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Time for marketing to embrace reinforcement learning

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2022/12/12 19:35
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Data Science
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Authors
Laura Murphy Fernando Perales Anand Gopal Yordanka Gyurdieva Victor Gueorguiev and Pratyush Shandilya Laura Murphy: Amplify Analytix BV The Netherlands Fernando Perales: JOT Internet Media Spain Anand Gopal: Voiro India Yordanka Gyurdieva: Amplify Analytix Campus X Victor Gueorguiev: Bul. Simeonovsko Shose 110 Bulgaria Pratyush Shandilya: Data Scientist Amplify Analytix India
Abstract
Since COVID-19 upended the world marketers can no longer rely on historical data to inform their decisions. Channel splits have changed and online conversations have exploded. Marketing budgets have decreased as a percentage of revenue meaning marketing funds must be used more effectively and efficiently than ever. Fortunately the relatively new application of reinforcement learning — a data science approach — in marketing offers additional opportunities to gain competitive advantage using artificial intelligence. Unlike other types of machine learning reinforcement learning uses algorithms that do not typically rely only on historical data sets to learn to make predictions. Rather these algorithms learn as humans often do through trial and error adjusting their ‘behaviour’ based on the outcomes of their actions. While the algorithms and computations behind reinforcement learning can be complex and sophisticated its ability to deal with real-time decision making makes it an attractive option for marketers. This paper shows that with the right ‘business translator’ — that is a person or team operating as the ‘glue’ between data science and business performance — sophisticated data science becomes accessible to commercial teams looking to drive performance improvements.
Keywords
reinforcement learning ; data science ; marketing analytics ; change management ; artificial intelligence ; digital marketing (search for similar items in EconPapers)
Year Published
2022
Series
Journal of Digital & Social Media Marketing 2022 vol. 10 issue 2 135-142
Rank
0.4
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