Algorithmic Bias in Marketing

Ayelet  Israeli; Eva  Ascarza

Teaching Note | HBS Case Collection | September 2020

Algorithmic Bias in Marketing

Abstract

Teaching Note for HBS No. 521-020. This note focuses on algorithmic bias in marketing. First, it presents a variety of marketing examples in which algorithmic bias may occur. The examples are organized around the 4 P’s of marketing – promotion, price, place and product—characterizing the marketing decision that generates the bias and highlighting the consequences of such a bias. Then, it explains the potential causes of algorithmic bias and offers some solutions to mitigate or reduce this bias.

Keywords: Marketing; Race; Ethnicity; Gender; Diversity; Prejudice and Bias; Decision Making; Ethics; Customer Relationship Management; Retail Industry; Technology Industry; Apparel and Accessories Industry; United States;

Language: English

Format: Print

10 pages

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Citation:

Israeli, Ayelet, and Eva Ascarza. "Algorithmic Bias in Marketing." Harvard Business School Teaching Note 521-035, September 2020.

Does the adoption of descriptive analytics impact online retailer performance, and if so, how? We use the synthetic control method to analyze the staggered adoption of a retail analytics dashboard by more than 1,000 e-commerce websites, and find an increase of 13–20% in average weekly revenues post-adoption. 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 prospecting and personalization 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 repeated customers. In contrast, there is no change in basket size. Put together, these findings are consistent with an indirect effect of descriptive analytics, and they suggest that the adoption of analytics serves as a monitoring device that allows retailers to assess the value of new technologies. Without using the descriptive dashboard, retailers are unable to reap the benefits associated with these technologies.

Teaching Note for HBS No. 521-021,521-022,521-037,521-043. 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 exercises are designed such that the issues of algorithmic bias and discrimination would emerge inductively, “surprising” the students in the act of recommending a strategy that, inadvertently, is discriminating against customers who belong to minority groups. This is achieved via the combination of hands-on exercises, where students would make decisions based on data analyses and visualization, and in-class discussions, where students would defend their proposed strategies, discover the (discriminating) implications of those actions, and discuss possible solutions.

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Algorithmic Bias in Marketing

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