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مدلسازی ترکیبی Pareto/NBD و RFM موزون فازی بهمنظور بخشبندی مشتریان در روابط غیرقراردادی | ||
| مدیریت بازرگانی | ||
| مقاله 1، دوره 6، شماره 3، مهر 1393، صفحه 417-440 اصل مقاله (530.28 K) | ||
| نوع مقاله: مقاله علمی پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jibm.2014.51381 | ||
| نویسندگان | ||
| امیر البدوی* 1؛ اشرف نوروزی2؛ محمدمهدی سپهری3؛ محمد رضا امین ناصری3 | ||
| 1استاد مهندسی صنایع، دانشکدۀ فنی و مهندسی، دانشگاه تربیت مدرس، تهران، ایران | ||
| 2دانشجوی دکتری مهندسی صنایع، دانشکدۀ فنی و مهندسی، دانشگاه تربیت مدرس، تهران، ایران | ||
| 3دانشیار مهندسی صنایع، دانشکدۀ فنی و مهندسی، دانشگاه تربیت مدرس، تهران، ایران | ||
| چکیده | ||
| درآمدسازی در شرکت‌ها از طریق ایجاد رابطه با مشتریان و حفظ این روابط در درازمدت صورت می‌پذیرد. از این رو توانایی پیشبینی مناسب روابط با مشتریان نکته‌ای اساسی در مدیریت رابطه با مشتریان است. بخشبندی روشی است که طی آن با تفکیک مشتریان به بخش‌های متجانس با رفتار خرید مشابه، تلاش می‌شود تا ارزش آتی رابطه با مشتریان پیشبینی شود. روش RFM یکی ازمتداول‌ترین روش‌های بخشبندی است که از تحلیل پایگاه دادۀ تراکنشی برای رده‌بندی ارزش مشتریان استفاده میکند. پژوهش حاضر تلاش دارد تا از ترکیب مدلسازی Pareto/NBD ـ که به مدلی قدرتمند در پیشبینی رفتار مشتریان مشهور است ـ با روش معمول RFM، کیفیت بخشبندی مشتریان را ارتقا بخشد. در این پژوهش از روش Pareto/NBD برای تخمین سه مؤلفۀ مقدار انتظار احتمال فعالیت آتی، تعداد تراکنش‌های آتی و متوسط ارزش پولی استفاده شده است. سپس نتایج بخشبندی مشتریان با استفاده از این مؤلفهها با کاربرد روش مرسوم RFM مقایسه شده است. نتایج حاصل بیانگر بهبود کیفیت بخشبندی در رده‌بندی ارزش آتی مشتریان، بهویژه در ردههای ارزشمند مشتری با کمک رویکرد پیشنهادی است. | ||
| کلیدواژه‌ها | ||
| روابط غیرقراردادی؛ فرایند تحلیل سلسلهمراتبی فازی؛ مدل RFM؛ مدلسازی Pareto/NBD | ||
| عنوان مقاله [English] | ||
| An Integrated Pareto/NBD- fuzzy weighted RFM model for customer segmentation in non-contractual setting | ||
| نویسندگان [English] | ||
| Amir Albadvi1؛ Ashraf Norouzi2؛ Mohammad Mehdi Sepehri3؛ Mohammad Reza Amin Naseri3 | ||
| 1Prof. Amir Albadvi, Faculty of Engineering, Tarbiat Modares University, Tehran, Iran | ||
| 2PhD. Candidatein Industrial Engineering, Faculty of Engineering, Tarbiat Modares University, Tehran, Iran | ||
| 3Associate Prof., Mohammad Mehdi Sepehri, Faculty of Engineering, Tarbiat Modares University, Tehran, Iran | ||
| چکیده [English] | ||
| Companies create revenue through creating customer relationships and sustenance of these relations in long-term. Therefore, an appropriate prediction of customer relationships is central for CRM. Segmentation divides the customer base into homogenous parts where every group of customers has similar characteristics of buying behavior and therefore has similar expected value. One of the most popular segmentation techniques is RFM analysis which uses transactional databases to provide predictions about customer lifetime value. This study aims to combine Pareto/NBD model, as a powerful tool in predicting customer’s future behavior with RFM analysis to improve the segmentation quality. The Pareto/NBD model is used to estimate the probability of customers’ activity in future periods, the expected number of transactions and the expected average monetary value. The result of this segmentation is being compared to the application of traditional RFM technique. Experimental results indicate that the proposed approach improves the quality of segmentation in order to estimate the customer’s future value, especially in the case of more valuable segments. | ||
