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Practice paper

Using multi-armed bandit experimentation to optimise multichannel digital marketing campaigns

Ian Thomas
Applied Marketing Analytics: The Peer-Reviewed Journal, 3 (2), 146-156 (2017)
https://doi.org/10.69554/VMMC5877

Abstract

Multichannel campaign optimisation using a machine-learning technique called multi-armed bandit experimentation is a powerful, though nascent, alternative to traditional campaign attribution approaches for maximising return on marketing investment. The technique works by treating the various attributes of a digital marketing campaign as combinatorial treatments in an ongoing controlled experiment and continuously optimising delivery towards the combinations that deliver the best results. Performing true multichannel optimisation requires significant investment in experiment design and maturity in data, marketing automation technology and organisational alignment. However, despite these challenges, organisations can start to move towards a full optimisationdriven approach for their digital marketing by identifying campaigns within their existing channels which could benefit from this technique and using those campaigns to establish the appropriate processes and technical capabilities, before scaling out efforts more broadly across multiple channels. This paper provides an overview of this new campaign optimisation approach, including some existing in-market solutions that use it, and examines some of the factors and prerequisites that organisations will need to consider in implementing such a technique.

Keywords: optimisation; experimentation; multichannel; digital; multi-armed bandit; CRM

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Author's Biography

Ian Thomas has been involved in online measurement and customer intelligence for over 15 years. Since joining Microsoft in 2006, Ian has led a number of major data initiatives that help the company understand and better serve its customers in areas including online advertising, the Bing search engine, Office and Windows. In his current role, he is responsible for bringing together customer behavioural, attitudinal and demographic data to power personalised experiences in Microsoft products and marketing communications through real-time targeting and message optimisation across multiple digital channels.

Citation

Thomas, Ian (2017, May 9). Using multi-armed bandit experimentation to optimise multichannel digital marketing campaigns. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 3, Issue 2. https://doi.org/10.69554/VMMC5877.

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cover image, Applied Marketing Analytics: The Peer-Reviewed Journal
Applied Marketing Analytics: The Peer-Reviewed Journal
Volume 3 / Issue 2
© Henry Stewart
Publications LLP

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