Data Mining as a Tool to Predict Churn Behavior of Customers

نویسنده

  • Vivek Bhambri
چکیده

Customer is the heart and soul of any organization. The era of globalization and cut throat competition has changed the basic concept of marketing, now marketing is not confined to selling the products to the customers, but the objective is to reach to the hearts of the customers so that they feel belongingness towards the organizations and hence should remain the loyal customers. In the dynamic market scenarios where companies are coming up with varied options every now and then, customer retention is a critical area to ponder upon, as customers usually churn from one company to another quite often and this too is happening at an alarming rate and is becoming the most important issue in customer relationship management. So prediction of the customer behaviour and hence taking remedial actions before hand is the need of the hour. But the ever growing data bases make it difficult to analyze the data and to forecast the future trends. The solution lies in the use of Data Mining tools for predicting the churn behavior of the customers. This paper throws light on the underlying technology and the perspective applications of data mining in predicting the churn behavior of the customers and hence paving path for better customer relationship management.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Customer Behavior Mining Framework (CBMF) using clustering and classification techniques

The present study proposes a Customer Behavior Mining Framework on the basis of data mining techniques in a telecom company. This framework takes into account the customers’ behavior patterns and predicts the way they may act in the future. Firstly, clustering technique is used to implement portfolio analysis and previous customers are divided based on socio-demographic features using k</em...

متن کامل

A New Model to Speculate CLV Based on Markov Chain Model

The present study attempts to establish a new framework to speculate customer lifetime value by a stochastic approach. In this research the customer lifetime value is considered as combination of customer’s present and future value. At first step of our desired model, it is essential to define customer groups based on their behavior similarities, and in second step a mechanism to count current ...

متن کامل

Hierarchical Alpha-cut Fuzzy C-means, Fuzzy ARTMAP and Cox Regression Model for Customer Churn Prediction

As customers are the main asset of any organization, customer churn management is becoming a major task for organizations to retain their valuable customers. In the previous studies, the applicability and efficiency of hierarchical data mining techniques for churn prediction by combining two or more techniques have been proved to provide better performances than many single techniques over a nu...

متن کامل

Predicting Customer Churn Using CLV in Insurance Industry

Today, increased level of customer awareness caused themto access to the other suppliers easily and they can get their servicesfrom the competitors with similar or even better quality and same price.Therefore, focusing on customers and preventing them to leave, has beenthe most important strategy for any company. Researches have shownthat retaining former customers is cheaper than attracting ne...

متن کامل

Customer Churn Prediction in Cloud Computing by using Fuzzy Boosted Trees

Organizations always take part in competition and as the competition grows; they are more concern about their customers rather than products. Organization always focuses on customer’s behaviour to retain in market competition. Churn prediction models are developed to manage and control customer churn in order to retain existing customers. Churn prediction aims to predict profitable customers. T...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2012