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- Faculty Publications (143)
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- All HBS Web (313)
- Faculty Publications (143)
- 07 Mar 2016
- HBS Seminar
Scott Stern, MIT Sloan School of Management
- August 2021 (Revised February 2024)
- Case
Data Science at the Warriors
By: Iavor I. Bojinov and Michael Parzen
The case explores the development and early growth of a data science team at the Golden State Warriors, an NBA team based in San Francisco. The case begins by explaining the initial rationale for investing in data science, then covers a debate on the appropriate team... View Details
Keywords: Digital Marketing; Analysis; Forecasting and Prediction; Technological Innovation; Information Technology; Analytics and Data Science; Sports Industry; San Francisco; United States
Bojinov, Iavor I., and Michael Parzen. "Data Science at the Warriors." Harvard Business School Case 622-048, August 2021. (Revised February 2024.)
- Program
Competing in the Age of AI—Virtual
will delve into diverse applications of AI, machine learning, predictive modeling, and data science; explore network effects and platform strategies; and learn how to build an AI factory that enables your company to compete successfully... View Details
Jill J. Avery
Dr. Jill Avery is a Senior Lecturer of Business Administration and C. Roland Christensen Distinguished Management Educator in the marketing unit at Harvard Business School. She is a respected authority on branding and brand management, customer relationship... View Details
Keywords: consumer products; arts; advertising; automobiles; retailing; fashion; hotels ＆ motels; food; beverage
- April 2008
- Tutorial
Finance: An Introductory Online Course
By: Timothy A. Luehrman, Brenda W. Chia and Michelle Rendall
The Finance Online Course provides a fundamental understanding of the principles, analytical tools, and knowledge needed to make good investment and financing decisions. The course introduces students to finance ratios, forecasting methods, capital structure theory,... View Details
- July 2023 (Revised July 2023)
- Background Note
Generative AI Value Chain
By: Andy Wu and Matt Higgins
Generative AI refers to a type of artificial intelligence (AI) that can create new content (e.g., text, image, or audio) in response to a prompt from a user. ChatGPT, Bard, and Claude are examples of text generating AIs, and DALL-E, Midjourney, and Stable Diffusion are... View Details
Keywords: AI; Artificial Intelligence; Model; Hardware; Data Centers; AI and Machine Learning; Applications and Software; Analytics and Data Science; Value
Wu, Andy, and Matt Higgins. "Generative AI Value Chain." Harvard Business School Background Note 724-355, July 2023. (Revised July 2023.)
- Article
Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy
By: Edward Glaeser, Andrew Hillis, Scott Duke Kominers and Michael Luca
The proliferation of big data makes it possible to better target city services like hygiene inspections, but city governments rarely have the in-house talent needed for developing prediction algorithms. Cities could hire consultants, but a cheaper alternative is to... View Details
Keywords: User-generated Content; Operations; Tournaments; Policy-making; Machine Learning; Online Platforms; Analytics and Data Science; Mathematical Methods; City; Infrastructure; Business Processes; Government and Politics
Glaeser, Edward, Andrew Hillis, Scott Duke Kominers, and Michael Luca. "Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy." American Economic Review: Papers and Proceedings 106, no. 5 (May 2016): 114–118.
- Article
Is it Better to Average Probabilities or Quantiles?
By: Kenneth C. Lichtendahl, Yael Grushka-Cockayne and Robert L. Winkler
We consider two ways to aggregate expert opinions using simple averages: averaging probabilities and averaging quantiles. We examine analytical properties of these forecasts and compare their ability to harness the wisdom of the crowd. In terms of location, the two... View Details
Keywords: Probability Forecasts; Quantile Forecasts; Expert Combination; Linear Opinion Pooling; Forecasting and Prediction
Lichtendahl, Kenneth C., Yael Grushka-Cockayne, and Robert L. Winkler. "Is it Better to Average Probabilities or Quantiles?" Management Science 59, no. 7 (July 2013): 1594–1611.
- June 2021
- Technical Note
Introduction to Linear Regression
By: Michael Parzen and Paul Hamilton
This technical note introduces (from an applied point of view) the theory and application of simple and multiple linear regression. The motivation for the model is introduced, as well as how to interpret the summary output with regard to prediction and statistical... View Details
- 2010
- Working Paper
The Unbundling of Advertising Agency Services: An Economic Analysis
By: Mohammad Arzaghi, Ernst R. Berndt, James C. Davis and Alvin J. Silk
We address a longstanding puzzle surrounding the unbundling of services occurring over several decades in the U.S. advertising agency industry: What accounts for the shift from bundling to unbundling of services and the slow pace of change? Using Evans and Salinger's... View Details
Keywords: Advertising; Change; Forecasting and Prediction; Cost; Price; Analytics and Data Science; Surveys; Marketing Strategy; Media; Service Operations; Agency Theory; Mathematical Methods; Advertising Industry; United States
Arzaghi, Mohammad, Ernst R. Berndt, James C. Davis, and Alvin J. Silk. "The Unbundling of Advertising Agency Services: An Economic Analysis." Harvard Business School Working Paper, No. 11-039, September 2010.
