What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
Dirk Van den Poel focuses on Customer relationship management, Econometrics, Data mining, Random forest and Marketing.
His Customer relationship management research is multidisciplinary, incorporating elements of Customer retention, Customer intelligence, Actuarial science, Financial services and Purchasing.
His work carried out in the field of Econometrics brings together such families of science as Financial institution, Financial analysis and Parametric statistics.
Dirk Van den Poel combines subjects such as Artificial neural network, Cluster analysis and Variables with his study of Data mining.
As part of one scientific family, Dirk Van den Poel deals mainly with the area of Random forest, narrowing it down to issues related to the Logistic regression, and often Customer base.
His studies in Marketing integrate themes in fields like Context and Operations research.
His most cited work include:
- Customer attrition analysis for financial services using proportional hazard models (324 citations)
- Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting (318 citations)
- Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques (307 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Customer relationship management, Data mining, Marketing, Econometrics and Artificial intelligence.
His study looks at the relationship between Customer relationship management and topics such as Financial services, which overlap with Actuarial science.
His research integrates issues of Artificial neural network, Predictive analytics and Cluster analysis in his study of Data mining.
Dirk Van den Poel has researched Econometrics in several fields, including Statistics, Empirical research, Markov chain and Sample.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Pattern recognition.
In his study, Added value is inextricably linked to Social media, which falls within the broad field of Random forest.
He most often published in these fields:
- Customer relationship management (19.51%)
- Data mining (19.51%)
- Marketing (18.70%)
What were the highlights of his more recent work (between 2012-2021)?
- Data mining (19.51%)
- Social media (6.50%)
- Artificial intelligence (17.48%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Data mining, Social media, Artificial intelligence, Random forest and Machine learning.
His research in Data mining intersects with topics in Contrast, Artificial neural network, Cluster analysis, Customer relationship management and The Internet.
The Customer relationship management study combines topics in areas such as Context, Data quality and Customer intelligence.
His Artificial intelligence research focuses on subjects like Statistics, which are linked to Deep learning.
His Random forest research incorporates elements of Lift, Boosting and Support vector machine, AdaBoost.
His Machine learning research is multidisciplinary, relying on both Technical analysis and Data analysis.
Between 2012 and 2021, his most popular works were:
- Evaluating multiple classifiers for stock price direction prediction (155 citations)
- Cash demand forecasting in ATMs by clustering and neural networks (55 citations)
- Identifying new product ideas : waiting for the wisdom of the crowd or screening ideas in real time (47 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
The scientist’s investigation covers issues in Data mining, Artificial intelligence, Social media, Data science and Statistics.
His study in Data mining is interdisciplinary in nature, drawing from both Artificial neural network, Cluster analysis, Multi-objective optimization, The Internet and Web mining.
As part of his studies on Artificial intelligence, Dirk Van den Poel frequently links adjacent subjects like Machine learning.
In the subject of general Machine learning, his work in Random forest is often linked to Latent semantic indexing, thereby combining diverse domains of study.
His studies deal with areas such as Marketing and Profitability index as well as Data science.
His Statistics study integrates concerns from other disciplines, such as Bankruptcy prediction, Bankruptcy and Econometrics.
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