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M. Kasslin, J. Kangas, O. Simula (1992)
Process State Monitoring Using Self-Organizing Maps
O. Simula, P. Vasara, Juha Vesanto, R. Helminen (1999)
The Self-Organizing Map in Industry Analysis
T. Kohonen (1995)
Self-Organizing and Associative Memory
Florian Zettelmeyer (2000)
Expanding to the Internet: Pricing and Communications Strategies When Firms Compete on Multiple ChannelsJournal of Marketing Research, 37
N. Kasabov, R. Kozma, J. Kacprzyk (1999)
Neuro-Fuzzy Techniques for Intelligent Information Systems
A. Yeo, K. Smith‐Miles, R. Willis, M. Brooks (2001)
Modelling the Effect of Premium Changes on Motor Insurance Customer Retention Rates Using Neural Networks
Piew Datta, B. Masand, D. Mani, Bin Li (2000)
Automated Cellular Modeling and Prediction on a Large ScaleArtificial Intelligence Review, 14
J. Peppard (2000)
Customer Relationship Management (CRM) in Financial ServicesEuropean Management Journal, 18
H. Song, Jae Kim, S. Kim (2001)
Mining the change of customer behavior in an internet shopping mallExpert Syst. Appl., 21
R. Agrawal, R. Srikant (1994)
Fast Algorithms for Mining Association Rules in Large Databases
T. Kohonen (1990)
The self-organizing mapNeurocomputing, 21
T. Kohonen (1989)
Self-organization and associative memory: 3rd edition
E. Alhoniemi, J. Hollmen, O. Simula, J. Vesanto (1999)
Process Monitoring and Modeling Using the Self-Organizing MapIntegrated CAE, 6
A. Eiben, A. Koudijs, F. Slisser (1998)
Genetic Modelling of Customer Retention
Juha Vesanto (1999)
SOM-based data visualization methodsIntell. Data Anal., 3
T. Kohonen, E. Oja, O. Simula, Aari Visa, J. Kangas (1996)
Engineering applications of the self-organizing mapProc. IEEE, 84
KianSing Ng, Huan Liu (2000)
Customer Retention via Data MiningArtificial Intelligence Review, 14
K. Kira, L. Rendell (1992)
The Feature Selection Problem: Traditional Methods and a New Algorithm
G. Chakraborty, B. Chakraborty (2000)
A novel normalization technique for unsupervised learning in ANNIEEE transactions on neural networks, 11 1
Ron Kohavi, George John (1997)
Wrappers for Feature Subset SelectionArtif. Intell., 97
Ellie Trubik, Malcolm Smith (2000)
Developing a model of customer defection in the Australian banking industryManagerial Auditing Journal, 15
A. Berson, Stephen Smith, Kurt Thearling (1999)
Building Data Mining Applications for CRM
Jens Berfenfeldt (2010)
CUSTOMER RELATIONSHIP MANAGEMENT: A STRATEGIC IMPERATIVE IN THE WORLD OF E-BUSINESS
E. Alhoniemi, Jaakko Hollmén, O. Simula, Juha Vesanto (1999)
Process Monitoring and Modeling Using the Self-Organizing MapIntegr. Comput. Aided Eng., 6
J. Lee, S. You, Sang-Chan Park (2001)
A new intelligent SOFM-based sampling plan for advanced process controlExpert Syst. Appl., 20
R. Cooley, B. Mobasher, J. Srivastava (1999)
Data Preparation for Mining World Wide Web Browsing PatternsKnowledge and Information Systems, 1
M. Hall, L. Smith (1998)
Practical feature subset selection for machine learning
Nandini Raghavan, Robert Bell, Matthias Schonlau (2000)
Defection detection: using activity profiles to predict ISP customer vulnerability
R. Agrawal, T. Imielinski, A. Swami (1993)
Mining association rules between sets of items in large databasesProceedings of the 1993 ACM SIGMOD international conference on Management of data
M. Lejeune (2001)
Measuring the impact of data mining on churn managementInternet Res., 11
(1991)
Self-organizing Feature Maps for Process Control in Chemistry
(2000)
Target Selection via Scoring Using Association Rules
K. Smith‐Miles, R. Willis, M. Brooks (2000)
An analysis of customer retention and insurance claim patterns using data mining: a case studyJournal of the Operational Research Society, 51
H. Almuallim, Thomas Dietterich (1994)
Learning Boolean Concepts in the Presence of Many Irrelevant FeaturesArtif. Intell., 69
Y. Ma, B. Liu, C. Wong, Philip Yu, Shuik Lee (2000)
Targeting the right students using data mining
Bettina Berendt, M. Spiliopoulou (2000)
Analysis of navigation behaviour in web sites integrating multiple information systemsThe VLDB Journal, 9
M. Mozer, R. Wolniewicz, David Grimes, Eric Johnson, Howard Kaushansky (2000)
Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industryIEEE transactions on neural networks, 11 3
Customer retention is an increasinglypressing issue in today's competitiveenvironment. This paper proposes a personalizeddefection detection and prevention procedurebased on the observation that potentialdefectors have a tendency to take a couple ofmonths or weeks to gradually change theirbehaviour (i.e., trim-out their usage volume)before their eventual withdrawal. For thispurpose, we suggest a SOM (Self-Organizing Map)based procedure to determine the possiblestates of customer behaviour from pastbehaviour data. Based on this staterepresentation, potential defectors aredetected by comparing their monitoredtrajectories of behaviour states with frequentand confident trajectories of past defectors.Also, the proposed procedure is extended toprevent the defection of potential defectors byrecommending the desirable behaviour state forthe next period so as to lower the likelihoodof defection. For the evaluation of theproposed procedure, a case study has beenconducted for a Korean online game site. Theresult demonstrates that the proposed procedureis effective for defection prevention andefficiently detects potential defectors withoutdeterioration of prediction accuracy whencompared to that of the MLP (Multi-LayerPerceptron) neural networks.
Artificial Intelligence Review – Springer Journals
Published: Oct 10, 2004
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