طبقه بندی مشتریان بانک صادرات براساس ارزش مشتری با استفاده از درخت تصمیم
محورهای موضوعی : مدیریت بازرگانی
1 - استادیار و عضو هیئت علمی دانشگاه تبریز
2 - دانش‌آموخته کارشناسی ارشد آمار ریاضی، دانشگاه تبریز
کلید واژه: مدیریت ارتباط با مشتری, درخت تصمیم, داده کاوی, ارزش مشتری و ارزش عمر مشتری,
چکیده مقاله :
با توجه به اینکه امروزه کسب رضایت مشتری در محیط تجاری اهمیت زیادی پیدا کرده است ، بسیاری از شرکت‌ها به منظور افزایش سود و رضایت مشتری بر روی ارزش مشتری تمرکز دارند. مدیریت ارتباط با مشتری 3(CRM) ابزار بالا بردن ارتباط مشتری به عنوان اصل رقابت در شرکت‌ها ظهور پیدا کرده است. ساختار موفق CRM در شرکت‌ها از شناسایی ارزش درست مشتری شروع می‌شود، زیرا ارزش مشتری اطلاعات مهمی را به منظور گسترش هدف و مدیریت فراهم می‌کند. تکنیک‌هایی مثل داده کاوی سبب شده است که مدیریت ارتباط با مشتری در حوزه جدید رقابت پیشرفت کند به طوری که شرکت‌ها بتوانند در رقابت تجاری سود داشته باشند. از طریق داده کاوی -کشف دانش پنهان از پایگاه داده- سازمان‌ها می‌توانند مشتری ارزشمندشان را بشناسند و رفتار آینده آنها را پیش‌بینی و تصمیمات مفید و دانش محور را اتخاذ کنند. هدف از انجام این تحقیق بدست آوردن معیار‌های موثر در انتخاب مشتری ارزشمند است که بتوان مشتریان را براساس ویژگی‌های جمعیت شناختی‌شان و سایر متغیرهای مربوط به معاملات به طبقات سود خیلی کم، کم سود ، سودبالا و سود خیلی بالا طبقه بندی کرد. در این تحقیق تاثیر ویژگی‌های جمعیت شناختی افراد از جمله سن ، تحصیلات و شغل افراد همچنین تاثیر درجه شعبه، مکان شعبه بانک و تعداد تراکنش افراد برروی ارزش مشتری بررسی می شود. متغیر وابسته در این تحقیق مقدار ارزش مشتری است که به چهار طبقه دسته بندی شده است. جامعه آماری در این تحقیق مشتریان دارای حساب جاری فعال نزد بانک صادرات ایران در شهر تبریز است و مشتریانی را در نظر گرفتیم که حداقل یک سال سابقه فعالیت بانکی نزد بانک صادرات دارند. برای بررسی هدف موردنظر، درخت تصمیم CHAID یکی از الگوریتم های داده کاوی مورد استفاده قرار گرفت. نتایج نشان داد متغیرهای سن، تحصیلات مشتری و درجه شعبه بانک تاثیر معنی داری بر ارزش مشتری ندارند. تعداد تراکنش مشتری با بانک موثرترین ویژگی مشتری در تشخیص طبقه مشتری می باشد. 3- Customer Relationship Management
Customer satisfaction in today's business environment has gained importance. Many companies to increase profits and customer satisfaction are focused on customer value. Customer relationship Management (CRM) is tools to enhance customer relationships has emerged as the principal competing companies. Successful customer relationship management in companies start from identifying customer value beacuse Customer value provides important information for the development and management. Techniques such as data mining has led to the development of customer relationship management in new competition areas so that companies can be profitable in business competition. Through data mining -the discovery of hidden knowledge database- organizations can identify valuable customer and predict their future behavior and take useful and knowledge-based decisions. The purpose of this research is to gain the effective features to select valuable customer that can classify customers based on population characteristics and other variables relating to transactions in classes very low profit, low profit, high profit and very high profit. In this study the influence of demographic characteristics including age, education and job level also affect the degree of branch, bank branch location and number of transactions on customer value will be checked. Dependent variable in this research is customer value that is classified into four categories. Statistical population is the all customers have an active checking account with Bank Saderat Iran in Tabriz city. To review the case, the CHAID decision tree data mining algorithms were used.Results showed that the variables age, education level of customer and bank branches have no significant effect on customer value and the number of customer transactions with bank has most effective in identifying the class of customer.
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- Kim, J., Suh, E., Hwang, H. (2003), A model for evaluating the effectiveness of CRM using the balanced scorecard. Journal of Interactive Marketing, 17(2), 5–19
- Hyunseok, H., Taesoo, J., Euiho, S. (2004), An LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry.
