New Approach for Customer Clustering by Integrating the LRFM Model and Fuzzy Inference System

Document Type : Research Paper


1 Department of Management, Faculty of Economics & Administrative Sciences, Ferdowsi University of Mashhad; Researcher at ACECR: Academic Center for Education, Culture and Research-Khorasan Razavi, Mashhad, Iran

2 Department of Management, Faculty of Economics & Administrative Sciences, Ferdowsi University of Mashhad; Researcher at Boshra Research Institute, Mashhad, Iran


This study aimed at providing a systematic method to analyze the characteristics of customers’ purchasing behavior in order to improve the performance of customer relationship management system. For this purpose, the improved model of LRFM (including Length, Recency, Frequency, and Monetary indices) was utilized which is now a more common model than the basic RFM model apt for analyzing the customer lifetime value. Since the RFM model does not take the customers’ loyalty into consideration, the LRFM model has instead been applied for making amendments. Contrary to most of the past studies in which the statistical clustering techniques were used besides the RFM or LRFM model, the current study has provided the possibility of clustering analysis by importing the LRFM indices into the framework of a fuzzy inference system. The results obtained for a wholesale firm based on the proposed approach indicated that there was a significant difference between clusters in terms of the four indices of LRFM. Therefore, this approach can be well utilized for clustering the customers and for studying their characteristics. The strong point of this approach compared to the older ones is its high flexibility, because in which it is not needed to re-cluster the customers and to reformulate the strategies when the number of customers is increased or decreased. Finally, after analyzing the attributes of each cluster, some suggestions on marketing strategies were made to be compatible with clusters, and totally, to improve the performance of customer relationship management system.


Main Subjects

Article Title [Persian]

رویکردی جدید برای خوشه‌بندی مشتریان با یکپارچه‌سازی مدل LRFM و سیستم استنتاج فازی

Authors [Persian]

  • علی علیزاده زوارم 1
  • احمدرضا کریمی مزیدی 2
1 گروه مدیریت، دانشکدة علوم اداری و اقتصادی، دانشگاه فردوسی مشهد؛ پژوهشگر جهاد دانشگاهی خراسان رضوی، مشهد، ایران
2 گروه مدیریت، دانشکدة علوم اداری و اقتصادی، دانشگاه فردوسی مشهد؛ پژوهشگر مؤسسة مطالعات راهبردی بشرا پژوه، مشهد، ایران
Abstract [Persian]

این مطالعه بر ارائة یک روش نظام‌مند برای تجزیه‌وتحلیل ویژگی‌های رفتار خرید مشتریان درراستای بهبود عملکرد سیستم مدیریت ارتباط با مشتری، هدف‌گذاری شده است. بدین‌منظور و برای تحلیل ارزش طول‌عمر مشتری، مدل بهبودیافتة LRFM (دربرگیرندة شاخص‌های طول‌مدت رابطه، تازگی رابطه، تعداد دفعات خرید، و ارزش پولی خرید) به‌کار گرفته شد، که در حال حاضر نسبت به مدل پایه‌ای RFM رایج‌تر است. از آنجا که مدل RFM وفاداری مشتریان را لحاظ نمی‌کند، به‌جای آن مدل اصلاح شدة LRFM مورد استفاده قرار گرفته است. برخلاف غالب مطالعات پیشین که در آنها تکنیک‌های خوشه‌بندی آماری در کنار مدل RFM یا LRFM مورد استفاده بوده، مطالعة حاضر امکان تحلیل خوشه‌بندی با وارد کردن شاخص‌های LRFM به چارچوب یک سیستم استنتاج فازی را فراهم آورده است. نتایج به‌دست آمده برای یک شرکت عمده‌فروشی براساس رویکرد پیشنهادی، نشان داد که در رابطه با چهار شاخص LRFM تفاوت معناداری بین خوشه‌ها وجود دارد. بنابراین، این رویکرد را می‌توان به‌خوبی برای خوشه‌بندی مشتریان و مطالعة ویژگی‌های آنها مورد استفاده قرار داد. نقطه‌قوت این رویکرد نسبت به موارد پیشین انعطاف‌پذیری آن است؛ چراکه در آن با افزایش یا کاهش تعداد مشتریان، نیازی به خوشه‌بندی مجدد آنها و تدوین دوبارة استراتژی‌ها نیست. درنهایت پس از تحلیل خصایص هر خوشه، برای استراتژی‌های بازاریابی هم‌ساز با خوشه‌ها و به‌طور کلی برای بهبود عملکرد سیستم مدیریت ارتباط با مشتری، پیشنهاداتی ارائه شد.

Keywords [Persian]

  • مدیریت ارتباط با مشتری
  • ارزش طول عمر مشتری
  • مدل LRFM
  • تحلیل خوشه‌بندی مشتری
  • سیستم استنتاج فازی
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