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      • 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... View Details
      Keywords: Targeted Advertising; Algorithmic Data; Bias; Advertising; 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. "Spreadsheet Supplement to Artea Teaching Note." Harvard Business School Spreadsheet Supplement 521-705, September 2020. (Revised June 2023.)
      • September 2020 (Revised September 2021)
      • Case

      Student Success at Georgia State University (A)

      By: Michael W. Toffel, Robin Mendelson and Julia Kelley
      Georgia State University had developed a reputation for driving student success by nearly doubling its graduation rate for students of all racial, ethnic, and socioeconomic backgrounds. It did so while growing its student body and the proportion of Black/African... View Details
      Keywords: Education; Higher Education; Learning; Curriculum and Courses; Demographics; Diversity; Ethnicity; Income; Race; Leadership; Goals and Objectives; Measurement and Metrics; Operations; Organizations; Mission and Purpose; Organizational Culture; Outcome or Result; Performance; Performance Effectiveness; Performance Evaluation; Service Operations; Performance Improvement; Planning; Strategic Planning; Social Enterprise; Nonprofit Organizations; Social Issues; Wealth and Poverty; Equality and Inequality; Information Technology; Digital Platforms; Education Industry; Atlanta
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      Toffel, Michael W., Robin Mendelson, and Julia Kelley. "Student Success at Georgia State University (A)." Harvard Business School Case 621-006, September 2020. (Revised September 2021.)
      • September 2020 (Revised March 2022)
      • Case

      JOANN: Joannalytics Inventory Allocation Tool

      By: Kris Ferreira and Srikanth Jagabathula
      Michael Joyce, Vice President of Inventory Management at JOANN, championed an effort to develop and implement an inventory allocation analytics tool that used advanced analytics to predict in-season demand of seasonal items for each of JOANN’s nearly 900 stores and... View Details
      Keywords: Analytics; Machine Learning; Optimization; Inventory Management; Mathematical Methods; Decision Making; Operations; Supply Chain Management; Resource Allocation; Distribution; Technology Adoption; Applications and Software; Change Management; Fashion Industry; Consumer Products Industry; Retail Industry; United States; Ohio
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      Ferreira, Kris, and Srikanth Jagabathula. "JOANN: Joannalytics Inventory Allocation Tool." Harvard Business School Case 621-055, September 2020. (Revised March 2022.)
      • August 2020 (Revised September 2020)
      • Technical Note

      Assessing Prediction Accuracy of Machine Learning Models

      By: Michael W. Toffel, Natalie Epstein, Kris Ferreira and Yael Grushka-Cockayne
      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... View Details
      Keywords: Machine Learning; Statistics; Econometric Analyses; Experimental Methods; Data Analysis; Data Analytics; Forecasting and Prediction; Analytics and Data Science; Analysis; Mathematical Methods
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      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
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      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

      By: Iavor I Bojinov, Chiara Farronato, Yael Grushka-Cockayne, Willy C. Shih and Michael W. Toffel
      This note describes sampling and t-tests, two fundamental statistical concepts. View Details
      Keywords: Statistics; Econometric Analyses; Experimental Methods; Data Analysis; Data Analytics; Analytics and Data Science; Analysis; Surveys; Mathematical Methods
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      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.
      • August 2020
      • Teaching Note

      People Analytics at McKinsey

      By: Jeffrey T. Polzer and Olivia Hull
      Teaching note to accompany "People Analytics at McKinsey," HBS No. 418-023. View Details
      Keywords: People Analytics; Analytics; Human Resource Management; Business Analytics
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      Polzer, Jeffrey T., and Olivia Hull. "People Analytics at McKinsey." Harvard Business School Teaching Note 421-022, August 2020.
      • July 2020
      • Case

      Karen Bruck: Growing Managers at MercadoLibre

      By: Joshua D. Margolis, Fernanda Miguel and Mariana Cal
      Karen Bruck, Corporate Sales Director at MercadoLibre, Latin America's largest e-commerce platform, needs to make a decision about one of her managers, who, while analytically savvy, has an approach that does not fit in with the company's culture. View Details
      Keywords: Performance Evaluation; Employee Relationship Management; Decision Making; Interpersonal Communication; Organizational Culture; Retail Industry; Latin America; Argentina
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      Margolis, Joshua D., Fernanda Miguel, and Mariana Cal. "Karen Bruck: Growing Managers at MercadoLibre." Harvard Business School Case 421-013, July 2020.
      • July 2020
      • Case