- Witten, I. H., Frank, E. (2005), Data mining: Practical machine learning tools and techniques. California: Morgan Kaufmann.
- Su-Yeon, K., Tae-Soo, J., Eui-Ho, S., Hyun-Seok, H. (2006), Customer segmentation and strategy development based on customer lifetime value: A case study.
- Huang, M. J., Chen, M. Y., Lee, S. C. (2007), Integrating data mining with case-based reasoning for chronic diseases prognosis and diagnosis. Expert Systems with Applications, 32(3), 856–867.
- Kumar, P. R., Ravi, V. (2007), Bankruptcy prediction in banks and firms via statistical and intelligent techniques-A review. European Journal of Operational Research, 180, 1–28.
- Hung, C., Tsai, C. F. (2008), Marketing segmentation based on hierarchical self-organizing map for markets of multimedia on demand. Expert Systems with Application, 34(1), 780–787.
- Benoit, D. F., Van de Poel, D. (2009),Benefits of quantile regression for the analysis of customer lifetime value in a contractual setting: An application in the financial services. Expert Systems with Applications, 36, 1045–10484.
- Liang, Y. H. (2010), Integration of data mining technologies to analyze customer value for the automotive maintenance industry. Expert Systems with Applications. doi:10.1016/j.eswa.2010.04. 097
- Soman, k. p. Diwaker, S., Ajay, v. (2010), Insight into Data mining theory and practice -book. PHI Learning.
- Shui, H., Shui, X., Stephen, L., Leung. (2011), Segmentation of telecom customers based on customer value by decision tree model.
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- Jin, K., R and Traiandis, C., H. (1997), “Management on Unmanageable”, Tehran, Institute of Education and Research of Defense Industries.
- Khaki, G. (2008), “Research method in management”, Baztab publication.
- Dwyer,F.R.(1997),Customer lifetime valuation to support marketing decision making. Journal of Interactive Marketing, 11(4),6–13.
- Hawkes, V. A. (2000), The heart of the matter: The challenge of customer lifetime value. CRM Forum Resources, 1-10.
- Kim, J (2000), e-CRM for e-Business,Gurm.
- Verhoef, P. C.,Donkers, B. (2001),Predicting customer potential value an application in the insurance industry. Decision Support Systems, 32,189–199.
- Newton’s telecom dictionary, Harry newton, CMP Books. http://www.cmpbooks.com
- Zeithaml, V., Rust, R. T., Lemon, K. N. (2001), The customer pyramid: Creating and serving profitable customers. California Management Review, 42(4), 118–142.
- Chrise, R. Juen-Cheng, W., Dawid, C, Y. (2002), Data mining techniques for customer relationship management. Technology in society,483-502.
- Kim, J., Suh, E., Hwang, H. (2003), A model for evaluating the effectiveness of CRM using the balanced scorecard. Journal of Interactive Marketing, 17(2), 5–19
- Hyunseok, H., Taesoo, J., Euiho, S. (2004), An LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry.
- Witten, I. H., Frank, E. (2005), Data mining: Practical machine learning tools and techniques. California: Morgan Kaufmann.
- Su-Yeon, K., Tae-Soo, J., Eui-Ho, S., Hyun-Seok, H. (2006), Customer segmentation and strategy development based on customer lifetime value: A case study.
- Huang, M. J., Chen, M. Y., Lee, S. C. (2007), Integrating data mining with case-based reasoning for chronic diseases prognosis and diagnosis. Expert Systems with Applications, 32(3), 856–867.
- Kumar, P. R., Ravi, V. (2007), Bankruptcy prediction in banks and firms via statistical and intelligent techniques-A review. European Journal of Operational Research, 180, 1–28.
- Hung, C., Tsai, C. F. (2008), Marketing segmentation based on hierarchical self-organizing map for markets of multimedia on demand. Expert Systems with Application, 34(1), 780–787.
- Benoit, D. F., Van de Poel, D. (2009),Benefits of quantile regression for the analysis of customer lifetime value in a contractual setting: An application in the financial services. Expert Systems with Applications, 36, 1045–10484.
- Liang, Y. H. (2010), Integration of data mining technologies to analyze customer value for the automotive maintenance industry. Expert Systems with Applications. doi:10.1016/j.eswa.2010.04. 097
- Soman, k. p. Diwaker, S., Ajay, v. (2010), Insight into Data mining theory and practice -book. PHI Learning.
- Shui, H., Shui, X., Stephen, L., Leung. (2011), Segmentation of telecom customers based on customer value by decision tree model.