Model of a module for dynamic generation of personalized offers of additional services for airline passengers

Abstract

Aim. The presented study aims to develop an optimal model of a software module for generating personalized offers.Tasks. The authors investigate the functional requirements of a module for dynamic generation of personalized offers; analyze the necessary integrations (inputs and outputs); choose the optimal architecture for the module based on the tasks assigned to it and best practices; develop a model of a module for the specified types of generated offers.Methods. This study uses general scientific methods (analysis, synthesis, monographic method, grouping). Best practices of building recommendation systems designed to work with big data are investigated, and the most promising approaches in terms of further development and potential for integration are highlighted.Results. The functional requirements of a module for dynamic generation of personalized offers and the tasks performed by it are formulated. Best practices of building recommendation systems designed to work with big data are analyzed. The most promising technological configuration of a module for dynamic generation of personalized offers is determined. A model of a module for generating personal offers is developed.Conclusions. The capabilities of processing large amounts of data make it possible to significantly increase the effectiveness of marketing tools, particularly by training software to automatically generate advertising offers of additional services, choosing the optimal delivery channel, type, class of service, and stage of the customer lifecycle. A module for generating personalized offers can work only as part of software that performs several advanced functions (such as collecting, storing and processing customer data, clustering customers, collecting feedback on customer actions, training based on user actions, and many others). Therefore, the architecture of a dynamic offer generation module should be designed for effective integration with software and work with large amounts of data generated by the airline. During the study, a model of a module for generating personalized offers satisfying the requirements described above is developed.