Api Motivational Quotes

When you come down to it, alignment is about helping people understand what you want them to do. Most contributors will be motivated to ladder up to the top-line OKRs—assuming they know where to set the ladder. As our team got larger and more layered, we confronted new issues. One product manager was working on Premium, the enhanced subscription version of our app. Another focused on our API platform, to enable third parties like Fitbit to connect to MyFitnessPal and write data to it or applications on top of it. The third addressed our core login experience. All three had individual OKRs for what they hoped to accomplish—so far, so good. The problem was our shared engineering team, which got caught in the middle. The engineers weren’t aligned with the product managers’ objectives. They had their own infrastructure OKRs, to keep the plumbing going and the lights on. We assumed they could do it all—a big mistake. They got confused about what they should be working on, which could change without notice. (Sometimes it boiled down to which product manager yelled loudest.) As the engineers switched between projects from week to week, their efficiency dragged.

It’s always good to motivate migration by means of some kind of bonus. For example, offer functionality in the new API that cannot be achieved in the old version.

Finding the Competitive Levers When there’s a battle between two networks, there are competitive levers that shift users from one into the other—what are they? The best place to focus in the rideshare market was the hard side of the network: drivers. More drivers meant that prices would be lower, attracting valuable high-frequency riders that often comparison shop for fares. Attract more riders, and it more efficiently fills the time of drivers, and vice versa. There was a double benefit to moving drivers from a competitor’s network to yours—it would push their network into surging prices while yours would lower in price. Uber’s competitive levers would combine financial incentives—paying up for more sign-ups, more hours—with product improvements to improve Acquisition, Engagement, and Economic forces. Drawing in more drivers through product improvements is straightforward—the better the experience of picking up riders and routing the car to their destination, the more the app would be used. Building a better product is one of the classic levers in the tech industry, but Uber focused much of its effort on targeted bonuses for drivers. Why bonuses? Because for drivers, that was their primary motivation for using the app, and improving their earnings would make them sticky. But these bonuses weren’t just any bonuses—they were targeted at quickly flipping over the most valuable drivers in the networks of Uber’s rivals, targeting so-called dual apping drivers that were active on multiple networks. They were given large, special bonuses that compelled them to stick to Uber, and every hour they drove was an hour that the other networks couldn’t utilize. There was a sophisticated effort to tag drivers as dual appers. Some of these efforts were just manual—Uber employees who took trips would just ask if the drivers drove for other services, and they could mark them manually in a special UI within the app. There were also behavioral signals when drivers were running two apps—they would often pause their Uber session for a few minutes while they drove for another company, then unpause it. On Android, there were direct APIs that could tell if someone was running Uber and Lyft at the same time. Eventually a large number of these signals were fed into a machine learning model where each driver would receive a score based on how likely they were to be a dual apper. It didn’t have to be perfect, just good enough to aid the targeting.