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Show Results For
- All HBS Web
(166)
- News (36)
- Research (108)
- Multimedia (1)
- Faculty Publications (46)
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- 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.
- September 17, 2021
- Article
AI Can Help Address Inequity—If Companies Earn Users' Trust
By: Shunyuan Zhang, Kannan Srinivasan, Param Singh and Nitin Mehta
While companies may spend a lot of time testing models before launch, many spend too little time considering how they will work in the wild. In particular, they fail to fully consider how rates of adoption can warp developers’ intent. For instance, Airbnb launched a... View Details
Keywords: Artificial Intelligence; Algorithmic Bias; Technological Innovation; Perception; Diversity; Equality and Inequality; Trust; AI and Machine Learning
Zhang, Shunyuan, Kannan Srinivasan, Param Singh, and Nitin Mehta. "AI Can Help Address Inequity—If Companies Earn Users' Trust." Harvard Business Review Digital Articles (September 17, 2021).
- November 2021 (Revised December 2021)
- Supplement
PittaRosso (B): Human and Machine Learning
By: Ayelet Israeli
This case supplements the "PittaRosso: Artificial Intelligence-Driven Pricing and Promotion" case, and provides major highlights on what happened at the company since the first case. View Details
Keywords: Artificial Intelligence; Pricing; Pricing Algorithm; Pricing Decisions; Pricing Strategy; Pricing Structure; Promotion; Promotions; Online Marketing; Data-driven Decision-making; Data-driven Management; Retail; Retail Analytics; Price; Advertising Campaigns; Analytics and Data Science; Analysis; Digital Marketing; Budgets and Budgeting; Marketing Strategy; Marketing; Transformation; Decision Making; AI and Machine Learning; Retail Industry; Italy
Israeli, Ayelet. "PittaRosso (B): Human and Machine Learning." Harvard Business School Supplement 522-047, November 2021. (Revised December 2021.)
- May 2023
- Technical Note
Dynamic Pricing: Timing is Everything
By: Elie Ofek
This note provides a comprehensive exposition to the topic of dynamic pricing (whereby the fee customers are charged is time-dependent). It covers the motivation for firms to engage in dynamic pricing, provides a typology of the main formats dynamic pricing can take,... View Details
Ofek, Elie. "Dynamic Pricing: Timing is Everything." Harvard Business School Technical Note 523-110, May 2023.
- 24 Apr 2019
- Research & Ideas
The 'Amazon Effect' Is Changing Online Price Competition—and the Fed Needs to Pay Attention
Cavallo’s research didn’t extend to why prices are changing more frequently, but he has some theories. Technology like pricing algorithms has cut both the labor and decision... View Details
- September 2020 (Revised June 2023)
- Exercise
Artea: Designing Targeting Strategies
By: Eva Ascarza and Ayelet Israeli
This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and targeting. Parts (B),(C),(D) Introduce algorithmic bias. The... View Details
Keywords: Algorithmic Data; Race And Ethnicity; Experimentation; Promotion; ＂Marketing Analytics＂; Marketing And Society; Big Data; Privacy; Data-driven Management; Data Analytics; Data Analysis; E-Commerce Strategy; Discrimination; Targeted Advertising; Targeted Policies; Targeting; Pricing Algorithms; A/B Testing; Ethical Decision Making; Customer Base Analysis; Customer Heterogeneity; Coupons; Algorithmic Bias; 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
Ascarza, Eva, and Ayelet Israeli. "Artea: Designing Targeting Strategies." Harvard Business School Exercise 521-021, September 2020. (Revised June 2023.)
- 09 Dec 2015
- Research Event
How Do You Predict Demand and Set Prices For Products Never Sold Before?
the researchers then developed an efficient multi-product price optimization algorithm to concurrently recommend prices for all products on Rue La La’s site in a given day.... View Details
- Research Summary
Overview
Professor Ferreira's research primarily focuses on how retailers can use algorithms to make better revenue management decisions, including pricing, product display, and assortment planning. In the retail industry, anticipating consumer demand is arguably one of the... View Details
- 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
- 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... View Details
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
- 25 May 2021
- Research & Ideas
White Airbnb Hosts Earn More. Can AI Shrink the Racial Gap?
White people who host rental properties on Airbnb earn significantly more per year than Black hosts, but a “race blind” pricing algorithm could help close that income gap, new research shows. Black hosts who... View Details
- 05 Oct 2015
- Working Paper Summaries
Online Network Revenue Management Using Thompson Sampling
- November–December 2018
- Article
Online Network Revenue Management Using Thompson Sampling
By: Kris J. Ferreira, David Simchi-Levi and He Wang
We consider a network revenue management problem where an online retailer aims to maximize revenue from multiple products with limited inventory constraints. As common in practice, the retailer does not know the consumer's purchase probability at each price and must... View Details
Keywords: Online Marketing; Revenue Management; Revenue; Management; Marketing; Internet and the Web; Price; Mathematical Methods
Ferreira, Kris J., David Simchi-Levi, and He Wang. "Online Network Revenue Management Using Thompson Sampling." Operations Research 66, no. 6 (November–December 2018): 1586–1602.
