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Practice paper

Recommendations and personalisation: Three strategies for activating customer behaviour analytics insights

Seth Earley
Applied Marketing Analytics: The Peer-Reviewed Journal, 7 (4), 329-336 (2022)
https://doi.org/10.69554/MWOM4404

Abstract

This paper describes three approaches to personalisation and explains the methods for implementing each one. The reasons why many organisations have difficulty achieving effective personalisation are discussed, including lack of maturity in the required processes, architecture and data quality. Differences and similarities between personalisation and recommendations are reviewed, as well as the methods described for segmenting customers to present them with products and information tailored to their interests and needs. The paper identifies four dimensions that can be used for personalisation; preferences, location, topics and products/solutions. Methods required to carry out personalisation at scale are explained, and suggestions made regarding the development of the attributes needed for personalisation. The author highlights the importance of correctly modelling the customer journey in order to present the right information or products in response to signals from the customer.

Keywords: personalisation; customer journey; customer data; data models; customer behavioural analytics; product recommendations; artificial intelligence; customer attributes

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Author's Biography

Seth Earley is Founder and CEO of Earley Information Science. He is an expert with more than twenty years’ experience in artificial intelligence, knowledge strategy, data and information architecture, search-based applications and information findability solutions. He has worked with a diverse roster of Fortune 1000 companies, helping them to achieve higher levels of operating performance by making information more findable, usable and valuable through integrated enterprise architectures supporting analytics, e-commerce and customer experience applications.

Citation

Earley, Seth (2022, March 1). Recommendations and personalisation: Three strategies for activating customer behaviour analytics insights. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 7, Issue 4. https://doi.org/10.69554/MWOM4404.

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cover image, Applied Marketing Analytics: The Peer-Reviewed Journal
Applied Marketing Analytics: The Peer-Reviewed Journal
Volume 7 / Issue 4
© Henry Stewart
Publications LLP

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