What does Uber do? Uber helps people to commute by connecting them with the riders.

Why Uber Pool is launched? So that users can book more cheaper rides and to increase the availability of rides for user.

Assumptions

• It is launched in Bangalore.
• It is launched of intra city commute via cabs.

Metrics to be Tracked

Clarification

Just to get on the same page regarding what Uber Pool is. Uber Pool is essentially carpooling for the Uber app. Riders can request a ride to their destination and they will share a ride with other users heading in a similar direction. While less convenient this is more affordable than a standard Uber ride.

Context

Before we dive into talking metrics, let’s make sure we understand the bigger picture surrounding the feature. Uber’s mission statement is to make transportation as reliable as running water everywhere and for everyone. Uber Pool helps further this mission by offering a more affordable option, allowing users who were previously priced out to now have access to Uber.

 

Users

• Drivers

• Riders

 

Product Goal

For any newly launched product our first priority is going to be aggressively confirming whether or not we’ve found product-market fit. We spent all this time doing market research and user interviews to establish that users want / need a more affordable option and now we’ve built this solution, Uber Pool, which we’ve theorized will solve their need. Now we need to make sure our solution is actually solving their problem and meeting their need. The best way to do that is by monitoring both engagement and retention. If users are repeatedly engaging with our product then it’s safe to say they are getting value out of it and we’ve hit our mark.

• Confirm product-market fit via monitoring Engagement + Retention

 

The only caveat here is that in order to assess our product-market fit, we are going to first need some base level of adoption. We can’t measure the success of a feature if no one is using it as there won’t be enough data or we won’t have a representative sample.  I would start with looking at adoption, but if we look and see there isn’t much adoption then we could work our way upstream and look at things like feature awareness and activation.

• Ensure we have at least a sufficient base level of adoption amongst users first

 

Metrics

 

Adoption

1. MAU (Riders) – Number of users who have taken an Uber Pool in the last month

2. WAU (Drivers) – Number of drivers who have given a ride for Uber Pool in the last week

3. Total number of Uber Pool rides taken in a week

 

Engagement

1. Average number of monthly Uber Pool rides per user

2. Number of users who have taken at least 3 Uber Pool rides in the last month

3. Average number of weekly Uber Pool rides per driver

4. Number of drivers who have completed at least 5 Uber Pool rides in the last week

 

Retention

1. % of users who have taken at least 3 Uber Pool rides in the last month, who did the same this month

2. % of of drivers who have completed at least 5 Uber Pool rides in the last week

 

North Star Metric

If I was forced to select one metric from the above, I would suggest we focus on the metric listed below. This metric will let us know how many users are getting significant value out of Uber Pool. By looking at completed rides we are also accounting for the drivers and not just the riders because you need types of users to make a ride happen. Additionally, it is an absolute metric and not a relative metric so it will help us monitor growth as well.

• Number of users who have taken at least 3 Uber Pool rides in the last month

 

Concerns

While I’m confident in the above metrics, no set of metrics is perfect so let’s spend a moment talking about any potential downsides, trade offs, or gaps.

1. A top level concern of mine is that we don’t want Uber Pool to too greatly cannibalize normal rides and our above metrics don’t help us monitor to what degree this is happening. We don’t want Uber Pool to be successful to the detriment of the greater product.

2. While intentional, all of our above metrics are very top level and don’t offer much in terms of granularity. Certain geographic areas or different use cases might perform better than others. Additionally, we aren’t gaining a very nuanced understanding of the user experience which we might get when looking at things like average time spent waiting for a ride or how the experience of sharing a car with strangers is.

 

Summary

As Uber Pool is a newly launched product we’re going to first monitor adoption and then when we have a sufficient amount of users we will try to confirm product-market fit by monitoring engagement and retention with the following metric as our North Star:

 

• Number of users who have taken at least 3 Uber Pool rides in the last month