ارايه‌ي مدلي مبتني بر داده‌كاوي براي پيش‌بيني جذب مشتري با استفاده از درخت تصميم در «مديريت ارتباط با مشتري»

PRESENTING A MODEL BASED ON DATA MINING TO FORECAST CUSTOMER ATTRACTION USING DECISION TREE IN CUSTOMER RELATIONSHIP MANAGEMENT

در دنياي رقابتي و كيفيت‌گراي امروز، جذب مشتري از اهميت زيادي برخوردار است. از اين رو، «مديريت روابط با مشتري» به‌عنوان هسته‌ي اصلي استراتژي سازمان در چهار بعد: شناسايي، جذب، نگه‌داري و رضايت مشتري به ايفاي نقش مي‌پردازد. سازمان‌ها با تجزيه و تحليل چرخه‌ي زندگي مشتري به افزايش ارزش مشتري دست يافته‌اند. اين ادبيات با كاربرد عملي داده‌كاوي در شناسايي مشتريان بالقوه، سعي دارد كه معيارهاي شناسايي اين مشتريان را در محيط رقابتي كسب و كارشان تعيين، و سازوكاري براي بالفعل‌شدنِ آنان ارايه دهد. در اين مطالعه، با استفاده از ابزار درخت تصميم معيارهاي اصلي و زيرمعيارهايي را شناسايي و سپس ميزان اهميت آن‌ها را تعيين مي‌‌كنيم. از اين طريق به سازمان اين امكان داده مي شود كه در هر مراجعه فرايند فروش را به سمتي سوق دهد كه منجر به خريد از سوي مراجعه‌كننده شود.

With the rapid change in the business competitive environment, enterprise resource integration and innovative issues of business operation have gradually become the most important issues for businesses. Furthermore, many organizations have implemented novel information technology and developed innovative application systems to enhance their competitive advantages. CRM systems can help organizations to gain potential new customers. Customer relationship management is a multi-perspective business paradigm which aims to maximize the benefits gained from relationships with customers. Today, in the quality-based and competitive world, which is known as knowledge time, customer attraction is very important. In line with the slogan, “the customer is always right”, customer relation management is at the core of an organizational strategy. It plays an important role in four aspects: customer identification, customer attraction, customer retaining, and customer satisfaction. By analysis of customer life cycles, commercial organizations have obtained increases in customer value. Data store and data mining tools, and other customer relation management methods, have provided new opportunities for business. Data mining (DM) methodology has been of tremendous assistance to researchers in extracting hidden knowledge and information inherited in their data. This paper, by the practical use of data mining in identification of potential customers, tries to help organizations to determine identification criteria of potential customers in the competitive environment of their business. It also presents mechanisms for identification of potential customers who have the ability to become real customers. In this paper, using a decision tree tool, we identify main criteria and determine their importance levels. We also consider that each main criterion consists of several sub-criteria, and we determine their importance in turning potential customers into real. We allow organizations to sell the process to each attendant in a direction which results in attendant (future customer) purchases, considering the criteria and sub-criteria identified.