Classifying services by attributes important to customers

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.