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Aurélie Lemmens
Researcher at Erasmus University Rotterdam
Publications - 40
Citations - 1625
Aurélie Lemmens is an academic researcher from Erasmus University Rotterdam. The author has contributed to research in topics: Granger causality & Consumer confidence index. The author has an hindex of 15, co-authored 38 publications receiving 1437 citations. Previous affiliations of Aurélie Lemmens include Tilburg University & Katholieke Universiteit Leuven.
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Journal ArticleDOI
Analytics for customer engagement
Tammo H. A. Bijmolt,Peter S.H. Leeflang,Frank Block,Maik Eisenbeiss,Bruce G. S. Hardie,Aurélie Lemmens,Peter Saffert +6 more
TL;DR: In this paper, the authors discuss the state of the art of models for customer engagement and the problems that are inherent to calibrating and implementing these models, and discuss several organizational issues of analytics for user engagement.
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Bagging and Boosting Classification Trees to Predict Churn
Aurélie Lemmens,Christophe Croux +1 more
TL;DR: The authors apply the bagging and boosting classification techniques to a customer database of an anonymous U.S. wireless telecommunications company, and both significantly improve accuracy in predicting churn.
Posted Content
Measuring and Testing Granger Causality over the Spectrum: An Application to European Production Expectation Surveys
TL;DR: In this paper, the authors compare two existing approaches in the frequency domain, proposed originally by Pierce (1979) and Geweke (1982), and introduce a new testing procedure for the Pierce spectral Granger causality measure.
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Sales Growth of New Pharmaceuticals Across the Globe: The Role of Regulatory Regimes
TL;DR: It is found that differences in regulation substantially contribute to cross-country variation in sales and that national culture, economic wealth, introduction timing, lagged sales and competition, also affect drug sales.
Journal ArticleDOI
In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions
Eva Ascarza,Scott A. Neslin,Oded Netzer,Zachery Anderson,Peter S. Fader,Sunil Gupta,Bruce G. S. Hardie,Aurélie Lemmens,Barak Libai,David T. Neal,Foster Provost,Rom Y. Schrift +11 more
TL;DR: In this paper, the authors present an integrated framework for managing retention that leverages emerging opportunities offered by new data sources and new methodologies such as machine learning, highlighting the importance of distinguishing between which customers are at risk and which should be targeted.