      Applying Data Science and Analytics at P&G

      By: Srikant M. Datar, Sarah Mehta and Paul Hamilton
      Set in December 2019, this case explores how P&G has applied data science and analytics to cut costs and improve outcomes across its business units. The case provides an overview of P&G’s approach to data management and governance, and reviews the challenges associated... View Details
      Keywords: Data Science; Analytics; Analysis; Information; Information Management; Information Types; Innovation and Invention; Strategy; Analytics and Data Science; Consumer Products Industry; United States; Ohio
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      Datar, Srikant M., Sarah Mehta, and Paul Hamilton. "Applying Data Science and Analytics at P&G." Harvard Business School Case 121-006, July 2020.
      • July 2020
      • Case

      Driving Transformation at the Majid Al Futtaim Group

      By: Suraj Srinivasan and Esel Çekin
      The case opens with Alain Bejjani, CEO of Majid Al Futtaim (MAF) Holding, anticipating on the Group’s next phase in the multi-year transformation journey and reflecting on the initiatives he implemented to create the Group’s growth-oriented culture. Founded in 1995,... View Details
      Keywords: Transformation; Organizational Change and Adaptation; Organizational Culture; Growth and Development Strategy; Retail Industry; United Arab Emirates; Middle East; Dubai
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      Srinivasan, Suraj, and Esel Çekin. "Driving Transformation at the Majid Al Futtaim Group." Harvard Business School Case 121-002, July 2020.
      • June 2020
      • Background Note

      Customer Management Dynamics and Cohort Analysis

      By: Elie Ofek, Barak Libai and Eitan Muller
      The digital revolution has allowed companies to amass considerable amounts of data on their customers. Using this information to generate actionable insights is fast becoming a critical skill that firms must master if they wish to effectively compete and win in today’s... View Details
      Keywords: Cohort Analysis; Customers; Analytics and Data Science; Segmentation; Analysis; Customer Value and Value Chain
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      Ofek, Elie, Barak Libai, and Eitan Muller. "Customer Management Dynamics and Cohort Analysis." Harvard Business School Background Note 520-122, June 2020.
      • 2021
      • Working Paper

      The Project on Impact Investments' Impact Investment Database

      By: M. Diane Burton, Shawn Cole, Abhishek Dev, Christina Jarymowycz, Leslie Jeng, Josh Lerner, Fanele Mashwama, Yue (Cynthia) Xu and T. Robert Zochowski
      Impact investing has grown significantly over the past 15 years. From a niche investing segment with only $25 billion AUM in 2013 (WEF 2013), it experienced double-digit growth and developed into a market with an estimated $502 billion AUM (Mudaliapar and Dithrich... View Details
      Keywords: Impact Investing; Investment; Analytics and Data Science; Analysis
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      Burton, M. Diane, Shawn Cole, Abhishek Dev, Christina Jarymowycz, Leslie Jeng, Josh Lerner, Fanele Mashwama, Yue (Cynthia) Xu, and T. Robert Zochowski. "The Project on Impact Investments' Impact Investment Database." Harvard Business School Working Paper, No. 20-117, May 2020. (Revised August 2021.)
      • May 8, 2020
      • Article

      Which Covid-19 Data Can You Trust?

      By: Satchit Balsari, Caroline Buckee and Tarun Khanna
      The COVID-19 pandemic has produced a tidal wave of data, but how much of it is any good? And as a layperson, how can you sort the good from the bad? The authors suggest a few strategies for dividing the useful data from the misleading: Beware of data that’s too broad... View Details
      Keywords: COVID-19 Pandemic; Health Pandemics; Analytics and Data Science
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      Balsari, Satchit, Caroline Buckee, and Tarun Khanna. "Which Covid-19 Data Can You Trust?" Harvard Business Review (website) (May 8, 2020).
      • May 2020
      • Article

      Inventory Auditing and Replenishment Using Point-of-Sales Data

      By: Achal Bassamboo, Antonio Moreno and Ioannis Stamatopoulos
      Spoilage, expiration, damage due to employee/customer handling, employee theft, and customer shoplifting usually are not reflected in inventory records. As a result, records often report phantom inventory, i.e., units of good not available for sale. We derive an... View Details
      Keywords: Shelf Availability; Inventory Record Inaccuracy; Optimal Replenishment; Retail Analytics; Performance Effectiveness; Analysis; Mathematical Methods
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      Bassamboo, Achal, Antonio Moreno, and Ioannis Stamatopoulos. "Inventory Auditing and Replenishment Using Point-of-Sales Data." Production and Operations Management 29, no. 5 (May 2020): 1219–1231.
      • 2020
      • Article

