Academic Papers about Analytical Customer Relationship Management (CRM) / Business Intelligence in Marketing / Customer Intelligence / Predictive Analytics / Customer Data Mining

by the Modeling Cluster of the

Department of Marketing

at Ghent University, Belgium

(Prof. Dr. Dirk Van den Poel)

 

Updated August 25th, 2009

 

VIDEO:

 

See www.mma.UGent.be/video.htm (click on navigation list on the left-hand side).

 

View a 42-minute video lecture by Anita Prinzie & Dirk Van den Poel about “Generalizing Random Forests Principles to other methods: Random MultiNomial Logit, Random Naďve Bayes, …” (1.4 GB video stream in Apple’s Quicktime, so you may have to download the software using the link to obtain the advanced high-quality H.264 codec, download of the video may take several minutes before it starts (depending on your connection speed!)). Click here for the pdf of the slides used in the presentation.

 

 

Click on paper titles to obtain the full electronic version (then continue by clicking on the “Downloads” link)

 

n      Data Augmentation

n      NEW – BAECKE Ph & VAN DEN POEL D. (2009), Data Augmentation by Predicting Spending Pleasure Using Commercially Available External Data, Forthcoming in Journal of Intelligent Information Systems.

n      BUCKINX W., VERSTRAETEN G., VAN DEN POEL D. (2007), Predicting Customer Loyalty Using the Internal Transactional Database, Expert Systems with Applications, 32 (1), 125-134.

 

 

n      Customer profitability

n      NEW – BENOIT D.F., VAN DEN POEL D. (2009), Benefits of quantile regression for the analysis of customer lifetime value in a contractual setting: An application in financial services, Forthcoming in Expert Systems with Applications.

n      LARIVIERE B., VAN DEN POEL D. (2005), Predicting Customer Retention and Profitability by Using Random Forest and Regression Forest Techniques, Expert Systems with Applications, 29 (2), 472-484.

 

n      Churn

n      COUSSEMENT Kristof, VAN DEN POEL Dirk (2009), Improving Customer Attrition Prediction by Integrating Emotions from Client/Company Interaction Emails and Evaluating Multiple Classifiers, Expert Systems with Applications, 36 (3), 6127-6134.

n      BUREZ Jonathan, VAN DEN POEL Dirk (2009), Handling Class Imbalance in Customer Churn Prediction, Expert Systems with Applications, Forthcoming.

n      BUREZ Jonathan, VAN DEN POEL Dirk (2008), Separating Financial From Commercial Customer Churn: A Modeling Step Towards Resolving The Conflict Between The Sales And Credit Department, Expert Systems with Applications, 35 (1), 497-514.

n      COUSSEMENT Kristof, VAN DEN POEL Dirk (2008), Churn Prediction in Subscription Services: An Application of Support Vector Machines While Comparing Two Parameter-Selection Techniques, Expert Systems with Applications, 34 (1), 313-327.

n      BUREZ Jonathan, VAN DEN POEL Dirk (2007), CRM at a Pay-TV Company: Using Analytical Models to Reduce Customer Attrition by Targeted Marketing for Subscription Services, Expert Systems with Applications, 32 (2), 277-288.

n      PRINZIE Anita, VAN DEN POEL Dirk (2006), “Incorporating sequential information into traditional classification models by using an element/position-sensitive SAM”, Decision Support Systems, 42 (2), 508-526.

n      BUCKINX Wouter, VAN DEN POEL Dirk (2005), “Customer Base Analysis: Partial Defection of Behaviorally-Loyal Clients in a Non-Contractual FMCG Retail Setting”, European Journal of Operational Research, 164 (1), 252-268 .

n      LARIVIERE Bart & VAN DEN POEL Dirk (2004), "Investigating the role of product features in preventing customer churn,by using survival analysis and choice modeling: The case of financial services", Expert Systems with Applications, 27 (2), 277-285.

n      VAN DEN POEL Dirk, LARIVIČRE Bart (2004), “Customer Attrition Analysis for Financial Services Using Proportional Hazard Models”, European Journal of Operational Research, 157 (1), 196-217.

