Predicting Customer Satisfaction from Dental Implants Perception Data

Abstract

<p>In recent years, measuring customer satisfaction has become one of the key concerns of market research studies. One of the basic features of leading companies is their success in fulfilling their customers’ demands. For that reason, companies attempt to find out what essential factors dominate their customers’ purchasing habits.</p> <p>Millennium Research Group (MRG) - a global authority on medical tech- nology market intelligence - uses a web-based survey tool to collect informa- tion about customers’ level of satisfaction. One of their surveys is designed to gather information about the practitioner’s level of satisfaction on different brands of dental implants. The Dental Implants dataset obtained from the survey tool has thirty-four attributes, and practitioners were asked to rank or specify their level of satisfaction by assigning a score to each attribute.</p> <p>The basic question asked by the company was whether the attributes were useful to make customer behavior predictions. The aim of this study is to assess the reliability and accuracy of these measures and to build a model for future predictions, then, determine the attributes that are most influential</p> <p>in the practitioners’ purchasing decisions. Classification and regression trees (CART) and Partial least squares regression (PLSR) are the two statistical approaches used in this study to build a prediction model for the Dental Implants dataset.</p> <p>The prediction models generated, using both of the techniques, have rel- atively small prediction powers; which may be perceived as an indication of deficiency in the dataset. However, getting a small prediction power is gener- ally expected in market research studies. The research then attempts to find ways to improve the power of these models to get more accurate results. The model generated by CART analysis tends to have better prediction power and is more suitable for future predictions. Although PLSR provides extremely small prediction power, it helps finding out the most important attributes that influence the practitioners’ purchasing decisions. Improvements in pre- diction are sought by restricting the cases in the data to subsets that show better alignment between predictors and customer purchasing behaviour.</p>