Automated user segmentation using RFM analysis in marketing strategies

The relevance of the study is determined by the need to enhance the effectiveness of marketing strategies through automated and customizable customer segmentation. This work proposes a universal customer data management system based on RFM segmentation with the ability to configure flexible logic, as well as the capability to integrate with various external systems. Traditional CRM systems and manual RFM segmentation methods are limited in functionality and do not always meet the business needs for flexibility and integration with various data sources. The study identifies the shortcomings of traditional CRM systems and suggests points for improvement in the described system. Additionally, an experiment was conducted comparing the RFM segments generated using the proposed architecture with Yandex's auto-strategies in the Yandex.Direct advertising platform. The application of the system showed significant advantages over auto-strategies, including a 30.71% increase in purchases in the case of a clothing store. The results confirm the practical value of the system for optimizing marketing campaigns and improving conversion. The results are of practical importance for companies in need of customized solutions and integrations. Further development is proposed, focusing on improving the RFM segmentation method by implementing machine learning algorithms and exploring additional effective channels for utilizing the generated segments.

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