Customer Retention in AI Tool Subscription Services: A Marketing Analytics Study of User Reviews

DOI

10.2991/978-94-6239-777-4_50How to use a DOI?

Keywords

AI tool subscription services; customer retention; marketing analytics; user reviews; perceived value; customer satisfaction; generative AI

Abstract

AI tool subscription services have become increasingly important in digital markets, yet customer retention remains a major challenge for providers. This study examines retention-related themes in AI tool subscription services from a marketing analytics perspective by analysing user review data from generative AI applications. Using two publicly available Kaggle datasets, the study applies rating-based sentiment classification, keyword-assisted thematic coding and frequency analysis to identify customer experience themes associated with satisfaction, dissatisfaction and potential retention risk. The primary dataset contains 7,736 reviews of five generative AI applications, and a supplementary ChatGPT dataset containing over one million reviews is used to validate the main findings. The results show that satisfaction and perceived value are the most frequent themes in user reviews, while price fairness, technical issues and retention-risk signals are more strongly associated with negative reviews. Trust / accuracy concerns show a different pattern, appearing more often in neutral reviews while remaining visible in negative reviews. These findings suggest that retention-related expressions in AI tool reviews are associated not only with technical capability, but also with perceived usefulness, pricing transparency, service reliability, trust and customer experience. The study contributes to digital marketing and consumer behaviour research by demonstrating how user review analytics can be used to identify retention-related themes in AI-enabled subscription services.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zihang Su
PY  - 2026
DA  - 2026/09/30
TI  - Customer Retention in AI Tool Subscription Services: A Marketing Analytics Study of User Reviews
BT  - Proceedings of the 2026 8th International Conference on Economic Management and Cultural Industry (ICEMCI 2026)
PB  - Atlantis Press
SP  - 459
EP  - 470
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-777-4_50
DO  - 10.2991/978-94-6239-777-4_50
ID  - Su2026
ER  -