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

Abstract

Low engagement rates and high attrition rates have been formidable challenges to mobile apps and their long-term success. 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 as 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. The structural estimates from the FHMM with the field-experimental data enable us to identify heterogeneity in the treatment effects. 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 generate substantially higher revenues than the experience-based targeting strategy applied by current industry practices and targeting strategies based on alternative customer segmentation models. 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.

One app to trace them all? Examining app specifications for mass acceptance of contact-tracing apps

“…Rutz et al, 2019) points to considerable heterogeneity across apps. While consumer apps or other health apps can be optimised to fit the needs of a particular target group (Zhang et al, 2019), there can only be one set of specifications for tracing apps. That set must then succeed in convincing a majority of the populationpotentially even critics -to install and use the app (Morley et al, 2020).…”

“…Studies on the drivers of mobile app acceptance in general (Ghose & Han, 2014) and health apps in particular (Briz-Ponce & García-Peñalvo, 2015; Kim & Park, 2012) have highlighted the importance of identifying mobile app specifications that best meet the requirements of the target group (Zhang et al, 2019). However, the situation is more complex for the case of tracing apps, which must reach the majority of a country's population to work effectively.…”

“…Our finding that benefit appeals, privacy design, and convenience design have varying impacts across different propensities for acceptance implies that focusing on aggregate effects of antecedents, which has proven valuable for explaining acceptance in other contexts (Ghose & Han, 2014;Venkatesh et al, 2003), can be misleading in this one. As the context of mass acceptance does not allow for flexibility or configuring the app for different target groups (Zhang et al, 2019), identifying the needs of citizens -particularly those sceptical of the apprequires a move beyond aggregate impacts. Thus, we reveal that mass acceptance is a nuanced and complex phenomenon where one size does not fit all.…”

Governance and Design of Digital Platforms: A Review and Future Research Directions on a Meta-Organization

“…Enacting value-creating activities may require continued inducements, and that tends to take the form of promotional rewards (Li & Agarwal, 2017). By implementing platform designs such as recommendation, certification, and featuring (Dinerstein, Einav, Levin, & Sundaresan, 2018;He, Fang, Liu, & Li, 2019;Rietveld et al, 2019;Rietveld, Seamans, & Meggiorin, 2021;Sun, Fan, & Tan, 2020), platform owners can help selected complementors enhance their reputation, draw customers' attention, drive website traffic, and improve sales growth (Chen, Wei, & Zhu, 2018;Horton, 2019;Huang, Singh, & Srinivasan, 2014;Zhang, Li, Luo, & Wang, 2019b). In their study of mobile apps, Liang, Shi, and Raghu (2019) first show that offering editor recommendations has a positive influence on the sales of featured products.…”

Content Spending and Network Quality in Mobile Channels: A Hidden Markov Model of User Engagement and Content Consumption

“…Wu et al [39] develop a framework to capture the underlying mechanism of mobile app usage. Zhang et al [40] identify user latent engagement states and proposed a personalized targeting strategy based on engagement states. While Huang et al [38] explore users' motivational factors, Wu et al [39] examine contextual factors, and Zhang et al [40] focus on the mobile app's pricing strategies, our study investigates an operational factor, i.e., network delay, in addition to a financial factor, users' prior spending.…”