Abstract
Over the past two decades, digital platforms have become integral to meeting the diverse needs of consumers. Given the wide choice of platforms, organizations employ various mechanisms to retain them. These include personalized recommendations, alerts about offers and rewards, reminders to rate items, subscribing for premium services, etc. However, excessive notifications could overwhelm users, resulting in dissatisfaction and loss of user base. To address this problem, we introduce NudgeX, a framework for intelligent nudges to balance user engagement and user experience. NudgeX, which is in its beta stage, orchestrates and optimizes the timing and type of nudges across three key user touchpoints: onboarding, performing interactions, and feedback provision. The nudges are initiated based on triggers, defined by user actions, predefined scenarios, or periods of inactivity. The users are segmented as blue (new or recently signed up users), red (inactive or less engaged users), or green (active users) based on their level of engagement. NudgeX is novel in its approach to integrate various machine learning models powered by decision tree classifier, singular value decomposition, random forest classifier, linear SVC, and cosine similarity to provide personalized nudges, display recommendations, and predict user item ratings. The framework also provides explainability to provide the rationale behind the selection and timing of the nudges delivered. The models are evaluated based on accuracy and F1 score. In a simulated environment with 300 users and 2000 transactions, NudgeX performed with accuracy ranging from 0.92 to 0.99 across scenarios.
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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Subramanian, G., Agarwal, R. (2024). Smart Nudge Framework for Digital Platforms. In: Uddin, M.S., Bansal, J.C. (eds) Proceedings of International Joint Conference on Advances in Computational Intelligence. IJCACI 2022. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-97-0180-3_50
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DOI: https://doi.org/10.1007/978-981-97-0180-3_50
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