The article presents the development results of a module for the retail network mobile applicationmodernization. A feature of the presented mobile application module is the display of personalizedmessages with advertising and promotions of the retail network. A mathematical modelof machine learning is used to collect and analyze data in a mobile application. The process ofchoosing a mathematical model, the operation algorithm and the model training stages on trainingdata are described in detail. The quality of the classifier's work was evaluated on a test and trainingsample. Test sample objects classification and the real value of the class comparison with the resultingclassification were performed. The authors in the article presented the main stages of the algorithmsdevelopment for processing statistical data from customer receipts. The program codes for thereceipt analysis module implementation and display the mobile application personalized advertisingare presented. To implement the database as a tool, the authors used the relational data managementsystem MS SQL Server. The modules of the mobile application are developed in the Android Studioenvironment for the Android operating system family. The authors presented the algorithm mainstages and testing the implemented modules operability in the paper. Based on the data on purchasesmade by the buyer, information about preferred products is collected based on the fixation of productgroups and product items from the receipt. The loyalty card of the retail network is linked to the mobileapplication, and receipts for purchases are linked to loyalty cards, in turn. Previously, the applicationdisplayed ads for all products participating in promotions. The actual task is to display personalizedadvertising, which has proven its effectiveness. The mobile application is distributed forfree through the Play Market and is designed for smartphones running the Android OS line.The purpose of the development is to display in the application on the buyer's device first advertisingfrequently purchased goods, and then the rest of the promotional goods. The mobile application haspassed load testing in real use by customers conditions of the retail network.