Maximizing Process Performance with Maze, Uber’s Funnel Visualization Platform

At Uber, we spend a considerable amount of resources making the driver sign-up experience as easy as possible. At Uber’s scale, even a one percent increase in the rate of sign-ups to first trips (the driver conversion rate) carries a monumental impact.

In December 2016, Uber data scientist Andrey Liscovich hypothesized that traditional funnel analytics tools were not adequate for studying the actual driver sign-up experience because they treated it as a fixed sequence of steps, while in practice, the path from sign-up to first trip is a complex maze that any two drivers might navigate differently. To gain a more realistic understanding of how users interact with the sign-up technology, he started a cross-functional effort to develop a new funnel visualization platform, called Maze, that recognized the underlying complexity of the funnel.

By applying Maze to the logs captured during driver sign-up, we can visualize the actual paths drivers take when signing up with Uber, and identify bottlenecks that occur in the process. Maze’s application at Uber has since expanded beyond the sign-up use case, and it is now used to visualize many processes—from rider pick-up and drop-off to user interactions with our website. Read on to learn how the Uber Visualization team developed Maze and why this new solution offers unparalleled insight into the Uber user experience.

Navigating the maze: why we built a new tool
Before 2016, we visualized the driver sign-up experience as a simple series of chronological steps in a fixed order. At any given step in the process, a certain percentage of aspiring drivers would drop out, or “churn.”
Figure 1. This simplistic view of a funnel highlights where user drop off might occur.

In this model, the conversion rate is the product of the percentage of conversions at each step. So, to improve our conversion rate, we needed to reduce churn. This approach, while valuable, was limited because it didn’t take into account the order in which driver sign-up events happened or whether they happened at all.

There are many points of entry to the driver sign-up flow (web pages, email, and app, to name a few), varying by region. Not all driver candidates go through the exact same sign-up process. And even for those who do: what happens if they start the process on their phone then choose to continue on a desktop computer? What if they pause their application and resume it two months later? What if they go back one step and change innocuous information (ex. car type, car color, etc.) they had already submitted? The answers to these questions have a notable impact on our conversion rate.

The long way to conversion
When we started measuring the number of steps from sign-up to activation–that is, all the events we were able to detect—we discovered huge variability in the number of steps actually taken by drivers. In some cases it took a few dozen steps to go from sign-up to first trip, but in others, it took far longer.  In some cases, driver candidates might go through hundreds of events before dropping from the funnel.
Contrary to what we long-assumed, there is not one most effective path to conversion, but rather thousands, some short, some long, some linear, some crooked. Indeed, the journey to conversion is a real maze.

Entering the Maze
To better understand the sign-up process, we built Maze, a tool to visualize aggregated sequences of events, enabling us to answer questions such as:
How many drivers went through event A?
How many went through event A, then event B?
How many went through event A, then event C?
How many went through event A, then B, then D?
How many went through event A, then B, then D, but not C?
Answering these questions through traditional means (for instance, a SQL query) is cumbersome and error-prone. Instead, our tool finds the size of every sequence of events and represents the entire funnel visually.