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      • Faculty Publications  (42)

      A/b Testing Remove A/b Testing →

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      The Surprising Power of Online Experiments: Getting the Most Out of A/B and Other Controlled Tests
      Avoid the Pitfalls of A/B Testing
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      • February 2021
      • Tutorial

      T-tests: Theory and Practice

      By: Michael Parzen, Natalie Epstein, Chiara Farronato and Michael Toffel
      This video provide an introduction to hypothesis testing, sampling, t-tests, and p-values. It provides examples of A/B testing and t-testing to assess whether difference between two groups are statistically significant. This video can be assigned in conjunction with...  View Details
      Keywords: Data Analysis; Data Analytics; Experiment Design; Experimentation
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      Parzen, Michael, Natalie Epstein, Chiara Farronato, and Michael Toffel. T-tests: Theory and Practice. Harvard Business School Tutorial 621-707, February 2021.
      • September 2020 (Revised December 2020)
      • 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; E-commerce Strategy; Discrimination; Targeted Advertising; Targeted Policies; Targeting; Pricing Algorithms; A/b Testing; Ethical Decision Making; Customer Base Analysis; Customer Heterogeneity; Coupons; Marketing; Race; Gender; Diversity; Customer Relationship Management; Marketing Communications; Advertising; Decision Making; Ethics; 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 December 2020.)
      • September 2020
      • 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: 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.
      • September 2020
      • 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: 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.
      • September 2020
      • 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: 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 (D): Discrimination through Algorithmic Bias in Targeting." Harvard Business School Exercise 521-043, September 2020.
      • September 2020 (Revised December 2020)
      • 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; 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 December 2020.)
      • September 2020 (Revised December 2020)
      • 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: Race; Gender; Diversity; Marketing; 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. "Spreadsheet Supplement to Artea Teaching Note." Harvard Business School Spreadsheet Supplement 521-705, September 2020. (Revised December 2020.)
      • September 2020 (Revised November 2020)
      • Supplement

      Student Success at Georgia State University (B)

      By: Michael W. Toffel, Robin Mendelson and Julia Kelley
      This is a supplement to the Student Success at Georgia State University (A) case. The (B) case includes the results of a randomized control trial that Georgia State conducted to test education technology start-up AdmitHub’s chatbot solution as a strategy for improving...  View Details
      Keywords: Education; Higher Education; Learning; Curriculum And Courses; Demographics; Diversity; Ethnicity; Income; Race; Values And Beliefs; Leadership; Goals And Objectives; Measurement And Metrics; Operations; Organizations; Mission And Purpose; Organizational Culture; Outcome Or Result; Performance; Performance Effectiveness; Performance Evaluation; Performance Improvement; Planning; Strategic Planning; Social Enterprise; Nonprofit Organizations; Social Issues; Wealth And Poverty; Equality And Inequality; Technology; Technology Platform; Education Industry; Atlanta
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      Toffel, Michael W., Robin Mendelson, and Julia Kelley. "Student Success at Georgia State University (B)." Harvard Business School Supplement 621-039, September 2020. (Revised November 2020.)
      • July–September 2020
      • Article

      Innovation Contest: Effect of Perceived Support for Learning on Participation

      By: Olivia Jung, Andrea Blasco and Karim R. Lakhani
      Background: Frontline staff are well positioned to conceive improvement opportunities based on first-hand knowledge of what works and does not work. The innovation contest may be a relevant and useful vehicle to elicit staff ideas. However, the success of the...  View Details
      Keywords: Contest; Innovation; Employee Engagement; Organizational Learning; Health Care; Health Care Delivery; Innovation And Invention; Organizations; Learning; Employees; Perception; Health Care And Treatment
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      Jung, Olivia, Andrea Blasco, and Karim R. Lakhani. "Innovation Contest: Effect of Perceived Support for Learning on Participation." Health Care Management Review 45, no. 3 (July–September 2020): 255–266.
      • 2020
      • Chapter

