A novel decision rules approach for customer relationship management of the airline market

نویسنده

  • James J. H. Liou
چکیده

Customer churn means the loss of existing customers to a competitor. Accurately predicting customer behavior may help firms to minimize this loss by proactively building a lasting relationship with their customers. In this paper, the application of the factor analysis and the Variable Consistency Dominance-based Rough Set Approach (VC-DRSA) in the customer relationship management (CRM) of the airline market is introduced. A set of ‘‘if . . . then . . .” decision rules are used as the preference model to classify customers by a set of criteria and regular attributes. The proposed method can determine the competitive position of an airline by understanding the behavior of its customers based on their perception of choice, and so develop the appropriate marketing strategies. A large sample of customers from an international airline is used to derive a set of rules and to evaluate its prediction ability. 2008 Elsevier Ltd. All rights reserved.

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

Mining Airline Data for CRM Strategies

In today’s competitive climate, customer relationship management (CRM) has become an essential component in airline business strategies. CRM in the airline industry would be based on analyzing customer data in order to understand preferences and behavior. In this paper, we apply data mining techniques to real airline frequent flyer data in order to derive CRM recommendations and strategies. Clu...

متن کامل

Comparing performance of organization on implementation of customer relationship management systems using ANP and TOPSIS hybrid approach

As the customers are the main reason of the formation and survival of the organization, not only understanding their obvious needs, but also forecasting, determining and guiding their hidden needs, design and implementing plans of offering services for meeting these needs for attracting customers are among cornerstone of any activity in the organization. In this research, one compares the perfo...

متن کامل

Modelling Customer Attraction Prediction in Customer Relation Management using Decision Tree: A Data Mining Approach

In Today’s quality- based competitive world, known as knowledge age, customer attraction is of ultimate importance. In respect to the slogan “customer is always right”, customer relation management is the core of an organizational strategy playing an important role in four aspects of customer identification, customer attraction, customer retaining, and customer satisfaction. Commercial organiza...

متن کامل

A Dominance-based Rough Set Approach to customer behavior in the airline market

Market segmentation is a crucial activity in the present business environment. Data mining is a useful tool for identifying customer behavior patterns in large amounts of data. This information can then be used to help with decision-making in areas such as the airline market. In this study, we use the Dominance-based Rough Set Approach (DRSA) to provide a set of rules for determining customer a...

متن کامل

Retaining Customers Using Clustering and Association Rules in Insurance Industry: A Case Study

This study clusters customers and finds the characteristics of different groups in a life insurance company in order to find a way for prediction of customer behavior based on payment. The approach is to use clustering and association rules based on CRISP-DM methodology in data mining. The researcher could classify customers of each policy in three different clusters, using association rules. A...

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:
  • Expert Syst. Appl.

دوره 36  شماره 

صفحات  -

تاریخ انتشار 2009