a r t i c l e i n f o Keywords: Customer response predictive model Knowledge-based marketing RFM Neural networks Decision tree models Logistic regression Decision support techniques and models for marketing decisions are critical to retail success. Among different marketing domains, customer segmentation or profiling is recognized as an important area in research and industry practice. Various ...

Telecommunication Companies use data mining techniques to maintain good relationships with their existing customers and attract new customers and identifying profitable/unprofitable customers. Clustering leads to better understanding of customer and its results can be used to definition and decision-making for promotional schemes. In this study, we used the 999-customer purchase records in Sout...

Given the vast amount of data generated by customers’ online and offline purchases, many organizations today are turning to data analytics to help design their direct marketing campaigns and introduce personalized promotions for customers. Data analytics allows companies to implement more effective market segmentation strategies, customize promotional offers, allocate marketing resources effici...

Abstract Digitalization allows retailers to target customers with personalized promotions when they enter the store. Although traditional promotional retailer objectives, such as store visit, become obsolete once shopper is already in store, still tend based on indicators that drive recency, frequency, and monetary value (RFM). In order improve efficiency, authors propose targeting shoppers inf...

Customer Segmentation is the process of grouping the customers based on their purchase habit. Data mining is useful in finding knowledge from huge amounts of data. The clustering techniques in data mining can be used for the customer segmentation process so that it clusters the customers in such a way that the customers in one group behave similar when compared to the customers in the other gro...