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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
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Aparkalo (C): Normal Crisis or Sudden Death
Peeters, Claire; Zott, ChristophCase E-210-EEntrepreneurshipAparkalo was born as the brainchild of Luis París and Iván Rodríguez, who developed the initial business plan for the venture as part of an IESE EMBA course on entrepreneurship. Their simple yet appealing idea was to introduce the concepts of reservations and dynamic pricing as a business model innovation to parking lots, so that clients could reserve spots in advance, and parking lot managers could charge more during peak times (e.g., concerts o...Starting at €5.74
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Aparkalo (A), (B) & (C) - Teaching Note
Peeters, Claire; Zott, ChristophTeaching Note ET-54-EEntrepreneurshipAparkalo was born as the brainchild of Luis París and Ivan Rodriguez, who developed the initial business plan for the venture as part of an IESE EMBA course on entrepreneurship. Their simple yet appealing idea was to introduce the concepts of reservations and dynamic pricing as a business model innovation to parking lots, so that clients could reserve spots in advance, and parking lot managers could charge more during peak times (e.g., concerts o...Starting at €0.00
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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: 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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Aparkalo (A): Dynamizing a Sleepy Industry
Peeters, Claire; Zott, ChristophCase E-208-EEntrepreneurshipAparkalo was born as the brainchild of Luis París and Iván Rodríguez, who developed the initial business plan for the venture as part of an IESE EMBA course on entrepreneurship. Their simple yet appealing idea was to introduce the concepts of reservations and dynamic pricing as a business model innovation to parking lots, so that clients could reserve spots in advance, and parking lot managers could charge more during peak times (e.g., concerts o...Starting at €8.20
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Aparkalo (B): Revised Business Plan
Peeters, Claire; Zott, ChristophCase E-209-EEntrepreneurshipAparkalo was born as the brainchild of Luis París and Iván Rodríguez, who developed the initial business plan for the venture as part of an IESE EMBA course on entrepreneurship. Their simple yet appealing idea was to introduce the concepts of reservations and dynamic pricing as a business model innovation to parking lots, so that clients could reserve spots in advance, and parking lot managers could charge more during peak times (e.g., concerts o...Starting at €5.74
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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