 

n      Cross-sell

n      PRINZIE Anita & VAN DEN POEL Dirk (2008), Random Forests for Multiclass classification: Random Multinomial Logit, Expert Systems with Applications, 34 (3), 1721-1732.

n      PRINZIE Anita & VAN DEN POEL Dirk (2007), Predicting home-appliance acquisition sequences: Markov/MTD/MTDg and survival analysis for modeling sequential information in NPTB models, Decision Support Systems, 44 (1), 28-45.

n      PRINZIE Anita & VAN DEN POEL Dirk (2007), Random Multiclass Classification: Generalizing Random Forests to Random MNL and Random NB, Lecture Notes in Computer Science, LNCS 4653, 349-358.

n      PRINZIE Anita & VAN DEN POEL Dirk (2006), Exploiting Randomness for Feature Selection in Multinomial Logit: A CRM Cross-Sell Application, Lecture Notes in Artificial Intelligence, 4065, 310-323.

n      PRINZIE Anita & VAN DEN POEL Dirk (2006), “Investigating Purchasing Patterns for Financial Services using Markov, MTD and MTDg Models”, European Journal of Operational Research, 170 (3), 710-734.

n      BAESENS Bart, VERSTRAETEN Geert, VAN DEN POEL Dirk, EGMONT-PETERSEN M., VAN KENHOVE P., VANTHIENEN J. (2004), “Bayesian Network Classifiers for Identifying the Slope of the Customer-Lifecycle of Long-Life Customers”, European Journal of Operational Research, 156 (2), 508-523, 2004.

 

n      E-Commerce/Clickstream analysis

n      VAN DEN POEL Dirk, BUCKINX Wouter (2005), “Predicting Online-Purchasing Behavior”, European Journal of Operational Research, 166 (2), 2005, 557-575.

 

n      Database marketing

n      BUCKINX W., VAN DEN POEL D. (2009), Assessing and exploiting the profit function by modeling the net impact of targeted marketing, European Journal of Operational Research, Forthcoming.

n      PRINZIE Anita & VAN DEN POEL Dirk (2005), Constrained optimization of data-mining problems to improve model performance: A direct-marketing application, Expert Systems with Applications, 29 (3), 630-640.

n      BUCKINX Wouter et al. (2004), “Customer-Adapted Coupon Targeting Using Feature Selection”, Expert Systems with Applications, 26 (4), 509-518.

n      VAN DEN POEL Dirk, “Predicting Mail-Order Repeat Buying: Which Variables Matter?”, Tijdschrift voor Economie & Management, 48 (3), 371-403.

n      BAESENS Bart, VIAENE Stijn, VAN DEN POEL Dirk, VANTHIENEN Jan, DEDENE Guido (2002), “Bayesian Neural Network Learning for Repeat Purchase Modelling in Direct Marketing”, European Journal of Operational Research, 138 (1), 191-211.

 

n      Text Mining

n      COUSSEMENT K., VAN DEN POEL D. (2008), Integrating the Voice of Customers through Call Center Emails into a Decision Support System for Churn Prediction, Information and Management, 45 (3), 164-174.

n      COUSSEMENT K., VAN DEN POEL D. (2008), Improving Customer Complaint Management by Automatic Email Classification Using Linguistic Style Features as Predictors, Decision Support Systems, 44 (4), 870-882.

 

n      Other

n      PRINZIE Anita & VAN DEN POEL Dirk (2007), “Predicting home-appliance acquisition sequences: Markov/Markov for Discrimination and survival analysis for modeling sequential information in NPTB models”, Decision Support Systems, 44 (1), 28-45.

n      LARIVIČRE B. & VAN DEN POEL D. (2007), Banking behavior after the lifecycle event of “moving in together”: An exploratory study of the role of marketing investments, European Journal of Operational Research, 183 (1), 345-369..

n      LARIVIČRE B. & VAN DEN POEL D. (2005), Investigating the post-complaint period by means of survival analysis, Expert Systems with Applications, 29 (3), 667-677.

n      VINDEVOGEL B., VAN DEN POEL D., WETS G. (2005), Why promotion strategies based on market basket analysis do not work, Expert Systems with Applications, 28 (3), 583-590.

n      VAN DEN POEL Dirk et al. (2004), “Direct and Indirect Effects of Retail Promotions”, Expert Systems with Applications, 27 (1), 53-62.

n      JONKER J.J., PIERSMA N. & VAN DEN POEL D. (2004), “Joint Optimization of Customer Segmentation and Marketing Policy to Maximize Long-Term Profitability”, Expert Systems with Applications, 27 (2), 159-168.

 

 

 

The Department of Marketing of Ghent University offers a Master of Marketing Analysis, which is a one-year full-time degree (from October – June) specializing in CRM and market(ing) research and marketing communications. See http://www.mma.UGent.be  for more information.

 

 

Links to other great scholars in this field: