UDC 339.138
DOI: 10.36871/u.i.k.2025.05.01.005
Authors
Eugene V. Mischenko,
Russian-Armenian University, Yerevan, Armenia
Kirill S. Timofeev,
Mad Lead Printer, Belgrade, Serbia
Yelena S. Kozubskaya,
Food Solutions KZ, Almaty, Kazakhstan
Garik R. Egikyan,
Email Deliverability в Aether Group, Yerevan, Armenia
Abstract
The article explores the features and prospects of applying artificial intelligence (AI) and machine learning (ML) technologies in cohort analysis of customer behavior. The significance of cohort analysis in marketing research is substantiated, as it allows for the systematic consideration of the time factor and the identification of consumer groups with similar behavioral characteristics. It is noted that in the context of digital transformation and the increasing availability of large datasets, the integration of AI and ML for conducting cohort analysis is becoming particularly relevant. This integration provides an opportunity to more accurately segment customers, automate data collection and processing, and conduct predictive analysis, while considering complex and often implicit factors that influence and characterize patterns and trends in consumer behavior. The necessity of using not only quantitative but also qualitative data, which can be obtained from social media, is emphasized. It is revealed that the application of text processing methods expands the ability to identify hidden patterns and trends in consumer behavior, which proves to be viable in enhancing the effectiveness of cohort analysis. Special attention is given to the role of AI technologies in improving decision-making speed and ensuring personalization in decision-making processes related to cohort analysis. The study concludes that the application of AI in cohort analysis enhances the efficiency of marketing analytics.
Keywords
cohort analysis, artificial intelligence, machine learning, customer behavior, marketing analytics, personalization, predictive analysis, digital transformation, big data, segmentation
