Automated cluster generation and labelling of peer groups for marketing reporting

Dakota Crisp, Jonathan Prantner, Grant Miller, Jack Claucherty, Tom Thomas and Danielle Barnes
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Dakota Crisp: Analytics Manager, OneMagnify, USA
Jonathan Prantner: Chief Analytics Officer, OneMagnify, USA
Grant Miller: RXA, USA
Jack Claucherty: Analytics Manager, OneMagnify, USA
Tom Thomas: Vice President of Data Strategy, Analytics & Business Intelligence, FordDirect, USA
Danielle Barnes: Analytics Director, OneMagnify, USA

Applied Marketing Analytics: The Peer-Reviewed Journal, 2023, vol. 9, issue 2, 134-144

Abstract: In today's data-driven marketing landscape, clustering data helps businesses better understand themselves and their customers. However, clusters derived from machine learning can be difficult to interpret and obtain buy-in from stakeholders. This paper details a method for automated cluster generation and labelling using machine learning. Two automotive case studies are provided where clustering enhanced business value and gained stakeholder buy-in. The first details segmenting dealerships based on their media environment to produce higher quality media models for lead generation. The second entails the creation of peer groups to enhance performance reporting across a diverse set of dealerships.

Keywords: clustering; automotive; labelling; peer groups; segmentation (search for similar items in EconPapers)
JEL-codes: M3 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:aza:ama000:y:2023:v:9:i:2:p:134-144

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