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(1,987)
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Show Results For
- All HBS Web
(1,987)
- News (300)
- Research (1,281)
- Events (27)
- Multimedia (11)
- Faculty Publications (769)
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- Research Summary
Overview
By: Ayelet Israeli
Professor Israeli utilizes econometric methods and field experiments to study data driven decision making in marketing context. Her research focuses on data-driven marketing, with an emphasis on how businesses can leverage their own data, customer data, and market data... View Details
- November–December 2022
- Article
The Value of Descriptive Analytics: Evidence from Online Retailers
By: Ron Berman and Ayelet Israeli
Does the adoption of descriptive analytics impact online retailer performance, and if so, how? We use the synthetic difference-in-differences method to analyze the staggered adoption of a retail analytics dashboard by more than 1,500 e-commerce websites, and we find an... View Details
Keywords: Descriptive Analytics; Big Data; Synthetic Control; E-commerce; Online Retail; Difference-in-differences; Martech; Internet and the Web; Analytics and Data Science; Performance; Marketing; Retail Industry
Berman, Ron, and Ayelet Israeli. "The Value of Descriptive Analytics: Evidence from Online Retailers." Marketing Science 41, no. 6 (November–December 2022): 1074–1096.
- September 2019
- Case
Starling Trust Sciences: Measuring Trust in Organizations
By: Aiyesha Dey, Jonas Heese and James Weber
Stephen Scott needed to decide whether to keep his behavioral analytics startup in the people analytics sector or shift his company into the RegTech sector. Starling had develop technology that enabled its customers to anticipate and shape the behavior of their... View Details
Keywords: Behavioral Analytics; Financial Institutions; Banks and Banking; Entrepreneurship; Strategy; Banking Industry; Consulting Industry; Information Technology Industry; United States; United Kingdom
Dey, Aiyesha, Jonas Heese, and James Weber. "Starling Trust Sciences: Measuring Trust in Organizations." Harvard Business School Case 120-006, September 2019.
- April 2014 (Revised March 2015)
- Case
GE and the Industrial Internet
By: Karim R. Lakhani, Marco Iansiti and Kerry Herman
CEO Jeff Immelt considers whether GE is moving fast enough on its new Industrial Internet initiative. The undertaking includes building out an Industrial Internet, connecting machines and devices, collecting their data and operations, and providing services to clients... View Details
Keywords: Technology; Operations Management; Strategy; Big Data; Business Analysis; Corporate Strategy; Digital Technology; Digital Innovation; General Management; General Strategy; Global Competitiveness; Global Strategy; Innovation; Innovation And Management; Industrial Internet; GE; Innovation and Invention; Information Technology; Analytics and Data Science; Medical Devices and Supplies Industry; Medical Devices and Supplies Industry; Medical Devices and Supplies Industry; Medical Devices and Supplies Industry; Medical Devices and Supplies Industry; Medical Devices and Supplies Industry; Medical Devices and Supplies Industry; Medical Devices and Supplies Industry; Medical Devices and Supplies Industry; Medical Devices and Supplies Industry; North and Central America; Asia; Europe; Middle East; Latin America
Lakhani, Karim R., Marco Iansiti, and Kerry Herman. "GE and the Industrial Internet." Harvard Business School Case 614-032, April 2014. (Revised March 2015.)
- Article
Considerations of Fairness and Strategy: Experimental Data from Sequential Games
By: V. Prasnikar and A. E. Roth
Prasnikar, V., and A. E. Roth. "Considerations of Fairness and Strategy: Experimental Data from Sequential Games." Quarterly Journal of Economics 107, no. 3 (August 1992): 865–888.
- 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.
- October 2016
- Article
Looking Across and Looking Beyond the Knowledge Frontier: Intellectual Distance and Resource Allocation in Science
By: Kevin J. Boudreau, Eva Guinan, Karim R. Lakhani and Christoph Riedl
Selecting among alternative innovative projects is a core management task in all innovating organizations. In this paper, we focus on the evaluation of frontier scientific research projects. We argue that the "intellectual distance" between the knowledge embodied in... View Details
Keywords: Knowledge; Innovation; Novelty; Evaluation; Resource Allocation; Decision Choices and Conditions; Innovation and Management; Science-Based Business; Experience and Expertise
Boudreau, Kevin J., Eva Guinan, Karim R. Lakhani, and Christoph Riedl. "Looking Across and Looking Beyond the Knowledge Frontier: Intellectual Distance and Resource Allocation in Science." Management Science 62, no. 10 (October 2016).
