Filter Results:
(241)
Show Results For
- All HBS Web (323)
- Faculty Publications (144)
Show Results For
- All HBS Web (323)
- Faculty Publications (144)
Sort by
- January 2022
- Technical Note
Introduction to Capital Structure Analytics
By: Samuel Antill and Ted Berk
This technical note provides an overview of key analytical approaches that are useful in assessing the appropriateness of a firm’s capital structure and funding plan. This note introduces basic quantitative tools and metrics that are commonly used as inputs to this... View Details
Keywords: Budgets and Budgeting; Business Plan; Forecasting and Prediction; Borrowing and Debt; Corporate Finance; Capital Structure; Cash Flow; Financial Liquidity; Financial Management; Financing and Loans
Antill, Samuel, and Ted Berk. "Introduction to Capital Structure Analytics." Harvard Business School Technical Note 222-061, January 2022.
- Article
A Prescriptive Analytics Framework for Optimal Policy Deployment Using Heterogeneous Treatment Effects
By: Edward McFowland III, Sandeep Gangarapu, Ravi Bapna and Tianshu Sun
We define a prescriptive analytics framework that addresses the needs of a constrained decision-maker facing, ex ante, unknown costs and benefits of multiple policy levers. The framework is general in nature and can be deployed in any utility maximizing context, public... View Details
Keywords: Prescriptive Analytics; Heterogeneous Treatment Effects; Optimization; Observed Rank Utility Condition (OUR); Between-treatment Heterogeneity; Machine Learning; Decision Making; Analysis; Mathematical Methods
McFowland III, Edward, Sandeep Gangarapu, Ravi Bapna, and Tianshu Sun. "A Prescriptive Analytics Framework for Optimal Policy Deployment Using Heterogeneous Treatment Effects." MIS Quarterly 45, no. 4 (December 2021): 1807–1832.
- 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
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.
- March 2022 (Revised January 2025)
- Technical Note
Prediction & Machine Learning
This note provides an introduction to machine learning for an introductory data science course. The note begins with a description of supervised, unsupervised, and reinforcement learning. Then, the note provides a brief explanation of the difference between traditional... View Details
Keywords: Machine Learning; Data Science; Learning; Analytics and Data Science; Performance Evaluation; AI and Machine Learning
Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Prediction & Machine Learning." Harvard Business School Technical Note 622-101, March 2022. (Revised January 2025.)
- 2009
- Case
What People Want (and How to Predict It)
By: Thomas H. Davenport and Jeanne G. Harris
Historically, neither the creators nor the distributors of cultural products such as books or movies have used analytics -- data, statistics, predictive modeling -- to determine the likely success of their offerings. Instead, companies relied on the brilliance of... View Details
Keywords: Product Development; Creativity; Customer Satisfaction; Forecasting and Prediction; Markets; Business Model; Publishing Industry; Motion Pictures and Video Industry
Davenport, Thomas H., and Jeanne G. Harris. "What People Want (and How to Predict It)." 2009.
- 09 Feb 2007
- Working Paper Summaries
Do Corporate Social Responsibility Ratings Predict Corporate Social Performance?
- August 2018 (Revised September 2018)
- Supplement
Predicting Purchasing Behavior at PriceMart (B)
By: Srikant M. Datar and Caitlin N. Bowler
Supplements the (A) case. In this case, Wehunt and Morse are concerned about the logistic regression model overfitting to the training data, so they explore two methods for reducing the sensitivity of the model to the data by regularizing the coefficients of the... View Details
Keywords: Data Science; Analytics and Data Science; Analysis; Customers; Household; Forecasting and Prediction
Datar, Srikant M., and Caitlin N. Bowler. "Predicting Purchasing Behavior at PriceMart (B)." Harvard Business School Supplement 119-026, August 2018. (Revised September 2018.)
- July 2019
- Article
'Forward Flow': A New Measure to Quantify Free Thought and Predict Creativity
By: Kurt Gray, Stephen Anderson, Eric Evan Chen, John Michael Kelly, Michael S. Christian, John Patrick, Laura Huang, Yoed N. Kenett and Kevin Lewis
When the human mind is free to roam, its subjective experience is characterized by a continuously evolving stream of thought. Although there is a technique that captures people’s streams of free thought—free association—its utility for scientific research is undermined... View Details
Gray, Kurt, Stephen Anderson, Eric Evan Chen, John Michael Kelly, Michael S. Christian, John Patrick, Laura Huang, Yoed N. Kenett, and Kevin Lewis. "'Forward Flow': A New Measure to Quantify Free Thought and Predict Creativity." American Psychologist 74, no. 5 (July 2019): 539–554.
- Article
Algorithms Need Managers, Too
By: Michael Luca, Jon Kleinberg and Sendhil Mullainathan
Algorithms are powerful predictive tools, but they can run amok when not applied properly. Consider what often happens with social media sites. Today many use algorithms to decide which ads and links to show users. But when these algorithms focus too narrowly on... View Details
Keywords: Machine Learning; Algorithms; Predictive Analytics; Management; Big Data; Analytics and Data Science
Luca, Michael, Jon Kleinberg, and Sendhil Mullainathan. "Algorithms Need Managers, Too." Harvard Business Review 94, nos. 1/2 (January–February 2016): 96–101.
