Cosmetics manufacturer decides which products to add to the product line

Learn how GroupBWT helped a client in the beauty industry expand their product line based on competitor data.

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The Client Story

The client is among the top 10 cosmetic brands. Their products are available in over 80 countries worldwide and they update their product line every six months, constantly searching for new ideas. Their main competitors are L’Oréal, Maybelline, NYX.

Industry: Beauty
Cooperation: Since 2022
Location: Germany

Improved assortment planning system with the possibility to track real-time data on the market

Improved the client’s market share and boost their sales


Insights based on competitors' product analysis

By continuously analyzing major retailers such as Sephora, Boots, Douglas, Notino, Flaconi, etc., insights can be gained about market dynamics and competitors’ pricing strategies. In some cases, it’s even possible to gather information about the sales volumes of competitors.

Proper analysis of this data can serve as a key factor in deciding to launch new products that are popular among various consumer groups. Conversely, it can guide the decision to discontinue investment in underperforming products that do not sell well. The objective was to automate data collection for subsequent analysis, thereby reducing manual effort and enhancing the decision-making process.

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The Solution

Competitor monitoring system

We developed a platform that gathers data on products from specific categories found on the websites of the top 10 major retailers and competitors’ websites. This process collects data points such as SKU, GTIN, product name, and stock availability. The platform compiles data from all the locations where the retailers operate.

The data is collected daily. However, one of the challenges we faced was mapping products that lack a specific GTIN across multiple websites. To address this, we developed a mapping system using AI algorithms. This system compares multiple parameters, including images, descriptions, and titles, to ensure that product A on one website is the same as product B on another.

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We built a platform that daily collects product info from top retailers and competitors. The challenge? Matching products without specific IDs. So, we used AI to compare images, descriptions, and titles, making sure Product A on one site is the same as Product B on another.

Alex Yudin
Web Scraping Team Lead
The Results

Better management of the product line based on quality data

We have developed a data platform that scrapes information about the client’s competitors on a daily basis. This data is then properly visualized for the client, allowing them to analyze the market and identify the best sellers. This, in turn, helps them make educated decisions regarding their product line.

Special attention has been given to products that cannot be matched due to the absence of a unique ID. To match these products, we have developed an AI-based algorithm that relies on DeepMatcher models. One of the key success factors was the proper creation of the dataset to increase the accuracy of the results.

All of these efforts helped the client increase sales by 15% in the first quarter after they started using the software

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