      Public Sentiment and the Price of Corporate Sustainability

      By: George Serafeim
      Combining corporate sustainability performance scores based on environmental, social, and governance (ESG) data with big data measuring public sentiment about a company’s sustainability performance, I find that the valuation premium paid for companies with strong... View Details
      Keywords: Sustainability; ESG; ESG (Environmental, Social, Governance) Performance; Investment Management; Investment Strategy; Big Data; Machine Learning; Environment; Environmental Sustainability; Corporate Governance; Performance; Asset Pricing; Investment; Management; Strategy; Human Capital; Public Opinion; Value; Analytics and Data Science
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      Serafeim, George. "Public Sentiment and the Price of Corporate Sustainability." Financial Analysts Journal 76, no. 2 (2020): 26–46.
      • April 2020
      • Case

      Ment.io: Knowledge Analytics for Team Decision Making

      By: Yael Grushka-Cockayne, Jeffrey T. Polzer, Susie L. Ma and Shlomi Pasternak
      Ment.io was a software platform that used proprietary data analytics technology to help organizations make informed and transparent decisions based on team input. Ment was born out of founder Joab Rosenberg’s frustration that, while organizations collected ever... View Details
      Keywords: Decision Making; Information Technology; Knowledge; Knowledge Acquisition; Knowledge Management; Operations; Information Management; Product; Product Development; Entrepreneurship; Business Startups; Communications Industry; Information Industry; Information Technology Industry; Web Services Industry; Middle East; Israel
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      Grushka-Cockayne, Yael, Jeffrey T. Polzer, Susie L. Ma, and Shlomi Pasternak. "Ment.io: Knowledge Analytics for Team Decision Making." Harvard Business School Case 420-078, April 2020.
      • March 2020 (Revised June 2022)
      • Case

      GreenLight Fund

      By: Brian Trelstad, Julia Kelley and Mel Martin
      As Tara Noland, the Executive Director (ED) of GreenLight Cincinnati, reflected on her first few years on the job. Noland had delivered on what she had been hired to do in the city: work with leading philanthropists and nonprofit executives to use data and evidence to... View Details
      Keywords: Philanthropy; Venture Philanthropy; Replication; Philanthropy and Charitable Giving; Venture Capital; Social Issues; Decision Making; Analytics and Data Science; Cincinnati
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      Trelstad, Brian, Julia Kelley, and Mel Martin. "GreenLight Fund." Harvard Business School Case 320-053, March 2020. (Revised June 2022.)
      • March 2020
      • Supplement

      People Analytics at Teach For America (B)

      By: Jeffrey T. Polzer and Julia Kelley
      This is a supplement to the People Analytics at Teach For America (A) case. In this supplement, situated one year after the A case, Managing Director Michael Metzger must decide how to apply his team's predictive models generated from the previous year’s data. View Details
      Keywords: Analytics; Human Resource Management; Data; Workforce; Hiring; Talent Management; Forecasting; Predictive Analytics; Organizational Behavior; Recruiting; Analytics and Data Science; Forecasting and Prediction; Recruitment; Selection and Staffing; Talent and Talent Management
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      Polzer, Jeffrey T., and Julia Kelley. "People Analytics at Teach For America (B)." Harvard Business School Supplement 420-086, March 2020.
      • March 2020
      • Article

      Diagnosing Missing Always at Random in Multivariate Data

      By: Iavor I. Bojinov, Natesh S. Pillai and Donald B. Rubin
      Models for analyzing multivariate data sets with missing values require strong, often assessable, assumptions. The most common of these is that the mechanism that created the missing data is ignorable—a twofold assumption dependent on the mode of inference. The first... View Details
      Keywords: Missing Data; Diagnostic Tools; Sensitivity Analysis; Hypothesis Testing; Missing At Random; Row Exchangeability; Analytics and Data Science; Mathematical Methods
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      Bojinov, Iavor I., Natesh S. Pillai, and Donald B. Rubin. "Diagnosing Missing Always at Random in Multivariate Data." Biometrika 107, no. 1 (March 2020): 246–253.
      • 2020
      • Book

      The Power of Experiments: Decision-Making in a Data-Driven World

      By: Michael Luca and Max H. Bazerman
      Have you logged into Facebook recently? Searched for something on Google? Chosen a movie on Netflix? If so, you've probably been an unwitting participant in a variety of experiments—also known as randomized controlled trials—designed to test the impact of changes to an... View Details
      Keywords: Experiments; Randomized Controlled Trials; Organizations; Decision Making; Analytics and Data Science; Management Analysis, Tools, and Techniques
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      Luca, Michael, and Max H. Bazerman. The Power of Experiments: Decision-Making in a Data-Driven World. Cambridge, MA: MIT Press, 2020.
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