- June 30, 2020
- Article
Scaling Up Behavioral Science Interventions in Online Education
By: Rene F. Kizilcec, Justin Reich, Michael Yeomans, Christoph Dann, Emma Brunskill, Glenn Lopez, Selen Turkay, Joseph J. Williams and Dustin Tingley
Online education is rapidly expanding in response to rising demand for higher and continuing education, but many online students struggle to achieve their educational goals. Several behavioral science interventions have shown promise in raising student persistence and... View Details
Keywords: Online Learning; Behavioral Interventions; Scale; Education; Online Technology; Performance Improvement
Kizilcec, Rene F., Justin Reich, Michael Yeomans, Christoph Dann, Emma Brunskill, Glenn Lopez, Selen Turkay, Joseph J. Williams, and Dustin Tingley. "Scaling Up Behavioral Science Interventions in Online Education." Proceedings of the National Academy of Sciences 117, no. 26 (June 30, 2020).
- September 2012
- Article
Bringing Science to the Art of Strategy
By: A. G. Lafley, Roger L. Martin, Jan W. Rivkin and Nicolaj Siggelkow
For all its emphasis on data and number crunching, conventional strategic planning is not actually scientific. It lacks the hypothesis generation and testing that's at the heart of the scientific method. To produce novel and successful strategies, teams need to adopt a... View Details
Lafley, A. G., Roger L. Martin, Jan W. Rivkin, and Nicolaj Siggelkow. "Bringing Science to the Art of Strategy." Harvard Business Review 90, no. 9 (September 2012).
- December 2011
- Article
Data Impediments to Empirical Work on Health Insurance Markets
By: Leemore S. Dafny, David Dranove, Frank Limbrock and Fiona Scott Morton
We compare four datasets that researchers might use to study competition in the health insurance industry. We show that the two datasets most commonly used to estimate market concentration differ considerably from each other (both in levels and in changes over time),... View Details
Dafny, Leemore S., David Dranove, Frank Limbrock, and Fiona Scott Morton. "Data Impediments to Empirical Work on Health Insurance Markets." B.E. Journal of Economic Analysis & Policy 11, no. 2 (December 2011).
- August 2020 (Revised September 2020)
- Technical Note
Assessing Prediction Accuracy of Machine Learning Models
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
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
The Value of Descriptive Analytics: Evidence from Online Retailers
By: Ron Berman and Ayelet Israeli
Does the adoption of descriptive analytics impact online retailer performance, and if so, how? We use the synthetic difference-in-differences method to analyze the staggered adoption of a retail analytics dashboard by more than 1,500 e-commerce websites, and we find an... View Details
Keywords: Descriptive Analytics; Big Data; Synthetic Control; E-commerce; Online Retail; Difference-in-differences; Martech; Internet and the Web; Analytics and Data Science; Performance; Retail Industry
Berman, Ron, and Ayelet Israeli. "The Value of Descriptive Analytics: Evidence from Online Retailers." Harvard Business School Working Paper, No. 21-067, November 2020. (Revised December 2021. Accepted at Marketing Science.)
- July 2013
- Case
Sample6: Innovating to Make Food Safer
By: Robert F. Higgins and Kirsten Kester
Tim Curran, CEO of Sample6, a start-up biotechnology company developing a novel food safety diagnostics platform, must decide how to partner with food industry players. How can he best convince leaders in this mature industry to adopt a new technology and improve food... View Details
Keywords: Data Analytics; Food Safety; Biotechnology; Nutrition; Entrepreneurship; Product; Partners and Partnerships; Food; Technological Innovation; Business Startups; Governing Rules, Regulations, and Reforms; Product Development; Agribusiness; Information Technology; Globalization; Performance Improvement; Safety; Technology Adoption; Agriculture and Agribusiness Industry; Agriculture and Agribusiness Industry; Agriculture and Agribusiness Industry; Agriculture and Agribusiness Industry; United States; Boston; Massachusetts
Higgins, Robert F., and Kirsten Kester. "Sample6: Innovating to Make Food Safer." Harvard Business School Case 814-014, July 2013.
