شناسایی مشتریان سودآور بر اساس مدل RFM و تنوع محصولات در پلتفرم های خرده فروشی آنلاین

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش آموخته کارشناسی ارشد گروه مدیریت،دانشکده علوم اجتماعی و اقتصادی،دانشگاه الزهرا،تهران،ایران

2 دانشیار گروه مدیریت، دانشکده علوم اجتماعی و اقتصادی، دانشگاه الزهرا، تهران، ایران

چکیده
رفتار مصرف کننده یک جنبه حیاتی از استراتژی بازاریابی است که شامل درک عادات خرید، انگیزه ها و ترجیحات مشتریان است. درک بهتر رفتار مشتری از طریق روش های نوآورانه ذخیره سازی و تجزیه و تحلیل داده ها و اطلاعات مشتریان، تدوین استراتژی های اثربخش تری را موجب می شود. ظهور فناوری‌های محاسباتی جدید تغییرات عمده‌ای را در توانایی سازمان‌ها برای جمع‌آوری، ذخیره و تجزیه و تحلیل داده‌های کلان ایجاد کرده است. بسیاری از تحقیقات با استفاده از الگوریتم‌های یادگیری ماشین بدون نظارت مانند K-Means با استفاده از مدل معروفRFM به طبقه بندی مشتری پرداخته‌اند اما این مدل ها با نادیده گرفتن سایر پارامترهای مهم با توجه به حوزه کاربرد، ناکافی می باشند. این تحقیق از نظر هدف کاربردی و از نظر گردآوری داده ها توصیفی و از نوع تحقیقات کمی است که با استفاده از 200000 تراکنش مشتریان فروشگاه خرده فروشی آنلاین طی بازه زمانی 2013 تا 2018 انجام شده است. مدل، با افزودن تنوع "D" به عنوان پارامتر چهارم، با اشاره به تنوع محصولات خریداری شده توسط یک مشتری معین، اصلاح شده است. طبقه بندی بر اساس RFM-D در بازار خرده فروشی آنلاین به منظور شناسایی الگوهای رفتاری برای مشتری اعمال می شود. بررسی رفتار مشتریان خوشه ها نشان داد که تنوع محصولات به همراه سایر متغیرهای رفتاری، سودآوری بیشتری نسبت به مدل RFM ارائه کرده است.

کلیدواژه‌ها


عنوان مقاله English

Identifying profitable customers based on the RFM model and the variety of products in online retail platforms

نویسندگان English

Fatemeh Ghobaee arani 1
Masoumeh Hoseinzadeh Shahri 2
1 Master's degree in Management Department, Faculty of Social and Economic Sciences, Al-Zahra University, Tehran, Iran
2 Associate Professor
چکیده English

Consumer behavior is a critical aspect of marketing strategy that involves understanding customers' buying habits, motivations, and preferences. Better understanding of customer behavior through innovative methods of storing and analyzing customer data and information leads to formulation of more effective strategies. The emergence of new computing technologies has brought about major changes in the ability of organizations to collect, store and analyze big data. Many researches have done customer classification using unsupervised machine learning algorithms such as K-Means using the famous RFM model, but these models are insufficient by ignoring other important parameters according to the field of application. This research is applied in terms of purpose and descriptive in terms of data collection, and it is a quantitative type of research that was conducted using 200,000 transactions of online retail store customers during the period of 2013 to 2018. The model is modified by adding variety "D" as a fourth parameter, referring to the variety of products purchased by a given customer. Classification based on RFM-D is applied in the online retail market in order to identify behavioral patterns for customers. Examining the behavior of cluster customers showed that the variety of products, along with other behavioral variables, provided more profitability than the RFM model.

کلیدواژه‌ها English

Customer buying behavior
RFM-D
diversity
K-Means algorithm
customer classification
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