Title: Classifying services by attributes important to customers

Authors: Venkat Venkateswaran; John Maleyeff

Addresses: Department of Engineering and Science, Rensselaer Polytechnic Institute, 275 Windsor Street, Hartford, CT 06120, USA. ' Lally School of Management and Technology, Rensselaer Polytechnic Institute, 275 Windsor Street, Hartford, CT 06120, USA

Abstract: A scheme is developed that clusters services based on outcome attributes deemed important by customers. The algorithm that determined clusters used empirical field research data from 164 different services. The services are modelled as binary vectors. They are analysed using a clustering method based on the Ward algorithm. The analysis reveals six distinct clusters by customer desiderata. Additionally, the medoid is a natural representative of each cluster. The clusters are quite distinct from previously considered groupings based on process characteristics, and offer new insights into their common features. Implications for service innovation, strategic planning, and staffing are discussed.

Keywords: service management; service classification; cluster analysis; data mining; services science; service clusters; customer requirements; customer desires; service innovation; strategic planning; staffing.

DOI: 10.1504/IJBIDM.2011.044977

International Journal of Business Intelligence and Data Mining, 2011 Vol.6 No.4, pp.382 - 401

Received: 19 Oct 2011
Accepted: 21 Oct 2011
Published online: 22 Apr 2015 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article