Eva Ascarza

Eva Ascarza

Jakurski Family Associate Professor of Business Administration

Jakurski Family Associate Professor of Business Administration

Eva Ascarza is the Jakurski Family Associate Professor of Business Administration in the Marketing Unit, teaching the Marketing course in the MBA required curriculum.

As a marketing modeler, Professor Ascarza uses tools from statistics, economics, and machine learning to answer relevant marketing questions. Her main research areas are customer analytics and customer relationship management, with special attention to the problem of customer retention. She uses field experimentation (e.g., A/B testing) as well as econometric modeling and machine learning tools not only to understand and predict patterns of behavior, but also to optimize the impact of firms’ interventions. Her research has appeared in leading marketing journals including Marketing Science and Journal of Marketing Research. She received the 2014 Frank Bass award, awarded to the best marketing paper derived from a Ph.D. thesis published in an INFORMS-sponsored journal. Her research has been recognized as a Paul E. Green Award finalist in 2016 and 2017, awarded to the best article in the Journal of Marketing Research that demonstrates the greatest potential to contribute significantly to the practice of marketing research. She was named a Marketing Science Institute Young Scholar in 2017 and serves on the editorial review board of several top marketing journals including Marketing Science, Journal of Marketing Research, Journal of Marketing, and Quantitative Marketing and Economics.

Professor Ascarza earned a Ph.D. in marketing from London Business School, a B.S. in mathematics at the Universidad de Zaragoza (Spain), and a M.S. in economics and finance from Universidad de Navarra (Spain). Prior to joining HBS, she was an associate professor in the marketing department at Columbia Business School.

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Eva Ascarza
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Featured Work Publications Research Summary Awards & Honors

Eva Ascarza is the Jakurski Family Associate Professor of Business Administration in the Marketing Unit, teaching the Marketing course in the MBA required curriculum.

As a marketing modeler, Professor Ascarza uses tools from statistics, economics, and machine learning to answer relevant marketing questions. Her main research areas are customer analytics and customer relationship management, with special attention to the problem of customer retention. She uses field experimentation (e.g., A/B testing) as well as econometric modeling and machine learning tools not only to understand and predict patterns of behavior, but also to optimize the impact of firms’ interventions. Her research has appeared in leading marketing journals including Marketing Science and Journal of Marketing Research. She received the 2014 Frank Bass award, awarded to the best marketing paper derived from a Ph.D. thesis published in an INFORMS-sponsored journal. Her research has been recognized as a Paul E. Green Award finalist in 2016 and 2017, awarded to the best article in the Journal of Marketing Research that demonstrates the greatest potential to contribute significantly to the practice of marketing research. She was named a Marketing Science Institute Young Scholar in 2017 and serves on the editorial review board of several top marketing journals including Marketing Science, Journal of Marketing Research, Journal of Marketing, and Quantitative Marketing and Economics.

Professor Ascarza earned a Ph.D. in marketing from London Business School, a B.S. in mathematics at the Universidad de Zaragoza (Spain), and a M.S. in economics and finance from Universidad de Navarra (Spain). Prior to joining HBS, she was an associate professor in the marketing department at Columbia Business School.

Featured Work
Overcoming the Cold Start Problem of CRM using a Probabilistic Machine Learning Approach

The success of customer relationship management (CRM) programs ultimately depends on the firm’s ability to identify and leverage differences across customers—a difficult task when firms attempt to manage new customers, for whom only the first purchase has been observed. The lack of repeated observations for these customers poses a structural challenge for firms to infer unobserved differences across them. This is what the authors call the “cold start” problem of customer relationship management, whereby companies have difficulties leveraging existing data when they attempt to make inferences about customers at the beginning of their relationship. The authors propose a solution to the cold start problem by developing a probabilistic machine learning modeling framework that leverages the information collected at the moment of acquisition. The main aspect of the model is that it flexibly captures latent dimensions that govern the behaviors observed at acquisition as well as future propensities to buy and to respond to marketing actions using deep exponential families. The model can be integrated with a variety of demand specifications and is flexible enough to capture a wide range of heterogeneity structures. The authors validate their approach in a retail context and empirically demonstrate the model’s ability to identify high-value customers as well as those most sensitive to marketing actions right after their first purchase.

Why You Aren’t Getting More from Your Marketing AI

Fewer than 40% of companies that invest in AI see gains from it, usually because of one or more of these errors: (1) They don’t ask the right question, and end up directing AI to solve the wrong problem. (2) They don’t recognize the differences between the value of being right and the costs of being wrong, and assume all prediction mistakes are equivalent. (3) They don’t leverage AI’s ability to make far more frequent and granular decisions, and keep following their old practices. If marketers and data science teams communicate better and take steps to avoid these pitfalls, they’ll get much higher returns on their AI efforts.

