Dr A. (Aurélie) Lemmens
Aurélie Lemmens is a Full Professor at the Rotterdam School of Management, Erasmus University (The Netherlands). She holds the Chair in Customer Analytics in the Department of Marketing Management. She is also the Academic Director of the Expert Practice on Customer Analytics at the Erasmus Center for Data Analytics.
She obtained my Ph.D. degree from K.U. Leuven (Belgium), after obtaining an MSc as Business Engineer at Solvay Business School (Belgium). Before joining RSM, she held appointments at the Erasmus School of Economics in Rotterdam and Tilburg University (The Netherlands). She has also been a visiting scholar at Harvard Business School (USA).
Her research interests focus on the development of prescriptive analytics that can leverage consumer data in order to address key business decisions. She is interested in the design of new methodologies that can guide organizations in their customer-centric decision-making. Her research portfolio is organized according to the three fundamental stages of the customer lifecycle: (i) customer acquisition and new product introduction, (ii) customer development and engagement, and (iii) customer retention. A substantial part of her work involves machine learning and large-scale cluster and grid computing.
Her research has been published in leading academic journals, such as Marketing Science, the Journal of Marketing, the Journal of Marketing Research, and the International Journal of Research in Marketing. In 2012, she was the recipient of the 2012 IJRM Best Paper Award. I am also serving on the editorial board of the Journal of Marketing Research and the International Journal of Research in Marketing. Finally, I was awarded several prestigious grants, including a Marie Curie from the European Research Council, a Veni grant, and a Vidi grant from the Dutch Science Foundation, NWO and she was a finalist for the ERC consolidator grant.
Besides research, she teaches Customer Analytics in the Business Analytics and Management Msc, Customer Centricity in the Marketing Management Msc, and Creating Customer Value in the Executive MBA program. She has received several best teaching awards (2017 and 2019) for her course on Conjoint Analysis.
For more information, please find her CV here or visit www.aurelielemmens.com.
Publications
Article (15)
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Academic (15)
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Esterzon, E., Lemmens, A., & Van den Bergh, B. (2023). Enhancing donor agency to improve charitable giving: Strategies and heterogeneity. Journal of Marketing, 87(4), 636-655. https://doi.org/10.1177/00222429221148969
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Pieters, C., Pieters, R., & Lemmens, A. (2022). Six Methods for Latent Moderation Analysis in Marketing Research: A Comparison and Guidelines. Journal of Marketing Research, 59(5), 941-962. https://doi.org/10.1177/00222437221077266
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Lemmens, A., & Gupta, S. (2020). Managing churn to maximize profits. Marketing Science, 39(5), 956-973. https://doi.org/10.1287/mksc.2020.1229
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Ascarza, E., Neslin, S., Netzer, O., Anderson, Z., Fader, P., Gupta, S., Hardie, B., Lemmens, A., Libai, B., Neal, D., Provost, F., & Schrift, R. (2018). In pursuit of enhanced customer retention management. Customer Needs and Solutions, 5(1-2), 65-81. https://doi.org/10.1007/s40547-017-0080-0
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Glady, N., Lemmens, A., & Croux, C. (2015). Unveiling the relationship between the transaction timing, spending and dropout behavior of customers. International Journal of Research in Marketing, 32(1), 78-93. https://doi.org/10.1016/j.ijresmar.2014.09.005
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Verbeke, W., Bagozzi, RP., van den Berg, W., & Lemmens, A. (2013). Polymorphisms of the OXTR gene explain why sales professionals love to help customers. Frontiers in Behavioral Neuroscience, 7, 171. https://doi.org/10.3389/fnbeh.2013.00171
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Lemmens, A., Croux, C., & Stremersch, S. (2012). Dynamics in the international market segmentation of new product growth. International Journal of Research in Marketing, 29(1), 81-92. https://doi.org/10.1016/j.ijresmar.2011.06.003
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Bijmolt, THA., Leeflang, PSH., Block, F., Eisenbeiss, M., Hardie, BGS., Lemmens, A., & Saffert, P. (2010). Analytics for customer engagement. Journal of Service Research, 13(3), 341-356. https://doi.org/10.1177/1094670510375603
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Stremersch, S., & Lemmens, A. (2009). Sales Growth of New Pharmaceuticals Across the Globe: the Role of Regulatory Regimes. Marketing Science, 28(4), 690-708. https://doi.org/10.1287/mksc.1080.0440
