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Automated cluster generation and labelling of peer groups for marketing reporting
Applied Marketing Analytics: The Peer-Reviewed Journal,
9
(2),
134-144
(2023)
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
The full article is available to subscribers to the journal.
Author's Biography
Dakota Crisp is an analytics manager at OneMagnify. With a PhD from the University of Michigan, his hypothesis-driven approach to integrating concepts from neural engineering into the data science space provides a distinctive take on consumer behaviours.
Jonathan Prantner is the Chief Analytics Officer at OneMagnify. His approach to applied mathematics has pushed analytics to the limits for over two decades. Jonathan's career has spanned educational research, automotive, consumer packaged goods, travel and healthcare. At OneMagnify, he leads efforts surrounding applied artificial intelligence and machine learning as well as integrating advanced analytics with data visualisation platforms. Jonathan is a celebrated thought-leader and recipient of multiple data science patents.
Grant Miller uses his programming and statistical approaches to try and tackle standardisation, automation and deep insights.
Jack Claucherty is an analytics manager at OneMagnify with a BS and MSE in industrial engineering from the University of Michigan. His background as an industrial engineer helps him understand complex systems and he loves helping his clients find value in their data and the models created.
Tom Thomas is Vice President of Data Strategy, Analytics & Business Intelligence at FordDirect. Tom has 30 years of experience as an executive, consultant and entrepreneur delivering digital advertising, mobile application and enterprise resource planning solutions to the automotive, consumer packaged goods, hospitality and public utilities industries.
Danielle Barnes is an analytics director at OneMagnify. She is an accomplished analytics leader with extensive experience across the entire data lifecycle. Her work directing enterprise analytics initiatives for companies across various industries has made her a powerhouse for realising visions in complex environments. She is a Spartan superfan with a BA in mathematics, MS in statistics and currently pursuing a PhD in data science, all from Michigan State University.
Citation
Crisp, Dakota, Prantner, Jonathan, Miller, Grant, Claucherty, Jack, Thomas, Tom and Barnes, Danielle (2023, October 1). Automated cluster generation and labelling of peer groups for marketing reporting. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 9, Issue 2. https://doi.org/10.69554/UBFB7673.
© Henry Stewart
Publications LLP
Publications LLP