Eva Ascarza

Home | Curriculum Vitae | Research

WORKING PAPERS


  • Debiasing Treatment Effect Estimation for Privacy-Protected Data: A Model Auditing and Calibration Approach
             Ta-Wei Huang and Eva Ascarza (2025)
             [Paper]

  • Dynamic Personalization with Multiple Customer Signals: Multi-Response State Representation in Reinforcement Learning
             Ma, Liangzong, Ta-Wei Huang, Eva Ascarza and Ayelet Israeli (2025)
             [Paper]

  • Advancing Personalization: How to Experiment, Learn & Optimize
             Lemmens, Aurélie, Jason M.T. Roos, Sebastian Gabel, Eva Ascarza, Hernan Bruno, Brett R. Gordon, Ayelet Israeli, Elea McDonnell Feit, Carl Mela, and Oded Netzer (2025)
             [Paper]

  • Incrementality Representation Learning: Synergizing Past Experiments for Intervention Personalization
             Ta-Wei Huang, Eva Ascarza and Ayelet Israeli (2024)
             [Paper]

  • Policy-Aware Experimentation: Sampling Smart to Improve Targeting Policy
             Chen, Yi-Wen, Eva Ascarza and Oded Netzer (2024)
             [Paper]


    PUBLISHED / FORTHCOMING


  • Personalized Game Design for Improved User Retention and Monetization in Freemium Mobile Games
             Eva Ascarza, Oded Netzer and Julian Runge (2025)
             Forthcoming at the International Journal of Research in Marketing
             [Paper] [Web Appendix]

  • The Customer Journey as a Source of Information
             Nicolas Padilla, Eva Ascarza and Oded Netzer (2024)
             Forthcoming at Quantitative Marketing and Economics
             [Paper] [Web Appendix]

  • Doing More with Less: Overcoming Ineffective Long-term Targeting Using Short-Term Signals
             Ta-Wei Huang and Eva Ascarza (2024)
             Marketing Science (2024) 43(4), 863-884
             [Paper] [Replication Files]

  • Detecting Routines: Implications for Ridesharing CRM
             Ryan Dew, Eva Ascarza, Oded Netzer, and Nachum Sicherman (2024)
             Journal of Marketing Research (2024) 61(2), 368-392
             [Paper] [Web Appendix] [Replication Files]

  • Eliminating unintended bias in personalized policies using Bias Eliminating Adapted Trees (BEAT)
             Eva Ascarza and Ayelet Israeli (2022)
             Proceedings of the National Academy of Sciences (2022) 119(11)
             [Paper] [Web Appendix] [Replication Files] [Github]

  • Overcoming the Cold Start Problem of CRM using a Probabilistic Machine Learning Approach
             Nicolas Padilla and Eva Ascarza (2021)
             Journal of Marketing Research (2021) 58(5), 981-1006
             [Paper] [Web Appendix] [Replication Files]

  • Why You Aren't Getting More from Your Marketing AI
             Eva Ascarza, Michael Ross, and Bruce G.S. Hardie (2021)
             Harvard Business Review (2021) July-August.
             [Link]

  • Retention futility: Targeting high-risk customers might be ineffective
             Eva Ascarza (2018)
             Journal of Marketing Research (2018) 55(1), 80-98
             Winner, 2023 Weitz-Winer-O'Dell Award
             Winner, 2018 Paul E. Green Award
             [Paper] [Web Appendix]

  • In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions
             Eva Ascarza, Scott A. Neslin, Oded Netzer et al. (2018)
             Customer Needs and Solutions (2018) 5, 65-81
             Finalist, 2019 MSI Robert D. Buzzell Best Paper Award
             [Paper]

  • Some Customers Would Rather Leave Without Saying Goodbye
             Eva Ascarza, Oded Netzer and Bruce Hardie (2018)
             Marketing Science (2018) 37(1), 54-77
             [Paper] [Web Appendix]

  • Beyond the Target Customer: Social Effects of CRM Campaigns
             Eva Ascarza, Peter Ebbes, Oded Netzer and Matthew Danielson (2017)
             Journal of Marketing Research (2017) 54(3), 347-363
             Finalist, 2017 Paul E. Green Award
             [Paper] [Web Appendix]

  • The perils of proactive churn prevention using plan recommendations: Evidence from a field experiment
             Eva Ascarza, Raghuram Iyengar and Martin Schleicher (2016)
             Journal of Marketing Research (2016) 53(1), 46-60
             Finalist, 2021 Weitz-Winer-O'Dell Award
             Finalist, 2016 Paul E. Green Award
             [Paper] [Web Appendix]

  • A Joint Model of Usage and Churn in Contractual Settings
             Eva Ascarza and Bruce G.S. Hardie (2013)
             Marketing Science. (2013) 32(4), 570-590
             Winner, 2014 Frank M. Bass Outstanding Dissertation Award
             [Paper] [Web Appendix]

  • When Talk is Free: The Effect of Tariff Structure on Usage under Two and Three-Part Tariffs
             Eva Ascarza, Anja Lambrecht and Naufel Vilcassim (2012)
             Journal of Marketing Research (2012) 49(6), 882-899
             [Paper] [Web Appendix]


    BOOK CHAPTERS


  • Marketing Models for the Customer-Centric Firm
             Eva Ascarza, Peter S. Fader, and Bruce G.S. Hardie
             Handbook of Marketing Decision Models (2017), edited by Berend Wierenga and Ralf van der Lans, Springer.
             [Paper]


    ONLINE PUBLICATIONS


  • Research: When A/B Testing Doesn't Tell You the Whole Story
             Eva Ascarza
             Harvard Business Review Online (June 23, 2021) [Link]

  • Beyond Pajamas: Sizing Up the Pandemic Shopper
             Ayelet Israeli, Eva Ascarza and Laura Castrillo
             Working Knowledge (March 17, 2021) [Link]


    RESEARCH FEATURED IN OTHER OUTLETS


  • Navigating Consumer Data Privacy in an AI World [Link]
             Working Knowledge (June 4, 2024)
             Featuring "Debiasing Treatment Effect Estimation for Privacy-Protected Data: A Model Audition and Calibration Approach"

  • When Bias Creeps into AI, Managers Can Stop It by Asking the Right Questions [Link]
             Working Knowledge (Oct 18, 2022)
             Featuring "Eliminating unintended bias in personalized policies using Bias Eliminating Adapted Trees (BEAT)"

  • Identify Great Customers from Their First Purchase [Link]
             Working Knowledge (Dec 9, 2019)
             Featuring "Overcoming the Cold Start Problem of CRM using a Probabilistic Machine Learning Approach"

  • The Wrong Way to Reduce Churn [Link]
             Harvard Business School (October, 2015)
             Featuring "The perils of proactive churn prevention using plan recommendations: Evidence from a field experiment"

    Harvard Business School Website | Customer Intelligence Lab