Start Date
11-12-2016 12:00 AM
Description
Low engagement rate and high attrition rate have been major challenges for the success of mobile apps, especially for apps whose revenues mainly come from the in-app purchases. To date, little is known towards how companies can improve user engagement and business revenues through designing effective in-app pricing strategies. In this paper, we propose a structural econometric framework to model consumer latent engagement by accounting for both the time-varying nature of engagement and consumer forward-looking behavior. We instantiate our study by analyzing fine-grained mobile tapstream data on 2,143 consumers’ continuous content consumption behaviors in a popular mobile reading app in 2015. Our final results enable us to tailor optimal pricing strategy to each consumer based on the model detected engagement status. Interestingly, we found such engagement-specific pricing strategy leads to lower average price for consumers and higher overall business revenues, suggesting potential welfare improvement in the mobile app market.
Recommended Citation
Zhang, Yingjie; Li, Beibei; Luo, Xueming; and Wang, Xiaoyi, "Modeling User Engagement in Mobile Content Consumption with Tapstream Data" (2016). ICIS 2016 Proceedings. 13.
https://aisel.aisnet.org/icis2016/EBusiness/Presentations/13
Modeling User Engagement in Mobile Content Consumption with Tapstream Data
Low engagement rate and high attrition rate have been major challenges for the success of mobile apps, especially for apps whose revenues mainly come from the in-app purchases. To date, little is known towards how companies can improve user engagement and business revenues through designing effective in-app pricing strategies. In this paper, we propose a structural econometric framework to model consumer latent engagement by accounting for both the time-varying nature of engagement and consumer forward-looking behavior. We instantiate our study by analyzing fine-grained mobile tapstream data on 2,143 consumers’ continuous content consumption behaviors in a popular mobile reading app in 2015. Our final results enable us to tailor optimal pricing strategy to each consumer based on the model detected engagement status. Interestingly, we found such engagement-specific pricing strategy leads to lower average price for consumers and higher overall business revenues, suggesting potential welfare improvement in the mobile app market.