Personalized Recommendations

< Back to portfolio overview


Price comparison websites provide comparable information of financial products and are widely used by customers. However, it seems that visitors are overwhelmed by the amount of information presented with the financial recommendations, which makes it difficult for them to choose the right product. Numerous researches have been conducted on the operationalization of personalization based on the customerís preferences, however, the advantages of using customerís values and needs based on behavioral data was still unknown. Therefore, this study identified which behavioral data of customers is important when creating personalized financial recommendations. It was discovered that the consumption values and goals of the customer are important behavioral data to provide personalized financial recommendations.

A recommendation algorithm was developed with the dataset from the financial comparison website The prototype makes it easier for customers to find the most suitable financial product based on their values and goals. With personalization it creates personalized financial recommendations whereby the customer can compare the different recommended products. The personalized elements of the financial recommendations, such as labels and persuasive information, allow the customer to make an easier choice.

Another implication is the ethical usage of the customer data. The results of the user tests showed that participants have gained more trust in the prototypeís use of their data. The prototype explains what is done with the customer's data and that customers have full control over their own data. This means that customers have the option to retrieve their data from the database if they do not want their data to be stored.