Albert Heijn, the largest supermarket chain of the Netherlands, has a loyalty program, called the Bonus Card, allowing us to tie all purchased products to a customer whenever they scan the card, either in the store or online. This creates a huge potential for personalisation, which had previously not been utilised within product search. We will present about our journey going from popularity-based search for the broader customer base, to a tailored search experience for all of our unique customers using the information we gather from the Bonus Card. Specifically, we will focus on Learning-to-Rank (LTR). This transition was definitely not without it's challenges, on which we would love to share our experience: * Handling large quantities of data. Going from aggregated popularity to single user relevancy meant a million-fold increase in the quantities of data that we were handling. * Handling large amounts of point-in-time accurate features in offline feature stores. * Using distributed computing to train a model on this large quantity of data. * Redefining the concept of relevancy. How can we incorporate profitability? * Handling position bias in our data. * Using Kafka to facilitate the quick transfer of offline features to online features during inference. This presentation is relevant for anyone who is struggling to go from legacy popularity-based search to personalised search for big customer bases. You will learn how to face the challenges of moving to large quantities of personalized data, distributing a model to learn on this exploded quantity of data, and redefining the concept of relevancy.