Uber\u2019s use of data allows it create unique offerings for both the driver and rider.<\/p>\n
Rider<\/strong>: Uber\u2019s immense collection of data allows it to introduce offerings such as UberPool. Several years ago, when Uber decided to pursue UberPool, they realized that UberPool\u2019s profitability was important. The last thing Uber wanted to do was UberPool, only to remove it a couple years later due to lack of profit. Taking this into account, Uber determined which metrics needed to be met to make UberPool financially sustainable. These metrics included the \u201ctrip match %\u201d, \u201ctrip count\u201d, \u201c# of trips heading in the same direction by time of day\u201d, \u201cvarious driver metrics\u201d, etc\u2026Fortunately, Uber\u2019s data collection mechanism is standardized throughout the world, and updated in real time. Data scientists can quickly determine which cities in the world meet the criteria defined above. These cities are ranked in order of how successful they might be and passed along to the strategy team.<\/p>\n As new cities are introduced, the original metrics are updated, and Uber can go back to determining which cities meet the new metrics. Unlike other carpool offshoots, Uber knows upfront if their venture will be profitable, and is able to think long-term about rolling out the service.<\/p>\n Driver<\/strong>: Uber has been pursuing dynamic surge pricing for some time. The theory is that surge can be predicted on a block-by-block basis before it happens. This prediction is based on the number of people opening the Uber app at any point in time. The difficulty of dynamic pricing is that it requires an extensive amount of data collection and the creation of a very complicated algorithm that can predict the future. The algorithm needs to take into account ouside data such as weather conditions, recent news, local events, etc\u2026 Dynamic pricing is useful for drivers because it allows them to drive to areas before the surge. Currently, surge is updated on a 5-10 minute interval \u2013 so by the time a driver gets to a \u201csurge\u201d area, the surge could already be over. This data intensive system will allow them to get to the area and be guaranteed a higher fare.<\/p>\n <\/p>\n On the rider side Uber captures value as we pay for our ride. On the driver side, Uber takes a % of commissions from the fare.<\/p>\n <\/p>\n Uber\u2019s processes emphasize the use of data to make important product and pricing decisions. For example, Uber occasionally has \u201cguaranteed fares\u201d for drivers. The time and place of the guaranteed fares are determined by the team\u2019s data science expert. At headquarters, Uber has an entire cohort of statisticians to help city teams run statistically significant experiments. These experiments aim to determine the best ways to increase trips, motivate drivers to drive longer hours, etc\u2026<\/p>\n None of this would be possible without the capability to accurately use statistics to improve business decisions. Uber has gained this capability by hiring a LOT of PHDs. In the tech community, Uber has become the place to go for math majors to \u201cmake a difference in the world.\u201d I\u2019m not completely sure how they pull it off, but Uber is amazing at attracting world-class coders and math talent.<\/p>\n The resources Uber exhausts to make data available is epic. The cost of maintaining the servers alone is staggering. Since Uber is growing so rapidly, they constantly need to update and improve their hardware and software, which costs an unimaginable amount of time, money, and stress. Uber\u2019s high valuation allows for this \u2013 whereas a smaller company would likely be unable to compete.<\/p>\n <\/p>\n Culture. Uber\u2019s founders placed a heavy emphasis on creating a culture that values uncovering truth through data. Its founders and all of the initial hires had backgrounds in mathematics and statistics. In addition to this emphasis on data, Uber was able to create a \u201cgreater mission\u201d for its employees to follow \u2013 making the world a better place by giving people access to safe transportation around the world. As the company continued to grow, Uber implemented systems for hiring such as a mandatory analytical test, which ensured that new hires are mathematically focused.<\/p>\n The problem moving forward is Uber\u2019s ability to continue attracting the best people. Uber will have 6000 employees by the end of the year. Most employees are between the ages of 25 and 35. People aren\u2019t leaving the company, which means that there is little upward mobility. Either artificial layers of management need to be created (which goes against the \u201cstartup\u201d mentality), or really talented hires need to come into the company at very low levels with no hope of promotion. Uber will need to figure this out if they want to maintain the best resource \u2013 its coders, data scientists, and statisticians.<\/p>\n","protected":false},"excerpt":{"rendered":" Uber uses data to create unique value offerings. <\/p>\n","protected":false},"author":89,"featured_media":0,"comment_status":"open","ping_status":"closed","template":"","categories":[428,430,384],"class_list":["post-2705","hck-submission","type-hck-submission","status-publish","hentry","category-easy-taxi","category-lyft","category-uber"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-digit\/assignment\/data-driven-value-creation-value-capture-and-operating-models\/","yoast_head":"\nValue Capture<\/h2>\n
Operating Model<\/h2>\n
How it got here<\/h2>\n