- 2018
- Working Paper
Measuring Gentrification: Using Yelp Data to Quantify Neighborhood Change
By: Edward L. Glaeser, Hyunjin Kim and Michael Luca
We demonstrate that data from digital platforms such as Yelp have the potential to improve our understanding of gentrification, both by providing data in close to real time (i.e., nowcasting and forecasting) and by providing additional context about how the local... View Details
Keywords: Geographic Location; Local Range; Transition; Analytics and Data Science; Measurement and Metrics; Forecasting and Prediction
Glaeser, Edward L., Hyunjin Kim, and Michael Luca. "Measuring Gentrification: Using Yelp Data to Quantify Neighborhood Change." NBER Working Paper Series, No. 24952, August 2018.
- September 2010
- Article
Do Inventory and Gross Margin Data Improve Sales Forecasts for U.S. Public Retailers?
By: Saravanan Kesavan, Vishal Gaur and Ananth Raman
Firm-level sales forecasts for retailers can be improved if we incorporate cost of goods sold, inventory, and gross margin (defined here as the ratio of sales to cost of goods sold) as three endogenous variables. We construct a simultaneous equations model, estimated... View Details
Keywords: Sales; Forecasting and Prediction; Distribution; Goods and Commodities; Cost; Public Sector; Profit; Mathematical Methods; Analytics and Data Science; Retail Industry; United States
Kesavan, Saravanan, Vishal Gaur, and Ananth Raman. "Do Inventory and Gross Margin Data Improve Sales Forecasts for U.S. Public Retailers?" Management Science 56, no. 9 (September 2010): 1519–1533.
- 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.)
- 2011
- Chapter
Regional Trade Integration and Multinational Firm Strategies
By: Pol Antras and C. Fritz Foley
This paper analyzes the effects of the formation of a regional trade agreement on the level and nature of multinational firm activity. We examine aggregate data that captures the response of U.S. multinational firms to the formation of the ASEAN free trade agreement.... View Details
Keywords: Forecasting and Prediction; Trade; Foreign Direct Investment; Multinational Firms and Management; Globalized Markets and Industries; Analytics and Data Science; Agreements and Arrangements; United States
Antras, Pol, and C. Fritz Foley. "Regional Trade Integration and Multinational Firm Strategies." In Costs and Benefits of Regional Economic Integration in Asia, edited by Robert J. Barro and Jong-Wha Lee. Oxford University Press, 2011.
- September 2023 (Revised October 2024)
- Case
Forecasting Climate Risks: Aviva’s Climate Calculus
By: Mark Egan and Peter Tufano
In late 2021, Ben Carr, Director of Analytics and Capital Modeling at Aviva Plc (Aviva)—a leading insurer with core operations in the UK, Ireland and Canada,—was preparing for an upcoming presentation before the company's board which included its CEO, Amanda Blanc,... View Details
Keywords: Climate Risk; Climate Finance; Forecasting; Insurance; Risk Measurement; Climate Change; Risk Management; Forecasting and Prediction; Insurance Industry; United States
Egan, Mark, and Peter Tufano. "Forecasting Climate Risks: Aviva’s Climate Calculus." Harvard Business School Case 224-025, September 2023. (Revised October 2024.)
- 2016
- Working Paper
The Impact of Supplier Inventory Service Level on Retailer Demand
By: Nathan Craig, Nicole DeHoratius and Ananth Raman
To set inventory service levels, suppliers must understand how changes in inventory service level affect demand. We build on prior research, which uses analytical models and laboratory experiments to study the impact of a supplier's service level on demand from... View Details
Keywords: Customer Satisfaction; Forecasting and Prediction; Learning; Consumer Behavior; Service Delivery; Performance Expectations; Apparel and Accessories Industry; Service Industry
Craig, Nathan, Nicole DeHoratius, and Ananth Raman. "The Impact of Supplier Inventory Service Level on Retailer Demand." Working Paper. (Revised January 2016.)
- March–April 2023
- Article
Market Segmentation Trees
By: Ali Aouad, Adam Elmachtoub, Kris J. Ferreira and Ryan McNellis
Problem definition: We seek to provide an interpretable framework for segmenting users in a population for personalized decision making. Methodology/results: We propose a general methodology, market segmentation trees (MSTs), for learning market... View Details
Keywords: Decision Trees; Computational Advertising; Market Segmentation; Analytics and Data Science; E-commerce; Consumer Behavior; Marketplace Matching; Marketing Channels; Digital Marketing
Aouad, Ali, Adam Elmachtoub, Kris J. Ferreira, and Ryan McNellis. "Market Segmentation Trees." Manufacturing & Service Operations Management 25, no. 2 (March–April 2023): 648–667.
- 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
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).