Abstract
In order to increase sales, companies try their best to develop relevant offers that anticipate customer needs. One way to achieve this is by leveraging artificial intelligence algorithms that process data collected based on customer transactions, extract insights and patterns from them, and then present them in a user-friendly way to human or artificial intelligence decision makers. This study is based on a hybrid approach, it starts with an online marketplace dataset that contains many customers’ purchases and ends up with global personalized offers based on three different datasets. The first one, generated by a recommendation system, identifies for each customer a list of products they are most likely to buy. The second is generated with an Apriori algorithm. Apriori is used as an associate rule mining technique to identify and map frequent patterns based on support, confidence, and lift factors, and also to pull important rules between products. The third and last one describes, for each customer, their purchase probability in the next few weeks, based on the BG/NBD model and the average of transactions using the Gamma-Gamma model, as well as the satisfaction based on the CLV and RFMTS models. By combining all three datasets, specific and targeted promotion strategies can be developed. Thus, the company is able to anticipate customer needs and generate the most appropriate offers for them while respecting their budget, with minimum operational costs and a high probability of purchase transformation.