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• Case | HBS Case Collection | June 2020 (Revised October 2020)

  Time Out: The Evolution from Media to Markets

  Kate Barasz and Eva Ascarza

  Citation:

  Barasz, Kate, and Eva Ascarza. "Time Out: The Evolution from Media to Markets." Harvard Business School Case 520-128, June 2020. (Revised October 2020.)  View Details

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• Case | HBS Case Collection | August 2020 (Revised September 2020)

  Facelift at Olay (A)

  Sunil Gupta, Rajiv Lal and Olivia Hull

  By October 2017, Procter & Gamble’s skincare brand Olay has been struggling with declining sales for several years. The team has tried many remedies, but none has returned the brand to growth. As pressure grows from Olay’s competitors, including hundreds of new direct-to-consumer brands catering to millennial women, North American Skin Care General Manager Chris Heiert must identify a winning marketing strategy that will appeal to women of all ages.

  Keywords: Advertising; Advertising Campaigns; Demographics; Age; Brands and Branding; Marketing Channels; Competitive Advantage; Competitive Strategy; Online Advertising; Transformation; Marketing Strategy; Social Marketing; Beauty and Cosmetics Industry; United States; Ohio;

  Citation:

  Gupta, Sunil, Rajiv Lal, and Olivia Hull. "Facelift at Olay (A)." Harvard Business School Case 521-011, August 2020. (Revised September 2020.)  View Details

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• 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

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