Hugo Boss Logo

€ 4.9 BN




150 Days

Project Timeline

Worldwide rollout


Hugo Boss Logo

Hugo Boss

Creating a revolutionary shopping experience using AI.

Hugo Boss is a German public traded company with a market cap of 4.9 billion EUR. It employs 14.900 people and runs two separate brands: HUGO and BOSS.

Project Mission

Creation of an unprecedented online shopping experience to increase brand awareness of the HUGO brand among the target group.


90% of HUGO customers are not aware that HUGO is a sub-brand of HUGO BOSS.

The annual sales generated with the HUGO sub-brand amount to more than € 300M. Interestingly, however, customers are not aware that they have purchased the HUGO brand. For them, this is simply BOSS. BOSS, however, is also a sub-brand of HUGO BOSS and has a distinctly different style and target group.

The overarching project challenge was therefore to sensitize the target group to the HUGO brand. In addition, there were significantly more data-driven, economic goals such as reducing the return rate or increasing the conversion from shopping cart to payment.

Within HUGO BOSS, this project was regarded as a beacon project of digital transformation. Under the observation of various stakeholders, it was all the more important that words were followed by deeds.


Build a cutting-edge recommendation engine for the fashion industry.

The idea, as a result of the Blueprint Workshop, was to create a previously unseen user experience in the fashion industry by giving highly individual recommendations that adapt in real time to the user behaviour of the customers.

Recommendation Engines exist since the beginning of the internet. Amazon is recommending books to cross-sell and up-sell products since years. Netflix build a very sophisticated recommendation engine to recommend videos to their customers.

Because of cloud technology like AWS SageMaker it is now possible to build fully custom recommendation systems for customers at a cheaper cost. Moreover image recognition models and systems developed tremendously in the last years and are available as a service (Tensorflow, AWS). Because of Moore’s law it’s now possible to build a recommendation engine for the fashion industry and make it the backbone of a global lifestyle brand.

In the digital world, fashion is about shopping products online. Having a digital channel to shop or inform about products, isn’t really a premium brand experience. The value proposition for the Hugo digital platform is a customized and personalized experience, tailored to the target audience. Similar to AirBnB, which recommends the right place for customers all over the world or Netflix, which recommends the right movie to the customer’s personal need, the Hugo Digital platform should be a digital twin of the customer to support an entire different brand experience.

Today it’s possible for a few selected customers (top 1% customers of BOSS) to receive a 100% personalized, customized experience by the top sales managers. Technology allows us to provide a similiar experience to the masses.


Users only get to see what the algorithm feels is most appropriate.

Hugo Boss App

In order to better understand the target group behaviour when making purchasing decisions, and in particular to find out which direct factors can have a decisive influence on this purchasing behaviour, we first spoke with the top retail sellers.

In the process we have gained important insights, such as that the weather, the occasion or the location are decisive when what is bought. The sellers gave us a detailed description of the characteristics of their customers. These characteristics then served as the basis for the recommendation algorithm in the online shop.

The new website was a dynamic product. What the user sees changes at any time due to his behaviour or external circumstances. In addition, the recommendations were influenced by the customer's usage behavior on Instagram. By logging in with Instagram on the new HUGO website, the algorithm took into account all activities such as likes, follows, etc. in order to present the optimal product selection.

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