Using segment level stability to select target segments in data-driven market segmentation studies
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Using segment level stability to select target segments in data-driven market segmentation studies Sara Dolnicar1
· Friedrich Leisch2
© Springer Science+Business Media New York 2017
Abstract Market segmentation is widely used by industry to select the most promising target segment. Most organisations are interested in finding one or a small number of target segments to focus on. Yet, traditional criteria used to select a segmentation solution assess the global quality of the segmentation solution. This approach comes at the risk of selecting a segmentation solution with good overall quality criteria which, however, does not contain groups of consumers representing particularly attractive target segments. The approach we propose helps managers to identify segmentation solutions containing attractive individual segments (e.g., more profitable), irrespective of the quality of the global segmentation solution. We demonstrate the functioning of the newly proposed criteria using two empirical data sets. The new criteria prove to be able to identify segmentation solutions containing individual attractive segments which are not detected using traditional quality criteria for the overall segmentation solution. Keywords Market segmentation · Niche segments · Cluster analysis · Bootstrap
Sara Dolnicar
[email protected] Friedrich Leisch [email protected] 1
UQ Business School, The University of Queensland, Brisbane, Queensland 4072, Australia
2
Institute of Applied Statistics and Computing, University of Natural Resources and Life Sciences, Peter-Jordan-Straße 82, 1190, Vienna, Austria
Mark Lett
1 Introduction Market segmentation is a critical building block of strategic marketing (Iacobucci 2013) and “essential for marketing success” (Lilien and Rangaswamy 2003, p. 61). Conceptually, there are two possible approaches to market segmentation. Segments can be defined by using one single segmentation variable. For example, profitability can be used to split existing customers into a high, medium, and low profit potential segments. These three market segments can then be profiled using descriptor variables such as benefits sought from the product, socio-demographics, or media behavior. This approach has been referred to as a priori (Myers and Tauber 1977; Mazanec 2000; Wedel and Kamakura 2000), convenience-group (Lilien and Rangaswamy 2003), or commonsense (Dolnicar 2004) segmentation. Alternatively, multiple segmentation variables can be used. For example, benefits people seek when buying food in a fast food restaurant (save time, save money, keep kids happy, . . . ) may have been collected in a survey. The full set of benefits is used to extract market segments. As opposed to segmentations based on one variable, it is therefore not known in advance what the defining features of each of the market segments may be. Once the segments have been extracted from the data, they also need to be profiled in detail using descriptor variables, just like the high, medium, and low profit potential segments in the prev
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