Collecting and Analyzing Marketplace Data via AI Browser: How an AI Agent is Changing Business Analytics on Wildberries and Ozon

Collecting and Analyzing Marketplace Data via AI Browser: How an AI Agent is Changing Business Analytics on Wildberries and Ozon

June 2026. The Russian e-commerce market continues to grow: Wildberries and Ozon process millions of orders daily. For entrepreneurs, sellers, or analysts, the key question is how to stay competitive when prices, reviews, and assortment change every hour? Manual data collection has long been a bottleneck: it's slow, expensive, and error-prone. Enter the AI browser—an intelligent tool that automates data collection and analysis from marketplaces, turning information chaos into structured business analytics.

In this article, we'll break down how an AI agent collects data from websites via browser_scrape, which metrics are truly important for market analysis, and how to use this information for decision-making. No magic—only practical cases and proven approaches.

What is an AI Browser and browser_scrape?

An AI browser is not just a program for surfing the internet. It's a smart agent that can "see" web pages, understand their structure, and extract the needed data. The technology is based on browser_scrape—a method of automated information collection where AI analyzes HTML code, images, and dynamic content (e.g., lazily loaded product cards).

Unlike traditional parsing, which breaks at the slightest change in layout, an AI browser adapts. It recognizes key elements: product name, price, rating, review count, stock levels. This is especially important for Wildberries and Ozon, where page design and structure are updated regularly.

Why Collect Data from Marketplaces?

Data collection is the first step to understanding the market. Without quality analytics, you're acting blindly. Here are the key tasks automated collection solves:

• Competitor monitoring. Which sellers have risen to the top in your category? How has their price changed over the week?
• Demand analysis. Which products are gaining popularity? Where are sales declining?
• Price optimization. What price should you set to stay competitive without losing margin?
• Reputation assessment. What are reviews saying? Are there mass complaints about defects or delays?

How an AI Agent Works in Practice: Step-by-Step Breakdown

Let's examine the process using the example of analyzing the "wireless headphones" niche on Ozon. An AI agent, launched via browser_scrape, performs the following steps:

1. Query and navigation. The agent opens the search page for the keyword "TWS wireless headphones."
2. Data extraction. It collects from each card: name, brand, price before and after discount, rating, review count, presence of a "Bestseller" badge.
3. Handling dynamics. If the page loads new products on scroll, the AI browser waits for full loading.
4. Structuring. The data is saved into a table. Example result:

1. Analysis. The AI agent itself can calculate the average price in the niche, identify leading brands by review count, and plot a graph of price changes over the day.

What Data to Collect for Business Analytics?

Not all metrics are equally useful. To avoid drowning in numbers, focus on key indicators:

• Price range. Minimum, average, and maximum price in the category.
• Review dynamics. How many new reviews appeared per day—this is an indirect sales indicator.
• Assortment. How many unique products are available in the niche.
• Sellers. Which brands or stores dominate.
• Seasonality. How the number of offers changes depending on the month.

Practical Tips for Setting Up Collection

To make the AI browser work efficiently