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  • All HBS Web  (1,193)
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  • All HBS Web  (1,193)
    • News  (210)
    • Research  (832)
    • Events  (12)
    • Multimedia  (2)
  • Faculty Publications  (324)
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  • August 2005 (Revised December 2006)
  • Case

Procter & Gamble: Electronic Data Capture and Clinical Trial Management

By: Robert S. Huckman and Mark J. Cotteleer
Considers whether the management of Procter & Gamble (P&G) Pharmaceuticals should adopt Web-based electronic data capture (EDC) as the default standard for the management of its clinical drug trials. Provides a detailed description of the existing paper-based process... View Details
Keywords: Health Testing and Trials; Internet and the Web; Information Technology; Adoption; Business Processes; Industry Structures; Technological Innovation; Service Operations; Pharmaceutical Industry; United States
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Huckman, Robert S., and Mark J. Cotteleer. "Procter & Gamble: Electronic Data Capture and Clinical Trial Management." Harvard Business School Case 606-033, August 2005. (Revised December 2006.)
  • 21 Jan 2010
  • Working Paper Summaries

Going Through the Motions: An Empirical Test of Management Involvement in Process Improvement

Keywords: by Anita L. Tucker; Health
  • June 2023
  • Simulation

Artea Dashboard and Targeting Policy Evaluation

By: Ayelet Israeli and Eva Ascarza
Companies deploy A/B experiments to gain valuable insights about their customers in order to answer strategic business problems. In marketing, A/B tests are often used to evaluate marketing interventions intended to generate incremental outcomes for the firm. The Artea... View Details
Keywords: Algorithm Bias; Algorithmic Data; Race And Ethnicity; Experimentation; Promotion; Marketing And Society; Big Data; Privacy; Data-driven Management; Data Analysis; Data Analytics; E-Commerce Strategy; Discrimination; Targeted Advertising; Targeted Policies; 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
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Israeli, Ayelet, and Eva Ascarza. "Artea Dashboard and Targeting Policy Evaluation." Harvard Business School Simulation 523-707, June 2023.
  • 27 Feb 2024
  • Research & Ideas

Why Companies Should Share Their DEI Data (Even When It’s Unflattering)

products. “At the moment, many companies aren’t disclosing data on their workforce diversity,” Nam explains. “Simply disclosing this information is enough to improve customer attitudes.” The research comes amid mounting concern that DEI... View Details
Keywords: by Shalene Gupta
  • 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.)
  • Teaching Interest

Overview

Paul is primarily interested in teaching data science to management students through the case method. This includes technical topics (programming and statistics) as well as higher-level management issues (digital transformation, data governance, etc.) As a research... View Details
Keywords: A/B Testing; AI; AI Algorithms; AI Creativity; Algorithm; Algorithm Bias; Algorithmic Bias; Algorithmic Fairness; Algorithms; Analytics; Application Program Interface; Artificial Intelligence; Causality; Causal Inference; Computing; Computers; Data Analysis; Data Analytics; Data Architecture; Data As A Service; Data Centers; Data Governance; Data Labeling; Data Management; Data Manipulation; Data Mining; Data Ownership; Data Privacy; Data Protection; Data Science; Data Science And Analytics Management; Data Scientists; Data Security; Data Sharing; Data Strategy; Data Visualization; Database; Data-driven Decision-making; Data-driven Management; Data-driven Operations; Datathon; Economics Of AI; Economics Of Innovation; Economics Of Information System; Economics Of Science; Forecast; Forecast Accuracy; Forecasting; Forecasting And Prediction; Information Technology; Machine Learning; Machine Learning Models; Prediction; Prediction Error; Predictive Analytics; Predictive Models; Analysis; AI and Machine Learning; Analytics and Data Science; Applications and Software; Digital Transformation; Information Management; Digital Strategy; Technology Adoption
  • March 2022 (Revised January 2025)
  • Technical Note

