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- August 2021
- Case
Orchadio’s First Two Split Experiments
By: Iavor I. Bojinov, Marco Iansiti and David Lane
Orchadio, a direct-to-consumer grocery business, needs to conduct its first two A/B tests—one to evaluate the effectiveness and functioning of its newly redesigned website, and one to market-test four versions of a new banner for the website. To do so, it will rely on...
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Keywords:
Information Management;
Technological Innovation;
Knowledge Use and Leverage;
Resource Allocation;
Marketing;
Measurement and Metrics;
Customization and Personalization;
Information Technology;
Internet and the Web;
Digital Platforms;
Information Technology Industry;
Food and Beverage Industry
- July 2021 (Revised July 2022)
- Case
Brigham & Women's Hospital: Using Patient Reported Outcomes to Improve Breast Cancer Care
By: Robert S. Kaplan, Navraj S. Nagra and Syed S. Shehab
Dr. Andrea Pusic, breast cancer reconstruction surgeon, wants to extend outcomes measurement beyond traditional surgical metrics of infections, complications, and survival rates. The case describes her development of a new mobile phone app, which collects patients’...
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- 2021
- Working Paper
Dirty Money: How Banks Influence Financial Crime
By: Joseph Pacelli, Janet Gao, Jan Schneemeier and Yufeng Wu
On September 21st, 2020, a consortium of international journalists leaked nearly 2,500 suspicious activity reports (SAR) obtained from the U.S. Financial Crimes Enforcement Network, exposing nearly $2 trillion of money laundering activity. The event raises important...
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- July 2021
- Article
Information Transparency, Multihoming, and Platform Competition: A Natural Experiment in the Daily Deals Market
By: Hui Li and Feng Zhu
Platform competition is shaped by the likelihood of multi-homing (i.e., complementors or consumers adopt more than one platform). To take advantage of multi-homing, platform firms often attempt to motivate their rivals’ high-performing complementors to adopt their own...
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- June 23, 2021
- Article
Research: When A/B Testing Doesn't Tell You the Whole Story
By: Eva Ascarza
When it comes to churn prevention, marketers traditionally start by identifying which customers are most likely to churn, and then running A/B tests to determine whether a proposed retention intervention will be effective at retaining those high-risk customers. While...
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Keywords:
Customer Retention;
Churn;
Targeting;
Market Research;
Marketing;
Investment Return;
Customers;
Retention;
Research
- May–June 2021
- Article
Why Start-ups Fail
If you’re launching a business, the odds are against you: Two-thirds of start-ups never show a positive return. Unnerved by that statistic, a professor of entrepreneurship at Harvard Business School set out to discover why. Based on interviews and surveys with hundreds...
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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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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
- March 2021
- Case
VideaHealth: Building the AI Factory
By: Karim R. Lakhani and Amy Klopfenstein
Florian Hillen, co-founder and CEO of VideaHealth, a startup that used artificial intelligence (AI) to detect dental conditions on x-rays, spent the early years of his company laying the groundwork for an AI factory. A process for quickly building and iterating on new...
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Keywords:
Artificial Intelligence;
Innovation and Invention;
Disruptive Innovation;
Technological Innovation;
Information Technology;
Applications and Software;
Technology Adoption;
Digital Platforms;
Entrepreneurship;
AI and Machine Learning;
Technology Industry;
Medical Devices and Supplies Industry;
North and Central America;
United States;
Massachusetts;
Cambridge
- September 2020 (Revised July 2022)
- 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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- 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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- 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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- 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 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;
E-commerce;
Analytics and Data Science;
Retail Industry;
Apparel and Accessories Industry;
United States
- September 2020 (Revised July 2022)
- 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
- Article
Customer Supercharging in Experience-Centric Channels
By: David R. Bell, Santiago Gallino and Antonio Moreno
We conjecture that for online retailers, experience-centric offline store formats do not simply expand market coverage, but rather, serve to significantly amplify future positive customer behaviors, both online and offline. We term this phenomenon “supercharging” and...
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- Article
Forgoing Earned Incentives to Signal Pure Motives
By: Erika L. Kirgios, Edward H. Chang, Emma E. Levine, Katherine L. Milkman and Judd B. Kessler
Policy makers, employers, and insurers often provide financial incentives to encourage citizens, employees, and customers to take actions that are good for them or for society (e.g., energy conservation, healthy living, safe driving). Although financial incentives are...
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- May–June 2020
- Article
The New-Market Conundrum
By: Rory McDonald and Kathleen M. Eisenhardt
Brand-new markets are like the wormholes of science fiction, where the usual rules of time and space do not apply. When a market has just been born, the forces of competition there are constantly in flux, it’s unclear who your customers really are, and conventional...
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Keywords:
New Markets;
Markets;
Business Model;
Strategy;
Framework;
Innovation and Invention;
Value Creation
- 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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- March–April 2020
- Article
Building A Culture of Experimentation
By: Stefan Thomke
Why don’t organizations test more? After examining this question for several years, I can tell you that the central reason is culture. As companies try to scale up their experimentation capacity, they often find that the obstacles are not tools and technology but...
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- 2020
- Book
Experimentation Works: The Surprising Power of Business Experiments
By: Stefan Thomke
Don’t fly blind. See how the power of experiments works for you. When it comes to improving customer experiences, trying out new business models, or developing new products, even the most experienced managers often get it wrong. They discover that intuition,...
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