provide high resistance to systems that analyze browser metadata to detect scrapers.

The use of anti-detection browsers, often in combination with high-quality proxy servers (rotating, residential, or mobile), is critically important for long-term and large-scale parsing of marketplaces and other secure web resources.

Tools for Web Scraping

Tools for self-development. You will have to write code manually, but the flexibility and power of the tools obtained will handle practically any task.

• Python + Requests/BeautifulSoup: A combo for beginners and simple tasks. Requests downloads the HTML code of a webpage, while BeautifulSoup "parses" it and extracts data.
• Selenium/Playwright/Puppeteer: Browser automation tools. Used when content is loaded dynamically via JavaScript. They simulate scrolling, clicks, and text input.
• Scrapy: A powerful and fast framework for creating complex parsers. Ideal for large-scale data scraping from multiple pages.

Technology to bypass protection and maintain the anonymity and security of your hard-working parser.

• Anti-detection browsers (e.g. Undetectable): Provide a unique digital fingerprint of the browser, allowing scripts to appear as multiple different real users.
• Proxy servers: Rotation and use of multiple different IP addresses for load distribution and prevention of blocking.

Ready-made platforms (No-Code/Low-Code) are used when the task is not too complex or the website from which data needs to be collected has a standard structure.

• Universal parsers: ParseHub, Octoparse, Dexi.io. Allow setting up data scraping without programming through a visual constructor.
• Specialized e-commerce services: Competera, Priceva, DataFeedWatch. Offer ready-made solutions for monitoring prices, assortment, and promotions of competitors.

Main Challenges and How to Overcome Them

The most common and critical issue when parsing data from marketplaces is IP address blocking. The security systems of large platforms easily detect bots due to abnormally high activity from a single address. An effective solution to this problem is the use of proxy servers, which mask the real IP address of the scraper. Residential (ISP) and mobile proxies are best suited to bypass complex anti-bot systems since their IP addresses belong to real internet providers and communication operators, making the traffic indistinguishable from that of a live user. For greater reliability, proxy rotation is used – automatic change of IP address after each request or session, distributing the load and further concealing traces of automation.

Another serious challenge is the defense against bots, such as CAPTCHA or complex JavaScript challenges that verify if the visitor is human. Tools like browser automation tools, for example Selenium or Playwright, are used to overcome them. These tools can emulate real user behavior: scrolling through the page, mouse movements, and delays between actions. If CAPTCHA still appears, it can be passed to solve it using special services like Anti-Captcha or 2Captcha, where operators manually recognize the code, and the answer is automatically returned to the script.

The third group of issues is related to frequent changes in the website structure - developers regularly update the design and layout, which disrupts the work of parsers configured for the old site structure. To minimize delays, it is necessary to regularly update the parser logic and use more flexible and reliable methods to search for elements on the page. Instead of using classic HTML selectors, which depend on the specific location of elements, it is recommended to apply CSS selectors and XPath, which allow finding data based on unique attributes (for example, data-product-id), less prone to changes during cosmetic layout edits.

Legality and Ethics of Marketplace Scraping

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From a legal perspective, the key aspect of data parsing legality is compliance with the rules established by the marketplace itself. The robots.txt file serves as an indicator of the site owner's intentions, but its violation usually does not lead to direct legal responsibility, unlike a violation of the Terms of Service. Violating the ToS, which explicitly prohibits automated data collection, can be grounds for a lawsuit for unauthorized access (especially in jurisdictions with strict laws, such as the USA), if data collection is carried out in a manner that violates the Computer Fraud and Abuse Act (CFAA). Therefore, before starting any data collection, it is necessary to carefully study these rules.

The principle of confidentiality is no less important from an ethical and legal perspective, prohibiting the collection of users' personal data without their explicit consent. This requirement is enshrined in international regulations such as GDPR in Europe and CCPA in California, and its violation carries significant fines. In addition, ethical web scraping involves technical responsibility: despite marketplaces usually being located in powerful data centers, it is advisable to avoid making too frequent requests to marketplace servers, using delays between requests to avoid creating excessive load that could be perceived as a cyberattack.

Conclusion

In conclusion, it can be confidently stated that parsing and marketplace analytics have become not just tools, but a strategic necessity for building a successful e-commerce business. It allows companies to transition from intuitive decisions to a precise data-driven approach, providing them with a real competitive advantage through a deep understanding of the market, prices, and assortment.

The choice of a specific implementation method - from independent programming (using Python, Scrapy) to ready-made No-Code services - is a strategic task that depends on the available resources and the scale of the goals. In this case, for large-scale and sustainable data parsing, the use of anti-detection browsers and proxy servers becomes critically important. These technologies add the necessary level of anonymity and security, helping to bypass complex protection systems and minimize the risk of IP address blocking.

However, it is important to understand that data collection itself is just the first step. The main value lies in the subsequent analysis of the obtained information and its proper integration into business processes such as dynamic pricing, assortment management, and marketing campaigns.

It is always necessary to maintain a balance between efficiency and legality, strictly adhering to legal norms and ethical standards, so that benefiting does not lead to reputational and legal risks.