Dirk Van den Poel

发表

Purchase Prediction in Database Marketing with the ProbRough System

Dirk Van den Poel, Zdzislaw Piasta, D. V. Poel, 1998, Rough Sets and Current Trends in Computing.

Benefits of quantile regression for the analysis of customer lifetime value in a contractual setting: An application in financial services

Dirk Van den Poel, Dries F. Benoit, D. V. Poel, 2009, Expert Syst. Appl..

Temporary Staffing Services: A Data Mining Perspective

Dirk Van den Poel, Jeroen D'Haen, D. V. Poel, 2012, 2012 IEEE 12th International Conference on Data Mining Workshops.

The Dangers of Using Intention as a Surrogate for Retention in Brand Positioning Decision Support Systems

Michel Ballings, Dirk Van den Poel, D. V. Poel, 2012, GfKl.

Investigating the post-complaint period by means of survival analysis

Dirk Van den Poel, Bart Larivière, D. V. Poel, 2005, Expert Syst. Appl..

Data augmentation by predicting spending pleasure using commercially available external data

Dirk Van den Poel, Philippe Baecke, 2009, Journal of Intelligent Information Systems.

Separating financial from commercial customer churn: A modeling step towards resolving the conflict between the sales and credit department

Dirk Van den Poel, Jonathan Burez, D. V. Poel, 2008, Expert Syst. Appl..

Machine learning refinery sensor data to predict catalyst saturation levels

Dirk Van den Poel, Bram Steurtewagen, D. V. Poel, 2020, Comput. Chem. Eng..

CRM in social media: Predicting increases in Facebook usage frequency

Michel Ballings, Dirk Van den Poel, D. V. Poel, 2015, Eur. J. Oper. Res..

Semantic weak signal tracing

Dirk Thorleuchter, Dirk Van den Poel, Tobias Scheja, 2014, Expert Syst. Appl..

Cash demand forecasting in ATMs by clustering and neural networks

Vadlamani Ravi, Dirk Van den Poel, Kamini Venkatesh, 2014, Eur. J. Oper. Res..

Combining Unstructured/Structured information into a traditional churn prediction model (poster presentation)

Kristof Coussement, Dirk Van den Poel, 2007 .

Usability Based Modeling for Advanced IT-Security – An Electronic Engineering Approach

Dirk Thorleuchter, Dirk Van den Poel, Gerhard Weck, 2012 .

Kernel Factory: An ensemble of kernel machines

Michel Ballings, Dirk Van den Poel, D. V. Poel, 2013, Expert Syst. Appl..

Including spatial interdependence in customer acquisition models: A cross-category comparison

Dirk Van den Poel, Philippe Baecke, D. V. Poel, 2012, Expert Syst. Appl..

Improved marketing decision making in a customer churn prediction context using generalized additive models

Kristof Coussement, Dirk Van den Poel, Dries F. Benoit, 2010, Expert Syst. Appl..

Extracting Consumers Needs for New Products - A Web Mining Approach

Dirk Thorleuchter, Dirk Van den Poel, Anita Prinzie, 2010, 2010 Third International Conference on Knowledge Discovery and Data Mining.

Extraction of Ideas from Microsystems Technology

Dirk Thorleuchter, Dirk Van den Poel, 2012, CSIE 2012.

Improving Purchasing Behavior Predictions by Data Augmentation with Situational Variables

Dirk Van den Poel, Philippe Baecke, D. V. Poel, 2010, Int. J. Inf. Technol. Decis. Mak..

Incorporating sequential information into traditional classification models by using an element / position-sensitive SAM

Dirk Van den Poel, Anita Prinzie, D. V. Poel, 2005 .

Proof Running Two State-Of-The-Art Pattern Recognition Techniques in the Field of Direct Marketing

Bart Baesens, Jan Vanthienen, Guido Dedene, 2002, ICEIS.

Predicting customer profitability during acquisition: Finding the optimal combination of data source and data mining technique

Dirk Thorleuchter, Dirk Van den Poel, Jeroen D'Haen, 2013, Expert Syst. Appl..

Predicting customer loyalty using the internal transactional database

Dirk Van den Poel, Wouter Buckinx, Geert Verstraeten, 2007, Expert Syst. Appl..

Constrained optimization of data-mining problems to improve model performance: A direct-marketing application

Dirk Van den Poel, Anita Prinzie, D. V. Poel, 2005, Expert Syst. Appl..

