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    • Faculty Publications  (322)

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    • All HBS Web  (120,049)
      • Faculty Publications  (322)

      Analytics and Data ScienceRemove Analytics and Data Science →

      ← Page 2 of 322 Results →
      • April–May 2024
      • Article

      Gone with the Big Data: Institutional Lender Demand for Private Information

      By: Jung Koo Kang
      I explore whether big-data sources can crowd out the value of private information acquired through lending relationships. Institutional lenders have been shown to exploit their access to borrowers’ private information by trading on it in financial markets. As a shock... View Details
      Keywords: Analytics and Data Science; Borrowing and Debt; Financial Markets; Value; Knowledge Dissemination; Financing and Loans
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      Kang, Jung Koo. "Gone with the Big Data: Institutional Lender Demand for Private Information." Art. 101663. Journal of Accounting & Economics 77, nos. 2-3 (April–May 2024).
      • March 2024
      • Supplement

      Madrigal: Conducting a Customer-Base Audit

      By: Eva Ascarza, Peter Fader, Bruce G.S. Hardie and Michael Ross
      This case presents a scenario where Madrigal, a U.S. retailer with a rich 20-year history and a solid loyalty program, faces a turning point with the arrival of a new CEO. This leadership change reveals a critical gap in understanding the customer base, prompting an... View Details
      Keywords: Customer Relationship Management; Analytics and Data Science; Growth and Development Strategy; Retail Industry; United States
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      Ascarza, Eva, Peter Fader, Bruce G.S. Hardie, and Michael Ross. "Madrigal: Conducting a Customer-Base Audit." Harvard Business School PowerPoint Supplement 524-068, March 2024.
      • March 2024
      • Supplement

      Madrigal: Conducting a Customer-Base Audit

      By: Eva Ascarza, Bruce Hardie, Peter S. Fader and Michael Ross
      This case presents a scenario where Madrigal, a U.S. retailer with a rich 20-year history and a solid loyalty program, faces a turning point with the arrival of a new CEO. This leadership change reveals a critical gap in understanding the customer base, prompting an... View Details
      Keywords: Customer Relationship Management; Analytics and Data Science; Growth and Development Strategy; Retail Industry; United States
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      Ascarza, Eva, Bruce Hardie, Peter S. Fader, and Michael Ross. "Madrigal: Conducting a Customer-Base Audit." Harvard Business School Spreadsheet Supplement 524-707, March 2024.
      • March 2024
      • Supplement

      Madrigal: Conducting a Customer-Base Audit

      By: Eva Ascarza, Bruce Hardie, Peter S. Fader and Michael Ross
      This case presents a scenario where Madrigal, a U.S. retailer with a rich 20-year history and a solid loyalty program, faces a turning point with the arrival of a new CEO. This leadership change reveals a critical gap in understanding the customer base, prompting an... View Details
      Keywords: Customer Relationship Management; Analytics and Data Science; Growth and Development Strategy; Retail Industry; United States
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      Ascarza, Eva, Bruce Hardie, Peter S. Fader, and Michael Ross. "Madrigal: Conducting a Customer-Base Audit." Harvard Business School Spreadsheet Supplement 524-706, March 2024.
      • March 2024
      • Teaching Note

      Madrigal: Conducting a Customer-Base Audit

      By: Eva Ascarza, Peter S. Fader, Bruce Hardie and Michael Ross
      Teaching Note for HBS Case No. 524-046. This case presents a scenario where Madrigal, a U.S. retailer with a rich 20-year history and a solid loyalty program, faces a turning point with the arrival of a new CEO. This leadership change reveals a critical gap in... View Details
      Keywords: Customer Relationship Management; Analytics and Data Science; Growth and Development Strategy; Customer Value and Value Chain; Retail Industry; United States
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      Ascarza, Eva, Peter S. Fader, Bruce Hardie, and Michael Ross. "Madrigal: Conducting a Customer-Base Audit." Harvard Business School Teaching Note 524-047, March 2024.
      • March 2024
      • Case

      Madrigal: Conducting a Customer-Base Audit

      By: Eva Ascarza, Bruce Hardie, Michael Ross and Peter S. Fader
      This case presents a scenario where Madrigal, a U.S. retailer with a rich 20-year history and a solid loyalty program, faces a turning point with the arrival of a new CEO. This leadership change reveals a critical gap in understanding the customer base, prompting an... View Details
      Keywords: Customer Relationship Management; Analytics and Data Science; Growth and Development Strategy; Customer Value and Value Chain; Retail Industry; United States
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      Ascarza, Eva, Bruce Hardie, Michael Ross, and Peter S. Fader. "Madrigal: Conducting a Customer-Base Audit." Harvard Business School Case 524-046, March 2024.
      • 2023
      • Working Paper

