Koen W. De Bock

Audencia Business School | AUDENCIA · Marketing

Deep neural network (DNN) architectures such as recurrent neural networks and transformers display outstanding performance in modeling sequential unstructured data. However, little is known about their merit to model customer churn with time-varying data. The paper provides a comprehensive evaluation of the ability of recurrent neural networks and...

Recommendation systems help companies construct online personalization strategies for customers who are often overwhelmed by the abundance of product choices available. To extend existing operations research literature on recommendation systems, this article proposes a decision analytic framework for interpretable recommendation systems with multip...

An important business domain that relies heavily on advanced statistical- and machine learning algorithms to support operational decision-making is customer retention management. Customer churn prediction is a crucial tool to support customer retention. It allows an early identification of customers who are at risk to abandon the company and provid...

In order to assess risks associated with establishing relationships with corporate partners such as clients, suppliers, debtors or contractors, decision makers often turn to business failure prediction models. While a large body of literature has focused on optimizing and evaluating novel methods in terms of classification accuracy, recent research...

Off-the-shelf machine learning algorithms for prediction such as regularized logistic regression cannot exploit the information of time-varying features without previously using an aggregation procedure of such sequential data. However, recurrent neural networks provide an alternative approach by which time-varying features can be readily used for...

This study investigates the value added by incorporating textual data into customer churn prediction (CCP) models. It extends the previous literature by benchmarking convolutional neural networks (CNNs) against current best practices for analyzing textual data in CCP, and, using real life data from a European financial services provider, validates...

Marketing messages are most effective if they reach the right customers. Deciding which customers to contact is an important task in campaign planning. The paper focuses on empirical targeting models. We argue that common practices to develop such models do not account sufficiently for business goals. To remedy this, we propose profit-conscious ens...

Multiple classifier systems combine the decisions from individual classifiers to obtain a more accurate classifier. Multiple classifier systems are also known as ensemble methods, committee of classifiers, and mixture of experts. Three popular ways of creating the individual classifiers for multiple classifiers systems are bagging, random subspace...

Customer relationship management (CRM) is becoming a very hot topic nowadays in academia and business environments. Indeed, companies are constantly searching for new innovative ways to create or maintain their competitive advantage. Due to the recent advances in Internet and technology, CRM predictive analytics is becoming an important tool in the...

Decision trees and logistic regression are two very popular algorithms in customer churn prediction with strong predictive performance and good comprehensibility. Despite these strengths, decision trees tend to have problems to handle linear relations between variables and logistic regression has difficulties with interaction effects between variab...

This study proposes a decision support framework to help e-commerce companies select the best collaborative filtering algorithms (CF) for generating recommendations on the basis of online binary purchase data. To create this framework, an experimental design applies several CF configurations, which are characterized by different data-reduction tech...

Numerous organizations and companies rely upon business failure prediction to assess and minimize the risk of initiating business relationships with partners, clients, debtors or suppliers. Advances in research on business failure prediction have been largely dominated by algorithmic development and comparisons led by a focus on improvements in mod...

For roughly two decades, the domains of database marketing, customer intelligence, and analytical CRM have nourished the thought that ever-increasing access to customer data can and should be leveraged in order to differentiate and innovate. The recent hype surrounding big data that spawned a multitude of new technologies related to distributed dat...

Customer relationship management (CRM) is becoming a very hot topic nowadays in academia and business environments. Indeed, companies are constantly searching for new innovative ways to create or maintain their competitive advantage. Due to the recent advances in Internet and technology, CRM predictive analytics is becoming an important tool in the...

Churn modeling is important to sustain profitable customer relationships in saturated consumer markets. A churn model predicts the likelihood of customer defection. This is important to target retention offers to the right customers and to use marketing resources efficiently. The prevailing approach toward churn model development, supervised learni...

Companies greatly benefit from knowing how problems with data quality influence the performance of segmentation techniques and which techniques are more robust to these problems than others. This study investigates the influence of problems with data accuracy – an important dimension of data quality – on three prominent segmentation techniques for...

The online gambling industry is one of the most revenue generating branches of the entertainment business, resulting in fierce competition and saturated markets. Therefore it is essential to efficiently retain gamblers. Churn prediction is a promising new alternative in customer relationship management (CRM) to analyze customer retention. It is the...

To build a successful customer churn prediction model, a classification algorithm should be chosen that fulfills two requirements: strong classification performance and a high level of model interpretability. In recent literature, ensemble classifiers have demonstrated superior performance in a multitude of applications and data mining contests. Ho...

Several studies have demonstrated the superior performance of ensemble classification algorithms, whereby multiple member classifiers are combined into one aggregated and powerful classification model, over single models. In this paper, two rotation-based ensemble classifiers are proposed as modeling techniques for customer churn prediction. In Rot...

Customer churn prediction is one of the most important elements of a company’s Customer Relationship Management (CRM) strategy. In this study, two strategies are investigated to increase the lift performance of ensemble classification models, i.e. (i) using probability estimation trees (PETs) instead of standard decision trees as base classifiers,...

Several recent studies have explored the virtues of behavioral targeting and personalization for online advertising. In this paper, we add to this literature by proposing a cost-effective methodology for the prediction of demographic website visitor profiles that can be used for web advertising targeting purposes. The methodology involves the trans...

Generalized additive models (GAMs) are a generalization of generalized linear models (GLMs) and constitute a powerful technique which has successfully proven its ability to capture nonlinear relationships between explanatory variables and a response variable in many domains. In this paper, GAMs are proposed as base classifiers for ensemble learning...

Several recent studies have explored the virtues of behavioral targeting and personalization for online advertising. In this paper, we add to this literature by proposing a cost-effective methodology for the prediction of demographic web site visitor profiles that can be used for web advertising targeting purposes. The methodology involves the tran...