Building a hyper-personalized maturity model: A strategic path for businesses in the data ERA

Nguyen Van Thuy () and Chu Thi Hong Hai ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 8, 268-278

Abstract: In the era of big data and artificial intelligence, businesses face increasing demands for personalized customer experiences. This study introduces the Hyper-Personalization Maturity Model (HPMM), a strategic framework designed to assess and enhance an organization's personalization capabilities. The model is structured around three foundational pillars—data, technology, and business strategy—collectively supporting the evolution of hyper-personalization efforts. It delineates five progressive maturity levels: Basic, Standardized, Integrated, Automated, and Optimized, each characterized by specific criteria that guide businesses in evaluating their current status and identifying areas for improvement. The development of HPMM followed a mixed-methods approach, integrating insights from a comprehensive literature review and expert interviews. An empirical assessment was conducted on 50 businesses operating in Vietnam's trade and service sectors to validate the model. The findings reveal that 50% of the surveyed businesses remain at the Basic level, relying primarily on demographic data to deliver uniform customer experiences. In contrast, higher maturity-level businesses leverage multi-channel data integration and AI-driven decision-making to achieve more sophisticated personalization. The study underscores the critical role of data-driven strategies and advanced technologies in enhancing customer engagement and competitive advantage.

Keywords: Artificial intelligence; Competitive advantage; Customer experience; Data analytics; Hyper-personalization; Maturity model. (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://learning-gate.com/index.php/2576-8484/article/view/9281/3058 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ajp:edwast:v:9:y:2025:i:8:p:268-278:id:9281

Access Statistics for this article

More articles in Edelweiss Applied Science and Technology from Learning Gate
Bibliographic data for series maintained by Melissa Fernandes ().