تحليل رفتار مشتريان در خريد و ارسال پيامك گروهي آنلاين با استفاده از داده‌كاوي برمبناي مدل RFM

Analysis of Customer Behavior in Purchasing and Sending Online Group SMS using Data Mining Based on the RFM Model

در دنياي رقابتي امروز با كاهش هزينه‌هاي جابه‌جايي مشتريان، سازمان‌ها براي موفقيت در كسب‌وكار بايد مشتريان خود را به‌درستي بشناسند، نيازها و خواسته‌هاي آن‌ها را پيش‌بيني كنند و مانع رويگرداني مشتريان خود شوند. در تحقيق حاضر چارچوبي جديد مبتني بر مدل RFM براي تحليل رفتار و عملكرد مشتريان در فرايندهاي خدمت‌رساني به مشتري ‌ارايه مي‌شود. هدف اصلي از ارايه‌ي اين چارچوب تشخيص نقاط قوت و ضعف سازمان و تعيين روند حركت سازمان در جذب و نگه‌داري مشتريان است. موردكاوي روي 51534 ركورد از داده‌هاي يك شركت فعال در صنعت پيامك گروهي آنلاين انجام شده است. نتايج حاكي از آن است كه شركت مذكور در بازه ‌زماني مورد مطالعه در فرايند ارسال پيامك با مشكلات جدي روبه‌رو بوده و به‌تدريج مشتريان قديمي و پرسودش را از دست مي‌دهد، اگرچه در فرايند خريد پيامك موفق بوده و تعداد زيادي مشتري جديد جذب كرده است و بايد براي حفظ اين مشتريان راهكارهايي اتخاذ كند.

For effective Customer Relationship Management (CRM), it is important to gather information on customer value. The concept of segmentation is central to CRM. Segmentation is the method of knowing the customers and partitioning a population of customers into smaller groups. In other words, it means partitioning a population of customers into different segments, considering the most within-segment homogeneity and between-segment heterogeneity. It also allows a company to differentially treat consumers in different segments. The mass marketing approach cannot satisfy the needs of today’s diverse customers. This diversity should be satisfied using segmentation that divides markets into customer clusters with similar needs and/or characteristics that are likely to exhibit similar purchasing behaviors. One-to-one marketing is the ideal marketing strategy, in which every marketing campaign or product is optimally targeted at each individual customer; but this is not always possible. Thus, segmentation is required to distinguish similar clients and put them together in a segment. Doubtlessly, using segmentation to understand customer need is much easier, faster and more economical than uniquely investing in an understanding of individuals. This paper provides a proper framework for customer segmentation based on their lifetime value. In this study, first, we try to calculate RFM variables; recency (R), frequency (F) and monetary (M), in two groups of data including purchasing SMS (331 clients) and sending SMS (248 clients). Then, values are weighted with research and based on the advice of experts, and the optimal number of clusters based on Davis was determined. The results of the present study are to analyze and compare customer behavior in both the purchasing and sending processes. Then, we identified customer segmentation, customer lifetime value in the form of a pyramid, and the value of the companyʹs key customers. Finally, some suggestions are offered to improve the company purchasing and sending systems.