- 18 Oct 2022
- Research & Ideas
When Bias Creeps into AI, Managers Can Stop It by Asking the Right Questions
algorithm generates fair outcomes. As the algorithm sorts through information to optimize its objective, BEAT detects and eliminates bias at key points in the training process. For instance, BEAT could help... View Details
Keywords: by Rachel Layne
- 2023
- Working Paper
Dynamic Pricing, Intertemporal Spillovers, and Efficiency
By: Alexander J. MacKay, Dennis Svartbäck and Anders G. Ekholm
Pricing technology that allows firms to rapidly adjust prices has two potential benefits.
Time-varying prices can respond to high-frequency demand shocks to generate greater revenues,
and they can also be used to smooth out demand to reduce costs. Using data... View Details
MacKay, Alexander J., Dennis Svartbäck, and Anders G. Ekholm. "Dynamic Pricing, Intertemporal Spillovers, and Efficiency." Harvard Business School Working Paper, No. 23-007, July 2022. (Revised December 2023.)
- 2025
- Working Paper
Is Love Blind? AI-Powered Trading with Emotional Dividends
By: De-Rong Kong and Daniel Rabetti
We leverage the non-fungible tokens (NFTs) setting to assess the valuation of emotional dividends (LOVE), a long-standing empirical challenge in private-value markets such as art, antiques, and collectibles. Having created and validated our proxy, we use deep learning... View Details
Kong, De-Rong, and Daniel Rabetti. "Is Love Blind? AI-Powered Trading with Emotional Dividends." Working Paper, February 2025.
- March 2019
- Case
DayTwo: Going to Market with Gut Microbiome
By: Ayelet Israeli and David Lane
DayTwo is a young Israeli startup that applies research on the gut microbiome and machine learning algorithms to deliver personalized nutritional recommendations to its users in order to minimize blood sugar spikes after meals. After a first year of trial rollout in... View Details
Keywords: Start-up Growth; Startup; Positioning; Targeting; Go To Market Strategy; B2B2C; B2B Vs. B2C; Health ＆ Wellness; AI; Machine Learning; Female Ceo; Female Protagonist; Science-based; Science And Technology Studies; Ecommerce; Applications; DTC; Direct To Consumer Marketing; US Health Care; ＂USA,＂; Innovation; Pricing; Business Growth; Segmentation; Distribution Channels; Growth and Development Strategy; Business Startups; Science-Based Business; Health; Innovation and Invention; Marketing; Information Technology; Business Growth and Maturation; E-commerce; Applications and Software; Health Industry; Technology Industry; Insurance Industry; Information Technology Industry; Food and Beverage Industry; Israel; United States
Israeli, Ayelet, and David Lane. "DayTwo: Going to Market with Gut Microbiome." Harvard Business School Case 519-010, March 2019.
- November 2020
- Teaching Note
DayTwo: Going to Market with Gut Microbiome
By: Ayelet Israeli
Teaching Note for HBS Case No. 519-010. DayTwo is a young Israeli startup that applies research on the gut microbiome and machine learning algorithms to deliver personalized nutritional recommendations to its users in order to minimize blood sugar spikes after meals.... View Details
Keywords: Start-up Growth; Startup; Positioning; Targeting; Go To Market Strategy; B2B Vs. B2C; B2B2C; Health ＆ Wellness; AI; Machine Learning; Female Ceo; Female Protagonist; Science-based; Science And Technology Studies; Ecommerce; Applications; DTC; Direct To Consumer Marketing; US Health Care; ＂USA,＂; Innovation; Pricing; Business Growth; Segmentation; Distribution Channels; Growth and Development Strategy; Business Startups; Science-Based Business; Health; Innovation and Invention; Marketing; Information Technology; Business Growth and Maturation; E-commerce; Applications and Software; Health Industry; Technology Industry; Insurance Industry; Information Technology Industry; Food and Beverage Industry; Israel; United States
- Oct 2020
- Conference Presentation
Optimal, Truthful, and Private Securities Lending
By: Emily Diana, Michael J. Kearns, Seth Neel and Aaron Leon Roth
We consider a fundamental dynamic allocation problem motivated by the problem of securities lending in financial markets, the mechanism underlying the short selling of stocks. A lender would like to distribute a finite number of identical copies of some scarce resource... View Details
Diana, Emily, Michael J. Kearns, Seth Neel, and Aaron Leon Roth. "Optimal, Truthful, and Private Securities Lending." Paper presented at the 1st Association for Computing Machinery (ACM) International Conference on AI in Finance (ICAIF), October 2020.
- May 2018
- Article
Nowcasting Gentrification: Using Yelp Data to Quantify Neighborhood Change
By: Edward L. Glaeser, Hyunjin Kim and Michael Luca
Data from digital platforms have the potential to improve our understanding of gentrification and enable new measures of how neighborhoods change in close to real time. Combining data on businesses from Yelp with data on gentrification from the Census, Federal Housing... View Details
Keywords: Forecasting Models; Simulation Methods; Regional Economic Activity: Growth, Development, Environmental Issues, And Changes; Geographic Location; Local Range; Transition; Analytics and Data Science; Measurement and Metrics; Economic Growth; Forecasting and Prediction
Glaeser, Edward L., Hyunjin Kim, and Michael Luca. "Nowcasting Gentrification: Using Yelp Data to Quantify Neighborhood Change." AEA Papers and Proceedings 108 (May 2018): 77–82.