دسته‌بندی و پیش بینی دسته های مشتریان به کمک تلفیق روش LRFM، چندک‌ها و روش های داده‌کاوی چندکلاسه

نوع مقاله : مطالعه موردی

نویسندگان

1 استادیار، گروه مهندسی کامپیوتر، دانشکده مهندسی، دانشگاه اراک، اراک، ایران.

2 فارغ التحصیل کارشناسی ارشد، گروه فناوری اطلاعات، گرایش تجارت الکترونیک، دانشگاه غیرانتفاعی اترک قوچان، قوچان، ایران.

10.48301/kssa.2022.316104.1852

چکیده

امروزه مدیریت ارتباط با مشتری به ضرورتی اجتناب‌ناپذیر در سازمان­ها تبدیل شده است. با این حال، یکی از مشکلات اولیه در این زمینه، فقدان معیار مشخص برای طبقه­بندی مشتریان است. ایجاد مدل­های پیش‌بینی از دسته­های مشتری نیز یکی از معضلات مدیریت ارتباط با مشتری است. برای این منظور در این مقاله، از تلفیق روش LRFM به همراه مفاهیم چندک­ها و روش­های داده‌کاوی چندکلاسه برای دسته­بندی مشتریان استفاده شده است. در این راهکار، ابتدا رکوردهای اطلاعاتی مشتریان بررسی و پالایش شدند تا داده­های نامعتبر حذف گردند. سپس با ترکیب مفاهیم روش LRFM و چندک­ها، مشتریان دسته­بندی شده­اند. در ادامه، به‌منظور تشخیص و طبقه­بندی مشتریان جدید، خروجی­های به‌دست‌آمده، در معرض انتخاب ویژگی قرار گرفتند و ویژگی­های اضافی آنها حذف شدند. سپس، ویژگی‌های باقی‌مانده برای ایجاد مدل‌های پیش‌بینی­کننده دسته مشتریان به طبقه‌بند‌های مختلف منتقل ‌شدند. برخلاف تحقیقات پیشین، در این مقاله، از معیارهای ارزیابی چندکلاسه میکروسکوپی و ماکروسکوپی برای ارزیابی عملکرد پیش‌بینی‌کننده‌ها استفاده شده است. به‌منظور ارزیابی نتایج، در یک مطالعه موردی، بخشی از اطلاعات مشتریان شرکت سرچین خراسان مورد استفاده قرار گرفته است. ارزیابی­ها نشان می‌دهد که دقت مدل‌های پیش‌بینی روش پیشنهادی در طبقه‌بندی مشتریان، بالاتر از دقت روش مرسوم دسته­بندی مشتریان به کمک K-Means است. همچنین روش پیشنهادی در تشخیص گروهی رکوردها عملکرد بهتری دارد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Classification and Prediction of Customer Categories Using Combination of LRFM Method, Quartiles and Multi-Class Data Mining Methods

نویسندگان [English]

  • Hossein Ghaffarian 1
  • Alireza Bamohabbat 2
1 Assistant Professor, Department of Computer Engineering, Faculty of Engineering, Arak University, Arak, Iran.
2 MSc. Graduated from department of Information Technology, E-commerce, Atrak Quchan Non-profit University. Quchan, Iran.
چکیده [English]

Today, Customer Relationship Management (CRM( has become an inevitable necessity in organizations. However, one of the primary problems in this area is the lack of a clear criterion for classifying customers. Creating predictive models from customer categories is also one of the challenges of customer relationship management. For this purpose, in this paper, the combination of LRFM method with quartile concepts and multi-class data mining methods was used to categorize customers. In this solution, customer information records were first reviewed and refined to remove invalid data. Customers were then categorized by combining the concepts of the LRFM method with quartiles. In order to identify and classify new customers, the obtained outputs are subjected to feature selection and their additional features removed. Then, the remaining features were transferred to different classifiers to create predictive customer category models. Contrary to previous research achievements, in this paper, multi-class microscopic and macroscopic evaluation criteria were used to evaluate the performance of predictors. In order to evaluate the results, in a case study, part of the customer information of Sarchin Khorasan Company was used. Evaluations showed that the accuracy of the predicted models of the proposed method in customer classification was higher than the conventional method of customer classification using K-Means. In addition, the proposed method performed better in group detection of records.

کلیدواژه‌ها [English]

  • Customer relationship management Data mining Clustering LRFM Classification Multi
  • class performance evaluation metrics

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علوم انسانی
شهریور 1402
صفحه 777-800

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  • تاریخ دریافت: 09 دی 1400
  • تاریخ بازنگری: 19 فروردین 1401
  • تاریخ پذیرش: 26 اردیبهشت 1401

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