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All HBS Web
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- Faculty Publications (397)
- January 2021 (Revised March 2021)
- Exercise
E-Commerce Analytics for CPG Firms (C): Free Delivery Terms
By: Ayelet Israeli and Fedor (Ted) Lisitsyn
The E-Commerce Analytics group at the traditional CPG firm was in charge of compiling various online sales reports, as well as making data-driven recommendations for sales and marketing tactics. In a series of exercises, students address different data challenges for...
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Keywords:
Data;
Data Analysis;
Data Analytics;
Data Sharing;
CPG;
Consumer Packaged Goods (CPG);
Delivery Planning;
Customer Lifetime Value;
Online Channel;
Retail;
Retail Analytics;
Retailing Industry;
Ecommerce;
Grocery;
Grocery Delivery;
Margins;
Analytics and Data Science;
Retention;
E-commerce;
Retail Industry;
Consumer Products Industry;
United States
Israeli, Ayelet, and Fedor (Ted) Lisitsyn. "E-Commerce Analytics for CPG Firms (C): Free Delivery Terms." Harvard Business School Exercise 521-080, January 2021. (Revised March 2021.)
- January 2021 (Revised February 2021)
- Case
Tech with a Side of Pizza: How Domino's Rose to the Top
By: Boris Groysberg, Sarah L. Abbott and Susan Seligson
After hitting an all-time low in 2008, Domino’s Pizza underwent a vigorous rebranding, product development, and embraced innovative technologies to become the world’s leading international fast-food retailer. Domino’s considered itself as much a tech company as it was...
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Keywords:
Digital Marketing;
Digital Technology;
Innovation;
Scaling;
Data Analytics;
Turnaround;
Technological Innovation;
Information Technology;
Strategy;
Management;
Marketing;
Operations;
Human Resources;
Entrepreneurship;
Change Management;
Analysis;
Performance;
Customers;
Growth and Development;
Competitive Advantage;
Employees;
Training;
Leadership Development;
Food and Beverage Industry;
Technology Industry;
United States
Groysberg, Boris, Sarah L. Abbott, and Susan Seligson. "Tech with a Side of Pizza: How Domino's Rose to the Top." Harvard Business School Case 421-057, January 2021. (Revised February 2021.)
- January 2021 (Revised June 2023)
- Case
Biobot Analytics
In 2017, Newsha Ghaeli and Mariana Matus were deciding whether to leave their labs at the Massachusetts Institute of Technology, put other job opportunities aside, and dive full-time into founding a wastewater analysis start-up, Biobot. Ghaeli, an architect, and Matus,...
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Keywords:
Entrepreneurship;
Information Technology;
City;
Analytics and Data Science;
Personal Development and Career;
Technology Industry;
Utilities Industry;
Health Industry;
Information Technology Industry;
Information Industry;
Biotechnology Industry;
United States;
Kuwait;
Korean Peninsula
Kluender, Raymond, Joshua Krieger, and Mitchell Weiss. "Biobot Analytics." Harvard Business School Case 821-045, January 2021. (Revised June 2023.)
- June 2021
- Article
From Predictions to Prescriptions: A Data-driven Response to COVID-19
By: Dimitris Bertsimas, Léonard Boussioux, Ryan Cory-Wright, Arthur Delarue, Vassilis Digalakis Jr, Alexander Jacquillat, Driss Lahlou Kitane, Galit Lukin, Michael Lingzhi Li, Luca Mingardi, Omid Nohadani, Agni Orfanoudaki, Theodore Papalexopoulos, Ivan Paskov, Jean Pauphilet, Omar Skali Lami, Bartolomeo Stellato, Hamza Tazi Bouardi, Kimberly Villalobos Carballo, Holly Wiberg and Cynthia Zeng
The COVID-19 pandemic has created unprecedented challenges worldwide. Strained healthcare providers make difficult decisions on patient triage, treatment and care management on a daily basis. Policy makers have imposed social distancing measures to slow the disease, at...
