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- News (72)
- Research (251)
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- Faculty Publications (148)
Show Results For
-
All HBS Web
(379)
- News (72)
- Research (251)
- Events (6)
- Multimedia (1)
- Faculty Publications (148)
- Mar 2021
- Conference Presentation
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning
By: Seth Neel, Aaron Leon Roth and Saeed Sharifi-Malvajerdi
We study the data deletion problem for convex models. By leveraging techniques from convex optimization and reservoir sampling, we give the first data deletion algorithms that are able to handle an arbitrarily long sequence of adversarial updates while promising both...
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Neel, Seth, Aaron Leon Roth, and Saeed Sharifi-Malvajerdi. "Descent-to-Delete: Gradient-Based Methods for Machine Unlearning." Paper presented at the 32nd Algorithmic Learning Theory Conference, March 2021.
- 2023
- Working Paper
Digital Lending and Financial Well-Being: Through the Lens of Mobile Phone Data
By: AJ Chen, Omri Even-Tov, Jung Koo Kang and Regina Wittenberg-Moerman
To mitigate information asymmetry about borrowers in developing economies, digital lenders utilize machine-learning algorithms and nontraditional data from borrowers’ mobile devices. Consequently, digital lenders have managed to expand access to credit for millions of...
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Keywords:
Borrowing and Debt;
Credit;
AI and Machine Learning;
Welfare;
Well-being;
Developing Countries and Economies;
Equality and Inequality
Chen, AJ, Omri Even-Tov, Jung Koo Kang, and Regina Wittenberg-Moerman. "Digital Lending and Financial Well-Being: Through the Lens of Mobile Phone Data." Harvard Business School Working Paper, No. 23-076, April 2023. (Revised November 2023. SSRN Working Paper Series, November 2023)
- June 2020
- Article
Real-time Data from Mobile Platforms to Evaluate Sustainable Transportation Infrastructure
By: Omar Isaac Asensio, Kevin Alvarez, Arielle Dror, Emerson Wenzel, Catharina Hollauer and Sooji Ha
By displacing gasoline and diesel fuels, electric cars and fleets reduce emissions from the transportation sector, thus offering important public health benefits. However, public confidence in the reliability of charging infrastructure remains a fundamental barrier to...
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Keywords:
Environmental Sustainability;
Transportation;
Infrastructure;
Behavior;
AI and Machine Learning;
Demand and Consumers
Asensio, Omar Isaac, Kevin Alvarez, Arielle Dror, Emerson Wenzel, Catharina Hollauer, and Sooji Ha. "Real-time Data from Mobile Platforms to Evaluate Sustainable Transportation Infrastructure." Nature Sustainability 3, no. 6 (June 2020): 463–471.
- 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...
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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
- 10 Jun 2017
- News
How big data helped secure Emmanuel Macron’s astounding victory
- 21 Aug 2017
- Lessons from the Classroom
Companies Love Big Data But Lack the Strategy To Use It Effectively
managers, HR specialists, and data scientists work together to use data to improve employee-related decisions and practices. New analytic approaches and new sources of digital View Details
Keywords:
by Dina Gerdeman
- October 2017
- Case
Quantopian: A New Model for Active Management
Keywords:
Big Data;
Hedge Fund;
Crowdsourcing;
Investment Fund;
Quantitative Hedge Fun;
Algorithmic Data;
Analytics and Data Science
Fleiss, Sara, Adi Sunderam, Luis M. Viceira, and Caitlin Carmichael. "Quantopian: A New Model for Active Management." Harvard Business School Case 218-046, October 2017.
- 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,...
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Keywords:
Big Data;
Monetization;
Data and Data Sets;
Advertising;
Mobile Technology;
Customization and Personalization;
Performance Improvement
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.
- 12 Apr 2022
- Research & Ideas
Swiping Right: How Data Helped This Online Dating Site Make More Matches
we had no real idea what would prevail. So, this is where the scientific mystery lies,” he says. Applying dating algorithms to other industries, cautiously Platonic platforms could follow similar, industry-appropriate revelation models....
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Keywords:
by Kara Baskin
- 2023
- Working Paper
Learning About Demand in the Long-tail: The Role of Competitive Monitoring
By: Ayelet Israeli and Eric Anderson
With the growth of e-commerce and increasing price transparency, there has been tremendous interest in the adoption and competitive consequences of algorithmic pricing. A premise of algorithmic pricing is that firms monitor competitors' prices and then their pricing...
