Development,analysis and applications of a quantitative methodology for assessing customer satisfaction using evolutionary optimization |
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| Affiliation: | 1. Virtual Systems Research Centre, University of Skövde, Skövde 54128, Sweden;2. Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA;3. Amazon Development Centre (India) Pvt. Ltd., Bengaluru 560055, India;4. Department of Mechanical Engineering, Walchand College of Engineering, Sangli, Maharashtra 416415, India;5. General Motors R&D Center, Warren, MI 48090, USA;1. Département génie électrique, Ecole Mohamamdia d’Ingénieurs (EMI), Université Mohammed V Agdal, Rabat, Morocco;2. Laboratoire de Recherche en Economie de l’Energie, Environnement et Ressources, Département d’Economie, University Caddy Ayyad, Marrakech, Morocco;1. Simsoft Computer Technologies, Middle East Technical University, Teknokent Bolgesi, 06800 Ankara, Turkey;2. Microsoft, 1 Microsoft Way, Redmond, WA 98052, United States;3. Computer Engineering, Middle East Technical University, 06800 Ankara, Turkey;1. Centre for Biomedical Engineering, Transportation Research Alliance, Universiti Teknologi Malaysia, Skudai, Malaysia;2. Faculty of Bioscience and Medical Engineering, Universiti Teknologi Malaysia, Skudai, Malaysia;1. Department of Computer Engineering, Electrical Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, Italy;2. CORISA, Department of Computer Science, University of Salerno, 84084 Fisciano, Italy;1. College of Finance, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China;2. School of Economics and Management, Southeast University, Nanjing 210096, Jiangsu, China |
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| Abstract: | Consumer-oriented companies are getting increasingly more sensitive about customer's perception of their products, not only to get a feedback on their popularity, but also to improve the quality and service through a better understanding of design issues for further development. However, a consumer's perception is often qualitative and is achieved through third party surveys or the company's recording of after-sale feedback through explicit surveys or warranty based commitments. In this paper, we consider an automobile company's warranty records for different vehicle models and suggest a data mining procedure to assign a customer satisfaction index (CSI) to each vehicle model based on the perceived notion of the level of satisfaction of customers. Based on the developed CSI function, customers are then divided into satisfied and dissatisfied customer groups. The warranty data are then clustered separately for each group and analyzed to find possible causes (field failures) and their relative effects on customer's satisfaction (or dissatisfaction) for a vehicle model. Finally, speculative introspection has been made to identify the amount of improvement in CSI that can be achieved by the reduction of some critical field failures through better design practices. Thus, this paper shows how warranty data from customers can be utilized to have a better perception of ranking of a product compared to its competitors in the market and also to identify possible causes for making some customers dissatisfied and eventually to help percolate these issues at the design level. This closes the design cycle loop in which after a design is converted into a product, its perceived level of satisfaction by customers can also provide valuable information to help make the design better in an iterative manner. The proposed methodology is generic and novel, and can be applied to other consumer products as well. |
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| Keywords: | Customer satisfaction index (CSI) Quantitative modeling Evolutionary optimization Customer relationship management (CRM) |
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