Statistical Inference

By: Iavor I. Bojinov, Michael Parzen and Paul Hamilton
This note provides an overview of statistical inference for an introductory data science course. First, the note discusses samples and populations. Next the note describes how to calculate confidence intervals for means and proportions. Then it walks through the logic... View Details
Keywords: Data Science; Statistics; Mathematical Modeling; Mathematical Methods; Analytics and Data Science
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Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Statistical Inference." Harvard Business School Technical Note 622-099, March 2022. (Revised January 2025.)
  • Article

Vungle Inc. Improves Monetization Using Big-Data Analytics

By: Bert De Reyck, Ioannis Fragkos, Yael Grushka-Cockayne, Casey Lichtendahl, Hammond Guerin and Andrew Kritzer
The advent of big data has created opportunities for firms to customize their products and services to unprecedented levels of granularity. Using big data to personalize an offering in real time, however, remains a major challenge. In the mobile advertising industry,... View Details
Keywords: Big Data; Monetization; Data and Data Sets; Advertising; Mobile Technology; Customization and Personalization; Performance Improvement
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De Reyck, Bert, Ioannis Fragkos, Yael Grushka-Cockayne, Casey Lichtendahl, Hammond Guerin, and Andrew Kritzer. "Vungle Inc. Improves Monetization Using Big-Data Analytics." Interfaces 47, no. 5 (September–October 2017): 454–466.
  • Article

Online Experimentation: Benefits, Operational and Methodological Challenges, and Scaling Guide

By: Iavor Bojinov and Somit Gupta
In the past decade, online controlled experimentation, or A/B testing, at scale has proved to be a significant driver of business innovation. The practice was first pioneered by the technology sector and, more recently, has been adopted by traditional companies... View Details
Keywords: A/B Testing; Experimentation; Data-driven Culture; Product Development; Innovation and Invention; Digital Transformation
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Bojinov, Iavor, and Somit Gupta. "Online Experimentation: Benefits, Operational and Methodological Challenges, and Scaling Guide." Harvard Data Science Review, no. 4.3 (Summer, 2022).
  • February 2021
  • Tutorial

T-tests: Theory and Practice

By: Michael Parzen, Natalie Epstein, Chiara Farronato and Michael Toffel
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... View Details
Keywords: Data Analysis; Data Analytics; Experiment Design; Experimentation; Analytics and Data Science; Analysis
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Parzen, Michael, Natalie Epstein, Chiara Farronato, and Michael Toffel. T-tests: Theory and Practice. Harvard Business School Tutorial 621-707, February 2021.
  • February 2024
  • Module Note

Data-Driven Marketing in Retail Markets

By: Ayelet Israeli
This note describes an eight-class sessions module on data-driven marketing in retail markets. The module aims to familiarize students with core concepts of data-driven marketing in retail, including exploring the opportunities and challenges, adopting best practices,... View Details
Keywords: Data; Data Analytics; Retail; Retail Analytics; Data Science; Business Analytics; "Marketing Analytics"; Omnichannel; Omnichannel Retailing; Omnichannel Retail; DTC; Direct To Consumer Marketing; Ethical Decision Making; Algorithmic Bias; Privacy; A/B Testing; Descriptive Analytics; Prescriptive Analytics; Predictive Analytics; Analytics and Data Science; E-commerce; Marketing Channels; Demand and Consumers; Marketing Strategy; Retail Industry
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Israeli, Ayelet. "Data-Driven Marketing in Retail Markets." Harvard Business School Module Note 524-062, February 2024.
  • October 2024
  • Article

Sampling Bias in Entrepreneurial Experiments

By: Ruiqing Cao, Rembrand Koning and Ramana Nanda
Using data from a prominent online platform for launching new digital products, we document that ‘sampling bias’—defined as the difference between a startup’s target customer base and the actual sample on which early ‘beta tests’ are conducted—has a systematic and... View Details
Keywords: Target Market; Sampling Biases; Beta Testing; Product Launch; Entrepreneurship; Gender
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Cao, Ruiqing, Rembrand Koning, and Ramana Nanda. "Sampling Bias in Entrepreneurial Experiments." Management Science 70, no. 10 (October 2024): 7283–7307.
  • 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.)
  • November 2016 (Revised April 2017)
  • Case

