Modeling User Redemption Behavior in Complex Incentive Digital Environment: An Empirical Study Using Large-Scale Transactional Data

Abstract

The digital economy implements complex incentive systems to retain users through point redemption. Understanding user behavior in such complex incentive structures presents a fundamental challenge, especially in estimating the value of these digital assets against traditional money. This study tackles this question by analyzing large-scale, real-world transaction data from a popular personal finance application that captures both monetary spending and point-based transactions. We find that point usage is linked to demographics. Our analysis using a natural experiment and a causal inference technique reveals that a large point grant stimulated an increase in point spending without a detectable effect on cash expenditure. We then find an association between consumers' shopping styles and their point redemption patterns. This study, on a massive real-world economic ecosystem, examines how consumers behave in multi-currency environments, with direct implications for modeling economic behavior and designing digital platforms.