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Lemmens, A., Croux, C., & Dekimpe, MG. (2008). Measuring and Testing Granger Causality over the Spectrum: An Application to European Production Expectation Surveys. International Journal of Forecasting, 24(3), 414-431. https://doi.org/10.1016/j.ijforecast.2008.03.004
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Lemmens, A., Croux, C., & Dekimpe, MG. (2007). Consumer Confidence in Europe: Unity or Diversity. International Journal of Research in Marketing, 24(2), 113-127. https://doi.org/10.1016/j.ijresmar.2006.10.006
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Gelper, S., Lemmens, A., & Croux, C. (2007). Consumer Sentiment and Consumer Spending: Decomposing the Granger Causal Relationship in the Time Domain. Applied Economics, 39(1), 1-11. https://doi.org/10.1080/00036840500427791
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Croux, C., Joossens, K., & Lemmens, A. (2007). Trimmed Bagging. Computational Statistics & Data Analysis, 52(1), 362-368. https://doi.org/10.1016/j.csda.2007.06.012
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Lemmens, A., & Croux, C. (2006). Bagging and Boosting Classification Trees to Predict Churn. Journal of Marketing Research, 43(2), 276-286. https://doi.org/10.1509/jmkr.43.2.276
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Lemmens, A., Croux, C., & Dekimpe, MG. (2005). On the Predictive Content of Production Surveys: a Pan-European Study. International Journal of Forecasting, 21(2), 363-375. https://doi.org/10.1016/j.ijforecast.2004.10.004
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Conference proceeding (2)
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Academic (2)
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Puha, Z., Kaptein, M., & Lemmens, A. (2021). Batch Mode Active Learning for Individual Treatment Effect Estimation. In G. Di Fatta, V. Sheng, A. Cuzzocrea, C. Zaniolo, & X. Wu (Eds.), Proceedings - 20th IEEE International Conference on Data Mining Workshops, ICDMW 2020 (pp. 859-866). Article 9346484 IEEE Computer Society. https://doi.org/10.1109/ICDMW51313.2020.00123
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Croux, C., Joossens, K., & Lemmens, A. (2004). Bagging a stacked classifier. In Proceedings in Computational Statistics (pp. 839-855)
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Working paper (2)
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Academic (2)
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Lemmens, A., & Gupta, S. (2019). Managing churn to maximize profits. Harvard Business School. http://hdl.handle.net/1765/121652
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Pieters, C., & Lemmens, A. (2015). Acquiring customers via word-of-mouth referrals. Marketing Science Institute.
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PhD Tracks (5)
- Role: Member Doctoral Committee
- PhD Candidate: Jeroen Binken
- Time frame: 2003 - 2010
- Role: Co-promotor
- PhD Candidate: Bob Jan Jouke Rombach
- Time frame: 2021 -
- Role: Co-promotor
- PhD Candidate: Marina Lenkovskaya
- Time frame: 2021 -
- Role: Co-promotor
- PhD Candidate: Ting-Yi Lin
- Time frame: 2021 -
- Role: Member Doctoral Committee
- PhD Candidate: Martina Pocchiari
- Time frame: 2017 - 2022
Course (1)
- Current Topics in Marketing Research (2023/2024)
PhD Vacancy (1)
The Marketing group at Rotterdam School of Management, Erasmus University seeks a highly motivated PhD student with strong quantitative skills to study the problem of algorithmic biases in marketing.
As machines are trained to analyse complex problems, many tasks that previously required humans are now guided by Artificial Intelligence. Marketing is no exception in this domain. Increasingly, companies use algorithms to design targeted marketing campaigns. Causal Machine Learning is an emerging research field that can learn the causal effect of an intervention and how it varies within a population based on a large set of potential moderating variables. Its use in marketing has been rapidly growing over the last years (Lemmens and Gupta 2020; Esterzon, Lemmens, Van den Bergh 2023).
Unfortunately, algorithms can be discriminatory. The number of cases reporting biases in algorithms has exploded. Algorithms reproduce and amplify biases present in human decisions. They may even inadvertently create new discriminatory outcomes.
This PhD project ambitions to tackle this crucial managerial and societal challenge. The goal will be to better understand the problem of algorithmic biases in the context of targeting marketing campaigns and to develop a novel methodological framework to design effective and fair personalized policies. The project will include large-scale field experiments in collaboration with company partners.
Strong applicants typically have backgrounds in computer science, statistics or econometrics but should have an intrinsic interest for marketing problems. The PhD will be supervised by Prof. Dr. Aurélie Lemmens and funded by a VICI NWO grant.
Events (3)
Address
Office: T10-10
Burgemeester Oudlaan 50
3062 PA Rotterdam
Postbus 1738
3000 DR Rotterdam
Netherlands
