Ayelet Israeli

Ayelet Israeli

Marvin Bower Associate Professor

Marvin Bower Associate Professor

Marketing
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Ayelet Israeli
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(617) 495-3062
Featured Work Publications Research Summary Awards & Honors
Ayelet Israeli is the Marvin Bower Associate Professor of Business Administration at the Harvard Business School Marketing Unit. She teaches the Rethinking Retail course in the MBA elective curriculum (EC), the Data Driven Marketing course in the Harvard Business Analytics Program (HBAP), and in various Executive Education programs. She has previously taught the Marketing course in the first year MBA required curriculum (RC), and the E-Commerce: Strategy, Growth, and Analytics course in the EC. 

In her research, Ayelet studies omni-channel and e-commerce markets. In particular, she focuses on pricing, channel management, online marketing, and marketing analytics. Her research has been published in leading marketing journals including Marketing Science, Journal of Marketing Research, and Management Science. Her dissertation won the 2014 INFORMS Society for Marketing Science Doctoral Dissertation Proposal Award, and she was named a finalist for the 2018 and the 2019 Frank M. Bass Award. Her work has been cited by The Wall Street Journal, The Atlantic, MSN Money, and Harvard Business Review.

Ayelet received her PhD in marketing from the Kellogg School of Management at Northwestern University. She holds an MBA from the Hebrew University of Jerusalem, where she also earned her MSc and BSc in computer science. In addition to her academic experience, Ayelet served as a lieutenant in the Intelligence Corps of the Israeli Defense Forces and worked as an engineer at Israel Aerospace Industries and at Intel Corporation in Israel.

Featured Work
Should Your Company Sell on Amazon? Reach comes at a price - Harvard Business Review
Selling on Amazon allows brands to reach millions of consumers—but that exposure comes with costs. They include smaller margins, more competition, the risk of commoditization, and less knowledge about customers.

In this article, the authors present a scorecard to help brands determine whether the benefits exceed those costs. Companies can evaluate themselves on such topics as ease of shipping their products, degree of customization needed, distribution issues, and counterfeiting concerns.

Companies that choose to sell on the platform will need to make smart decisions about assortment offerings, page design, and fulfillment options so that they can take advantage of Amazon’s scale while protecting the long-term value of their brands.
The Value of Descriptive Analytics: Evidence from Online Retailers - Marketing Science
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 increase of 4%–10% in average weekly revenues postadoption. We demonstrate that only retailers that adopt and use the dashboard reap these benefits.

The increase in revenue is not explained by price changes or advertising optimization. Instead, it is consistent with the addition of customer relationship management, personalization, and prospecting technologies to retailer websites. The adoption and usage of descriptive analytics also increases the diversity of products sold, the number of transactions, the numbers of website visitors and unique customers, and the revenue from repeat customers. In contrast, there is no change in basket size. These findings are consistent with a complementary effect of descriptive analytics that serve as a monitoring device that helps retailers control additional martech tools and amplify their value. Without using the descriptive dashboard, retailers are unable to reap the benefits associated with these technologies.
Eliminating unintended bias in personalized policies using Bias Eliminating Adapted Trees (BEAT) - PNAS

An inherent risk of algorithmic personalization is disproportionate targeting of individuals from certain groups (or demographic characteristics such as gender or race), even when the decision maker does not intend to discriminate based on those “protected” attributes. This unintended discrimination is often caused by underlying correlations in the data between protected attributes and other observed characteristics used by the algorithm (or machine learning (ML) tool) to create predictions and target individuals optimally. Because these correlations are hidden in high dimensional data, removing protected attributes from the database does not solve the discrimination problem; instead, removing those attributes often exacerbates the problem by making it undetectable and, in some cases, even increases the bias generated by the algorithm.

