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Show Results For
- All HBS Web
(268)
- News (30)
- Research (155)
- Events (4)
- Multimedia (7)
- Faculty Publications (157)
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- September 2020 (Revised July 2022)
- Exercise
Artea (D): Discrimination through Algorithmic Bias in Targeting
By: Eva Ascarza and Ayelet Israeli
This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and targeting. Parts (B),(C),(D) Introduce algorithmic bias. The... View Details
Keywords: Targeted Advertising; Discrimination; Algorithmic Data; Bias; Advertising; Race; Gender; Marketing; Diversity; Customer Relationship Management; Prejudice and Bias; Analytics and Data Science; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States
Ascarza, Eva, and Ayelet Israeli. "Artea (D): Discrimination through Algorithmic Bias in Targeting." Harvard Business School Exercise 521-043, September 2020. (Revised July 2022.)
- August 2022
- Background Note
Retail Media Networks
By: Eva Ascarza, Ayelet Israeli and Celine Chammas
In 2022, retail media was one of the fastest growing segments in digital advertising. A retail media network (RMN) allows a retailer to use its assets for advertising. Retailers set up an advertising business by allowing marketers to buy advertising space across their... View Details
Keywords: Advertisers; Advertising Media; Media And Broadcasting Industry; Retail; Retail Analytics; Retail Promotion; Retailing; Ecommerce; E-Commerce Strategy; E-commerce; Marketing Communication; Targeting; Targeted Advertising; Targeted Marketing; Advertising; Marketing; Marketing Communications; Marketing Strategy; Brands and Branding; Media; Marketing Channels; Retail Industry; Consumer Products Industry; Advertising Industry; United States
Ascarza, Eva, Ayelet Israeli, and Celine Chammas. "Retail Media Networks." Harvard Business School Background Note 523-029, August 2022.
- December 2019 (Revised January 2022)
- Supplement
Othellonia: Growing a Mobile Game
- 13 Apr 2016
- Research Event
What Does 'Diversity' Really Mean?
a beauty feature on the needs of women with different types of skin, showcasing a range of actresses of different backgrounds with large close-up shots. Holmes began to throw out some names, including Eva Mendes, Lucy Liu, and Alfre... View Details
Keywords: by Dina Gerdeman
- 2025
- Working Paper
Dynamic Personalization with Multiple Customer Signals: Multi-Response State Representation in Reinforcement Learning
Reinforcement learning (RL) offers potential for optimizing sequences of customer interactions by modeling the relationships
between customer states, company actions, and long-term value. However, its practical implementation often faces significant
challenges.... View Details
Keywords: Dynamic Policy; Deep Reinforcement Learning; Representation Learning; Dynamic Difficulty Adjustment; Latent Variable Models; Customer Relationship Management; Customer Value and Value Chain; Foreign Direct Investment; Analytics and Data Science
Ma, Liangzong, Ta-Wei Huang, Eva Ascarza, and Ayelet Israeli. "Dynamic Personalization with Multiple Customer Signals: Multi-Response State Representation in Reinforcement Learning." Harvard Business School Working Paper, No. 25-037, February 2025.
- April 2024
- Article
Detecting Routines: Applications to Ridesharing CRM
By: Ryan Dew, Eva Ascarza, Oded Netzer and Nachum Sicherman
Routines shape many aspects of day-to-day consumption. While prior work has established the importance of habits in consumer behavior, little work has been done to understand the implications of routines—which we define as repeated behaviors with recurring, temporal... View Details
Keywords: Ride-sharing; Routine; Machine Learning; Customer Relationship Management; Consumer Behavior; Segmentation
Dew, Ryan, Eva Ascarza, Oded Netzer, and Nachum Sicherman. "Detecting Routines: Applications to Ridesharing CRM." Journal of Marketing Research (JMR) 61, no. 2 (April 2024): 368–392.
