Nowadays, banks are working on finding a suitable campaign for every customer profile. With this study, we aimed to develop a recommendation system that will direct the customer to the appropriate campaign. With the data received from a private bank, credit card transactions of the users were analyzed, and spending habits were modeled. We aimed to recommend the most suitable campaign to the users through the created models. Within the scope of the study, 662.088 credit card transactions performed by 4997 customers within three months were analyzed, and three campaigns were proposed for each customer as a result of the study. The ALS (Alternating Least Square) algorithm was used on Spark to establish the recommendation system. The primary purpose of the study is to increase customer satisfaction by finding unique users based on spending habits instead of campaigns that are applied collectively to customers by making a personalized campaign offer.