Basecamp: Pricing

By: Frank Cespedes and Robb Fitzsimmons
A data analyst at Basecamp is evaluating the results of pricing research and its potential implications for the venture’s latest version of its project management software product. View Details
Keywords: Pricing; Entrepreneurial Management; Data Analysis; Marketing; Customer Acquisition; Customer Retention; Value Proposition; Sales Management; Product Management; Market Research; Life Time Value; Testing; Entrepreneurship; Analytics and Data Science; Customers; Value; Sales; Product Marketing; United States
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Cespedes, Frank, and Robb Fitzsimmons. "Basecamp: Pricing." Harvard Business School Case 817-067, November 2016. (Revised April 2017.)
  • 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.)
  • 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.)
  • 16 Apr 2019
  • Working Paper Summaries

Can Biometric Tracking Improve Healthcare Provision and Data Quality? Experimental Evidence from Tuberculosis Control in India

Keywords: by Thomas Bossuroy, Clara Delavallade, and Vincent Pons; Health; Medical Devices & Supplies
  • 2020
  • Working Paper

Is Accounting Useful for Forecasting GDP Growth? A Machine Learning Perspective

By: Srikant Datar, Apurv Jain, Charles C.Y. Wang and Siyu Zhang
We provide a comprehensive examination of whether, to what extent, and which accounting variables are useful for improving the predictive accuracy of GDP growth forecasts. We leverage statistical models that accommodate a broad set of (341) variables—outnumbering the... View Details
Keywords: Big Data; Elastic Net; GDP Growth; Machine Learning; Macro Forecasting; Short Fat Data; Accounting; Economic Growth; Forecasting and Prediction; Analytics and Data Science
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Datar, Srikant, Apurv Jain, Charles C.Y. Wang, and Siyu Zhang. "Is Accounting Useful for Forecasting GDP Growth? A Machine Learning Perspective." Harvard Business School Working Paper, No. 21-113, December 2020.
  • Article

Evaluating and Managing Tramp Shipping Lines Performances: A New Methodology Combining Balanced Scorecard and Network DEA

By: Ying-Chen Hsu, Cheng-Chi Chung, Hsuan-Shih Lee and H. David Sherman
The shipping industry is essential for the economic development of nations like Taiwan as a means delivering and receiving cargo. Shipping has been depressed since 2008 as a result of the financial crisis increasing pressure for the shipping lines to operate more... View Details
Keywords: Network Data Envelopment Analysis; Shipping Line; Centralized Approach; Cross-efficiency; Balanced Scorecard; Performance Evaluation
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Hsu, Ying-Chen, Cheng-Chi Chung, Hsuan-Shih Lee, and H. David Sherman. "Evaluating and Managing Tramp Shipping Lines Performances: A New Methodology Combining Balanced Scorecard and Network DEA." INFOR: Information Systems and Operational Research 51, no. 3 (August 2013): 130–141.
  • November 2021
  • Article

Panel Experiments and Dynamic Causal Effects: A Finite Population Perspective

By: Iavor Bojinov, Ashesh Rambachan and Neil Shephard
In panel experiments, we randomly assign units to different interventions, measuring their outcomes, and repeating the procedure in several periods. Using the potential outcomes framework, we define finite population dynamic causal effects that capture the relative... View Details
Keywords: Panel Data; Dynamic Causal Effects; Potential Outcomes; Finite Population; Nonparametric; Mathematical Methods
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Bojinov, Iavor, Ashesh Rambachan, and Neil Shephard. "Panel Experiments and Dynamic Causal Effects: A Finite Population Perspective." Quantitative Economics 12, no. 4 (November 2021): 1171–1196.
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