- July–August 2013
- Article
A Joint Model of Usage and Churn in Contractual Settings
By: Eva Ascarza and Bruce G.S. Hardie
As firms become more customer-centric, concepts such as customer equity come to the fore. Any serious attempt to quantify customer equity requires modeling techniques that can provide accurate multiperiod forecasts of customer behavior. Although a number of researchers... View Details
Keywords: Churn; Retention; Contractual Settings; Access Services; Hidden Markov Models; RFM; Latent Variable Models; Customer Value and Value Chain; Consumer Behavior
Ascarza, Eva, and Bruce G.S. Hardie. "A Joint Model of Usage and Churn in Contractual Settings." Marketing Science 32, no. 4 (July–August 2013): 570–590.
- Article
Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)
By: Eva Ascarza and Ayelet Israeli
An inherent risk of algorithmic personalization is disproportionate targeting of individuals from certain groups (or demographic characteristics such as gender or race), even when the decision maker does not intend to discriminate based on those “protected”... View Details
Keywords: Algorithm Bias; Personalization; Targeting; Generalized Random Forests (GRF); Discrimination; Customization and Personalization; Decision Making; Fairness; Mathematical Methods
Ascarza, Eva, and Ayelet Israeli. "Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)." e2115126119. Proceedings of the National Academy of Sciences 119, no. 11 (March 8, 2022).
- December 24, 2020
- Article
How Businesses Can Find “Hidden Workers”
By: Joseph B. Fuller, Manjari Raman, Eva Sage-Gavin and Ladan Davarzani
Even before the COVID-19 pandemic, low- and middle-skill workers struggled to find and retain steady work. Now, many of these workers are considered “essential,” while many others are unemployed and struggling to find work. As the pandemic eases throughout 2021,... View Details
Fuller, Joseph B., Manjari Raman, Eva Sage-Gavin, and Ladan Davarzani. "How Businesses Can Find 'Hidden Workers'." Harvard Business Review Digital Articles (December 24, 2020).
- Article
Experiments in Open Innovation at Harvard Medical School
By: Eva C. Guinan, Kevin J. Boudreau and Karim R. Lakhani
Harvard Medical School seems an unlikely organization to open up its innovation process. By most measures, the more than 20,000 faculty, research staff and graduate students affiliated with Harvard Medical School are already world class and at the top of the medical... View Details
Guinan, Eva C., Kevin J. Boudreau, and Karim R. Lakhani. "Experiments in Open Innovation at Harvard Medical School." Art. 3. MIT Sloan Management Review 54, no. 3 (Spring 2013): 45–52.
- July–August 2021
- Article
Why You Aren't Getting More from Your Marketing AI
By: Eva Ascarza, Michael Ross and Bruce G.S. Hardie
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... View Details
Keywords: Artificial Intelligence; Marketing; Decision Making; Communication; Framework; AI and Machine Learning
Ascarza, Eva, Michael Ross, and Bruce G.S. Hardie. "Why You Aren't Getting More from Your Marketing AI." Harvard Business Review 99, no. 4 (July–August 2021): 48–54.
- 2023
- Working Paper
Personalized Game Design for Improved User Retention and Monetization in Freemium Games
By: Eva Ascarza, Oded Netzer and Julian Runge
One of the most crucial aspects and significant levers that gaming companies possess in designing
digital games is setting the level of difficulty, which essentially regulates the user’s ability to
progress within the game. This aspect is particularly significant in... View Details
Keywords: Freemium; Retention/churn; Field Experiment; Field Experiments; Gaming; Gaming Industry; Mobile App; Mobile App Industry; Monetization; Monetization Strategy; Games, Gaming, and Gambling; Mobile and Wireless Technology; Customers; Retention; Product Design; Strategy
Ascarza, Eva, Oded Netzer, and Julian Runge. "Personalized Game Design for Improved User Retention and Monetization in Freemium Games." Harvard Business School Working Paper, No. 21-062, November 2020. (Revised December 2023.)
- Article
When Talk Is "Free": The Effect of Tariff Structure on Usage Under Two- and Three-Part Tariffs
By: Eva Ascarza, Anja Lambrecht and Naufel Vilcassim
In many service industries, firms introduce three-part tariffs to replace or complement existing two-part tariffs. In contrast with two-part tariffs, three-part tariffs offer allowances, or “free” units of the service. Behavioral research suggests that the attributes... View Details
Keywords: Pricing; Nonlinear Pricing; Discrete/continuous Choice Model; Three-part Tariffs; Free Products; Price; Consumer Behavior; Analysis; Learning; Risk and Uncertainty
Ascarza, Eva, Anja Lambrecht, and Naufel Vilcassim. When Talk Is "Free": The Effect of Tariff Structure on Usage Under Two- and Three-Part Tariffs. Journal of Marketing Research (JMR) 49, no. 6 (December 2012): 882–900.
