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- Faculty Publications (119)
EVA →
- 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...
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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.)
- September 2020 (Revised July 2022)
- Teaching Note
Algorithmic Bias in Marketing
By: Ayelet Israeli and Eva Ascarza
Teaching Note for HBS No. 521-020. This note focuses on algorithmic bias in marketing. First, it presents a variety of marketing examples in which algorithmic bias may occur. The examples are organized around the 4 P’s of marketing – promotion, price, place and...
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- September 2020 (Revised July 2022)
- Technical Note
Algorithmic Bias in Marketing
By: Ayelet Israeli and Eva Ascarza
This note focuses on algorithmic bias in marketing. First, it presents a variety of marketing examples in which algorithmic bias may occur. The examples are organized around the 4 P’s of marketing – promotion, price, place and product—characterizing the marketing...
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Keywords:
Algorithmic Data;
Race And Ethnicity;
Promotion;
"Marketing Analytics";
Marketing And Society;
Big Data;
Privacy;
Data-driven Management;
Data Analysis;
Data Analytics;
E-Commerce Strategy;
Discrimination;
Targeting;
Targeted Advertising;
Pricing Algorithms;
Ethical Decision Making;
Customer Heterogeneity;
Marketing;
Race;
Ethnicity;
Gender;
Diversity;
Prejudice and Bias;
Marketing Communications;
Analytics and Data Science;
Analysis;
Decision Making;
Ethics;
Customer Relationship Management;
E-commerce;
Retail Industry;
Apparel and Accessories Industry;
United States
Israeli, Ayelet, and Eva Ascarza. "Algorithmic Bias in Marketing." Harvard Business School Technical Note 521-020, September 2020. (Revised July 2022.)
- September 2020 (Revised February 2024)
- Teaching Note
Artea (A), (B), (C), and (D): Designing Targeting Strategies
By: Eva Ascarza and Ayelet Israeli
Teaching Note for HBS No. 521-021,521-022,521-037,521-043. 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...
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- September 2020 (Revised July 2022)
- Exercise
Artea (B): Including Customer-level Demographic Data
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...
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Keywords:
Targeting;
Algorithmic Bias;
Race;
Gender;
Marketing;
Diversity;
Customer Relationship Management;
Demographics;
Prejudice and Bias;
Retail Industry;
Apparel and Accessories Industry;
Technology Industry;
United States
Ascarza, Eva, and Ayelet Israeli. "Artea (B): Including Customer-level Demographic Data." Harvard Business School Exercise 521-022, September 2020. (Revised July 2022.)
- September 2020 (Revised July 2022)
- Exercise
Artea (C): Potential Discrimination through Algorithmic 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...
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Keywords:
Targeting;
Algorithmic Bias;
Race;
Gender;
Marketing;
Diversity;
Customer Relationship Management;
Prejudice and Bias;
Retail Industry;
Apparel and Accessories Industry;
Technology Industry;
United States
Ascarza, Eva, and Ayelet Israeli. "Artea (C): Potential Discrimination through Algorithmic Targeting." Harvard Business School Exercise 521-037, September 2020. (Revised July 2022.)
- 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...
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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 2020 (Revised June 2023)
- Exercise
Artea: Designing Targeting Strategies
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:
Algorithmic Data;
Race And Ethnicity;
Experimentation;
Promotion;
"Marketing Analytics";
Marketing And Society;
Big Data;
Privacy;
Data-driven Management;
Data Analytics;
Data Analysis;
E-Commerce Strategy;
Discrimination;
Targeted Advertising;
Targeted Policies;
Targeting;
Pricing Algorithms;
A/B Testing;
Ethical Decision Making;
Customer Base Analysis;
Customer Heterogeneity;
Coupons;
Algorithmic Bias;
Marketing;
Race;
Gender;
Diversity;
Customer Relationship Management;
Marketing Communications;
Advertising;
Decision Making;
Ethics;
E-commerce;
Analytics and Data Science;
Retail Industry;
Apparel and Accessories Industry;
United States
Ascarza, Eva, and Ayelet Israeli. "Artea: Designing Targeting Strategies." Harvard Business School Exercise 521-021, September 2020. (Revised June 2023.)
- September 2020 (Revised July 2022)
- Supplement
Spreadsheet Supplement to "Artea: Designing Targeting Strategies"
By: Eva Ascarza and Ayelet Israeli
Spreadsheet Supplement to "Artea: Designing Targeting Strategies" (521-021).
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- September 2020 (Revised July 2022)
- Supplement
Spreadsheet Supplement to Artea (B) and (C)
By: Eva Ascarza and Ayelet Israeli
Spreadsheet Supplement to "Artea (B): Including Customer-level Demographic Data" and "Artea (C): Potential Discrimination through Algorithmic Targeting"
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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...
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- 2020
- Working Paper
When Do Experts Listen to Other Experts? The Role of Negative Information in Expert Evaluations for Novel Projects
By: Jacqueline N. Lane, Misha Teplitskiy, Gary Gray, Hardeep Ranu, Michael Menietti, Eva C. Guinan and Karim R. Lakhani
The evaluation of novel projects lies at the heart of scientific and technological innovation, and yet literature suggests that this process is subject to inconsistency and potential biases. This paper investigates the role of information sharing among experts as the...
