How to Maximize Clicks for Display Advertisement in Digital Marketing? A Reinforcement Learning Approach

Vinay Singh, Brijesh Nanavati, Arpan Kumar Kar () and Agam Gupta
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Vinay Singh: BASF SE
Brijesh Nanavati: BASF Services Europe GmbH
Arpan Kumar Kar: Indian Institute of Technology Delhi
Agam Gupta: Indian Institute of Technology Delhi

Information Systems Frontiers, 2023, vol. 25, issue 4, No 19, 1638 pages

Abstract: Abstract One of the core challenges in digital marketing is that the business conditions continuously change, which impacts the reception of campaigns. A winning campaign strategy can become unfavored over time, while an old strategy can gain new traction. In data driven digital marketing and web analytics, A/B testing is the prevalent method of comparing digital campaigns, choosing the winning ad, and deciding targeting strategy. A/B testing is suitable when testing variations on similar solutions and having one or more metrics that are clear indicators of success or failure. However, when faced with a complex problem or working on future topics, A/B testing fails to deliver and achieving long-term impact from experimentation is demanding and resource intensive. This study proposes a reinforcement learning based model and demonstrates its application to digital marketing campaigns. We argue and validate with actual-world data that reinforcement learning can help overcome some of the critical challenges that A/B testing, and popular Machine Learning methods currently used in digital marketing campaigns face. We demonstrate the effectiveness of the proposed technique on real actual data for a digital marketing campaign collected from a firm.

Keywords: Digital marketing; Computational advertising; Reinforcement learning; Upper confidence bound (UCB) algorithm; Big data analytics; Machine learning; Marketing analytics (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10796-022-10314-0

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