Uber: Applying Machine Learning to Improve the Customer Pickup Experience, Kellogg Case Assignment Questions: 1) What are the potential pain points of the pickup experience for the different customer personas? Use the persona information in the case and the narrative in Exhibit 5. 2) Develop a list of hypotheses Uber could use to predict a riders pickup location with information such as the riders previous trips and current destination, as well as historical patterns related to the pickup location. 3) Create a quantitative pickup quality metric using attributes derived from the passive, active, and third-party signals available to Uber. Discuss why your selected attributes represent a robust pickup quality metric. What weighs would you assign to the features you chose for your pickup model? 4) Discuss the steps involved in setting up a machine learning (ML) model for automating pickups at scale. Use the framework of the seven-step model in the case (Exhibit 7) to elaborate on how Uber should apply this framework to the ML model. (Hint: You can create a table and list the tasks under each step.