Customer attrition analysis for financial services using proportional hazard models

Dirk Van den Poel, Bart Larivière, D. V. Poel, 2004, Eur. J. Oper. Res..

Investigating purchasing-sequence patterns for financial services using Markov, MTD and MTDg models

Dirk Van den Poel, Anita Prinzie, D. V. Poel, 2006, Eur. J. Oper. Res..

Weak signal identification with semantic web mining

Dirk Thorleuchter, Dirk Van den Poel, D. Thorleuchter, 2013, Expert Syst. Appl..

Wrapped input selection using multilayer perceptrons for repeat-purchase modeling in direct marketing

Bart Baesens, Jan Vanthienen, Guido Dedene, 2001, Intell. Syst. Account. Finance Manag..

Identifying Soccer Players on Facebook Through Predictive Analytics

Michel Ballings, Dirk Van den Poel, Matthias Bogaert, 2017, Decis. Anal..

Predicting e-commerce company success by mining the text of its publicly-accessible website

Dirk Thorleuchter, Dirk Van den Poel, D. Thorleuchter, 2012, Expert Syst. Appl..

Latency Measurement of Fine-Grained Operations in Benchmarking Distributed Stream Processing Frameworks

Dirk Van den Poel, Bram Steurtewagen, Giselle van Dongen, 2018, 2018 IEEE International Congress on Big Data (BigData Congress).

The added value of social media data in B2B customer acquisition systems: A real-life experiment

Michel Ballings, Dirk Van den Poel, Matthijs Meire, 2017, Decis. Support Syst..

Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models

Koen W. De Bock, Dirk Van den Poel, D. V. Poel, 2012, Expert Syst. Appl..

Improved multilevel security with latent semantic indexing

Dirk Thorleuchter, Dirk Van den Poel, D. Thorleuchter, 2012, Expert Syst. Appl..

Joint optimization of customer segmentation and marketing policy to maximize long-term profitability

Dirk Van den Poel, Nanda Piersma, Jedid-Jah Jonker, 2002, Expert Syst. Appl..

Investigating the role of product features in preventing customer churn, by using survival analysis and choice modeling: The case of financial services

Dirk Van den Poel, Bart Larivière, 2004, Expert Syst. Appl..

Predicting home-appliance acquisition sequences: Markov/Markov for Discrimination and survival analysis for modeling sequential information in NPTB models

Dirk Van den Poel, Anita Prinzie, D. V. Poel, 2007, Decis. Support Syst..

Customer event history for churn prediction: How long is long enough?

Michel Ballings, Dirk Van den Poel, D. V. Poel, 2012, Expert Syst. Appl..

Modeling partial customer churn: On the value of first product-category purchase sequences

João Falcão e Cunha, Dirk Van den Poel, Vera L. Miguéis, 2012, Expert systems with applications.

Predicting customer retention and profitability by using random forests and regression forests techniques

Dirk Van den Poel, Bart Larivière, D. V. Poel, 2005, Expert Syst. Appl..

Predicting cross-selling opportunities using a sequential architecture.

Dirk Van den Poel, Anita Prinzie, Patrick Van Kenhove, 2002 .

Random Multiclass Classification: Generalizing Random Forests to Random MNL and Random NB

Dirk Van den Poel, Anita Prinzie, D. V. Poel, 2007, DEXA.

Evaluating the Added Value of Pictorial Data for Customer Churn Prediction

Michel Ballings, Dirk Van den Poel, E. Verhagen, 2013 .

Dynamic Bayesian Networks for Acquisition Pattern Analysis: A Financial-Services Cross-Sell Application

Dirk Van den Poel, Anita Prinzie, D. V. Poel, 2009, PAKDD Workshops.

Using Webcrawling of Publicly Available Websites to Assess E-commerce Relationships

Dirk Thorleuchter, Dirk Van den Poel, D. Thorleuchter, 2012, 2012 Annual SRII Global Conference.

Improving Customer Churn Prediction by Data Augmentation Using Pictorial Stimulus-Choice Data

Michel Ballings, Dirk Van den Poel, Emmanuel Verhagen, 2012, IS-MiS.

Integrating the voice of customers through call center emails into a decision support system for churn prediction

Kristof Coussement, Dirk Van den Poel, D. V. Poel, 2008, Inf. Manag..

Evaluating the Importance of Different Communication Types in Tie Strength Prediction on Social Media

Michel Ballings, Dirk Van den Poel, Matthias Bogaert, 2016 .

