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- 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...
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- February 2021
- Tutorial
T-tests: Theory and Practice
This video provides 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...
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- September 2020 (Revised April 2021)
- 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...
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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
- September 2020 (Revised April 2021)
- 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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- September 2020 (Revised April 2021)
- 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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- September 2020 (Revised March 2021)
- 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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- 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...
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- September 2020 (Revised March 2021)
- 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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- 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...
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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
- 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...
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- 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...
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- 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...
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- 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...
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- 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...
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- 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...
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- 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...
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- 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...
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- 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...
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Keywords:
Charter Schools;
Nonprofit Organizations;
Strategy;
Teaching;
Talent And Talent Management;
Innovation;
Education;
Early Childhood Education;
Middle School Education;
Organizational Structure;
Performance Consistency;
Growth and Development Strategy;
Innovation and Invention;
Education Industry
- 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...
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