Prof. dr. Gui Liberali

Gui Liberali

Professor of Digital Marketing
Rotterdam School of Management (RSM)
Erasmus University Rotterdam

Profile

Gui Liberali is the Endowed Professor of Digital Marketing at Rotterdam School of Management (RSM) of the Erasmus University. He holds a doctorate in marketing and a B.Sc. in computer science. His work has appeared on Marketing Science, Management Science, International Journal of Marketing Research, Sloan Management Review, and European Journal of Operational Research. His research interests include optimal learning, multi-armed bandits, digital experimentation, natural language processing, morphing theory and applications (e.g., website morphing, ad morphing), dynamic programming, machine learning, and product line optimization. Personal webpage: *www.guiliberali.org*  For more details on professional experience, education, and latest news please visit my linkedin page at  www.linkedin.com/in/gui-liberali/

Publications

  • Academic (10)
    • Giesecke, K., Liberali, G., Nazerzadeh, H., Shanthikumar, G., & Teo, CP. C-P. (2018). Special Issue on Data-Driven Prescriptive Analytics. Management Science, 64(6), 2972-2972. https://doi.org/10.1287/mnsc.2018.3120

    • Liberali, G., Muller, E., Rust, RT., & Stremersch, S. (2015). Introduction to the IJRM Special Issue on Marketing and Innovation. International Journal of Research in Marketing, 32(3), 235-237. https://doi.org/10.1016/j.ijresmar.2015.08.001

    • Urban, G., Liberali, G., Bordley, R., Macdonald, E., & Hauser, J. (2014). Morphing Banner Advertising. Marketing Science, 33(1), 27-46. https://doi.org/10.1287/mksc.2013.0803

    • Hauser, J., Liberali, G., & Urban, G. (2014). Website Morphing 2.0: Technical and Implementation Advances and a Field Experiment. Management Science, 60(6), 1594-1616. https://doi.org/10.1287/mnsc.2014.1961

    • Liberali, G., Urban, G., & Hauser, J. (2012). Competitive Information, Trust, Brand Consideration and Sales: Two Field Experiments. International Journal of Research in Marketing, 30(2), 101-113. [1]. https://doi.org/10.1016/j.ijresmar.2012.07.002

    • Liberali, G., Gruca, T., & Nique, W. (2011). Effects of Sensitization and Habituation in Durable Goods Markets. European Journal of Operational Research, 212(2), 398-410. https://doi.org/10.1016/j.ejor.2011.01.038

    • Liberali, G. (2011). Comments on Product Line Design Optimization. International Journal of Research in Marketing, 28(1), 28-29. https://doi.org/10.1016/j.ijresmar.2011.01.002

    • Hauser, J., Urban, G., Liberali, G., & Braun, M. (2009). Website Morphing. Marketing Science, 28(2), 202-223. https://doi.org/10.1287/mksc.1080.0459

    • Hauser, J., Urban, G., Liberali, G., & Braun, M. (2009). Rejoinder Response to Comments on "Website Morphing". Marketing Science, 28(2), 227-228. https://doi.org/10.1287/mksc.1080.0485

    • Urban, G., Hauser, J., Liberali, G., Braun, M., & Sultan, F. (2009). Morphing the Web - Building Empathy, Trust, and Sales. MIT Sloan Management Review, 50(4), 53-61.

  • Professional (1)
    • Liberali, G. (2014). Morphing advertising to improve online campaign success. RSM Discovery - Management Knowledge, 20(4), 12-14. http://hdl.handle.net/1765/77381

  • Popular (1)
    • Liberali, G. (2018). Learning with a purpose: the balancing acts of machine learning and individuals in the digital society. Erasmus Research Institute of Management. ERIM Inaugural Address Series Research in Management http://hdl.handle.net/1765/107428

Additional activities (2)

  • Management Science (Journal)

    Editorial work (Academic)

  • International Journal of Research in Marketing (Journal)

    Editorial work (Academic)

Media

Media item

  • Nieuwe hoogleraar digital marketing

    Gui Liberali hasbeen appointed as a professor of digital marketing with an inaugural speech entitled 'Learning with a purpose: The balancing acts of machine learning and individuals in the digital society'.

Courses

Learning from big data

  • Study year: 2021/2022
  • Code: B3MIN1039
  • Level: Bachelor, Bachelor 3, Bachelor 3

Using Business Analytics and Machine Learning for New Products

  • Study year: 2021/2022, 2020/2021, 2019/2020, 2018/2019, 2017/2018, 2016/2017, 2015/2016
  • Code: BM-IM04CC
  • Level: Master

Unsupervised Machine Learning & Reinforcement Learning

  • Study year: 2021/2022, 2020/2021
  • Code: EBDS20103
  • Level: Master

Unsupervised Machine Learning & Reinforcement Learning

  • Study year: 2021/2022, 2020/2021
  • Code: EBDS20103-F
  • Level: Master

Past courses

BDS-Thesis

  • Study year: 2020/2021
  • Code: EBDS-THESIS

Current Topics in Marketing Research

  • Study year: 2020/2021, 2019/2020
  • Code: BERMASC040
  • ECTS: 5 Level: Master, PhD

Innovation Management

  • Study year: 2020/2021
  • Code: BM-IM09CC
  • ECTS: 8 Level: Master

Learning from big data

  • Study year: 2020/2021, 2019/2020, 2018/2019, 2017/2018, 2016/2017, 2015/2016
  • Code: BKBMIN039
  • Level: Bachelor, Bachelor 3, Bachelor 3

Advanced Marketing Decision Models

  • Study year: 2019/2020
  • Code: BERMASC044
  • ECTS: 4 Level: Master

Advanced Mathematics

  • Study year: 2019/2020
  • Code: TI1821
  • ECTS: 4 Level: Master

IM Research clinic

  • Study year: 2019/2020, 2018/2019
  • Code: BM-IMRC
  • Level: Master

Unsupervised & Reinforcement Machine Learning

  • Study year: 2019/2020
  • Code: TI192
  • ECTS: 3 Level: Master

Unsupervised Machine Learning & Reinforcement Learning

  • Study year: 2019/2020
  • Code: TI199
  • ECTS: 4 Level: Master

Advanced Marketing Decision Models

  • Study year: 2016/2017
  • Code: BERMASC041
  • ECTS: 7 Level: Master

Global marketing: Developing and marketing new products

  • Study year: 2014/2015
  • Code: IM04CC
  • ECTS: 8 Level: Master

Seminar Innovation and Marketing

  • Study year: 2014/2015, 2013/2014, 2012/2013
  • Code: FEM11080
  • ECTS: 12 Level: Master

Exploration of New Markets Through Innovation

  • Study year: 2013/2014, 2012/2013
  • Code: FEB53112M
  • ECTS: 15 Level: Bachelor 3

ERIM Research Clinic (Seminars)

  • Study year: 2012/2013
  • Code: BERMRC000
  • Level: Master