Faculteit Economie En Bedrijfskunde Hoveniersberg 24 B-9000 Gent Predicting Mail-order Repeat Buying: Which Variables Matter? Predicting Mail-order Repeat Buying: Which Variables Matter?

Dirk Van den Poel, D. V. Poel, Dirk Van den Poel, 2022 .

Customer-adapted coupon targeting using feature selection

Geert Wets, Dirk Van den Poel, Wouter Buckinx, 2004, Expert Syst. Appl..

Evaluation of Stream Processing Frameworks

Dirk Van den Poel, Giselle van Dongen, 2020, IEEE Trans. Parallel Distributed Syst..

Enhanced decision support in credit scoring using Bayesian binary quantile regression

Dirk Van den Poel, Dries F. Benoit, Vera L. Miguéis, 2013, J. Oper. Res. Soc..

Equity price direction prediction for day trading: Ensemble classification using technical analysis indicators with interaction effects

Michel Ballings, Dirk Van den Poel, Celine Chesterman, 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

Dynamics between social media engagement, firm-generated content, and live and time-shifted TV viewing

Ewa Maslowska, Dirk Van den Poel, Edward C. Malthouse, 2018 .

Knowledge discovery in a direct marketing case using least squares support vector machines

Johan A. K. Suykens, Bart De Moor, Bart Baesens, 2001, Int. J. Intell. Syst..

Predicting Movie Watching Behavior using Facebook data and Information-fusion Sensitivity Analysis

Michel Ballings, Dirk Van den Poel, Matthias Bogaert, 2017 .

Mining Social Behavior Ideas of Przewalski Horses

Dirk Thorleuchter, Dirk Van den Poel, Sarah Herberz, 2011 .

Knowledge Discovery Using Least Squares Support Vector Machine Classifiers: A Direct Marketing Case

Johan A. K. Suykens, Bart De Moor, Bart Baesens, 2000, PKDD.

Exploiting Randomness for Feature Selection in Multinomial Logit: A CRM Cross-Sell Application

Dirk Van den Poel, Anita Prinzie, 2006, Industrial Conference on Data Mining.

Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting

Dirk Van den Poel, Wouter Buckinx, D. V. Poel, 2005, Eur. J. Oper. Res..

Evaluating multiple classifiers for stock price direction prediction

Michel Ballings, Dirk Van den Poel, Nathalie Hespeels, 2015, Expert Syst. Appl..

Predicting Self-declared Movie Watching Behavior Using Facebook Data and Information-Fusion Sensitivity Analysis

Michel Ballings, Dirk Van den Poel, Matthias Bogaert, 2019, Decis. Sci..

Extracting Consumers Needs for New Products

Dirk Thorleuchter, Dirk Van den Poel, Anita Prinzie, 2010 .

Improved Emergency Management by a Loosely Coupled Logistic System

Dirk Thorleuchter, Dirk Van den Poel, Joachim Schulze, 2012, Future Security.

Extracting Information from Sequences of Financial Ratios with Markov for Discrimination: An Application to Bankruptcy Prediction

Dirk Van den Poel, Andrey Volkov, D. V. Poel, 2012, 2012 IEEE 12th International Conference on Data Mining Workshops.

Banking behaviour after the lifecycle event of "moving in together": An exploratory study of the role of marketing investments

Dirk Van den Poel, Bart Larivière, D. V. Poel, 2007, Eur. J. Oper. Res..

Protecting research and technology from espionage

Dirk Thorleuchter, Dirk Van den Poel, D. Thorleuchter, 2013, Expert Syst. Appl..

Quantifying Credit-scoring Performance

Dirk Van den Poel, Geert Verstraeten, D. V. Poel, 2003 .

Manufacturer-retailer promotion competition: customisation of coupon target selection

Dirk Van den Poel, Wouter Buckinx, D. V. Poel, 2003 .

Using Text Summarizing to Support Planning of Research and Development

Dirk Thorleuchter, Dirk Van den Poel, 2014, WorldCIST.

Detecting Sequential Patterns For Cross-selling Fast Moving Consumer Goods

Dirk Van den Poel, Anita Prinzie, Patrick Van Kenhove, 2003 .

Faculteit Economie En Bedrijfskunde Hoveniersberg 24 B-9000 Gent Bayesian Kernel-based Classification for Financial Distress Detection Dirk Van Den Poel 4 Bayesian Kernel Based Classification for Financial Distress Detection

Johan A. K. Suykens, Bart Baesens, Dirk Van den Poel, 2022 .

