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.