      An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits

      By: Biyonka Liang and Iavor I. Bojinov
      Typically, multi-armed bandit (MAB) experiments are analyzed at the end of the study and thus require the analyst to specify a fixed sample size in advance. However, in many online learning applications, it is advantageous to continuously produce inference on the... View Details
      Keywords: Analytics and Data Science; AI and Machine Learning; Mathematical Methods
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      Liang, Biyonka, and Iavor I. Bojinov. "An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits." Harvard Business School Working Paper, No. 24-057, March 2024.
      • 2024
      • Working Paper

      Anytime-Valid Inference in Linear Models and Regression-Adjusted Causal Inference

      By: Michael Lindon, Dae Woong Ham, Martin Tingley and Iavor I. Bojinov
      Linear regression adjustment is commonly used to analyze randomized controlled experiments due to its efficiency and robustness against model misspecification. Current testing and interval estimation procedures leverage the asymptotic distribution of such estimators to... View Details
      Keywords: Mathematical Methods; Analytics and Data Science
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      Lindon, Michael, Dae Woong Ham, Martin Tingley, and Iavor I. Bojinov. "Anytime-Valid Inference in Linear Models and Regression-Adjusted Causal Inference." Harvard Business School Working Paper, No. 24-060, March 2024.
      • 2023
      • Working Paper

      Design-Based Inference for Multi-arm Bandits

      By: Dae Woong Ham, Iavor I. Bojinov, Michael Lindon and Martin Tingley
      Multi-arm bandits are gaining popularity as they enable real-world sequential decision-making across application areas, including clinical trials, recommender systems, and online decision-making. Consequently, there is an increased desire to use the available... View Details
      Keywords: Analytics and Data Science; Mathematical Methods
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      Ham, Dae Woong, Iavor I. Bojinov, Michael Lindon, and Martin Tingley. "Design-Based Inference for Multi-arm Bandits." Harvard Business School Working Paper, No. 24-056, March 2024.
      • February 2024
      • Teaching Note

      AB InBev: Brewing Up Forecasts during COVID-19

      By: Mark Egan and C. Fritz Foley
      Teaching Note for HBS Case No. 224-020. In July 2021, the CEO of AB InBev's European operations and his team strategized to position the company for success post-pandemic. As the world's largest beer company, boasting over 500 brands, revenue of $46 billion, and a... View Details
      Keywords: Forecasting; Investor Relations; Beverage Industry; Corporate Finance; Decisions; Forecasting and Prediction; Health Pandemics; Analytics and Data Science; Digital Transformation; Crisis Management; Business Model; Food and Beverage Industry; United States; Europe
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      Egan, Mark, and C. Fritz Foley. "AB InBev: Brewing Up Forecasts during COVID-19." Harvard Business School Teaching Note 224-074, February 2024.
      • February 6, 2024
      • Article

      Find the AI Approach That Fits the Problem You’re Trying to Solve

      By: George Westerman, Sam Ransbotham and Chiara Farronato
      AI moves quickly, but organizations change much more slowly. What works in a lab may be wrong for your company right now. If you know the right questions to ask, you can make better decisions, regardless of how fast technology changes. You can work with your technical... View Details
      Keywords: Technology Adoption; AI and Machine Learning; Organizational Change and Adaptation; Technological Innovation; Analytics and Data Science
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      Westerman, George, Sam Ransbotham, and Chiara Farronato. "Find the AI Approach That Fits the Problem You’re Trying to Solve." Harvard Business Review Digital Articles (February 6, 2024).
      • February 2024
      • Case

      ReSpo.Vision: The Kickstart of an AI Sports Revolution

      By: Paul A. Gompers, Elena Corsi and Nikolina Jonsson
      This case study explores the growth journey of Polish computer vision sports start-up ReSpo.Vision in an emerging entrepreneurial ecosystem. By providing 3D data and analysis to soccer clubs, ReSpo.Vision achieved significant milestones with a €1 million seed round, an... View Details
      Keywords: Business Startups; Business Plan; Experience and Expertise; Talent and Talent Management; Decisions; Decision Choices and Conditions; Forecasting and Prediction; Entrepreneurship; Venture Capital; AI and Machine Learning; Analytics and Data Science; Applications and Software; Business Strategy; Sports Industry; Technology Industry; Poland; Europe
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      Gompers, Paul A., Elena Corsi, and Nikolina Jonsson. "ReSpo.Vision: The Kickstart of an AI Sports Revolution." Harvard Business School Case 824-151, February 2024.
      • February 2024 (Revised February 2024)
      • Teaching Note

