Eva Ascarza

Home | Curriculum Vitae | Research

WORKING PAPERS


  • Learning When to Quit in Sales Conversations
             Emaad Manzoor, Eva Ascarza and Oded Netzer (2025)

  • In Privacy We Trust: The Effect of Privacy Regulations on Data Sharing Behavior
             Ozge Demirci, Eva Ascarza and Ayelet Israeli (2025) [Paper]

  • Policy-Aware Experimentation: Strategic Sampling for Optimized Targeting Policies
             Yi-Wen Chen, Eva Ascarza and Oded Netzer (2025) [Paper]

  • Enhancing Treatment Effect Prediction on Privacy-Protected Data: An Honest Post-Processing Approach
             Ta-Wei Huang and Eva Ascarza (2025) [Paper]

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

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


    PUBLISHED / FORTHCOMING


  • Personalization and Targeting: How to Experiment, Learn & Optimize
             Aurélie Lemmens, Jason M.T. Roos, Sebastian Gabel, Eva Ascarza, Hernan Bruno, Brett R. Gordon, Ayelet Israeli, Elea McDonnell Feit, Carl Mela, and Oded Netzer (2025)
             Forthcoming at the International Journal of Research in Marketing
             [Paper]

  • 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