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Personalized Mobile Targeting with User Engagement Stages: Combining a Structural Hidden Markov Model and Field Experiment

Author

Listed:
  • Yingjie Zhang

    (Naveen Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080)

  • Beibei Li

    (Heinz College, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

  • Xueming Luo

    (Fox School of Business, Temple University, Philadelphia, Pennsylvania 19122)

  • Xiaoyi Wang

    (School of Management, Zhejiang University, 310058 Hangzhou, China)

Abstract

Low engagement rates and high attrition rates have been formidable challenges to mobile apps and their long-term success, especially for those whose revenues derive mainly from in-app purchases. To date, little is known about how companies can scientifically detect user engagement stages and optimize corresponding personalized-targeting promotion strategies to improve business revenues. This paper proposes a new structural forward-looking hidden Markov model (FHMM) combined with a randomized field experiment on app notification promotions. Our model can recover consumer latent engagement stages by accounting for both the time-varying nature of users’ engagement and their forward-looking consumption behavior. Although app users in most of the engagement stages are likely to become less dynamically engaged, this slippery slope of user engagement can be alleviated by randomized treatments of app promotions. The structural estimates from the FHMM with the field-experimental data also enable us to identify heterogeneity in the treatment effects, specifically in terms of the causal impact of app promotions on continuous app consumption behavior across different hidden engagement stages. Additionally, we simulate and optimize the revenues of different personalized-targeting promotion strategies with the structural estimates. Personalized dynamic engagement-based targeting based on the FHMM can, compared with nonpersonalized mass promotion, generate 101.84% more revenue for the price promotion and 72.46% more revenue for the free-content promotion. It also can generate substantially higher revenues than the experience-based targeting strategy applied by current industry practices and targeting strategies based on alternative customer segmentation models such as k -means or the myopic hidden Markov model. Overall, the novel feature of our paper is its proposal of a new personalized-targeting approach combining the FHMM with a field experiment to tackle the challenge of low engagement with mobile apps.

Suggested Citation

  • Yingjie Zhang & Beibei Li & Xueming Luo & Xiaoyi Wang, 2019. "Personalized Mobile Targeting with User Engagement Stages: Combining a Structural Hidden Markov Model and Field Experiment," Information Systems Research, INFORMS, vol. 30(3), pages 787-804, September.
    • Handle: RePEc:inm:orisre:v:30:y:2019:i:3:p:787-804
    DOI: 10.1287/isre.2018.0831
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    3. Ni Huang & Probal Mojumder & Tianshu Sun & Jinchi Lv & Joseph M. Golden, 2021. "Not Registered? Please Sign Up First: A Randomized Field Experiment on the Ex Ante Registration Request," Information Systems Research, INFORMS, vol. 32(3), pages 914-931, September.
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    5. Shaohui Wu & Yong Tan & Yubo Chen & Yitian (Sky) Liang, 2022. "How Is Mobile User Behavior Different? A Hidden Markov Model of Cross-Mobile Application Usage Dynamics," Information Systems Research, INFORMS, vol. 33(3), pages 1002-1022, September.
    6. Harang Ju & Michael Zhao & Sinan Aral, 2025. "Complementarity Between Paid and Organic Installs in Mobile App Advertising," Papers 2504.16151, arXiv.org.
    7. Qiang Wei & Yao Mu & Xunhua Guo & Weijie Jiang & Guoqing Chen, 2024. "Dynamic Bayesian Network–Based Product Recommendation Considering Consumers’ Multistage Shopping Journeys: A Marketing Funnel Perspective," Information Systems Research, INFORMS, vol. 35(3), pages 1382-1402, September.
    8. Yan Leng & Xiaowen Dong & Esteban Moro & Alex Pentland, 2024. "Long-Range Social Influence in Phone Communication Networks on Offline Adoption Decisions," Information Systems Research, INFORMS, vol. 35(1), pages 318-338, March.
    9. Wan, Qin & Yang, Shilei & Shi, Victor & Qiu, Martin, 2021. "Optimal strategies of mobile targeting promotion under competition," International Journal of Production Economics, Elsevier, vol. 237(C).
    10. Xiang (Shawn) Wan & Anuj Kumar & Xitong Li, 2024. "Retargeted vs. Generic Product Recommendations: When is it Valuable to Present Retargeted Recommendations?," Information Systems Research, INFORMS, vol. 35(3), pages 1403-1421, September.
    11. Reiter, Alan & Yildiz, Hélène & Mouline, Jean-Pierre & Vrontis, Demetris & Al-Kwifi, Osama S., 2025. "Convergence of reality and Virtuality: How virtual realism transforms player experience and behavior in virtual reality massively multiplayer Online games," Journal of Business Research, Elsevier, vol. 199(C).
    12. Ryo Kato & Takahiro Hoshino & Daisuke Moriwaki & Shintaro Okazaki, 2022. "Mobile Targeting: Exploring the Role of Area Familiarity, Store Knowledge, and Promotional Incentives," Discussion Paper Series DP2022-10, Research Institute for Economics & Business Administration, Kobe University.
    13. Ni Huang & Gordon Burtch & Yumei He & Yili Hong, 2022. "Managing Congestion in a Matching Market via Demand Information Disclosure," Information Systems Research, INFORMS, vol. 33(4), pages 1196-1220, December.

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