      Travelogo: Understanding Customer Journeys

      By: Eva Ascarza and Ta-Wei Huang
      Teaching Note for HBS Exercise 524-044. The exercise aims to teach students about 1) Customer Segmentation; and 2) constructing buying personas, 3) Get actionable insights from clickstream data. View Details
      Keywords: Customer Relationship Management; Analysis; Analytics and Data Science; Marketing Strategy; Segmentation; Consumer Behavior; Travel Industry; United States
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      Ascarza, Eva, and Ta-Wei Huang. "Travelogo: Understanding Customer Journeys." Harvard Business School Teaching Note 524-045, February 2024. (Revised February 2024.)
      • February 2024
      • Teaching Note

      CityScore: Big Data Comes to Boston

      By: Boris Groysberg and Sarah L. Abbott
      Teaching Note for HBS Case No. 422-050. In 2016, Mayor Marty Walsh of Boston introduced CityScore, a data dashboard that measured the city’s progress across a range of metrics. View Details
      Keywords: Government Administration; Leadership; Transformation; City; Analytics and Data Science; Measurement and Metrics; Public Administration Industry; United States; Boston
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      Groysberg, Boris, and Sarah L. Abbott. "CityScore: Big Data Comes to Boston." Harvard Business School Teaching Note 424-058, February 2024.
      • 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.
      • January 2024 (Revised February 2024)
      • Course Overview Note

      Managing Customers for Growth: Course Overview for Students

      By: Eva Ascarza
      Managing Customers for Growth (MCG) is a 14-session elective course for second-year MBA students at Harvard Business School. It is designed for business professionals engaged in roles centered on customer-driven growth activities. The course explores the dynamics of... View Details
      Keywords: Customer Relationship Management; Decision Making; Analytics and Data Science; Growth Management; Telecommunications Industry; Technology Industry; Financial Services Industry; Education Industry; Travel Industry
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      Ascarza, Eva. "Managing Customers for Growth: Course Overview for Students." Harvard Business School Course Overview Note 524-032, January 2024. (Revised February 2024.)
      • January 2024 (Revised February 2024)
      • Exercise

      Travelogo: Understanding Customer Journeys

      By: Eva Ascarza, Nicolas Padilla and Oded Netzer
      In late May 2023, Sarah Merino, the newly appointed manager of the Customer Insights group at Travelogo—an online travel booking platform—initiates a comprehensive analysis of clickstream data to understand the varied behaviors and needs of their users. In preparation... View Details
      Keywords: Customer Relationship Management; Analysis; Analytics and Data Science; Marketing Strategy; Segmentation; Consumer Behavior; Travel Industry; United States
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      Ascarza, Eva, Nicolas Padilla, and Oded Netzer. "Travelogo: Understanding Customer Journeys." Harvard Business School Exercise 524-044, January 2024. (Revised February 2024.)
      • 2025
      • Working Paper

      Enhancing Treatment Effect Prediction on Privacy-Protected Data: An Honest Post-Processing Approach

      By: Ta-Wei Huang and Eva Ascarza
      As firms increasingly rely on customer data for personalization, concerns over privacy and regulatory compliance have grown. Local Differential Privacy (LDP) offers strong individual-level protection by injecting noise into data before collection. While... View Details
      Keywords: Targeted Intervention; Conditional Average Treatment Effect Estimation; Differential Privacy; Honest Estimation; Post-processing; Analytics and Data Science; Consumer Behavior; Marketing
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      Huang, Ta-Wei, and Eva Ascarza. "Enhancing Treatment Effect Prediction on Privacy-Protected Data: An Honest Post-Processing Approach." Harvard Business School Working Paper, No. 24-034, December 2023. (Revised March 2025.)
      • 2023
      • Article

      Benchmarking Large Language Models on CMExam—A Comprehensive Chinese Medical Exam Dataset

      By: Junling Liu, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu and Michael Lingzhi Li
      Recent advancements in large language models (LLMs) have transformed the field of question answering (QA). However, evaluating LLMs in the medical field is challenging due to the lack of standardized and comprehensive datasets. To address this gap, we introduce CMExam,... View Details
      Keywords: Large Language Model; AI and Machine Learning; Analytics and Data Science; Health Industry
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      Liu, Junling, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu, and Michael Lingzhi Li. "Benchmarking Large Language Models on CMExam—A Comprehensive Chinese Medical Exam Dataset." Conference on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track 36 (2023).
      • 2023
      • Other Article

      The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications

      By: Mirac Suzgun, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers and Stuart Shieber
      Innovation is a major driver of economic and social development, and information about many kinds of innovation is embedded in semi-structured data from patents and patent applications. Though the impact and novelty of innovations expressed in patent data are difficult... View Details
      Keywords: USPTO; Natural Language Processing; Classification; Summarization; Patent Novelty; Patent Trolls; Patent Enforceability; Patents; Innovation and Invention; Intellectual Property; AI and Machine Learning; Analytics and Data Science
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      Suzgun, Mirac, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers, and Stuart Shieber. "The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications." Conference on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track 36 (2023).
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