| کلیدواژه‌ها [English] | ||
| Fuzzy Analytical Hierarchy Problem (FAHP), non-contractual setting, Pareto/NBD modeling, RFM Model | ||
| مراجع | ||
|
Ahmadi, A. & Yahyazadehfar, M. (2011). Investigation of Implementing Relational Marketing for Costumer relationship in Hospitality Industry (Case Study: of Persian Palace hotel). Iranian Business Management, 3(8): 23-40. (in Persian)
Ahmadi, P., Azar, A. & Samsami, F. (2011). Market Segmentation using Neural Networks (Case Study: Pharmaceutical Market in Iran). Iranian Business Management, 2(6): 1-20. (in Persian)
Buckleyd, J.J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17(3): 233-247.
Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European journal of operational research, 95(3): 649-655.
Cheng, C. H. & Chen, Y. S. (2009).Classifying the segmentation of customer value via RFM model and RS theory. Expert Systems with Applications, 36(3): 4176-4184.
Cheng, X., Gong, B. & Zhang, H. (2012). Customer Value Assessment Using the Fuzzy AHP and TOPSIS Methods: Application in Bank. Journal of Information & Computational Science, 9(12): 3431–3438.
Chiang, W. Y. (2014). Applying data mining with a new model on customer relationship management systems: a case of airline industry in Taiwan. Transportation Letters, 6(2): 89-97.
Deng, H. (1999). Multicriteria analysis with fuzzy pair wise comparisons. International Journal of Approximate Reasoning, 21 (3): 215-231.
Durango-Cohen, E. J., Torres, R. L. & Durango-Cohen, P. L. (2013). Donor Segmentation: When Summary Statistics Don't Tell the Whole Story. Journal of Interactive Marketing, 27(3): 172-184.
Fader, P. & Hardie, B. (2007). Probability Models for Customer-Base Analysis. 18th Annual Advanced Research Techniques Forum, June 10–13.Sante Fe, New Mexico.
Fader, P.S., Hardie, B.G.S. & Lee, K.L. (2005a).Counting Your Customers, the Easy Way: An Alternative to the Pareto/NBD Model. Marketing Science, 24(2): 275-284.
Fader, P. S., Hardie, B. G. S. & Lee. K. L. (2005b). RFM and CLV: Using iso-value curves for customer base analysis. Journal of Marketing Research, 42(4): 415-430.
Fader, P.S., Hardie, B.G.S. & Shang, J. (2010) Customer-Base Analysis in a Discrete-Time Noncontractual Setting. Marketing Science, 29(6): 1086–1108.
Gupta, S., Hanssens, D., Kahn, W., Kumar, V., Lin, N. & Ravishanker, N. )2006(. Modeling Customer Lifetime Value. Journal of Service Research, 9(2): 139-155.
Han, S. H., Lu, S. X. & Leung, S. C. (2012). Segmentation of telecom customers based on customer value by decision tree model. Expert Systems with Applications, 39(4): 3964-3973.
Hiziroglu, A. (2013). Soft computing applications in customer segmentation: State-of-art review and critique. Expert Systems with Applications, 40(16): 6491-6507.
Hsieh, N. C. (2004). An integrated data mining and behavioral scoring model for analyzing bank customers. Expert systems with applications, 27(4): 623-633.