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- 2024
- Working Paper
Improving Human-Algorithm Collaboration: Causes and Mitigation of Over- and Under-Adherence
By: Maya Balakrishnan, Kris Ferreira and Jordan Tong
Even if algorithms make better predictions than humans on average, humans may sometimes have private information
which an algorithm does not have access to that can improve performance. How can we help humans effectively use
and adjust recommendations made by...
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Keywords:
Cognitive Biases;
Algorithm Transparency;
Forecasting and Prediction;
Behavior;
AI and Machine Learning;
Analytics and Data Science;
Cognition and Thinking
Balakrishnan, Maya, Kris Ferreira, and Jordan Tong. "Improving Human-Algorithm Collaboration: Causes and Mitigation of Over- and Under-Adherence." Working Paper, February 2024.
- August 2022
- Supplement
Zalora: Data-Driven Pricing Recommendations
By: Ayelet Israeli
This exercise can be used in conjunction with the main case "Zalora: Data-Driven Pricing" to facilitate class discussion without requiring data analysis from the students. Instead, the exercise presents reports that were created by the data science team to answer the...
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Keywords:
Pricing;
Pricing Algorithms;
Dynamic Pricing;
Ecommerce;
Pricing Strategy;
Pricing And Revenue Management;
Apparel;
Singapore;
Startup;
Demand Estimation;
Data Analysis;
Data Analytics;
Exercise;
Price;
Internet and the Web;
Apparel and Accessories Industry;
Retail Industry;
Fashion Industry;
Singapore
Israeli, Ayelet. "Zalora: Data-Driven Pricing Recommendations." Harvard Business School Supplement 523-032, August 2022.
- Web
Data Science for Managers 2 - Course Catalog
(collect and clean data that suit their purposes) and expand their machine learning repertoire to include gradient boosting, neural networks, unsupervised algorithms and natural language processing. As in...
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- 2023
- Chapter
Marketing Through the Machine’s Eyes: Image Analytics and Interpretability
By: Shunyuan Zhang, Flora Feng and Kannan Srinivasan
he growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured data and can inform recommendations for increasing profits and consumer utility—if only the...
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Zhang, Shunyuan, Flora Feng, and Kannan Srinivasan. "Marketing Through the Machine’s Eyes: Image Analytics and Interpretability." Chap. 8 in Artificial Intelligence in Marketing. 20, edited by Naresh K. Malhotra, K. Sudhir, and Olivier Toubia. Review of Marketing Research. Emerald Publishing Limited, forthcoming.
- 22 Feb 2024
- Research & Ideas
How to Make AI 'Forget' All the Private Data It Shouldn't Have
predictions about the world. And now, even though generative AI feels very different from making a simple prediction, at a technical level, that's really what it is. In order to train these predictive systems, you need lots of example View Details
- 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...
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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;
Fashion Industry;
Retail Industry;
Apparel and Accessories Industry;
Consumer Products 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.)
- March 2019
- Case
Wattpad
By: John Deighton and Leora Kornfeld
How to run a platform to match four million writers of stories to 75 million readers? Use data science. Make money by doing deals with television and filmmakers and book publishers. The case describes the challenges of matching readers to stories and of helping writers...
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Keywords:
Platform Businesses;
Creative Industries;
Publishing;
Data Science;
Machine Learning;
Collaborative Filtering;
Women And Leadership;
Managing Data Scientists;
Big Data;
Recommender Systems;
Digital Platforms;
Information Technology;
Intellectual Property;
Analytics and Data Science;
Publishing Industry;
Entertainment and Recreation Industry;
Canada;
United States;
Philippines;
Viet Nam;
Turkey;
Indonesia;
Brazil
Deighton, John, and Leora Kornfeld. "Wattpad." Harvard Business School Case 919-413, March 2019.
- May 2021 (Revised February 2024)
- Teaching Note
THE YES: Reimagining the Future of E-Commerce with Artificial Intelligence (AI)
By: Ayelet Israeli and Jill Avery
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;
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;
Fashion Industry;
Retail Industry;
Apparel and Accessories Industry;
Consumer Products Industry;
United States
- Article
Ensembles of Overfit and Overconfident Forecasts
By: Y. Grushka-Cockayne, V.R.R. Jose and K. C. Lichtendahl
Firms today average forecasts collected from multiple experts and models. Because of cognitive biases, strategic incentives, or the structure of machine-learning algorithms, these forecasts are often overfit to sample data and are overconfident. Little is known about...
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Grushka-Cockayne, Y., V.R.R. Jose, and K. C. Lichtendahl. "Ensembles of Overfit and Overconfident Forecasts." Management Science 63, no. 4 (April 2017): 1110–1130.
- 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,...
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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.