Faculteit Economie En Bedrijfskunde Hoveniersberg 24 B-9000 Gent Bayesian Network Classifiers for Identifying the Slope of the Customer Lifecycle of Long-life Customers Bayesian Network Classifiers for Identifying the Slope of the Customer Lifecycle of Long-life Customers

Bart Baesens, Jan Vanthienen, Michael Egmont-Petersen, 2022 .

The added value of Facebook friends data in event attendance prediction

Michel Ballings, Dirk Van den Poel, Matthias Bogaert, 2016, Decis. Support Syst..

Social media optimization: Identifying an optimal strategy for increasing network size on Facebook

Michel Ballings, Dirk Van den Poel, Matthias Bogaert, 2016 .

Algorithms from and for Nature and Life - Classification and Data Analysis

Alfred Ultsch, Berthold Lausen, Dirk Van den Poel, 2013, Studies in Classification, Data Analysis, and Knowledge Organization.

Bayesian neural network learning for repeat purchase modelling in direct marketing

Bart Baesens, Jan Vanthienen, Guido Dedene, 2002, Eur. J. Oper. Res..

Leveraging latent representations for milk yield prediction and interpolation using deep learning

Dirk Van den Poel, Arno Liseune, Matthieu Salamone, 2020, Comput. Electron. Agric..

Incorporating Neighborhood Effects in Customer Relationship Management Models

Dirk Van den Poel, Philippe Baecke, 2011, ISMIS.

Using Eye-Tracking Data of Advertisement Viewing Behavior to Predict Customer Churn

Michel Ballings, Dirk Van den Poel, D. V. Poel, 2013, 2013 IEEE 13th International Conference on Data Mining Workshops.

RFM Variables Revisited Using Quantile Regression

Michel Ballings, Dirk Van den Poel, Dries Benoit, 2011, 2011 IEEE 11th International Conference on Data Mining Workshops.

Modeling complex longitudinal consumer behavior with Dynamic Bayesian networks: an Acquisition Pattern Analysis application

Dirk Van den Poel, Anita Prinzie, D. V. Poel, 2011, Journal of Intelligent Information Systems.

CRM at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services

Dirk Van den Poel, Jonathan Burez, D. V. Poel, 2007, Expert Syst. Appl..

Using NMF for Analyzing War Logs

Dirk Thorleuchter, Dirk Van den Poel, 2012, Future Security.

Handling class imbalance in customer churn prediction

Dirk Van den Poel, Jonathan Burez, D. V. Poel, 2009, Expert Syst. Appl..

Semantic compared cross impact analysis

Dirk Thorleuchter, Dirk Van den Poel, 2014, Expert Syst. Appl..

Response modeling for database marketing using binary classification

Dirk Van den Poel, 1999 .

Identifying new product ideas : waiting for the wisdom of the crowd or screening ideas in real time

Michel Ballings, Dirk Van den Poel, Edward C. Malthouse, 2017 .

The impact of sample bias on consumer credit scoring performance and profitability

Dirk Van den Poel, Geert Verstraeten, D. V. Poel, 2005, J. Oper. Res. Soc..

Predicting Website Audience Demographics forWeb Advertising Targeting Using Multi-Website Clickstream Data

Koen W. De Bock, Dirk Van den Poel, 2010, Fundam. Informaticae.

Direct and indirect effects of retail promotions on sales and profits in the do-it-yourself market

Geert Wets, Dirk Van den Poel, Jan De Schamphelaere, 2003, Expert Syst. Appl..

An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction

Koen W. De Bock, Dirk Van den Poel, D. V. Poel, 2011, Expert Syst. Appl..

Predicting web site audience demographics for web advertising targeting using multi-web site clickstream data

Koen W. De Bock, Dirk Van den Poel, S. Manigart, 2009 .

Mining ideas from textual information

Dirk Thorleuchter, Dirk Van den Poel, Anita Prinzie, 2010, Expert Syst. Appl..

Analyzing existing customers' websites to improve the customer acquisition process as well as the profitability prediction in B-to-B marketing

Dirk Thorleuchter, Dirk Van den Poel, Anita Prinzie, 2012, Expert Syst. Appl..

Evaluating the importance of different communication types in romantic tie prediction on social media

Michel Ballings, Dirk Van den Poel, Matthias Bogaert, 2016, Annals of Operations Research.

FACULTEIT ECONOMIE

Dirk Van den Poel, Anita Prinzie, D. V. Poel, 2007 .

