Time for marketing to embrace reinforcement learning

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.