- Teaching Interest
Overview
By: John A. Deighton
I teach about the ecosystem of big data, the role of data in advertising and creative industries, and customer management and personal privacy in an era of individual addressability. View Details
Keywords: Digital Marketing; Database Marketing; Social Media; Data Analytics; Information; Advertising; Marketing; Media; Technology; Entertainment and Recreation Industry; Entertainment and Recreation Industry; Entertainment and Recreation Industry; Entertainment and Recreation Industry; Entertainment and Recreation Industry
- October 2015 (Revised October 2016)
- Case
Building Watson: Not So Elementary, My Dear! (Abridged)
By: Willy C. Shih
This case is set inside IBM Research's efforts to build a computer that can successfully take on human challengers playing the game show Jeopardy! It opens with the machine named Watson offering the incorrect answer "Toronto" to a seemingly simple question during the... View Details
Keywords: Analytics; Big Data; Business Analytics; Product Development Strategy; Machine Learning; Machine Intelligence; Artificial Intelligence; Product Development; AI and Machine Learning; Information Technology; Analytics and Data Science; Information Technology Industry; United States
Shih, Willy C. "Building Watson: Not So Elementary, My Dear! (Abridged)." Harvard Business School Case 616-025, October 2015. (Revised October 2016.)
- 2023
- Working Paper
Feature Importance Disparities for Data Bias Investigations
By: Peter W. Chang, Leor Fishman and Seth Neel
It is widely held that one cause of downstream bias in classifiers is bias present in the training data. Rectifying such biases may involve context-dependent interventions such as training separate models on subgroups, removing features with bias in the collection... View Details
Chang, Peter W., Leor Fishman, and Seth Neel. "Feature Importance Disparities for Data Bias Investigations." Working Paper, March 2023.
- January 2021 (Revised March 2021)
- Case
THE YES: Reimagining the Future of E-Commerce with Artificial Intelligence (AI)
By: Jill Avery, Ayelet Israeli and Emma von Maur
THE YES, a multi-brand shopping app launched in May 2020 offered a new type of buying experience for women’s fashion, driven by a sophisticated algorithm that used data science and machine learning to create and deliver a personalized store for every shopper, based on... View Details
Keywords: Data; Data Analytics; Artificial Intelligence; AI; AI Algorithms; AI Creativity; Fashion; Retail; Retail Analytics; E-Commerce Strategy; Platform; Platforms; Big Data; Preference Elicitation; Preference Prediction; Predictive Analytics; App Development; ＂Marketing Analytics＂; Advertising; Mobile App; Mobile Marketing; Apparel; Online Advertising; Referral Rewards; Referrals; Female Ceo; Female Entrepreneur; Female Protagonist; Analytics and Data Science; Analysis; Creativity; Marketing Strategy; Brands and Branding; Consumer Behavior; Demand and Consumers; Forecasting and Prediction; Marketing Channels; Digital Marketing; Internet and the Web; Mobile and Wireless Technology; AI and Machine Learning; E-commerce; Digital Platforms; Apparel and Accessories Industry; Apparel and Accessories Industry; Apparel and Accessories Industry; Apparel and Accessories Industry; United States
Avery, Jill, Ayelet Israeli, and Emma von Maur. "THE YES: Reimagining the Future of E-Commerce with Artificial Intelligence (AI)." Harvard Business School Case 521-070, January 2021. (Revised March 2021.)
- November 1998
- Article
Modeling Large Data Sets in Marketing
By: Sridhar Balasubramanian, Sunil Gupta, Wagner Kamakura and Michel Wedel
Balasubramanian, Sridhar, Sunil Gupta, Wagner Kamakura, and Michel Wedel. "Modeling Large Data Sets in Marketing." Special Issue on Large Data Sets in Business Economics. Statistica Neerlandica 52, no. 3 (November 1998).
- Article
Some Uses of Happiness Data in Economics
By: Rafael Di Tella and Robert MacCulloch
Di Tella, Rafael, and Robert MacCulloch. "Some Uses of Happiness Data in Economics." Journal of Economic Perspectives 20, no. 1 (Winter 2006): 25–46.
- Article
Mitigating Bias in Adaptive Data Gathering via Differential Privacy
By: Seth Neel and Aaron Leon Roth
Data that is gathered adaptively—via bandit algorithms, for example—exhibits bias. This is true both when gathering simple numeric valued data—the empirical means kept track of by stochastic bandit algorithms are biased downwards—and when gathering more complicated... View Details
Neel, Seth, and Aaron Leon Roth. "Mitigating Bias in Adaptive Data Gathering via Differential Privacy." Proceedings of the International Conference on Machine Learning (ICML) 35th (2018).