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Show Results For
- All HBS Web
(258)
- News (28)
- Research (152)
- Events (4)
- Multimedia (7)
- Faculty Publications (151)
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- September 2020 (Revised June 2023)
- Supplement
Spreadsheet Supplement to Artea Teaching Note
By: Eva Ascarza and Ayelet Israeli
Spreadsheet Supplement to Artea Teaching Note 521-041. 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... View Details
- 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.)
- September 2023
- Supplement
Design and Evaluation of Targeted Interventions
By: Eva Ascarza
Targeted interventions serve as a pivotal tool in business strategy, streamlining decisions for enhanced efficiency and effectiveness. This note delves into two central facets of such interventions: first, the design of potent decision guidelines, or targeting... View Details
- 2020
- Working Paper
Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach
By: Eva Ascarza
The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to understand consumers' preferences and precisely capture how these preferences may differ across customers. Only by understanding customer heterogeneity, firms can... View Details
Keywords: Customer Management; Targeting; Deep Exponential Families; Probabilistic Machine Learning; Cold Start Problem; Customer Relationship Management; Customer Value and Value Chain; Consumer Behavior; Analytics and Data Science; Mathematical Methods; Retail Industry
Padilla, Nicolas, and Eva Ascarza. "Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach." Harvard Business School Working Paper, No. 19-091, February 2019. (Revised May 2020. Accepted at the Journal of Marketing Research.)
- 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.
- 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; Uncertainty; Learning; Free Products; Price; Consumer Behavior; Analysis
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.
- 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
- 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.
- Research Summary
Overview
By: Eva Ascarza
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... View Details
- November 2022 (Revised February 2024)
- Exercise
Managing Customer Retention at Teleko
By: Eva Ascarza
This exercise aims to teach students about 1) Targeting Policies; and 2) Algorithmic decision making, and 3) Retention management. View Details
Ascarza, Eva. "Managing Customer Retention at Teleko." Harvard Business School Exercise 523-005, November 2022. (Revised February 2024.)
- March 2022
- Teaching Note
Allianz Customer Centricity: Is Simplicity the Way Forward?
By: Eva Ascarza
Teaching Note for HBS Case No. 522-008. View Details
- September 2021
- Supplement
Interview with Julio Bruno (Time Out)
By: Eva Ascarza
Video supplement for HBS Case No. 520-128. View Details
Ascarza, Eva. "Interview with Julio Bruno (Time Out)." Harvard Business School Multimedia/Video Supplement 522-707, September 2021.
- August 2021 (Revised February 2022)
- Supplement
Melissa Wood Health: How to Win in the Creator Economy
By: Eva Ascarza
- August 2021 (Revised January 2024)
- Teaching Note
Melissa Wood Health: How to Win in the Creator Economy
By: Eva Ascarza
- February 2018
- Article
Retention Futility: Targeting High-Risk Customers Might Be Ineffective.
By: Eva Ascarza
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... View Details
Keywords: Retention/churn; Proactive Churn Management; Field Experiments; Heterogeneous Treatment Effect; Machine Learning; Customer Relationship Management; Risk Management
Ascarza, Eva. "Retention Futility: Targeting High-Risk Customers Might Be Ineffective." Journal of Marketing Research (JMR) 55, no. 1 (February 2018): 80–98.
- July–August 2024
- Article
Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals
By: Ta-Wei Huang and Eva Ascarza
Firms are increasingly interested in developing targeted interventions for customers with the best response,
which requires identifying differences in customer sensitivity, typically through the conditional average treatment
effect (CATE) estimation. In theory, to... View Details
Keywords: Long-run Targeting; Heterogeneous Treatment Effect; Statistical Surrogacy; Customer Churn; Field Experiments; Consumer Behavior; Customer Focus and Relationships; AI and Machine Learning; Marketing Strategy
Huang, Ta-Wei, and Eva Ascarza. "Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals." Marketing Science 43, no. 4 (July–August 2024): 863–884.
- March 2024
- Case
Unintended Consequences of Algorithmic Personalization
By: Eva Ascarza and Ayelet Israeli
“Unintended Consequences of Algorithmic Personalization” (HBS No. 524-052) investigates algorithmic bias in marketing through four case studies featuring Apple, Uber, Facebook, and Amazon. Each study presents scenarios where these companies faced public criticism for... View Details
Keywords: Race; Gender; Marketing; Diversity; Customer Relationship Management; Prejudice and Bias; Customization and Personalization; Technology Industry; Retail Industry; United States
Ascarza, Eva, and Ayelet Israeli. "Unintended Consequences of Algorithmic Personalization." Harvard Business School Case 524-052, March 2024.