{"id":27502,"date":"2025-05-27T14:26:00","date_gmt":"2025-05-27T12:26:00","guid":{"rendered":"https:\/\/www.rewe-group.com\/en\/?p=27502"},"modified":"2026-03-03T16:28:09","modified_gmt":"2026-03-03T15:28:09","slug":"how-ai-helps-us-analyze-data-and-make-better-decisions","status":"publish","type":"post","link":"https:\/\/www.rewe-group.com\/en\/press-and-media\/newsroom\/stories\/how-ai-helps-us-analyze-data-and-make-better-decisions\/","title":{"rendered":"How AI helps us analyze data and make better decisions"},"content":{"rendered":"\n
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Artificial intelligence (AI) is revolutionizing data analysis in companies, especially in the retail sector. By using AI technologies, companies can make informed decisions, increase customer satisfaction or improve the efficiency of stock management. To better understand data-driven decisions with the help of AI in our company, we spoke to the Head of Analytics, Dr. Lorenz Determann.<\/p>\n<\/div>\n <\/div>\n <\/div>\n <\/div><\/div>\n\n\n\n

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Digital Responsibility<\/h2>
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We recognized the benefits of AI early on and decided to integrate it as a complementary value driver in as many areas of the company as possible. We launched the first applications in the areas of personalization and assortment optimization as early as 2018. With the Analytics Transformation 2021, AI was also strategically and organizationally anchored in the REWE Group.<\/p>\n <\/div>\n\n <\/div>\n<\/div>\n\n

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About:<\/h2>

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Head of Analytics at the REWE Group<\/p>\n \n <\/div>\n <\/div>\n<\/div>\n<\/div>\n\n

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The use of AI in data analysis enables us to recognize complex patterns. In practical terms, this can be seen in stores when it comes to shelf maintenance or space optimization. Here, analytical AI has proven to be an invaluable tool. Another example is the search for suitable items to meet customer needs such as “reduced-sugar items” or “Asian cooking”.<\/p>\n

The use of AI enables us to significantly improve our customers’ shopping experience in our food retailers. With the help of AI, we anonymously analyze sales data and customer behavior to determine exactly which products need to be available at the right place at the right time. Overall, this not only leads to greater customer satisfaction, but also to less food spoilage, more efficient stock management and reduced costs.<\/p>\n <\/div>\n\n <\/div>\n<\/div>\n\n

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How long have we been using AI in data analysis and how has its use developed over time?<\/p>\n<\/div>\n

We started using data warehousing at REWE Group back in 2002. Even back then, the aim was to improve our decisions by processing large volumes of data. AI has multiplied both the possible applications and the benefits of data analysis in all areas of the company. Specifically, there are three main drivers that have changed since 2002: the storage and analysis of data has become almost infinitely scalable and significantly cheaper thanks to new cloud technologies. In addition, AI processes are no longer part of expensive software packages, but can be downloaded free of charge from the internet. What’s more, the number of well-trained young talents has increased significantly, allowing us to expand our workforce in addition to technical scaling.<\/p>\n<\/div>\n <\/div>\n <\/div>\n<\/div>\n\n

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How exactly does AI help us to optimize the shopping experience in our food retailers?<\/p>\n<\/div>\n

A good example is the HOLMES project, in which we specifically look at transaction data in the markets and analyze it with the help of AI. This allows us to recognize certain patterns, which we then use to explain anomalies in sales. Let’s assume someone buys a spice mix for mac and cheese, milk and grated cheese, but no macaroni. The AI then recognizes the missing product based on a previously trained model of item relationships and thus helps to identify gaps on the shelf or items that are not in the right place more quickly.<\/p>\n

The prerequisite for this was that we first fed the AI with billions of lines of receipts so that it could learn patterns. The AI is now continuing to refine these and is becoming better and better at recognizing anomalies. This is also supported by the store employees, who provide digital feedback on whether the AI has correctly identified anomalies without any additional effort (link to film). Together with the AI, we can significantly improve the availability of articles in the stores.<\/p>\n<\/div>\n <\/div>\n <\/div>\n<\/div>\n\n

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What challenges have there been or are there in integrating AI into our existing systems?<\/p>\n<\/div>\n

Good data is a challenge. Modern AI processes require up-to-date, detailed and quality-assured data in order to make precise predictions and recognize correlations. In the simpler processes of the past, a lot of calculations were based on averages, so an outlier in a market did not carry much weight – today, a different level of accuracy is required for market-specific recommendations of articles. Another challenge is the integration of analytical suggestions into the operational systems without system breaks and double entries. One example of how we have successfully mastered this challenge is a technical solution that transfers price suggestions directly into the input masks for operational price maintenance.<\/p>\n<\/div>\n <\/div>\n <\/div>\n<\/div>\n\n

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What future developments and innovations do you see in the field of AI-supported data analysis?<\/p>\n<\/div>\n

The better AI gets, the more use cases we will find. And new options are emerging practically every day. Since AI has been able to solve problems independently based on available information – known as reasoning in AI parlance – completely new possibilities are opening up. AI agents that independently collect, evaluate and communicate information within a defined framework, for example via interfaces or databases, are an exciting next step in development right now. But the question: How do you ensure that reliable and up-to-date information is displayed in ChatGPT and co.<\/q> is also currently an open issue.<\/p>\n<\/div>\n <\/div>\n <\/div>\n<\/div>\n\n

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