Marketing in the age of machine learning: How optimising personalisation granularity leads to better performance in a dynamic market

Julie Penzotti

Applied Marketing Analytics: The Peer-Reviewed Journal, 2016, vol. 2, issue 1, 41-51

Abstract: While personalisation has advanced formidably in our digital age, marketers remain challenged by how to deliver truly individualised customer experiences that are optimised for relevance, timeliness and value, and do so at scale. Marketers know that the more targeted a marketing interaction is, the stronger the customer response. Yet as customer behaviour continually changes, more advanced technologies capable of acting on dynamic data and increasing targeting granularity are required to discover and exploit optimal customer experiences at scale. In this paper the author discusses the application of machine learning to marketing personalisation, and how it can be used in conjunction with closed-loop experimentation to learn optimal combinations of targeting audiences, marketing experiences, and execution parameters to boost marketing performance even in highly competitive, dynamic markets.

Keywords: marketing personalisation; machine learning; continuous optimisation; controlled experimentation; customer analytics; customer experience (search for similar items in EconPapers)
JEL-codes: M3 (search for similar items in EconPapers)
Date: 2016
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