چكيده به لاتين
Abstract
In a competitive environment today, customers are the most important asset of any business. In targeted marketing, which is opposed to mass marketing, organizations focus their advertising activities on behalf of all customers on specific groups. The ability to identify profitable, loyal and skilled customers is a key success for customer-driven companies.
Customer segmentation allows organizations to tailor their marketing and sales strategies based on these segments. Customer clustering, the most important way of data mining used in marketing and customer relationship management (CRM), is to divide customers into meaningful categories and relatively homogeneous groups. Customers are categorized into distinct categories based on a variety of features such as behavioral, demographic, geographic, and psychological characteristics. By employing customer data and clustering them, businesses track track purchasing behavior and design innovative ways to achieve strategic goals.
Using definitive clustering models, each customer is attributed only to a cluster and does not provide more information, such as how each client belongs to each cluster. In this study, with the use of fuzzy clustering, 15-month customers of a drug distribution company were divided into four clusters. Then, the fuzzy analysis of each cluster and the allocation of the particular sales strategy for that cluster at different levels of membership ranked according to industry experts. This is very useful when confronted with a limited budget and the selection of customers with a higher priority. Finally, the clustering of new customers with fuzzy analysis has been predicted to determine the strategy of dealing with new customers through their degree of membership.
Keywords: Targeted Marketing, Customer Relationship Management, Data mining, Customer Clustering, Allocation of Sales Strategy.