We propose BEAT (Bias-Eliminating Adapted Trees) to address these issues. This approach allows decision makers to target individuals based on differences in their predicted behavior—hence capturing value from personalization—while ensuring a balanced allocation of resources across individuals, guaranteeing both group and individual fairness. Essentially, the method only extracts heterogeneity in the data that is unrelated to protected attributes. To do so, we build on the General Random Forest (GRF) framework (S. Athey et al., Ann. Stat. 47, 1148–1178 (2019)) and develop a targeting allocation that is “balanced” with respect to protected attributes.  We validate BEAT using simulations and an online experiment with N=3,146 participants. This approach can be applied to any type of allocation decision that is based on prediction algorithms, such as medical treatments, hiring decisions, product recommendations, or dynamic pricing.

Journal Articles
Working Papers
  • Anderson, Eric, Chaoqun Chen, Ayelet Israeli, and Duncan Simester. "Canary Categories." Working Paper, August 2022. View Details
Other Publications and Materials
Cases and Teaching Materials
Research Summary
Overview
Professor Israeli utilizes econometric methods and field experiments to study data driven decision making in marketing context. In her work, she focuses on pricing and channel management in omni-channel and e-commerce markets, and on marketing analytics. She has examined how the prevalence of the online channel affects the interactions between manufacturers and the retailers who are their downstream channel partners. Her findings indicate that a manufacturer is able to improve retailers’ compliance with pricing policies by creating policies that address the challenges of the online retail environment and credibly signal to the retailers that the manufacturer is monitoring their behavior and is prepared to enforce the policies.

Professor Israeli has also studied how the online channel affects the interactions between sellers and consumers in a large-scale field experiment in which callers requested price quotes from automotive repair shops. A key finding is that sellers alter their quotes depending on how informed individual consumers appear to be about market prices. This work demonstrates the benefit to consumers of conducting simple online research in order to appear savvy. The benefits are greater for women, for whom having the correct information alleviates price discrimination in repair shops. In another paper, she found that availability of inventory data online allows consumers to obtain lower prices for new cars.

In another stream of her research, Professor Israeli examines the value of analytics to firms. The main finding is that descriptive analytics are valuable for retailers. Specifically, adopting a descriptive dashboard resulted in increased revenue for online retailers. The underlying mechanism for these benefits was the use of descriptive analytics as a monitoring device that helped retailers monitor other marketing technologies (martech) and amplify their value.

Professor Israeli also investigates unintended consequences of leveraging data. An inherent risk of algorithmic personalization is disproportionate targeting of individuals from certain groups, even when the decision maker does not intend to discriminate based on any “protected” attributes. In this stream of work, she provides a practical solution for firms that want to avoid this bias, but still leverage data for personalization.
Keywords: Channel Management; Pricing; Pricing Policies; Online Marketing; E-commerce; Analytics; Econometrics; Field Experiments
Awards & Honors
Received the 2020–2021 Robert F. Greenhill Award.
Received the 2021 Apgar Award for Innovation in Teaching in recognition of outstanding work in the transition to online and hybrid teaching and learning.
Finalist for the 2019 Frank M. Bass Dissertation Paper Award for “Online MAP Enforcement: Evidence from a Quasi-Experiment” (Marketing Science, 2018). Awarded in 2020 for the best 2018–2019 marketing paper based on a Ph.D. thesis published in an INFORMS journal.
Winner of the 2020 Case Centre Award in the Marketing Category for “Predicting Consumer Tastes with Big Data at Gap” (HBS case 517-115) with Jill Avery.
Finalist for the 2018 Frank M. Bass Dissertation Paper Award for “Online MAP Enforcement: Evidence from a Quasi-Experiment” (Marketing Science, 2018). Awarded in 2019 for the best 2017–2018 marketing paper based on a Ph.D. thesis published in an INFORMS journal.
Winner of a 2014 Informs Society for Marketing Science (ISMS) Doctoral Dissertation Proposal Competition Award.
Named a 2012 AMA-Sheth Foundation Doctoral Consortium Fellow.
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