Publicação
Business customers segmentation with the use of K-means and self-organizing maps : an exploratory study in the case of a Slovenian bank
| dc.contributor.advisor | Henriques, Roberto André Pereira | |
| dc.contributor.author | Simic, Slavisa | |
| dc.date.accessioned | 2018-03-08T18:17:32Z | |
| dc.date.available | 2018-03-08T18:17:32Z | |
| dc.date.issued | 2016-01-18 | |
| dc.description | Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies Management | pt_PT |
| dc.description.abstract | Many companies of the contemporary economy have a large number of customers, and each of these represents almost as many different sets of needs and expectations which have become more and more complex, demanding and sophisticated over time. As it is impossible to treat every customer completely individually, let alone to provide them with fully customized products and services, it is clearly evident that they should be divided into a few groups in a reasonable manner, of course. Even though client segmentation has been present for many years, companies still struggle to use it correctly. They are trying to implement it properly as well as to integrate it into marketing strategy (Dibb & Simkin, 2009, p. 219). Instead of helping in more important, strategic areas, such as products and services innovation, pricing, and distribution channel selection, market segmentation has often been narrowly used for the needs of advertising (Yankelovich & Meer, 2006, p. 1). While the consumer market segmentation has been a challenging task for marketers, it has been an even more difficult job for those of industrial markets, or as Kukulas (2012, p. 2) had neatly illustrated with an example; whereas consumer marketers go fishing, business-to-business marketers have to fish for sharks. The business market segmentation is known to be much less developed in comparison to the consumer segmentation. However, some techniques of the latter can be also applied to the industrial markets. Yet, unless they want to be led into the wrong direction, practitioners have to be very careful about choosing and refining the appropriate variables on which to segment (Zimmerman & Blythe, 2013, p. 121). | pt_PT |
| dc.identifier.tid | 201125307 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10362/32047 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | CRM | pt_PT |
| dc.subject | Client segmentation | pt_PT |
| dc.subject | Industrial client segmentation | pt_PT |
| dc.subject | K-Means SOMS | pt_PT |
| dc.title | Business customers segmentation with the use of K-means and self-organizing maps : an exploratory study in the case of a Slovenian bank | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Gestão de Informação, especialização em Gestão de Sistemas e Tecnologias de Informação | pt_PT |