Quantifying the Effectiveness of Push Notifications on User Conversion and Retention in Digital Communities Using Time-Series Analysis and A/B Testing

Preetham Reddy Kaukuntla

doi:10.36948/ijfmr.2023.v05i06.38183

Quantifying the Effectiveness of Push Notifications on User Conversion and Retention in Digital Communities Using Time-Series Analysis and A/B Testing

Author(s)	Preetham Reddy Kaukuntla
Country	United States
Abstract	In this study, we consider the impact of push notifications on user conversion and retention within digital communities using time-series analysis and A/B testing. Our research explores how personalization, timing, and frequency of notifications affect user behavior. We compare personalized and generic notifications through A/B testing and track long-term engagement trends through time-series analysis. Results The outcome of personalized notifications is to boost conversion rates. The optimal timing of a notification, while users are engaged, matters most. Over-notification increases the possibility of notification fatigue, hence lower retention rates. In addition, lag effect research has shown that the effect of push notification on retention grows with time. Based on the recommendations of this study, an optimal mix of personalization, timing, and frequency needs to be balanced for maximal effectiveness. The limitations include concentration on a particular user base. Further research is required across other diverse platforms. Future studies could investigate multi-channel strategies and AI-driven personalization.
Keywords	Push Notifications, User Conversion, User Retention, Digital Communities, Time-Series Analysis, A/B Testing, Personalization, Notification Timing.
Field	Engineering
Published In	Volume 5, Issue 6, November-December 2023
Published On	2023-11-06
DOI	https://doi.org/10.36948/ijfmr.2023.v05i06.38183
Short DOI	https://doi.org/g86xrv

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Quantifying the Effectiveness of Push Notifications on User Conversion and Retention in Digital Communities Using Time-Series Analysis and A/B Testing

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