      Building Emergency Savings Through Employer-Sponsored Rainy-Day Savings Accounts

      By: John Beshears, James J. Choi, J. Mark Iwry, David C. John, David Laibson and Brigitte C. Madrian
      Roughly half of Americans live paycheck to paycheck. When financial shocks occur during their working life, many of these households tap into their retirement savings accounts. We explore the practical considerations and challenges associated with helping households...  View Details
      Keywords: Savings; Household; Saving
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      Beshears, John, James J. Choi, J. Mark Iwry, David C. John, David Laibson, and Brigitte C. Madrian. "Building Emergency Savings Through Employer-Sponsored Rainy-Day Savings Accounts." In Tax Policy and the Economy, Volume 34, edited by Robert A. Moffitt, 43–90. Chicago: University of Chicago Press, 2020.
      • Article

      The Impact of Penalties for Wrong Answers on the Gender Gap in Test Scores

      By: Katherine B. Coffman and David Klinowski
      Multiple-choice exams play a critical role in university admissions across the world. A key question is whether imposing penalties for wrong answers on these exams deters guessing from women more than men, disadvantaging female test-takers. We consider data from a...  View Details
      Keywords: Behavioral Economics; Standardized Testing; Gender; Higher Education; Prejudice And Bias
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      Coffman, Katherine B., and David Klinowski. "The Impact of Penalties for Wrong Answers on the Gender Gap in Test Scores." Proceedings of the National Academy of Sciences 117, no. 16 (April 21, 2020): 8794–8803.
      • March–April 2020
      • Article

      Avoid the Pitfalls of A/B Testing

      By: Iavor I. Bojinov, Guillaume Sait-Jacques and Martin Tingley
      Online experiments measuring whether “A,” usually the current approach, is inferior to “B,” a proposed improvement, have become integral to the product-development cycle, especially at digital enterprises. But often firms make serious mistakes in conducting these...  View Details
      Keywords: A/b Testing; Experiment Design; Social Networks; Product Development; Performance Improvement; Measurement And Metrics
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      Bojinov, Iavor I., Guillaume Sait-Jacques, and Martin Tingley. "Avoid the Pitfalls of A/B Testing." Harvard Business Review 98, no. 2 (March–April 2020): 48–53.
      • 2020
      • Working Paper

      The Effects of Hierarchy on Learning and Performance in Business Experimentation

      By: Sourobh Ghosh, Stefan Thomke and Hazjier Pourkhalkhali
      Do senior managers help or hurt business experiments? Despite the widespread adoption of business experiments to guide strategic decision-making, we lack a scholarly understanding of what role senior managers play in firm experimentation. Using proprietary data of live...  View Details
      Keywords: Experimentation; Innovation; Search; New Product Development; Innovation And Invention; Organizational Design; Learning; Performance
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      Ghosh, Sourobh, Stefan Thomke, and Hazjier Pourkhalkhali. "The Effects of Hierarchy on Learning and Performance in Business Experimentation." Harvard Business School Working Paper, No. 20-081, February 2020.
      • 2020
      • Working Paper

      Digital Experimentation and Startup Performance: Evidence from A/B Testing

      By: Rembrand Koning, Sharique Hasan and Aaron Chatterji
      Recent work argues that experimentation is the appropriate framework for entrepreneurial strategy. We investigate this proposition by exploiting the time-varying adoption of A/B testing technology, which has drastically reduced the cost of experimentally testing...  View Details
      Keywords: Experimentation; A/b Testing; Data-driven Decision-making; Entrepreneurship; Strategy; Business Startups; Technology; Performance
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      Koning, Rembrand, Sharique Hasan, and Aaron Chatterji. "Digital Experimentation and Startup Performance: Evidence from A/B Testing." Harvard Business School Working Paper, No. 20-018, August 2019. (Revised September 2020. SSRN Working Paper Series, No. 3440291, August 2019)
      • 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
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      Ascarza, Eva. "Retention Futility: Targeting High-Risk Customers Might Be Ineffective." Journal of Marketing Research (JMR) 55, no. 1 (February 2018): 80–98.
      • September–October 2017
      • Article

