مدل‎سازی ترکیبی Pareto/NBD و RFM موزون فازی به‎منظور بخش‌بندی مشتریان در روابط غیرقراردادی

نوع مقاله : مقاله علمی پژوهشی

نویسندگان

1 استاد مهندسی صنایع، دانشکدۀ فنی و مهندسی، دانشگاه تربیت مدرس، تهران، ایران

2 دانشجوی دکتری مهندسی صنایع، دانشکدۀ فنی و مهندسی، دانشگاه تربیت مدرس، تهران، ایران

3 دانشیار 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

موضوعات

عنوان مقاله [English]

An Integrated Pareto/NBD- fuzzy weighted RFM model for customer segmentation in non-contractual setting

نویسندگان [English]

  • Amir Albadvi 1
  • Ashraf Norouzi 2
  • Mohammad Mehdi Sepehri 3
  • Mohammad Reza Amin Naseri 3
1 Prof. Amir Albadvi, Faculty of Engineering, Tarbiat Modares University, Tehran, Iran
2 PhD. Candidatein Industrial Engineering, Faculty of Engineering, Tarbiat Modares University, Tehran, Iran
3 Associate 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

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