Quantitative cross impact analysis with latent semantic indexing

Dirk Thorleuchter, Dirk Van den Poel, 2014, Expert Syst. Appl..

Predicting partial customer churn using Markov for discrimination for modeling first purchase sequences

João Falcão e Cunha, Dirk Van den Poel, Ana S. Camanho, 2012, Adv. Data Anal. Classif..

Predicting online-purchasing behaviour

Dirk Van den Poel, Wouter Buckinx, D. V. Poel, 2005, Eur. J. Oper. Res..

Model-supported business-to-business prospect prediction based on an iterative customer acquisition framework

Dirk Van den Poel, Jeroen D'Haen, D. V. Poel, 2013 .

A divide-and-conquer analysis of very large asymmetric vehicle routing problems

Birger Raa, Dirk Van den Poel, Dauwe Vercamer, 2012 .

bayesQR: A Bayesian Approach to Quantile Regression

Dirk Van den Poel, Dries F. Benoit, D. Benoit, 2017 .

Rough Sets for Database Marketing

Dirk Van den Poel, D. V. Poel, 1998 .

Ensembles of Probability Estimation Trees for Customer Churn Prediction

Koen W. De Bock, Dirk Van den Poel, 2010, IEA/AIE.

Improving response prediction in direct marketing by optimizing for specific mailing depths

Dirk Van den Poel, Anita Prinzie, Patrick Van Kenhove, 2002 .

Mining Innovative Ideas to Support New Product Research and Development

Dirk Thorleuchter, Dirk Van den Poel, Anita Prinzie, 2010 .

LSI based profitability prediction of new customers

Dirk Thorleuchter, Dirk Van den Poel, Anita Prinzie, 2011 .

Technology classification with latent semantic indexing

Dirk Thorleuchter, Dirk Van den Poel, D. Thorleuchter, 2013, Expert Syst. Appl..

Improving customer attrition prediction by integrating emotions from client/company interaction emails and evaluating multiple classifiers

Kristof Coussement, Dirk Van den Poel, D. V. Poel, 2009, Expert Syst. Appl..

Analyzing Website Content for Improved R&T Collaboration Planning

Dirk Thorleuchter, Dirk Van den Poel, D. Thorleuchter, 2013, WorldCIST.

Ensemble classification based on generalized additive models

Kristof Coussement, Koen W. De Bock, Dirk Van den Poel, 2010, Comput. Stat. Data Anal..

The role of seed money and threshold size in optimizing fundraising campaigns: Past behavior matters!

Dirk Van den Poel, Griet Alice Verhaert, D. V. Poel, 2012, Expert Syst. Appl..

Predicting donation behavior: Acquisition modeling in the nonprofit sector using Facebook data

Dirk Van den Poel, Matthias Bogaert, Lisa Schetgen, 2020, Decis. Support Syst..

GAMens: Applies GAMens, GAMrsm and GAMbag ensemble classifiers. R Package version 1.11.

Kristof Coussement, Koen W. De Bock, Dirk Van den Poel, 2010 .

Improving Customer Complaint Management by Automatic Email Classification Using Linguistic Style Features as Predictors

Kristof Coussement, Dirk Van den Poel, D. V. Poel, 2007 .

Faculteit Economie En Bedrijfskunde Hoveniersberg 24 B-9000 Gent Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparing Two Parameter-selection Techniques Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparin

Kristof Coussement, Dirk Van den Poel, D. V. Poel, 2022 .

Improving customer acquisition models by incorporating spatial autocorrelation at different levels of granularity

Dirk Van den Poel, Philippe Baecke, D. V. Poel, 2012, Journal of Intelligent Information Systems.

Why promotion strategies based on market basket analysis do not work

Geert Wets, Dirk Van den Poel, Bernd Vindevogel, 2005, Expert Syst. Appl..

The Impact Of Product Features And Intermediaries On Customer Retention

Dirk Van den Poel, Bart Larivière, 2003 .

Web mining based extraction of problem solution ideas

Dirk Thorleuchter, Dirk Van den Poel, D. Thorleuchter, 2013, Expert Syst. Appl..

The added value of auxiliary data in sentiment analysis of Facebook posts

Michel Ballings, Dirk Van den Poel, Matthijs Meire, 2016, Decis. Support Syst..

Incorporating sequential information in bankruptcy prediction with predictors based on Markov for discrimination

Dirk Van den Poel, Dries F. Benoit, Andrey Volkov, 2017, Decis. Support Syst..

