More from the Author

• Technical Note | HBS Case Collection | September 2020

  Algorithmic Bias in Marketing

  Ayelet Israeli and Eva Ascarza

  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: algorithmic data; race and ethnicity; promotion; ＂marketing analytics＂; marketing and society; big data; privacy; Data-driven management; data analysis; Data Analytics; E-Commerce strategy; e-commerce; discrimination; targeting; targeted advertising; pricing algorithms; ethical decision making; customer heterogeneity; Marketing; Race; Ethnicity; Gender; Diversity; Prejudice and Bias; Marketing Communications; Data and Data Sets; Analysis; Decision Making; Ethics; Customer Relationship Management; Retail Industry; Apparel and Accessories Industry; United States;

  Citation:

  Israeli, Ayelet, and Eva Ascarza. "Algorithmic Bias in Marketing." Harvard Business School Technical Note 521-020, September 2020.  View Details

  CiteView DetailsEducatorsPurchase

  Related

• Teaching Note | HBS Case Collection | September 2020

  Algorithmic Bias in Marketing

  Ayelet Israeli and Eva Ascarza

  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;

  Citation:

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

  CiteView DetailsPurchase

  Related

• Teaching Note | HBS Case Collection | September 2020

  Artea (A), (B), (C), and (D): Designing Targeting Strategies

  Eva Ascarza and Ayelet Israeli

  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.

  Keywords: targeted advertising; targeting; Race; Gender; Diversity; Marketing; Customer Relationship Management; Prejudice and Bias; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States;

  Citation:

  Ascarza, Eva, and Ayelet Israeli. "Artea (A), (B), (C), and (D): Designing Targeting Strategies." Harvard Business School Teaching Note 521-041, September 2020.  View Details

  CiteView DetailsPurchase

  Related