‘We Cannot Diagnose the Patient's Illness…But Experience Tells us What Treatment Works’

Abstract

Having spent a lot of money collecting data to better understand the satisfaction of their customers, many clients want to know, with some certainty, what will happen if…? The traditional statistical techniques used to answer this question frequently struggle to cope with the complexity of real survey data, and in particular the interrelationships that exist between the various measures which make up ‘satisfaction’. In providing a solution, some analysts venture where others fear to tread, and many clients are obliged to follow whether they know the risks or not. The authors have developed a practical and intuitive solution to building ‘what if scenarios using an empirical approach, which overcomes many of the technical problems associated with analysing complex customer satisfaction data. Results are transparent to the client and can be explained without compromising the truth. As a consequence, the authors feel that clients will make better investment decisions. This paper describes the analytical approach to creating ‘what if scenarios and provides case studies, using real datasets, which had previously proved trouble some. Implications for the ubiquitous key driver analysis are also discussed.