Neural network survival analysis for personal loan data

Bart Baesens, Jan Vanthienen, Dirk Van den Poel, 2005, J. Oper. Res. Soc..

Evaluating multi-label classifiers and recommender systems in the financial service sector

Michel Ballings, Dirk Van den Poel, Matthias Bogaert, 2019, Eur. J. Oper. Res..

Integrating expert knowledge and multilingual web crawling data in a lead qualification system

Dirk Thorleuchter, Dirk Van den Poel, Dries F. Benoit, 2016, Decis. Support Syst..

Predicting the milk yield curve of dairy cows in the subsequent lactation period using deep learning

Dirk Van den Poel, Arno Liseune, Matthieu Salamone, 2021, Comput. Electron. Agric..

Granular Deleting in Multi Level Security Models – An Electronic Engineering Approach

Dirk Thorleuchter, Dirk Van den Poel, Gerhard Weck, 2012 .

Improving customer retention in financial services using kinship network information

Dirk Van den Poel, Dries F. Benoit, D. V. Poel, 2012, Expert Syst. Appl..

Investigating Purchasing Patterns for Financial Services using Markov , MTD and MTDg Models

Dirk Van den Poel, Anita Prinzie, D. V. Poel, 2004 .

Using Predicted Outcome Stratified Sampling to Reduce the Variability in Predictive Performance of a One-Shot Train-and-Test Split for Individual Customer Predictions

Dirk Van den Poel, Geert Verstraeten, 2006, Industrial Conference on Data Mining - Posters.

Box office sales and social media: A cross-platform comparison of predictive ability and mechanisms

Asil Oztekin, Michel Ballings, Dirk Van den Poel, 2021, Decis. Support Syst..

Adding interpretability to predictive maintenance by machine learning on sensor data

Dirk Van den Poel, Bram Steurtewagen, D. V. Poel, 2021, Comput. Chem. Eng..

Influencing Factors in the Scalability of Distributed Stream Processing Jobs

Dirk Van den Poel, Giselle van Dongen, 2021, IEEE Access.

Do Mobile Phone Data Provide a Better Denominator in Crime Rates and Improve Spatiotemporal Predictions of Crime?

Nico Van de Weghe, Thom Snaphaan, Wim Hardyns, 2021, ISPRS Int. J. Geo Inf..

Assessing and exploiting the profit function by modeling the net impact of targeted marketing

Dirk Van den Poel, Wouter Buckinx, 2005 .

WORKING PAPER Binary quantile regression: A Bayesian approach based on the asymmetric Laplace density

Dirk Van den Poel, Dries F. Benoit, D. Benoit, 2010 .

Mail-order versus retail store buying - The role of perceived risk and risk reduction strategies

Dirk Van den Poel, D. V. Poel, Joseph Leunis, 1992 .

Binary quantile regression: a Bayesian approach based on the asymmetric Laplace distribution

Dirk Van den Poel, Dries F. Benoit, D. Benoit, 2012 .

bayesQR: Bayesian quantile regression

Rahim Alhamzawi, Dirk Van den Poel, Dries F. Benoit, 2011 .

Perceived risk and rise reduction strategies in mail-order versus retail store buying

Dirk Van den Poel, D. V. Poel, Joseph Leunis, 1996 .

Demographic Classification of Anonymous Web Site Visitors Using Click Stream Information: A Practical Method for Supporting Online Advertising

Koen W. De Bock, Dirk Van den Poel, 2009 .

Database marketing modelling for financial services using hazard rate models

Dirk Van den Poel, D. V. Poel, Joseph Leunis, 1998 .

Preventing Customers from Running Away! Exploring Generalized Additive Models for Customer Churn Prediction

Kristof Coussement, Dirk Van den Poel, Dries F. Benoit, 2015 .

Data Mining for Database Marketing: a mail-order company application

Geert Wets, Dirk Van den Poel, 1996 .

Empathy as added value in predicting donation behavior

Dirk Van den Poel, D. V. Poel, Griet Verhaert, 2011 .

naam Integrating the voice of customers through call center emails into a churn predictions system

Kristof Coussement, Dirk Van den Poel, 2007 .

Assessing the principles of spatial competition between stores within a retail network

Nico Van de Weghe, Dirk Van den Poel, Matthias De Beule, 2015 .

Enterprise architecture management for small and medium sized enterprises: a case study and tool support

Dirk Van den Poel, M. Bernaert, 2012 .