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Longread

Personalise your digital product offering with a Recommender System

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Finding a product that fully meets the personal needs of every consumer is difficult – not to mention misplaced targeting that haunts customers for weeks after an online search. This is not exactly pleasant for consumers, but also not good for webshops, which miss out on a close connection and bond with both existing and potential customers. This can be fixed by providing personalised recommendations, both for products that are very similar to the initial search and for additional and complementary products. The solution? A Recommender System.

TAILORED PRODUCT RECOMMENDATIONS

There is a solution that will help consumers easily find the product they are looking for. By leveraging big data and machine learning, we can recommend products that are fully tailored on a personal level and specifically adapted to the tastes and expectations of the customer. These product recommendations can be generated in two flavours both with a different purpose:

  • Predicting alternative products, personalised and adapted to the customer’s wishes (one-on-one communication, algorithmically and no business rules). This includes offering an equivalent but different product based on your search.
  • Predicting complementary products, also personalised and adapted to the customer’s wishes (one-on-one communication, algorithmically and no business rules). This includes recommending additional items that complement the initial product and are often sold, also known as Frequently Bought Togethers (FBTs).

Thanks to the real-time character, the Recommender System is indispensable for website and media personalisation. Download our longread to find out how to implement and use this tool.