Towards Predicting Hexad User Types from Mobile Banking Data: An Expert Consensus Study

Automating the profiling of Hexad user types through mobile banking data offers a scalable alternative to traditional, survey-based methodologies for personalizing gamified financial systems. By mapping specific transaction categories and app interaction patterns to established user motivations, financial institutions can potentially gain deeper insights into customer behavior without relying on explicit user feedback. This approach shifts the focus toward behavioral analytics, enabling a more continuous and unobtrusive method for tailoring digital experiences to individual preferences.

Expert consensus among professionals in gamification and banking confirms that five of the six Hexad user types exhibit moderate to strong correlations with financial data. These findings suggest that the way individuals manage their finances—ranging from spending habits to engagement with banking features—serves as a reliable proxy for their underlying psychological motivations. Such predictive modeling could significantly enhance the efficacy of gamified features, such as savings challenges or loyalty programs, by aligning them more closely with the specific needs of different user segments.

While these exploratory results establish a promising foundation for behavioral profiling, the practical application of this framework requires further validation. Future research must prioritize longitudinal experiments to confirm the predictive accuracy of these identified relationships in real-world environments. By integrating established statistical methodologies with robust data analysis, the industry can move toward a more sophisticated, data-driven approach to gamification that enhances user engagement while maintaining the integrity of financial interactions.