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Development, analysis and applications of a quantitative methodology for assessing customer satisfaction using evolutionary optimization

机译:定量方法的开发,分析和应用,用于通过进化优化来评估客户满意度

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摘要

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.
机译:面向消费者的公司对客户对其产品的看法越来越敏感,不仅要获得其受欢迎程度的反馈,而且要通过更好地理解设计问题以进一步开发来提高质量和服务。但是,消费者的看法通常是定性的,可以通过第三方调查或公司通过明确调查或基于保修的承诺记录售后反馈来实现。在本文中,我们考虑了一家汽车公司针对不同车型的保修记录,并提出了一种数据挖掘程序,根据感知到的客户满意度水平为每个车型分配客户满意度指数(CSI)。然后根据已开发的CSI功能,将客户分为满意和不满意的客户组。然后针对每个组分别对保修数据进行聚类并进行分析,以找出可能的原因(现场故障)及其对车辆模型对客户满意度(或不满意程度)的相对影响。最后,进行了推测性自省,以识别通过改进设计实践减少某些关键现场故障而可以实现的CSI改进量。因此,本文显示了如何利用来自客户的保修数据来与市场上的竞争对手相比更好地了解产品的排名,并找出导致某些客户不满意并最终帮助解决这些问题的可能原因。设计水平。这样就关闭了设计周期循环,在该循环中,将设计转换为产品后,客户对它的满意程度也可以提供有价值的信息,以迭代的方式帮助改进设计。所提出的方法是通用且新颖的,并且也可以应用于其他消费产品。

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