Recommender System

Task:
Too much product selection overwhelms the human brain and minimizes the likelihood of a possible purchase, in short: “Paradox of Choice”. How do you keep the user on your platform and increase the shopping cart value at the same time?

Approach:
With a recommender system, a methodology for recommendation contexts, we use big data and self-learning algorithms to develop product recommendations. The user thus gets virtual expert advice at his side.

Objective:
Focus on: reducing uncertain purchase decisions, generating additional and impulse purchases, and increasing the average shopping cart value.

Conclusion:
A recommender system helps the user in the selection phase to maintain an overview instead of getting lost in a multitude of similar offers. Optimal recommendations create structure and accelerate the purchase decision – resulting in higher customer satisfaction and return customers.

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Applied Frameworks