Who are our clients: consumer segmentation through explorative data mining

Abstract

________________________________________________________________________The apparel industry aims to produce comfortable and aesthetically pleasing garments that fit populations well. However, repeated studies of apparel customers' levels of satisfaction indicate that their needs are often not being met. In order to produce better fitting clothes, it is crucial to understand the body profiles of typical consumers. Exploring the demographic profiles of correctly identified customer segments holds obvious benefit for production, marketing and ensuring return on investments. To this end, we explore a database, containing anthropometric measurements, demographic profiles and 3-D body scans, of samples of the North American, Italian and Dutch populations. Through the use of objective interestingness measures-based feature selection and feature extraction, we accurately discover the relevant subsets of body measurements that require special care when designing clothes. Furthermore, we apply association analysis to the demographic data, in order to target customers and thus potentially defining new market opportunities.

The market segmentation of higher education in Colombia reveals social inequalities

“…The processing of the data of the master database is oriented to find similarities among the academic programmes for clustering them using average values of their students (Erhardt & von Kotzebue, 2016). This kind of non-supervised models can deal with multidimensional databases to come upon useful patterns in customer profiling research (Viktor et al, 2012). Since the clustering strategy makes the visualisation of the entire master database difficult, this study uses the technique of dimension reduction (DR) from the beginning.…”

Data-driven marketing processes

“…Previous studies have focused on numerous themes that either overlap or surround data-driven marketing and marketing analytics practices (Chafey and Patron, 2012;Hauser, 2007;Järvinen and Karjaluoto, 2015;Jobs, Aukers, and Gilfoil, 2015;Liu, Singh, and Srinivasan, 2016;Martens et al, 2016;Netzer et al, 2012;Verhoef, Kooge, and Walk, 2016;Viktor, Pena, and Paquet, 2012;Wedel and Kannan, 2016;Wilson, 2010). Studies have highlighted both the fundamental shortage of marketing data professionals and the lack of knowledge regarding how much companies are currently using data-driven decisions in marketing (Erevelles, Fukawa, and Swayne, 2016).…”