Algorithmic Segmentation of the Electorate: New Clustering Practices in Political Consulting

The subject of the research is algorithmic segmentation of the electorate as a new paradigm of political and technological analysis in the context of the digitalization of political communication. The article examines the transformational processes occurring in the field of political consulting under the influence of big data, machine learning, and digital platforms, as well as their impact on the methods of forming electoral clusters, personalizing political messages, and mobilization strategies. Special attention is given to the transition from traditional sociodemographic models of segmentation to behavioral, psychographic, and dynamic models based on the processing of users' digital footprints. The analysis focuses on the changing logic of political influence – from static typologies of voters to algorithmically updated and adaptive segments formed based on streaming data. The study also covers the institutional and professional consequences of the algorithmization of political consulting, including the transformation of the consultant's role and changes in the structure of strategic decision-making in electoral campaigns. The paper employs a comprehensive interdisciplinary approach, including comparative political analysis, elements of institutional analysis, and a critical review of contemporary academic literature on digital communications, data-driven campaigning, and algorithmic governance. The scientific novelty of the research lies in conceptualizing algorithmic segmentation of the electorate not only as a technological tool of digital marketing but as an independent political and technological paradigm that alters the structure of electoral interaction and redistributes influence in public policy. The article demonstrates that algorithmic clustering establishes a new logic of political influence based on the dynamic modeling of behavioral patterns and predictive analytics, leading to the emergence of "floating" segments of the electorate and personalized communication strategies. It concludes that the effectiveness of algorithmic segmentation is accompanied by an increase in political and ethical risks, including heightened cognitive fragmentation, dependence of campaigns on technological infrastructure, and issues of algorithmic opacity. The necessity for institutionalization of professional standards and the development of mechanisms for algorithmic accountability in political consulting is justified as a condition for preserving the resilience of democratic processes in the digital age.