      The Surprising Power of Online Experiments: Getting the Most Out of A/B and Other Controlled Tests

      By: Ron Kohavi and Stefan Thomke
      In the fast-moving digital world, even experts have a hard time assessing new ideas. Case in point: At Bing, a small headline change an employee proposed was deemed a low priority and shelved for months until one engineer decided to do a quick online controlled...  View Details
      Keywords: Experiments; A/b Testing; Research; Consumer Behavior
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      Kohavi, Ron, and Stefan Thomke. "The Surprising Power of Online Experiments: Getting the Most Out of A/B and Other Controlled Tests." Harvard Business Review 95, no. 5 (September–October 2017): 74–82.
      • June 2017
      • Supplement

      Theranos: Small Volume Blood Testing (B)

      By: John A. Quelch and Irene Lu
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      Quelch, John A., and Irene Lu. "Theranos: Small Volume Blood Testing (B)." Harvard Business School Supplement 517-128, June 2017.
      • 2017
      • Supplement

      Uncommon Schools (B): Seeking Excellence at Scale through Standardized Practice

      By: John J-H Kim and Sarah McAra
      The (B) case provides an update to the (A) case by illustrating how charter school management organization Uncommon Schools responded to the disparity in its students’ 2013 standardized test results. In 2015, CEO Brett Peiser and his management team decided to align...  View Details
      Keywords: Education; Charter Schools; Nonprofit Organizations; Strategy; Early Childhood Education; Middle School Education; Teaching; Talent And Talent Management; Innovation; Organizational Structure; Education; Early Childhood Education; Middle School Education; Organizational Structure; Performance Consistency; Growth And Development Strategy; Innovation And Invention; Education Industry
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      Kim, John J-H, and Sarah McAra. "Uncommon Schools (B): Seeking Excellence at Scale through Standardized Practice." Harvard Business Publishing Supplement, 2017. (Case No. PEL-080.)
      • March 2016 (Revised January 2020)
      • Teaching Note

      Behavioural Insights Team (A) and (B)

      By: Michael Luca and Patrick Rooney
      The Behavioural Insights Team case introduces students to the concept of choice architecture and the value of experimental methods (sometimes called A/B testing) within organizational contexts. The exercise provides an opportunity for students to apply these principles...  View Details
      Keywords: Behavioral Economics; Experiments; Choice Architecture; Public Entrepreneurship; Decision Choices And Conditions; Mathematical Methods; United Kingdom
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      Luca, Michael, and Patrick Rooney. "Behavioural Insights Team (A) and (B)." Harvard Business School Teaching Note 916-050, March 2016. (Revised January 2020.)
      • 2015
      • Chapter

      How Leaders Use Values-based Guidance Systems to Create Dynamic Capabilities

      By: Rosabeth M. Kanter, Matthew Bird, Ethan Bernstein and Ryan Raffaelli
      How do strategic leaders create change-adept organizations? Based on qualitative field research, this chapter argues that well-defined institutionalized purpose, values, and principles act as an organizational guidance system that integrates and strengthens the...  View Details
      Keywords: Change; Dynamic Capabilities; Field Research; Intrinsic Motivation; Organizational Identity; Ecosystem; Organizational Change And Adaptation; Mission And Purpose; Motivation And Incentives; Research; Management Systems; Change
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      Kanter, Rosabeth M., Matthew Bird, Ethan Bernstein, and Ryan Raffaelli. "How Leaders Use Values-based Guidance Systems to Create Dynamic Capabilities." Chap. 2 in The Oxford Handbook of Dynamic Capabilities, edited by David J. Teece and Sohvi Leih. Oxford University Press, 2015. Electronic.
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      The Surprising Power of Online Experiments: Getting the Most Out of A/B and Other Controlled Tests
      Avoid the Pitfalls of A/B Testing
      → Search All HBS Web
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