Online Recommender System for Cross-Selling

Data-based product recommendations enable a consistent and individual customer approach at all touchpoints.


75% of the recommendations are rated as helpful by the sales experts

One algorithm for recommendations at multiple customer touchpoints

Enabling the further development of the recommendation system through know-how transfer


A B2B mail order company wants to increase its turnover through better cross-selling offers. Customers are to be addressed uniformly and individually both through classic sales and in the growing online area.


A recommendation algorithm is developed based on transaction data. The quality of the recommendations is continuously validated by sales experts during the development. Together with the client's IT department, a concept is developed for recording user activities in the web shop in order to enrich the algorithm with this data.


Customer-specific product recommendations are provided uniformly in the sales system and in the web shop. An automated feedback loop from the sales department enables the continuous further development of the recommendations.

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An automotive company would like to visualise various market-specific data in order to create a Competitive analysis for the US market.


There will be a interactive and Flexible application, including of different maps with two different views implemented.


Relevant markets are identifies, analyses and visualises. The dealer or the respective sales department have the possibility to compare the direct competition with their own product and to visualise the relevant data.

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