- January–February 2018
- Article
Some Customers Would Rather Leave Without Saying Goodbye
By: Eva Ascarza, Oded Netzer and Bruce G.S. Hardie
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... View Details
Keywords: Churn; Retention; Attrition; Customer Base Analysis; Hidden Markov Models; Latent Variable Models; Customer Relationship Management; Consumer Behavior
Ascarza, Eva, Oded Netzer, and Bruce G.S. Hardie. "Some Customers Would Rather Leave Without Saying Goodbye." Marketing Science 37, no. 1 (January–February 2018): 54–77.
- March 2024
- Supplement
Madrigal: Conducting a Customer-Base Audit
By: Eva Ascarza, Bruce Hardie, Peter S. Fader and Michael Ross
This case presents a scenario where Madrigal, a U.S. retailer with a rich 20-year history and a solid loyalty program, faces a turning point with the arrival of a new CEO. This leadership change reveals a critical gap in understanding the customer base, prompting an... View Details
- March 2024
- Case
Madrigal: Conducting a Customer-Base Audit
By: Eva Ascarza, Bruce Hardie, Michael Ross and Peter S. Fader
This case presents a scenario where Madrigal, a U.S. retailer with a rich 20-year history and a solid loyalty program, faces a turning point with the arrival of a new CEO. This leadership change reveals a critical gap in understanding the customer base, prompting an... View Details
Keywords: Customer Relationship Management; Analytics and Data Science; Growth and Development Strategy; Customer Value and Value Chain; Retail Industry; United States
Ascarza, Eva, Bruce Hardie, Michael Ross, and Peter S. Fader. "Madrigal: Conducting a Customer-Base Audit." Harvard Business School Case 524-046, March 2024.
- 2024
- Working Paper
Incrementality Representation Learning: Synergizing Past Experiments for Intervention Personalization
This paper introduces Incrementality Representation Learning (IRL), a novel multitask representation learning framework that predicts heterogeneous causal effects of marketing interventions. By leveraging past experiments, IRL efficiently designs and targets... View Details
Keywords: Heterogeneous Treatment Effect; Multi-task Learning; Representation Learning; Personalization; Promotion; Deep Learning; Field Experiments; Customer Focus and Relationships; Customization and Personalization
Huang, Ta-Wei, Eva Ascarza, and Ayelet Israeli. "Incrementality Representation Learning: Synergizing Past Experiments for Intervention Personalization." Harvard Business School Working Paper, No. 24-076, June 2024.
- December 2019 (Revised January 2022)
- Supplement
Othellonia: Growing a Mobile Game
- Article
The Perils of Proactive Churn Prevention Using Plan Recommendations: Evidence from a Field Experiment
By: Eva Ascarza, Raghuram Iyengar and Martin Schleicher
Facing the issue of increasing customer churn, many service firms have begun recommending pricing plans to their customers. One reason behind this type of retention campaign is that customers who subscribe to a plan suitable for them should be less likely to churn... View Details
Keywords: Churn/retention; Field Experiment; Pricing; Tariff/plan Choice; Targeting; Customer Relationship Management; Price; Performance Effectiveness
Ascarza, Eva, Raghuram Iyengar, and Martin Schleicher. "The Perils of Proactive Churn Prevention Using Plan Recommendations: Evidence from a Field Experiment." Journal of Marketing Research (JMR) 53, no. 1 (February 2016): 46–60.
- April 2025
- Teaching Note
Tabby: Winning Customers' Digital Wallets
By: Eva Ascarza
Teaching Note for HBS Case No. 524-056. Tabby, a Saudi-based fintech founded in 2019, rapidly became one of the MENA region’s first unicorns by offering buy-now-pay-later (BNPL) services with a unique twist: instead of charging end consumers, it partnered with... View Details