Artificial Intelligence-Powered Customer Experience and Consumer Retention: The Role of Personalization

Abstract

While the widespread integration of Artificial Intelligence into the world of Customer Experience Management has opened up new possibilities for personalising the experience on a scale never before seen, the ways in which such experiences can impact customer retention through personalisation have yet to be thoroughly explored—especially when it comes to new market contexts. The study was conducted using an AI-Powered Customer Experience (AIPCE) for online retail, streaming platforms, and banking apps, as well as customer personalization (CP) in the service area, within the context of the Personalization Theory and the Customer Relationship Management (CRM) Theory, and tried to determine the influence of AI-Powered Customer Experience (AIPCE) on the relationship between Customer personalization (CP) and Consumer Retention (CR). A quantitative causal research design was used and primary data gathered with a stratified sampling technique, ensuring proportional sampling across three sectors, 250 respondents. An instrument based on a structured questionnaire, with validated scales was used which included AIPCE (20 items), Personalization (14 items) and CR (10 items). The data was analyzed by SPSS and AMOS software version 28.0 and 26.0 respectively. Demographic analysis, descriptive statistics, reliability and validity testing, CFA and SEM were performed. The model was found to be satisfactory for being fit (CFI = 0.954, TLI = 0.947, RMSEA = 0.051, SRMR = 0.048) and was sufficiently valid for constructs (Tot = Good (CFI > 0.900 and TLI > 0.900, RMSEA < 0.050 and SRMR < 0.050)). SEM results demonstrated significant direct effects of AIPCE on Personalization (beta = 0.641, p < 0.001) and CR (beta = 0.298, p < 0.001), and of Personalization on CR (beta = 0.421, p < 0.001). There was partial mediation in the relationship between AIPCE and CR with the mediation model obtaining for personalization (indirect effect = 0.270, 95% CI: 0.189-0.351, p < 0.001) with 47.5% of the total effect. The model accounted for 62.3% of the variance in Consumer Retention. The AIPCE-CR direct relation was stronger in the banking sector, and the perceptions of the Personalization were higher among the users of streaming platforms. Findings contribute to the Personalization Theory for AI-service environments, and provide realistic insights to guide the CRM strategies in digitally-oriented service industries.