HBSP (USA)
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Ferrari
Thomke, Stefan; Corsi, Elena; Nimgade, AshokCase HBS-618047-EKnowledge and CommunicationFerrari is among the world's most powerful brands but how the company operates has remained mysterious. The case reveals the inner workings of the company - the Ferrari Way - from the way it designs, produces, and markets its cars, to how its leadership team is driving future growth. Central to Ferrari's strategy is its response to disruptive changes in the automotive industry and their impact on the company's products and brand.Starting at €8.20
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Othellonia: Growing a Mobile Game
Ascarza, Eva; Amano, Tomomichi; Gupta, SunilCase HBS-520016-EMarketingIn the summer of 2019, Yu Sasaki, Head of the Game Division of DeNA, a Japanese mobile gaming company, is evaluating various growth strategies for its recent game Othellonia. Sasaki needs to decide if he should focus on customer acquisition, retention, or monetization.Starting at €8.20
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Ferrari (Spanish version)
Thomke, Stefan; Corsi, Elena; Nimgade, AshokCase HBS-619S07StrategyFerrari is among the world's most powerful brands but how the company operates has remained mysterious. The case reveals the inner workings of the company - the Ferrari Way - from the way it designs, produces, and markets its cars, to how its leadership team is driving future growth. Central to Ferrari's strategy is its response to disruptive changes in the automotive industry and their impact on the company's products and brand.Starting at €8.20
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Artea (D): Discrimination through Algorithmic Bias in Targeting
Ascarza, Eva; Israeli, AyeletCase HBS-521043-EMarketingThis 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,...Starting at €5.74
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Amazon Shopper Panel: Paying Customers for Their Data
Ascarza, Eva; Israeli, AyeletCase HBS-521058-EMarketingThis case introduces a new Amazon program that has consumers upload their receipts from transactions outside of Amazon, in exchange for money. Through the discussion, the case aims to explore issues in customers' privacy in the digital age, the value of customers' own data, and the change in regulations aimed to protect consumers that move companies from using third party data to first party data. In addition, the case offers an opportunity to di...Starting at €8.20
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The Michelin Restaurant Guide: Charting a New Course
Khaire, Mukti; Corsi, Elena; Lenhardt, JeromeCase HBS-814088-EEntrepreneurshipCreated in 1900 by the tire manufacturer Michelin, the Michelin Restaurant Guide was widely considered the international benchmark of food rating, and, by 2013, boasted paper editions in 23 countries, and had recently expanded to the United States and Asia. Paper sales however had dropped, following the emergence of free, online guides, global players, and more broadly, the wider diffusion of the Internet. In 2012, the Guide had launched a new ra...Starting at €8.20
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Orsted Goes Global
Bower, Joseph L.; Corsi, ElenaCase HBS-918404-EStrategyThe European leader in offshore wind, the Danish Orsted is building a global position and entering markets where offshore wind is nascent.Starting at €8.20
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Artea: Designing Targeting Strategies
Ascarza, Eva; Israeli, AyeletCase HBS-521021-EMarketingThis 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 algorithmStarting at €8.20
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Artea (B): Including Customer-level Demographic Data
Ascarza, Eva; Israeli, AyeletCase HBS-521022-EMarketingThis 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 algorithmStarting at €5.74
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Artea (C): Potential Discrimination through Algorithmic Targeting
Ascarza, Eva; Israeli, AyeletCase HBS-521037-EMarketingThis 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 algorithmStarting at €5.74