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Publications

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    • All HBS Web  (269)
      • Faculty Publications  (124)

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      • May 2021 (Revised August 2021)
      • Case

      Melissa Wood Health: How to Win in the Creator Economy

      By: Eva Ascarza
      In October 2020, Melissa Wood-Tepperberg, founder of the digital subscription wellness platform Melissa Wood Health (MWH) and creator of ‘The MWH Method,’ was evaluating the strategic directions of her company. What had started as a way to share workouts and wellness... View Details
      Keywords: Lifestyle Brand; Brands and Branding; Strategy; Partners and Partnerships; Negotiation
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      Ascarza, Eva. "Melissa Wood Health: How to Win in the Creator Economy." Harvard Business School Case 521-086, May 2021. (Revised August 2021.)
      • Spring 2021
      • Article

      Building Cities' Collaborative Muscle

      By: Jorrit De Jong, Amy C. Edmondson, Mark Moore, Hannah Riley-Bowles, Jan Rivkin, Eva Flavia Martínez Orbegozo and Santiago Pulido-Gomez
      The most pressing social problems facing cities today require multiagency and cross-sector solutions. We offer tools and techniques to facilitate the process of diagnosing and solving problems by breaking down silos to build up cities. View Details
      Keywords: Collaboration; Cross-sector Collaboration; City; Governance; Problems and Challenges; Cooperation
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      De Jong, Jorrit, Amy C. Edmondson, Mark Moore, Hannah Riley-Bowles, Jan Rivkin, Eva Flavia Martínez Orbegozo, and Santiago Pulido-Gomez. "Building Cities' Collaborative Muscle." Stanford Social Innovation Review (website) (Spring 2021).
      • March 2021
      • Supplement

      Artea (A), (B), (C), and (D): Designing Targeting Strategies

      By: Eva Ascarza and Ayelet Israeli
      Power Point Supplement to 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... View Details
      Keywords: Targeted Advertising; Targeting; Algorithmic Data; Bias; A/B Testing; Experiment; Advertising; Gender; Race; Diversity; Marketing; Customer Relationship Management; Prejudice and Bias; Analytics and Data Science; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States
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      Ascarza, Eva, and Ayelet Israeli. "Artea (A), (B), (C), and (D): Designing Targeting Strategies." Harvard Business School PowerPoint Supplement 521-719, March 2021.
      • March 17, 2021
      • Other Article

      Beyond Pajamas: Sizing Up the Pandemic Shopper

      By: Ayelet Israeli, Eva Ascarza and Laura Castrillo
      A first look at how the COVID-19 pandemic impacted e-commerce apparel shopping in the US and the UK. Extensive analysis and interactive graphics utilizing millions of transactions.
      While the pandemic is still playing out, our preliminary investigations... View Details
      Keywords: Retail; Retail Analytics; Consumer; Pandemic; COVID; COVID-19; Apparel; Ecommerce; Online Shopping; Online Apparel; Online Sales; Returns; CRM; Customer Retention; Customer Experience; Customer Value; Digital; Customer Focus and Relationships; Customers; Health Pandemics; Consumer Behavior; Customer Relationship Management; Internet and the Web; Behavior; E-commerce; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States; United Kingdom
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      Israeli, Ayelet, Eva Ascarza, and Laura Castrillo. "Beyond Pajamas: Sizing Up the Pandemic Shopper." Harvard Business School Working Knowledge (March 17, 2021).
      • January 2021 (Revised May 2021)
      • Case

      Amazon Shopper Panel: Paying Customers for Their Data

      By: Eva Ascarza and Ayelet Israeli
      This case introduces a new Amazon program that has consumers upload their receipts from transactions outside of Amazon, in exchange for money. Through the discussion, the case aims to explore issues in customers’ privacy in the digital age, the value of customers’ own... View Details
      Keywords: Data Analytics; Data Privacy; Data Management; "Marketing Analytics"; Marketing Communication; Marketing Research; Data-driven Management; E-Commerce Strategy; Ethical Decision Making; CRM; Consumer Protection; Targeted Advertising; Targeted Policies; Data Ownership; Marketing; Research; Marketing Communications; Analytics and Data Science; Management; Customer Relationship Management; Ethics; E-commerce; Retail Industry; Technology Industry; United States
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      Ascarza, Eva, and Ayelet Israeli. "Amazon Shopper Panel: Paying Customers for Their Data." Harvard Business School Case 521-058, January 2021. (Revised May 2021.)
      • 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
      Keywords: Health Pandemics; Employees; Selection and Staffing
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      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).
      • 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
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      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... View Details
      Keywords: Marketing; Race; Ethnicity; Gender; Diversity; Prejudice and Bias; Decision Making; Ethics; Customer Relationship Management; Retail Industry; Technology Industry; Apparel and Accessories Industry; United States
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      Israeli, Ayelet, and Eva Ascarza. "Algorithmic Bias in Marketing." Harvard Business School Teaching Note 521-035, September 2020. (Revised July 2022.)
      • 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... View Details
      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
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      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... View Details
      Keywords: Targeted Advertising; Targeting; Race; Gender; Diversity; Marketing; Customer Relationship Management; Prejudice and Bias; Analytics and Data Science; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States
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      Ascarza, Eva, and Ayelet Israeli. "Artea (A), (B), (C), and (D): Designing Targeting Strategies." Harvard Business School Teaching Note 521-041, September 2020. (Revised February 2024.)
      • 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... View Details
      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
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      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... View Details
      Keywords: Targeting; Algorithmic Bias; Race; Gender; Marketing; Diversity; Customer Relationship Management; Prejudice and Bias; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States
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      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... 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
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      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
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      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). View Details
      Keywords: Race; Gender; Marketing; Diversity; Customer Relationship Management; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States
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      Ascarza, Eva, and Ayelet Israeli. Spreadsheet Supplement to "Artea: Designing Targeting Strategies". Harvard Business School Spreadsheet Supplement 521-703, September 2020. (Revised July 2022.)
      • 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" View Details
      Keywords: Gender; Race; Diversity; Marketing; Customer Relationship Management; Demographics; Prejudice and Bias; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States
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      Ascarza, Eva, and Ayelet Israeli. "Spreadsheet Supplement to Artea (B) and (C)." Harvard Business School Spreadsheet Supplement 521-704, September 2020. (Revised July 2022.)
      • 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
      Keywords: Targeted Advertising; Algorithmic Data; Bias; Advertising; Race; Gender; Diversity; Marketing; Customer Relationship Management; Prejudice and Bias; Analytics and Data Science; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States
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      Ascarza, Eva, and Ayelet Israeli. "Spreadsheet Supplement to Artea Teaching Note." Harvard Business School Spreadsheet Supplement 521-705, September 2020. (Revised June 2023.)
      • 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... View Details
      Keywords: Project Evaluation; Innovation; Knowledge Frontier; Negativity Bias; Projects; Innovation and Invention; Information; Diversity; Judgments
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      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... View Details
      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
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      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

      By: Eva Ascarza, Tomomichi Amano and Sunil Gupta
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      Ascarza, Eva, Tomomichi Amano, and Sunil Gupta. "Othellonia: Growing a Mobile Game." Harvard Business School PowerPoint Supplement 520-056, December 2019. (Revised January 2022.)
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