Retention Futility: Targeting High Risk Customers Might be Ineffective
Companies in a variety of sectors are increasingly managing customer churn proactively, generally by detecting customers at the highest risk of churning and targeting retention efforts towards them. While there is a vast literature on developing churn prediction models that identify customers at the highest risk of churning, no research has investigated whether it is indeed optimal to target those individuals. Combining two field experiments with machine learning techniques, the author demonstrates that customers identified as having the highest risk of churning are not necessarily the best targets for proactive churn programs. This finding is not only contrary to common wisdom but also suggests that retention programs are sometimes futile not because firms offer the wrong incentives but because they do not apply the right targeting rules. Accordingly, firms should focus their modeling efforts on identifying the observed heterogeneity in response to the intervention and to target customers on the basis of their sensitivity to the intervention, regardless of their risk of churning. This approach is empirically demonstrated to be significantly more effective than the standard practice of targeting customers with the highest risk of churning. More broadly, the author encourages firms and researchers using randomized trials (or A/B tests) to look beyond the average effect of interventions and leverage the observed heterogeneity in customers’ response to select customer targets.
Some Customers Would Rather Leave Without Saying Goodbye
We investigate the increasingly common business setting in which companies face the possibility of both observed and unobserved customer attrition (i.e., “overt” and “silent” churn) in the same pool of customers. This is the case for many online-based services where customers have the choice to stop interacting with the firm either by formally terminating the relationship (e.g., canceling their account) or by simply ignoring all communications coming from the firm. The standard contractual versus noncontractual categorization of customer–firm relationships does not apply in such hybrid settings, which means the standard models for analyzing customer attrition do not apply. We propose a hidden Markov model (HMM)-based framework to capture silent and overt churn. We apply our modeling framework to two different contexts—a daily deal website and a performing arts organization. In contrast to previous studies that have not separated the two types of churn, we find that overt churners in these hybrid settings tend to interact more, rather than less, with the firm prior to churning; that is, in settings where both types of churn are present, a high level of activity—such as customers actively opening emails received from the firm—is not necessarily a good indicator of future engagement; rather it is associated with higher risk of overt churn. We also identify a large number of “silent churners” in both empirical applications—customers who disengage with the company very early on, rarely exhibit any type of activity, and almost never churn overtly. Furthermore, we show how the two types of churners respond very differently to the firm’s communications, implying that a common retention strategy for proactive churn management is not appropriate in these hybrid settings.
Journal Articles
Book Chapters
Cases and Teaching Materials
Other Publications and Materials
Working papers
  • Dew, Ryan, Eva Ascarza, Oded Netzer, and Nachum Sicherman. "Detecting Routines in Ride-sharing: Implications for Customer Management." Working Paper, December 2020. View Details
  • Ascarza, Eva, Oded Netzer, and Julian Runge. "The Twofold Effect of Customer Retention in Freemium Settings." Harvard Business School Working Paper, No. 21-062, November 2020. View Details
  • Padilla, Nicolas, Eva Ascarza, and Oded Netzer. "The Customer Journey as a Source of Information." Working Paper, June 2019. View Details
Research Summary
Overview
Professor Ascarza’s research primarily focuses on providing researchers and marketers a better understanding of how to manage customer retention so as to reduce churn and increase firm’s profitability. She addresses these issues by building empirical models of customer relationship management with a focus on understanding and managing customer retention (i.e., reducing customer churn). While previous literature on customer relationship management (CRM) has predominantly used secondary data, she investigates most of these research questions from the lenses of causal inference (e.g., running field experiments). Some of her findings are counter-intuitive at first glance, but compelling once she pins down the underlying mechanisms. For example, some of her recent work challenges the very common practice of focusing on ‘risk of churning’ as the most important metric for proactive churn management. Combining two field experiments in different industries, professor Ascarza shows that, when the goal is to select customers for proactive/preventive retention efforts, identifying customers who have a high risk of churning might be missing the point. In turn, she empirically demonstrates that customers with the highest risk of churning and those who should be targeted are not necessarily the same. In another field study, Professor Ascarza investigates the role of social influence in retention campaigns. Specifically, she examines the role of the (telecommunications) network in influencing usage and retention decisions among customers who did not receive a marketing campaign, but who were connected to those who were targeted in the campaign. She finds a social multiplier of 1.28. That is, the effect of the campaign on first-degree connections of targeted customers is 28% of the effect of the campaign on the targeted customers.
Keywords: Customer Retention; Churn; Field Experiments
Awards & Honors
Finalist for the 2021 Weitz-Winer-O'Dell Award for “The Perils of Proactive Churn Prevention Using Plan Recommendations: Evidence from a Field Experiment” (Journal of Marketing Research (JMR), 2016) with Raghuram Iyengar and Martin Schleicher.
Selected as a Marketing Science Institute Scholar in 2020.
Winner of the 2019 Erin Anderson Award for Emerging Female Marketing Scholar and Mentor from the American Marketing Association.
Finalist for the 2019 MSI Robert D. Buzzell Award from the Marketing Science Institute for “In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions” (Customer Needs and Solutions, 2018) with Scott A. Neslin, Oded Netzer, Zachery Anderson, Peter S. Fader, Sunil Gupta, Bruce G.S. Hardie, Aurélie Lemmens, Barak Libai, David Neal, Foster Provost, and Rom Schrift.
Winner of the 2018 Paul E. Green Award from the Journal of Marketing Research for "Retention Futility: Targeting High-Risk Customers Might Be Ineffective" (February 2018).
Selected as an AMA-Sheth Foundation Doctoral Consortium Faculty Fellow by the American Marketing Association in 2015 and 2018.
Finalist for the 2017 Paul E. Green Award from the Journal of Marketing Research for “Beyond the Target Customer: Social Effects in CRM campaigns” (June 2017) with Peter Ebbes, Oded Netzer and Matthew Danielson.
Selected as a Marketing Science Institute Young Scholar in 2017.
Finalist for the 2016 Paul E. Green Award from the Journal of Marketing Research for “The Perils of Proactive Churn Prevention using Plan Recommendations: Evidence from a Field Experiment” (February 2016) with Raghuram Iyengar and Martin Schleicher.
Winner of the 2014 Frank M. Bass Dissertation Paper Award for “A Joint Model of Usage and Churn in Contractual Settings.”
Selected as an INFORMS Doctoral Consortium Fellow at the University of British Columbia in 2008.
Selected as an AMA-Sheth Foundation Doctoral Consortium Fellow by the American Marketing Association in 2007.
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