From simple retargeting on products consulted to the prediction of products suited to consumer tastes, personalized recommendations have given e-commerce a real boost! How does it work and why have they become essential for customer relations? In the form Finland Phone Number List of frames, pop-up windows or side menus at the end of the page, personalized recommendations offer content that may be of interest to Internet users surfing a news site, a social network or a marketplace. The formats and purposes of these recommendations vary depending on the site the user is on.

When personalized recommendations are put in place on your own website or on your distributor's website, this will allow you to optimize the effectiveness of your Finland Phone Number List website or your marketing campaigns , but also to better target your advertising investments . For example, if the Internet user surfs on a car comparison page and you offer him a recommendation to try your new scooter, not sure that it works. Personalized recommendations aren't just for the web. At the point of sale, interactive kiosks are very effective supports for offering additional products to customers and making additional sales. In supermarkets, the use of personalized coupons generated automatically at the time of checkout is a recent practice appreciated by customers. These coupons encourage consumers to return to the point of sale with an offer on a product they like.

These large-scale personalization tools are extremely complementary to the salespeople who remain the best authors of personalized recommendations! Without data collected, sorted, refined and analyzed, immediately forget about personalized recommendations! Indeed, to reach this level of customer relationship personalization, you will need to have Finland Phone Number List a well-established data strategy allowing you to know everything about your customers in order to associate this customer knowledge with your own objectives. You will therefore have to establish a relevant customer segmentation allowing you to define points of similarity between customer behavior (web browsing, frequency of purchases, type of products purchased, etc.) to find the specificities that drive a consumer (or a type of consumer) upon acquisition.