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Brand segmentation using implicit brand measures
Segmentation can be a difficult analytical project to pull off. Brand segmentation, in particular, has been challenged to the point that some research has aimed to demonstrate it does not exist. Although such studies have covered many different product categories, they have not addressed the issue using recent developments in the measurement of (brand) attitudes; neither have they used optimal analytical techniques. Both of these have the potential to affect segmentation solutions in a significant way. This paper aims to fill this gap in the literature. It investigates whether brand segments can be identified using implicit attitudes and using segmentation models that are best suited for the data at hand. The study finds evidence of meaningful brand segmentation in over 50 per cent of the categories studied.
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Marco Vriens has a PhD in marketing analytics and is a recognised expert in applied analytics. He led analytics teams for Microsoft, GE and supplier firms. Marco is the author of three books: ‘The Insights Advantage: Knowing How to Win’ (2012), ‘Handbook of Marketing Research’ (2006) and ‘Conjoint Analysis in Marketing’ (1995). Marco has been published in academic and industry journals and has won several best paper awards including the David K. Hardin Award.
Alessandro Martins Alves has a doctorate in production engineering. He leads an analytic team at Ipsos and he is responsible for developing, processing and supporting marketing research methodologies.
Song Chen has a PhD in Applied Mathematics and is an assistant professor at University of Wisconsin – La Crosse. He conducts research in scientific computing and data mining with applications in fields such as marketing. He is director of the data science group at University of Wisconsin – La Crosse and actively leads undergraduate collaborative projects with local industry.