Impacts of real-time usage monitoring on mobile plan choices: Modeling structural heterogeneity by Bayesian analysis

Abstract

With emergence of Smartphone era, consumers are now capable of optimizing their consumption by re-ceiving feedback or alarm services in various ways. Especially, technological advances of data service make real-time usage tracking possible for individual’s mobile phone and accordingly, they can change their price plan or usage levels in real-time. However, limited attention has been given to a real-time choice model. So far previous researches mainly emphasizes Lagged-Time Choices; consumers choose the plan that is conceived to fit most their expected consumption (ex ante) and then make decision for actual service consumption during the billing period (ex post). In order to model the changed consumer choice behaviors, we jointly combine two decision rules, Real-Time and Lagged-Time Choices, into a proposed model that incorporates structural heterogeneity. The result of empirical study clearly shows that the model fit of the proposed model prevails over those of one-sided models, which gives a better account for consumer heterogeneity. Some key findings are that ambidextrous consumers are primarily influenced by inertia and overusage of data service and rely more on lagged-time information of call service and real-time information of data service. In addition, heavy users generally prefer the Lagged-Time Choices, but when it comes to underage of call/data service, they become more sensitive to real-time information than light users. We also compare three different data sets from pre-Smartphone and Smartphone plan categories that represent the pervasiveness of Real-Time Choices. It is therefore useful for telecommunication companies to understand changes in consumer choice behaviors and develop diverse direct marketing strategies for future profitability.