studying about the customer segmentation and begetting customer ranking plan diverts more attention in recent years. in this regard, this study tries on providing a methodology for segmenting customers based on their value driver parameters which extracted from transaction data and then ranks customers with regard to their customer lifetime value (clv) score. discovering hidden pattern between ...

in order to survive and sustain the competitive advantage in the competitive markets, many organizations have turned to relationship marketing by focusing on maximizing the life time value of their customers and churn management. in fact, more and more companies do realize that their most precious asset is the existing customer base. customer retention is a valuable strategy to ensure long term...

This study aims to present a very recent literature review on customer churn prediction based 40 relevant articles published between 2010 and June 2020. For searching the literature, most according Google Scholar ranking were selected collected. Then, each of scrutinized six main dimensions: Reference; Areas Research; Main Goal; Dataset; Techniques; outcomes. The research has proven that widely...

RFM stands for Recency, Frequency and Monetary value. RFM analysis is a marketing technique used for analyzing customer behavior such as how recently a customer has purchased (recency), how often the customer purchases (frequency), and how much the customer spends (monetary). It is a useful method to improve customer segmentation by dividing customers into various groups for future personalizat...

Customer churn prediction models aim to detect customers with a high propensity to attrite. Predictive accuracy, comprehensibility, and justifiability are three key aspects of a churn prediction model. An accurate model permits to correctly target future churners in a retention marketing campaign, while a comprehensible and intuitive rule-set allows to identify the main drivers for customers to...

In today’s competitive world, organizations are in a constant struggle to retain their current customers while attracting new through various methods. Customer churn is major challenge different industries and companies. Despite initial successful attempts at customers, soon face the fact that may turn away toward rivals. By identifying candidates, will be able guarantee future success by revis...

The explosion of customer data in the last twenty years has increased the need for data mining aimed at customer relationship management (CRM) and understanding the customer. It is well known that the telecom sector consists of customers with a wide array of customer behaviors. These customers pose different risks, making it imperative to implement different treatment strategies to maximize sha...

Customer retention is a critical concern for most mobile network operators because of the increasing competition in the mobile services sector. This concern has driven companies to exploit data as an avenue to better understand customer needs. Data mining techniques such as clustering and classification have been adopted to understand customer retention in the mobile services industry. However,...

Data mining is a well-known technique, which can be used to extract hidden information about customers’ behaviors. It is used to improve the customer relationship management processes by various Organizations. Previous researches in constraint based pattern mining emphasis only on the concept of frequency.But the changes in the environment may occur frequently, so the frequently occuring patter...

With the development of telecom business, customer churn prediction becomes more and more important. An outstanding issue in customer churn prediction is high dimensional problem. Curse of dimensionality will easily occur if effective feature extraction is not applied during modeling. Among the most popular feature extraction approaches, principal component analysis (PCA) method based on induct...