How DECATHLON Optimized Asset Management Using AI and Automation - Maxence Malak, DAM Project Manager, DECATHLON

For DECATHLON, sport is a serious business. As are images. This is reflected in its Digital Asset Management platform (DAM), which has more than 2 million assets - media that must be documented in a qualitative and productive way.  

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What role does media play in DECATHLON's activities? Can you give some orders of magnitude?

Media, especially images, play a fundamental role since a product without images simply has no place on our e-commerce site. In addition, while about 80 people put 90% of the media online, nearly 20,000 people visit PixL - the name given to our Digital Asset Management (DAM) platform - each year to find a visual. This can be for a wide variety of purposes: external or internal communication, online and offline devices. About 2,000 new media files are added every day to the DAM, which already contains more than 2 million assets. This concerns only the "master" assets, to which multiple technical variations must be added for each of them. These figures can make you dizzy, but it should be noted that DECATHLON covers about a hundred sports and is located in 52 countries.

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When you took over responsibility of the DAM in 2017, what were your main concerns?

By reading the 150 support tickets issued each month, we identified two key issues. First, the time it takes to file images is far too long and obviously weighs on the daily lives of the employees who publish this media. Then, at the other end of the chain, all users complain about searches that are often too unsuccessful. Not surprisingly, they expect PixL to perform as well as Google and this is not the case....

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How do you deal with the first problem, the one related to the time it takes to deposit images?

With a lot of automation. Before, we proceeded in two steps: first, the extraction of the product tree from the Product Information Management (PIM); then, its import into PixL, Wedia's enterprise Digital Asset Management (DAM) platform. With this process, it was up to the user to search for metadata within this tree structure to enrich the documentation of the media. Tedious and time-consuming. We therefore worked on a much more fluid synchronization between the PIM and the DAM using APIs. Now, all the user has to do is enter the product code to automatically upload about fifteen metadata. The user just has to validate these suggestions. We quickly saw huge gains in team productivity, metadata quality and de facto adoption of PixL.

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This must have helped make searches more successful as well?

Yes, but it wasn't enough yet. To make a search more efficient, we took the time to analyze the requests. For example, we noticed that a search on football was bringing up a lot of product images, while users were first expecting images related to the world of football. With these findings in mind, we have simplified the metadata to better target autosuggestions (editor's note: the terms suggested by the engine to complete a request). With this too, we’ve seen a jump in performance. The success of the work undertaken on these two subjects, and the performance in upload and search, can be seen in the tickets filed to support: we have gone from 150 per month to just three!

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Digital Asset Management is a good playground for Artificial Intelligence. Have you evaluated this opportunity?

Yes, and this even resulted this year in a first Proof of Concept (POC) created with Wedia in the Azure cloud. At the origin of this project, there is a fundamental reflection. With the synchronization between the PIM and the DAM, and the efforts of standardizing the tree structure of our offer, we’ve improved performance of product images, pouring them into the DAM as well as finding them. But what about contextualized images, those that illustrate a use before matching a product model? We asked the following questions: can Artificial Intelligence (AI) recognize a bicycle and its associated use? Can it differentiate between a mountain bike (ATV) and a city bike? Identify a specific model? With the help of the Wedia team, we worked on the top 10 universe products such as bicycles, underwater and camping.

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With what results?

We can say that we were positively surprised. While the AI cannot yet identify a specific product - as we suspected - it is able to summarize an image. And quite well because it works by associations. If it identifies a bike and a mountain, it infers that it is an ATV. Better yet, it can generate a description like "man next to a tent preparing food". Such enrichment can significantly improve the search and reuse of images.

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Enough to go beyond the POC?

Although we still need to specify the large-scale ROI of a project, we will spend the first set of AI services in production during the summer. The results are sufficiently convincing to include the use of AI in our roadmap. Especially since we know how to guarantee the performance of the AI over time, as the product catalogue evolves. Indeed, Wedia has not only set up a neural network, but also interfaces to equip AI learning. In other words, throughout the content lifecycle, administrators will be able to simply validate (or reject) the detections made by AI and thus teach it to assimilate new objects, new scenes and ultimately new products.

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Do you already imagine other uses of AI?

There is no shortage of ideas. It should be noted, for example, that DECATHLON no longer uses models in its photos; in most cases, it is our employees who are being portrayed. An approach that is consistent with our strategy as collaborator-ambassadors. But, of course, if these employees leave the company, it must be possible to delete the images that include them. AI, based on the portraits of employees, could help to manage this type of legal constraint.

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In the short term, what are the most important projects?

We will work on the performance of the images. How do images impact sales? How many images are needed to convert a sale? What are the most efficient types of images? This involves correlating the data between the analytics of our e-commerce platform and those of the DAM. As for the AI POC, this project should give rise to a great deal of co-creation work between the Wedia and DECATHLON teams.

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Key Takeaways

API-First Integration: Synchronizing PIM and DAM via APIs reduces manual metadata tasks and drives 90% higher efficiency.

AI Contextual Awareness: Modern AI can summarize complex lifestyle scenes, significantly improving asset findability beyond basic keyword tagging.

Governance Automation: Facial recognition AI streamlines rights management by automatically identifying assets linked to specific individuals for legal compliance.

Data-Driven Creative: Correlating DAM analytics with e-commerce sales data reveals which visual formats maximize conversion and business ROI.

Frequently Asked Questions

Q: How does Digital Asset Management help large-scale global brands?
A: Digital Asset Management provides a centralized hub that ensures brand consistency across multiple regions. It streamlines the distribution of assets to 20,000+ users while automating technical variations for different channels.

Q: Can AI identify specific products in a Digital Asset Management system?
A: AI excels at summarizing scenes and identifying product categories, such as a mountain bike in a mountain setting. While specific model identification is evolving, AI-driven descriptions greatly improve the searchability of contextual imagery.

Q: Why is PIM and DAM synchronization important for e-commerce?
A: Synchronization ensures that product data and media are always aligned. When a product code is entered, the DAM can automatically pull 15+ metadata fields from the PIM, reducing manual errors and accelerating time-to-market.

Q: How does AI improve internal search within a multinational organization?
A: AI improves search by understanding intent and associations. By analyzing user requests and optimizing autosuggestions, it reduces unsuccessful searches and lowers support tickets significantly.

Q: What are the benefits of using employees as brand ambassadors in photography?
A: Using employees fosters authenticity and brand loyalty. However, it requires robust Digital Asset Management with AI tools to track facial data and manage usage rights if an employee leaves the organization.

Q: How can a global brand measure the ROI of its digital assets?
A: ROI is measured by correlating DAM engagement data with e-commerce conversion metrics. This allows organizations to see which images drive sales and optimize their creative budgets accordingly.

Q: What is the benefit of a "Single Source of Truth" for marketing assets?
A: An SSOT prevents content duplication and ensures all teams use the latest approved versions. This reduces asset recreation costs and protects the organization from using non-compliant or outdated media.

Q: How do APIs enhance team productivity in a Digital Asset Management environment?
A: APIs automate the transfer of data between systems, eliminating the need for manual uploads and tagging. This allows marketing teams to focus on strategy rather than tedious administrative tasks.

Bottom Line

Global brand DECATHLON demonstrates that scaling asset management to two million files requires a strategic mix of AI and deep systems integration. By moving from manual tagging to an automated, API-driven workflow, multinational organizations can drastically reduce operational friction and maximize the conversion value of their visual content.

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