تحلیل خوشه ای مشتریان بر مبنای مدل WRFM با رویکرد داده کاوی غیرنظارتی ( مورد مطالعه محصولات بهداشتی و آرایشی)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مدیریت، گروه مدیریت، دانشکده مدیریت و حسابداری، واحد رودهن، دانشگاه آزاد اسلامی، رودهن، ایران

2 دانشیار، گروه مدیریت ، دانشکده مدیریت دانشگاه تهران، تهران. ایران

3 دانشیار، گروه مدیریت، دانشکده مدیریت، واحد تهران مرکزی، دانشگاه آزاد اسلامی ، تهران، ایران

چکیده

در دنیای رقابتی امروز که شرکتها با حجم انبوهی از اطلاعات مشتریان به علت رشد و پیشرفت فناوری های اطلاعاتی و ایجاد پایگاه های داده ای مختلف مواجه‌اند استفاده از ابزار مدیریت ارتباط با مشتری که بتواند به درستی و به ‌موقع نیازها و انتظارات مشتریان را شناسایی و رصد کند بیش از پیش ضرورت می‌یابد؛ یکی از تکنیک‌هایی که می‌تواند در این برهه نقشی کلیدی و اساسی ایفاکند داده‌کاوی پایگاه داده‌هاست. هدف این پژوهش تحلیل داده‌های مشتریان بر پایه مدل WRFM به کمک روش‌های داده‌کاوی غیرنظارتی است؛ پژوهشگران درصددند که با کشف الگوهای موجود به ارایه استراتژی‌های مؤثرتری برای هر گروه از مشتریان و خصوصاً مشتریان کلیدی بپردازند تا برای سازمان سودآوری و عملکرد بهتری در برداشته باشد. .جامعه آماری این پژوهش مشتریان محصولات بهداشتی و آرایشی دربازه زمانی سال‌های1397-1398 است که به‌ روش نمونه‌گیری هدفمند در دسترس تعداد 64858 نمونه از پایگاه داده مشتریان انتخاب شده است. به کمک 3 تن از خبرگان (مدیران ارشد) فروش شرکت وزن شاخص‌های مدل WRFM تعیین شده است. برای تجزیه وتحلیل داده‌ها از نرم افزار کلمنتاین وSPSS استفاده شده است. با توجه به مدل پژوهش، 4دسته مشتری: خاص و کلیدی، طلایی بالقوه، نامطمئن ازدست رفته، نامطمئن جدید شناسایی و نامگذاری شدند که برای هر یک از این دسته‌ها استراتژی‌‌های متفاوتی ارایه شده است. ضمنا نتایج نشان می‌دهد که خوشه‌بندی K- میانگین شش خوشه‌ای با خلوص 0/744درصد نسبت به دیگر روش‌ها عملکرد بهتری داشته است.

کلیدواژه‌ها


عنوان مقاله [English]

Customers Clustring Analysis Based on WRFM Model Using Non-Supervisory Data Mining Approach (Case study of hygienic and cosmetic products)

نویسندگان [English]

  • omid bashardoust 1
  • Ezzatollah Asgharizadeh 2
  • Mohammad Ali Afshar Kazemi 3
1 PhD Student in Management, Department of Management, Faculty of Management and Accounting, Roudehen Branch, Islamic Azad University, Roudehen, Iran
2 Associate Professor, Department of Management, Faculty of Management, University of Tehran, Tehran, Iran
3 Associate Professor, Department of Management, School of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
چکیده [English]

In today's competitive world where companies are faced with a huge amount of customer information due to the growth and development of information technology and the creation of various databases, the use of customer relationship management tools that can accurately and timely identify and monitor customer needs and expectations Becomes more necessary;one of the techniques that can play a key and fundamental role in this period along with this important category is data mining of customer databases. The purpose of this study is to analyze customers clustering based on the WRFM model using non-supervisory data mining methods;the researchers seek to discover the existing rules and patterns to provide more effective strategies for each group of customers, especially key customers, in order to have a better profitability and performance for the organization.Using available purposive sampling method, 64858 samples have been selected from the database of customers who have used hygienic and cosmetic products in the period of 2018-2019.The weight of WRFM attributes has been determined by surveying 3 sales experts (senior managers) of the company. Clementine and SPSS soft wares were used for data analysis. According to the research model, 4 customer categories: Specific and key, Potential golden, lost Uncertainty, New Uncertain were identified and named, and different strategies have been presented for each of these customer categories Also the result show that K-mean clustering with six clusters and purity of 0.744% had better performance than other clustering methods.

کلیدواژه‌ها [English]

  • Anomaly detection
  • Data‌ mining
  • cluster analysis
  • Clustering
  • WRFM Model
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