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      • January 2021 (Revised March 2021)
      • Supplement

      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... View Details
      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; Retention; Analytics and Data Science; Analysis; Retail Industry; Consumer Products Industry; United States
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      Israeli, Ayelet, and Fedor (Ted) Lisitsyn. "E-Commerce Analytics for CPG Firms (C): Free Delivery Terms." Harvard Business School Spreadsheet Supplement 521-714, January 2021. (Revised March 2021.)
      • 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... View Details
      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
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      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... View Details
      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
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      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

      By: Raymond Kluender, Joshua Krieger and Mitchell Weiss
      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,... View Details
      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
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      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... View Details
      Keywords: COVID-19; Health Pandemics; AI and Machine Learning; Forecasting and Prediction; Analytics and Data Science
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      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

      By: Michael A. Wheeler
      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... View Details
      Keywords: Artificial Intelligence; Information Technology; Negotiation; AI and Machine Learning
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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.
      • 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... View Details
      Keywords: Model Persuasion; Analytics and Data Science; Forecasting and Prediction; Mathematical Methods; Framework
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      Schwartzstein, Joshua, and Adi Sunderam. "Using Models to Persuade." American Economic Review 111, no. 1 (January 2021): 276–323.
      • December 2020
      • Supplement

      People Analytics at Teach For America (B) (Data Set)

      By: Jeff Polzer
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      Polzer, Jeff. "People Analytics at Teach For America (B) (Data Set)." Harvard Business School Spreadsheet Supplement 421-054, December 2020.
      • 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... View Details
      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
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      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... View Details
      Keywords: Data Analytics; Machine Learning; Artificial Intelligence; Strategy; Business Startups; Markets; AI and Machine Learning; Telecommunications Industry; Utilities Industry; United States; Japan
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      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... View Details
      Keywords: Data Analytics; Machine Learning; Artificial Intelligence; Strategy; Business Startups; AI and Machine Learning; Telecommunications Industry; Utilities Industry; United States; Japan
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      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... View Details
      Keywords: Private Equity Returns; Prequin Data; Private Equity; Analytics and Data Science; Investment Return
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      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.
      • 2020
      • Working Paper

      Prioritarianism and Optimal Taxation

      By: Matti Tuomala and Matthew C. Weinzierl
      Prioritarianism has been at the center of the formal approach to optimal tax theory since its modern starting point in Mirrlees (1971), but most theorists’ use of it is motivated by tractability rather than explicit normative reasoning. We characterize analytically and... View Details
      Keywords: Prioritarianism; Optimal Taxation; Utilitarianism; Redistribution; Inverse-optimum; Taxation; Theory
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      Tuomala, Matti, and Matthew C. Weinzierl. "Prioritarianism and Optimal Taxation." Harvard Business School 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... View Details
      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
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      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.)
      • October 2020 (Revised November 2020)
      • Case

      Wilderness Safaris: Impact Investing and Ecotourism Conservation in Africa

      By: James E. Austin, Megan Epler Wood and Herman B. "Dutch" Leonard
      In 2018 the majority ownership of publicly owned Wilderness Safaris, the leading high-end ecotourism company in Africa with safari operations in eight countries, was acquired by The Rise Fund, one of the world’s largest private social impact investing funds, and by FS... View Details
      Keywords: Investing; Investing For Impact; Ecotourism; COVID-19; Equity Financing; Strategy Formulation; Profitability; Environmental And Social Sustainability; Sustainability; Conservation Planning; Corporate Social Responsibility; Investment; Social Enterprise; Social Entrepreneurship; Environmental Sustainability; Strategy; Financing and Loans; Corporate Social Responsibility and Impact; Health Pandemics; Tourism Industry; Africa; Rwanda; Angola
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      Austin, James E., Megan Epler Wood, and Herman B. "Dutch" Leonard. "Wilderness Safaris: Impact Investing and Ecotourism Conservation in Africa." Harvard Business School Case 321-020, October 2020. (Revised November 2020.)
      • 2020
      • Working Paper

      Reverse Information Sharing: Reducing Costs in Supply Chains with Yield Uncertainty

      By: Pavithra Harsha, Ashish Jagmohan, Retsef Levi, Elisabeth Paulson and Georgia Perakis
      Supply uncertainty in produce supply chains presents major challenges to retailers. Supply shortages create frequent disruptions in terms of promised delivery times, quantity and quality delivered. To alleviate these challenges, dual sourcing--a strategy in which... View Details
      Keywords: Information Sharing; Yield Uncertainty; Ration Gaming; Blockchain; Supply Chain; Risk and Uncertainty
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      Harsha, Pavithra, Ashish Jagmohan, Retsef Levi, Elisabeth Paulson, and Georgia Perakis. "Reverse Information Sharing: Reducing Costs in Supply Chains with Yield Uncertainty." MIT Sloan Research Paper, No. 6172-20, October 2020.
      • 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 (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.)
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