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Keywords:
COVID-19;
Health Pandemics;
AI and Machine Learning;
Forecasting and Prediction;
Analytics and Data Science
Bertsimas, Dimitris, Léonard Boussioux, Ryan Cory-Wright, Arthur Delarue, Vassilis Digalakis Jr, Alexander Jacquillat, Driss Lahlou Kitane, Galit Lukin, Michael Lingzhi Li, Luca Mingardi, Omid Nohadani, Agni Orfanoudaki, Theodore Papalexopoulos, Ivan Paskov, Jean Pauphilet, Omar Skali Lami, Bartolomeo Stellato, Hamza Tazi Bouardi, Kimberly Villalobos Carballo, Holly Wiberg, and Cynthia Zeng. "From Predictions to Prescriptions: A Data-driven Response to COVID-19." Health Care Management Science 24, no. 2 (June 2021): 253–272.
- Winter 2021
- Editorial
Introduction
This issue of Negotiation Journal is dedicated to the theme of artificial intelligence, technology, and negotiation. It arose from a Program on Negotiation (PON) working conference on that important topic held virtually on May 17–18. The conference was not the...
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Wheeler, Michael A. "Introduction." Special Issue on Artificial Intelligence, Technology, and Negotiation. Negotiation Journal 37, no. 1 (Winter 2021): 5–12.
- February 2021
- Article
Topic Classification of Electric Vehicle Consumer Experiences with Transformer-Based Deep Learning
By: Sooji Ha, Daniel J Marchetto, Sameer Dharur and Omar Isaac Asensio
The transportation sector is a major contributor to greenhouse gas (GHG) emissions and is a driver of adverse health effects globally. Increasingly, government policies have promoted the adoption of electric vehicles (EVs) as a solution to mitigate GHG emissions....
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Keywords:
Natural Language Processing;
Analytics and Data Science;
Environmental Sustainability;
Infrastructure;
Transportation;
Policy
Ha, Sooji, Daniel J Marchetto, Sameer Dharur, and Omar Isaac Asensio. "Topic Classification of Electric Vehicle Consumer Experiences with Transformer-Based Deep Learning." Art. 100195. Patterns 2, no. 2 (February 2021).
- January 2021
- Article
Using Models to Persuade
By: Joshua Schwartzstein and Adi Sunderam
We present a framework where "model persuaders" influence receivers’ beliefs by proposing models that organize past data to make predictions. Receivers are assumed to find models more compelling when they better explain the data, fixing receivers’ prior beliefs. Model...
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Keywords:
Model Persuasion;
Analytics and Data Science;
Forecasting and Prediction;
Mathematical Methods;
Framework
Schwartzstein, Joshua, and Adi Sunderam. "Using Models to Persuade." American Economic Review 111, no. 1 (January 2021): 276–323.
- December 2020 (Revised April 2021)
- Case
IBM Watson at MD Anderson Cancer Center
By: Shane Greenstein, Mel Martin and Sarkis Agaian
After discovering that their cancer diagnostic tool, designed to leverage the cloud computing power of IBM Watson, needed greater integration into the clinical processes at the MD Anderson Cancer Center, the development team had difficult choices to make. The Oncology...
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Keywords:
Decision Making;
Innovation Strategy;
Knowledge Management;
Knowledge Use and Leverage;
Operations;
Failure;
Information Technology;
Applications and Software;
Health Care and Treatment;
Product Development;
Health Industry;
Information Technology Industry;
Technology Industry;
United States;
Houston;
Texas
Greenstein, Shane, Mel Martin, and Sarkis Agaian. "IBM Watson at MD Anderson Cancer Center." Harvard Business School Case 621-022, December 2020. (Revised April 2021.)
- December 2020
- Case
VIA Science (A)
By: Juan Alcácer, Rembrand Koning, Annelena Lobb and Kerry Herman
Via (a) captures the early days of the data analytics startup as founders Gounden and Ravanis considered which markets offer the right opportunities for their firm and what kinds of experiments will help them narrow their choice. Supplement Via (b) reveals the...
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Keywords:
Data Analytics;
Machine Learning;
Artificial Intelligence;
Strategy;
Business Startups;
Markets;
AI and Machine Learning;
Telecommunications Industry;
Utilities Industry;
United States;
Japan
Alcácer, Juan, Rembrand Koning, Annelena Lobb, and Kerry Herman. "VIA Science (A)." Harvard Business School Case 721-367, December 2020.
- December 2020
- Supplement
VIA Science (B)
By: Juan Alcácer, Rembrand Koning, Annelena Lobb and Kerry Herman
Via (a) captures the early days of the data analytics startup as founders Gounden and Ravanis considered which markets offer the right opportunities for their firm and what kinds of experiments will help them narrow their choice. Supplement Via (b) reveals the...