- Winter 2016
- Article
Analytics for an Online Retailer: Demand Forecasting and Price Optimization
By: Kris J. Ferreira, Bin Hong Alex Lee and David Simchi-Levi
We present our work with an online retailer, Rue La La, as an example of how a retailer can use its wealth of data to optimize pricing decisions on a daily basis. Rue La La is in the online fashion sample sales industry, where they offer extremely limited-time... View Details
Ferreira, Kris J., Bin Hong Alex Lee, and David Simchi-Levi. "Analytics for an Online Retailer: Demand Forecasting and Price Optimization." Manufacturing & Service Operations Management 18, no. 1 (Winter 2016): 69–88.
- 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
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
- Working Paper
Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development
By: Daniel Yue, Paul Hamilton and Iavor Bojinov
Predictive model development is understudied despite its centrality in modern artificial
intelligence and machine learning business applications. Although prior discussions
highlight advances in methods (along the dimensions of data, computing power, and
algorithms)... View Details
Keywords: Analytics and Data Science
Yue, Daniel, Paul Hamilton, and Iavor Bojinov. "Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development." Harvard Business School Working Paper, No. 23-029, December 2022. (Revised April 2023.)
- August 2022
- Article
What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features
By: Shunyuan Zhang, Dokyun Lee, Param Vir Singh and Kannan Srinivasan
We study how Airbnb property demand changed after the acquisition of verified images (taken by Airbnb’s photographers) and explore what makes a good image for an Airbnb property. Using deep learning and difference-in-difference analyses on an Airbnb panel dataset... View Details
Keywords: Sharing Economy; Airbnb; Property Demand; Computer Vision; Deep Learning; Image Feature Extraction; Content Engineering; Property; Marketing; Demand and Consumers
Zhang, Shunyuan, Dokyun Lee, Param Vir Singh, and Kannan Srinivasan. "What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features." Management Science 68, no. 8 (August 2022): 5644–5666.
- 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
Ferreira, Kris, and Srikanth Jagabathula. "JOANN: Joannalytics Inventory Allocation Tool." Harvard Business School Case 621-055, September 2020. (Revised March 2022.)
- March 2017 (Revised March 2022)
- Case
Flashion: Art vs. Science in Fashion Retailing
By: Kris Ferreira and Karim R. Lakhani
Kate Wilson, retail analytics manager at Flashion, a fashion flash-sale site, is tasked with developing analytics to optimize pricing for first-exposure products on the site. Many in the industry have relied on years of experience and intuition to determine pricing—can... View Details
Keywords: Analytics; Pricing; Data; Service Operations; Forecasting and Prediction; Internet and the Web; Technology Adoption; Mathematical Methods; Decision Making; E-commerce; Retail Industry; Fashion Industry; United States
Ferreira, Kris, and Karim R. Lakhani. "Flashion: Art vs. Science in Fashion Retailing." Harvard Business School Case 617-059, March 2017. (Revised March 2022.)
- Article
Consumer Neuroscience: Advances in Understanding Consumer Psychology
By: Uma R. Karmarkar and Carolyn Yoon
While the study of consumer behavior has been enriched by improved abilities to generate new insights, many of the mechanisms underlying judgments and decision making remain difficult to investigate. In this review, we highlight some of the ways in which our... View Details
Keywords: Consumer Neuroscience; Neuroscience; Neuroeconomics; Consumer Psychology; Customer Behavior; Predictive Analytics; Neural Prediction; Neuroimaging; fMRI; Eye-tracking; Consumer Behavior; Marketing
Karmarkar, Uma R., and Carolyn Yoon. "Consumer Neuroscience: Advances in Understanding Consumer Psychology." Current Opinion in Psychology 10 (August 2016): 160–165.
- 09 Dec 2015
- Research Event
How Do You Predict Demand and Set Prices For Products Never Sold Before?
explained that the world of business analytics includes descriptive analytics (analyzing what has happened), predictive analytics (analyzing data... View Details
- 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
Israeli, Ayelet. "Data-Driven Marketing in Retail Markets." Harvard Business School Module Note 524-062, February 2024.
- February 2017 (Revised June 2017)
- Case
ExxonMobil: Business as Usual? (A)
By: George Serafeim, Shiva Rajgopal and David Freiberg
Climate change was becoming an important societal and business issue as more governments were introducing climate change related regulations and investors became increasibly worried about stranded assets within oil and gas firms. In September 2016, the U.S. Securities... View Details
Keywords: Oil ＆ Gas; Oil Prices; Oil Companies; Asset Impairment; Predictive Analytics; Sustainability; Environmental Impact; Innovation; Disclosure; Accounting; Valuation; Climate Change; Renewable Energy; Environmental Sustainability; Financial Reporting; Energy Industry
Serafeim, George, Shiva Rajgopal, and David Freiberg. "ExxonMobil: Business as Usual? (A)." Harvard Business School Case 117-046, February 2017. (Revised June 2017.)
- February 2017 (Revised June 2017)
- Supplement
ExxonMobil: Business as Usual? (B)
By: George Serafeim, Shiva Rajgopal and David Freiberg
The case presents ExxonMobil's response to growing pressure to disclose how climate change will impact their business. This includes multiple asset impairments and losing a proxy vote to shareholders to increase climate change related reporting. Supplements the (B)... View Details
Keywords: Oil ＆ Gas; Oil Prices; Oil Companies; Asset Impairment; Predictive Analytics; Sustainability; Environmental Impact; Innovation; Disclosure; Accounting; Valuation; Energy Sources; Ethics; Corporate Disclosure; Governance Compliance; Climate Change; Financial Reporting; Energy Industry; United States
Serafeim, George, Shiva Rajgopal, and David Freiberg. "ExxonMobil: Business as Usual? (B)." Harvard Business School Supplement 117-047, February 2017. (Revised June 2017.)