سیستم هوشمند تقسیم بندی مشتریان سودآور بر اساس ابزارهای هوش کسب و کار(هوش تجاری)
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|671||2005||8 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 29, Issue 1, July 2005, Pages 145–152
For the success of CRM, it is important to target the most profitable customers of a company. Many CRM researches have been performed to calculate customer profitability and develop a comprehensive model of it. Most of them, however, had some limitations and accordingly the customer segmentation based on the customer profitability model is still underutilized. This paper aims at providing an easy, efficient and more practical alternative approach based on the customer satisfaction survey for the profitable customers segmentation. We present a multi-agent-based system, called the survey-based profitable customers segmentation system that executes the customer satisfaction survey and conducts the mining of customer satisfaction survey, socio-demographic and accounting database through the integrated uses of business intelligence tools such as DEA (Data Envelopment Analysis), Self-Organizing Map (SOM) neural network and C4.5 for the profitable customers segmentation. A case study on a Motor company's profitable customer segmentation is illustrated.
In today's competitive business environment, the ability to identify profitable customers, build their long-term loyalty and steadily expand existing relationships is key competitive factors to a company. To meet these factors, companies across a wide range of industries have made Customer Relationship Management (CRM) one of the leading business strategies, integrating sales, marketing and service across multiple business units and customer contact points. CRM helps companies understand the value of customers, target their most profitable customers, cultivate and maintain high-quality relationships that increase loyalty and profits. Precise evaluation of customer profitability and targeting the most profitable customers are crucial elements for the success of CRM. Many CRM researches have been performed to calculate customer profitability based on customer lifetime value and develop a comprehensive model of it. Most of them, however, had some limitations by not considering such as the change of profit contribution resulted from the customer defection (Berger and Nasr, 1998 and Gupta and Lehmann, 2003). They need further extensions considering additional factors such as customer reactivation possibility, attracting/service cost and causes of customer defection. On the other hand, the customer segmentation based on their profitability to a company is still an underutilized approach. This study aims at providing an easy, efficient and more practical alternative approach based on the customer satisfaction survey for the profitable customers segmentation instead of using a customer profitability model, which is an important tool for marketing and managing customer relationships by providing the information of overall satisfaction level, repurchase intentions, word-of-mouth intentions, etc.
نتیجه گیری انگلیسی
To intelligently segment profitable customers of a company in terms of their profitability, we present an easy and efficient alternative approach based on the mining of customer satisfaction survey, socio-demographic and accounting database instead of using a complicated customer profitability model. First, the presented approach uses DEA to find out the customers with higher cost efficiency, High Efficiency Customer Group (HECG), among all the surveyed ones about their output from a company's input costs for them. And then it uses SOM to form the profitable customers group (PCG) by removing undesirable customers among HECG's customers. Finally, it successively uses C4.5 and SOM to decide the priority orders of non-PCG's customers. We also propose a survey-based profitable customers segmentation system (SPCSS) that conducts the customer satisfaction survey and those mining processes for the profitable customers segmentation. When our work is used in practice, the appropriate setting of efficiency score criterion in DEA analysis and similarity criteria in the SOM Classification is required. We expect that our study will offer an opportunity to use various survey data including customer satisfaction survey actively and develop an intelligent methodology for profitable customers segmentation.