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Keywords:
Data Analytics;
Machine Learning;
Artificial Intelligence;
Strategy;
Business Startups;
AI and Machine Learning;
Telecommunications Industry;
Utilities Industry;
United States;
Japan
Alcácer, Juan, Rembrand Koning, Annelena Lobb, and Kerry Herman. "VIA Science (B)." Harvard Business School Supplement 721-368, December 2020.
- 2020
- Working Paper
An Empirical Guide to Investor-Level Private Equity Data from Preqin
By: Juliane Begenau, Claudia Robles-Garcia, Emil Siriwardane and Lulu Wang
This note provides guidance on the use of investor-level private equity data from Preqin for empirical research. Preqin primarily sources its cash flow data through Freedom of Information Act (FOIA) requests with U.S. public pensions. Our focus is on the components of...
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Keywords:
Private Equity Returns;
Prequin Data;
Private Equity;
Analytics and Data Science;
Investment Return
Begenau, Juliane, Claudia Robles-Garcia, Emil Siriwardane, and Lulu Wang. "An Empirical Guide to Investor-Level Private Equity Data from Preqin." Working Paper, December 2020.
- 2021
- Working Paper
The Value of Descriptive Analytics: Evidence from Online Retailers
By: Ron Berman and Ayelet Israeli
Does the adoption of descriptive analytics impact online retailer performance, and if so, how? We use the synthetic difference-in-differences method to analyze the staggered adoption of a retail analytics dashboard by more than 1,500 e-commerce websites, and we find an...
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Keywords:
Descriptive Analytics;
Big Data;
Synthetic Control;
E-commerce;
Online Retail;
Difference-in-differences;
Martech;
Internet and the Web;
Analytics and Data Science;
Performance;
Retail Industry
Berman, Ron, and Ayelet Israeli. "The Value of Descriptive Analytics: Evidence from Online Retailers." Harvard Business School Working Paper, No. 21-067, November 2020. (Revised December 2021. Accepted at Marketing Science.)
- 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 (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...
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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;
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 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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- August 2020 (Revised September 2020)
- Technical Note
Assessing Prediction Accuracy of Machine Learning Models
The note introduces a variety of methods to assess the accuracy of machine learning prediction models. The note begins by briefly introducing machine learning, overfitting, training versus test datasets, and cross validation. The following accuracy metrics and tools...
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Keywords:
Machine Learning;
Statistics;
Econometric Analyses;
Experimental Methods;
Data Analysis;
Data Analytics;
Forecasting and Prediction;
Analytics and Data Science;
Analysis;
Mathematical Methods
Toffel, Michael W., Natalie Epstein, Kris Ferreira, and Yael Grushka-Cockayne. "Assessing Prediction Accuracy of Machine Learning Models." Harvard Business School Technical Note 621-045, August 2020. (Revised September 2020.)
- 2021
- Working Paper
Time and the Value of Data
By: Ehsan Valavi, Joel Hestness, Newsha Ardalani and Marco Iansiti
Managers often believe that collecting more data will continually improve the accuracy of their machine learning models. However, we argue in this paper that when data lose relevance over time, it may be optimal to collect a limited amount of recent data instead of... View Details
Keywords:
Economics Of AI;
Machine Learning;
Non-stationarity;
Perishability;
Value Depreciation;
Analytics and Data Science;
Value
Valavi, Ehsan, Joel Hestness, Newsha Ardalani, and Marco Iansiti. "Time and the Value of Data." Harvard Business School Working Paper, No. 21-016, August 2020. (Revised November 2021.)
- August 2020
- Technical Note
Comparing Two Groups: Sampling and t-Testing
This note describes sampling and t-tests, two fundamental statistical concepts.
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Keywords:
Statistics;
Econometric Analyses;
Experimental Methods;
Data Analysis;
Data Analytics;
Analytics and Data Science;
Analysis;
Surveys;
Mathematical Methods
Bojinov, Iavor I., Chiara Farronato, Yael Grushka-Cockayne, Willy C. Shih, and Michael W. Toffel. "Comparing Two Groups: Sampling and t-Testing." Harvard Business School Technical Note 621-044, August 2020.