Characterizing and Forecasting User Engagement with In-App Action Graphs: A Case Study of Snapchat
Yozen Liu, Xiaolin Shi, Lucas Pierce and Xiang Ren
While mobile social apps have become increasingly important in people’s daily life, we have limited understanding on what motivates users to engage with these apps. In this paper, we answer the question whether users’ in-app activity patterns help inform their future app engagement (e.g., active days in a future time window)? Previous studies on predicting user app engagement mainly focus on various macroscopic features (e.g., time-series of activity frequency), while ignoring fine-grained inter-dependencies between different in-app actions at the microscopic level. Here we propose to formalize individual user’s in-app action transition patterns as a temporally evolving action graph, and analyze its characteristics in terms of informing future user engagement. Our analysis suggested that action graphs are able to characterize user behavior patterns and inform future engagement. We derive a number of high-order graph features to capture in-app usage patterns and construct interpretable models for predicting trends of engagement changes and active rates. To further enhance predictive power, we design an end-to-end, multi-channel neural model to encode both temporal action graphs, activity sequences, and other macroscopic features. Experiments on predicting user engagement for 150k Snapchat new users over a 28-day period demonstrate the effectiveness of the proposed prediction models. The analysis and prediction framework is also deployed at Snapchat to deliver real world business insights. Our proposed framework is also general and can be applied to any online platform.
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