بخش‌بندی مشتریان صنایع تولید و پخش کالاهای پرگردش بر اساس مدل بهبود‌یافته RFM (مطالعه موردی: شرکت گلستان)

نوع مقاله : مقاله علمی پژوهشی

نویسندگان

1 استادیار گروه مهندسی صنایع، دانشکدة فنی و مهندسی، دانشگاه آزاد اسلامی واحد تهران‌شمال، تهران، ایران

2 کارشناس ارشد مهندسی صنایع، دانشکدة فنی و مهندسی، دانشگاه شمال، آمل، ایران

چکیده

بخش‌بندی مشتریان و تحلیل رفتار آنها در صنایع تولید و پخش کالاهای پرگردش، با تعداد کثیری از مشتریان متفاوت در نقاط پراکنده، سبب هدفمندشدن فعالیت­های بازاریابی و ارتباط مؤثر آنها با مشتریان می‌شود. بخش‌بندی مشتریان از رویکردهای داده‌کاوی که به کشف گروه‌های مشابه از مشتریان منجر می‌شود، عمدتاً براساس متغیرهای تازگی، تکرار و حجم خرید در مدل RFM انجام می‌شود. کیفیت بخش‌بندی، به انتخاب مناسب متغیرهای عملکردی مشتریان بستگی دارد. ارزیابی کیفیت بخش‌بندی مشتریان بزرگ­ترین شرکت تولید و پخش کالاهای پرگردش، مؤید فرضیة تأثیرگذاری اندک متغیر تازگی خرید بر بخش‌بندی مشتریان این صنایع است. در این مقاله، متغیر توالی خرید (C) به­عنوان متغیر عملکردی مشتریان در این صنایع معرفی شده و با جایگزینی آن با متغیر تازگی خرید در مدل RFM، کیفیت بخش‌بندی مشتریان در این صنایع بهبود داده شده است. کاهش 11 درصدی شاخص دیویس- بولدین در خوشه‌بندی مشتریان شرکت گلستان و افزایش 1 درصدی دقت پیش‌بینی خوشة مشتریان در مدل شبکه‌های عصبی براساس مدل پیشنهادی این تحقیق (CFM) در مقایسه با مدل RFM، بیانگر دقت بالاتر مدل CFM است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Customer segmentation in Fast Moving Consumer Goods (FMCG) Industries by using developed RFM model

نویسندگان [English]

  • Vahid Baradaran 1
  • Mohammad Biglari 2
1 Assistant Prof., Industrial Engineering Department, North Tehran Branch, Islamic Azad University, Tehran, Iran
2 MSc. in Industrail Engineering, Faculty of Engineering, University of Shomal, Amol, Iran
چکیده [English]

Customers segmentation and analyzing their behavior at fast moving costumer goods (FMGS) industries which deal with a large number of customers with a variety of characteristics causes the marketing activities to be targeted and leads to effective communication with the customers. Segmentation, a data mining approach, which leads to the discovery of similar groups of customers, is usually done by recency, frequency and purchased volume variables in RFM model. Using proper segmentation variables affects the quality of segmentation. Analyzing the quality of Golsetan customer segments, the biggest FMCG industry in Iran, confirms the hypothesis which the recency variable is not effective in customer segmentation in FMCG industries. In this paper, purchase sequence (continuity) variable is defined as a new customer performance variable in FMCG industries. By replacing the continuity variable (C) with recency in RFM model, the quality of segmentation has been improved. Customers of Golestan Company were segmented by two RFM and proposed (CFM) models. The Davis-Bouldin criterion reduced more than 11 percent and the forecast accuracy for customers cluster in artificial neural networks increased about 1 percent.

کلیدواژه‌ها [English]

  • customer segmentation
  • Data Mining
  • Fast-Moving Consumer Goods Industry
  • RFM Model

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