The following questions were addressed in the Harvard Business Review Uber Case Study:
• Identify customer pain points by using customer experience mapping and jobs-to-be-done framework by analyzing various people narratives.
• Create a short description of the ideal experience as well as list two or three outcomes and expectations for each persona.
• Develop a list of hypotheses Uber should use to predict a rider's pickup location with information such as previous trips, current destinations, and historical patterns related to the pickup location. Augment case information with your Uber personal experiences to suggest potential hypotheses.
• Create quantitative pickup quality metric using attributes derived from passive, active, or third-party signals available for uber. Discuss which of your selected attributes are robust to the pickup quality metric. Will you assign any specific weights to the features you chose for your pickup model?
• Based on the pickup quality metric, what actions do you suggest Uber's operations team should take to improve the pickup experience?
• Discuss the steps in setting up an ML mode for automatic pickups at scale. Use the seven-step model to elaborate on how you should apply this framework.