Hughes, A. M. (1994), Strategic database marketing, Chicago: Probus publishing.
Hwang, H., Jung, T. & Suh, E. (2004). An LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry. Expert systems with applications, 26(2): 181-188.
Jain, D. & Singh, S. S. (2002). Customer lifetime value research in marketing: A review and future directions. Journal of Interactive Marketing, 16(2): 34-46.
Keiningham, T. L., Aksoy, L. & Bejou, D. (2006). Approaches to measurement and management of customer value. Journal of relationship marketing, 5(2): 37-54.
Kim, S. Y., Jung, T. S., Suh, E. H. & Hwang, H. S. (2006). Customer segmentation and strategy development based on customer lifetime value: A case study. Expert Systems with Applications, 31(1): 101-107.
Kotler, P. (1997). Marketing management: Analysis, planning, implementation and control (9th ed.). Upper Saddle River, NJ, USA: Prentice-Hall.
Liu, D.-R., and Shih, Y.Y. (2005). Integrating AHP and data mining for product recommendation based on customer lifetime value. Information & Management, 42(3): 387-400.
Mikhailov, L., (2003). Deriving priorities from fuzzy pairwise comparison judgments. Fuzzy Sets and Systems, 134(3): 365-385.
Mollahosseini, A. & Alimirzaei, G. (2011). Segmentation and Identifying the Characteristics of Customers of Irankhodro & Saipa Groups in Kerman city, Iranian Business Management, 2(6): 135-146. (in Persian)Mortazavi, S., Asemandoreh, Y., Najafi Siahroudi, M. & Alavi, M. (2011). Benefit Sought Segmentation of Mobile Phone Market, Iranian Business Management, 3(8): 115-132. (in Persian)Mulhern, F. J. (1999). Customer profitability analysis: Measurement, concentration, and research directions. Journal of Interactive Marketing, 13(1): 25–40.
Niraj, R., Gupta, M. & Narasimhan, C. (2001). Customer profitability in a supply chain. Journal of Marketing, 65(3): 1-16.
Olson, D. L. & Chae, B. K. (2012). Direct marketing decision support through predictive customer response modeling. Decision Support Systems, 54(1): 443-451.
Qhodsipour, H. (2002). Analytical Hierarchy Process, Tehran: Amirkabir University Publication. (in Persian)Reinartz, W.J. & Kumar, V. (2000). On the Profitability of Long-Life Customers in a Non contractual Setting: An Empirical Investigation and Implications for Marketing. Journal of Marketing, 64(4): 17-35.
Rossiter, J. & Percy, L. (1997). Advertising communications and promotion management, (2nd ed.). New York: McGraw-Hill.
Schmittlein, D. C., Morrison, D. G. & Colombo, R. (1987). Counting your customers: who are they and what will they do next? Management Science, 33(1): 1-24.
Stone, B. (1995). Successful direct marketing methods, IL: NTC business books, Lincolnwood.
Tsai, C. F., Hu, Y. H. & Lu, Y. H. (2013). Customer segmentation issues and strategies for an automobile dealership with two clustering techniques. Expert Systems. DOI: 10.1111/exsy.12056.
Verhoef, P. C. & Donkers, B. (2001). Predicting customer potential value an application in the insurance industry. Decision Support Systems, 32(2): 189–199.
Wang, Y.M., Yang, J.B. & Xu, D.L. (2005). A two-stage logarithmic goal programming method for generating weights from interval comparison matrices. Fuzzy Sets and Systems, 152(3): 475-498.
Wu, R. S. & Chou, P. H. (2011).Customer segmentation of multiple category data in e-commerce using a soft-clustering approach. Electronic Commerce Research and Applications, 10(3): 331-341.
Xu, R. (2000). Fuzzy least-squares priority method in the analytic hierarchy process. Fuzzy Sets and Systems, 112(3): 395-404. | ||
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