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Keywords:
Project Evaluation;
Innovation;
Knowledge Frontier;
Negativity Bias;
Projects;
Innovation and Invention;
Information;
Diversity;
Judgments
Lane, Jacqueline N., Misha Teplitskiy, Gary Gray, Hardeep Ranu, Michael Menietti, Eva C. Guinan, and Karim R. Lakhani. "When Do Experts Listen to Other Experts? The Role of Negative Information in Expert Evaluations for Novel Projects." Harvard Business School Working Paper, No. 21-007, July 2020. (Revised November 2020.)
- June 2020 (Revised July 2023)
- Case
Time Out: The Evolution from Media to Markets
By: Kate Barasz and Eva Ascarza
In February 2020, Time Out’s chief executive officer Julio Bruno is evaluating the strategic direction of the company. Over the span of five decades, Time Out — the global media and entertainment brand — had gone from a self-published counterculture publication in...
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Keywords:
Branding;
Media Businesses;
Hospitality;
Hospitality Industry;
Digital;
Brands and Branding;
Media;
Marketing;
Marketing Strategy;
Organizational Change and Adaptation;
Strategy;
Media and Broadcasting Industry;
Food and Beverage Industry;
United Kingdom;
United States
Barasz, Kate, and Eva Ascarza. "Time Out: The Evolution from Media to Markets." Harvard Business School Case 520-128, June 2020. (Revised July 2023.)
- December 2019 (Revised January 2022)
- Supplement
Othellonia: Growing a Mobile Game
- December 2019 (Revised January 2022)
- Supplement
Othellonia: Growing a Mobile Game
- November 2019 (Revised December 2023)
- Teaching Note
Othellonia: Growing a Mobile Game
Teaching note for case 520-016
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- 2020
- Working Paper
Engineering Serendipity: When Does Knowledge Sharing Lead to Knowledge Production?
By: Jacqueline N. Lane, Ina Ganguli, Patrick Gaule, Eva C. Guinan and Karim R. Lakhani
We investigate how knowledge similarity between two individuals is systematically related to the likelihood that a serendipitous encounter results in knowledge production. We conduct a natural field experiment at a medical research symposium, where we exogenously...
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Keywords:
Cognitive Similarity;
Knowledge Creation;
Knowledge Sharing;
Knowledge Dissemination;
Relationships
Lane, Jacqueline N., Ina Ganguli, Patrick Gaule, Eva C. Guinan, and Karim R. Lakhani. "Engineering Serendipity: When Does Knowledge Sharing Lead to Knowledge Production?" Harvard Business School Working Paper, No. 20-058, November 2019. (Revised July 2020.)
- September 2019 (Revised June 2020)
- Case
Othellonia: Growing a Mobile Game
In the summer of 2019, Yu Sasaki, Head of the Game Division of DeNA, a Japanese mobile gaming company, is evaluating various growth strategies for its recent game Othellonia. Sasaki needs to decide if he should focus on customer acquisition, retention, or monetization.
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Keywords:
Targeting;
Retention/churn;
Freemium;
Monetization;
Customer Relationship Management;
Games, Gaming, and Gambling;
Mobile and Wireless Technology;
Growth and Development Strategy;
Marketing;
Customers;
Marketing Strategy;
Retention;
Acquisition;
Entertainment and Recreation Industry;
Japan
Ascarza, Eva, Tomomichi Amano, and Sunil Gupta. "Othellonia: Growing a Mobile Game." Harvard Business School Case 520-016, September 2019. (Revised June 2020.)
- 2023
- Working Paper
The Customer Journey as a Source of Information
By: Nicolas Padilla, Eva Ascarza and Oded Netzer
In the face of heightened data privacy concerns and diminishing third-party data access,
firms are placing increased emphasis on first-party data (1PD) for marketing decisions.
However, in environments with infrequent purchases, reliance on past purchases 1PD...
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Keywords:
Customer Journey;
Privacy;
Consumer Behavior;
Analytics and Data Science;
AI and Machine Learning;
Customer Focus and Relationships
Padilla, Nicolas, Eva Ascarza, and Oded Netzer. "The Customer Journey as a Source of Information." Harvard Business School Working Paper, No. 24-035, October 2023. (Revised October 2023.)
- Article
Use of Crowd Innovation to Develop an Artificial Intelligence-Based Solution for Radiation Therapy Targeting
By: Raymond H. Mak, Michael G. Endres, Jin Hyun Paik, Rinat A. Sergeev, Hugo Aerts, Christopher L. Williams, Karim R. Lakhani and Eva C. Guinan
Importance: Radiation therapy (RT) is a critical cancer treatment, but the existing radiation oncologist work force does not meet growing global demand. One key physician task in RT planning involves tumor segmentation for targeting, which requires substantial...
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Keywords:
Crowdsourcing;
AI Algorithms;
Health Care and Treatment;
Collaborative Innovation and Invention;
AI and Machine Learning
Mak, Raymond H., Michael G. Endres, Jin Hyun Paik, Rinat A. Sergeev, Hugo Aerts, Christopher L. Williams, Karim R. Lakhani, and Eva C. Guinan. "Use of Crowd Innovation to Develop an Artificial Intelligence-Based Solution for Radiation Therapy Targeting." JAMA Oncology 5, no. 5 (May 2019): 654–661.