**Topics::** [[Library/Data]] [[Epistemology]]
[[uberHOP]] is a little example from my experience. The product was a point-to-point (a.k.a UV express) service Uber launched in [[Manila]], along with [[Seattle]] and [[Toronto]].
The way it worked was simple: you would make a request to take a specific route during peak hours, and we would batch you in with up to 6 people to take a high occupancy vehicle along the route.
## uberHOP needed high occupancy to become profitable
The pricing was at a 70% discount to uberX (the traditional ride product), and drivers were guaranteed earnings, so there was a minimum average occupancy needed to hit profitability. To get to that high occupancy, we needed to ensure that the routes selected were of high quality.
![A slide showing a news article and the interface for uberHOP.](uberhop1.png)
## Initial approach: Clustering!
My first instinct as a data person was [[clustering]]. We needed to find pairs of longitude and latitude that had enough pickup and dropoff density in them to have a decent chance of becoming profitable.
The launch routes were selected using this method, but we had limited success, even after a novelty period, cancellation rates remained high. I tried different algorithms, distance metrics, using various map features, dispatch radiuses, all for very incremental gains.
![We used clustering to find initial approaches, but the results were not as expected.](uberhop2.png)
## Seeking ground truth
What did help was to actually seek ground truth, and the solution was embarrassingly obvious.
When we physically went to the most successful route's pickup, the two key factors were: (a) high density residential buildings (as opposed to commercial), and (b) a driveway so drivers weren't a moving target.
![SM Light Residences was a great pickup that embodied all the factors that were required for a good pickup](uberhop3.png)
We were able to turn the product profitable in a few weeks! This was easy to do because I was physically located in the market. However, this is a perennial challenge for distributed teams, so it's even more important to consciously seek ground truth in those situations.
Here's an abridged version in Twitter thread form:
<blockquote class="twitter-tweet"><p lang="en" dir="ltr">The map is not the territory; seek ground truth whenever possible to accelerate learning. 🧵<br><br>Here's a little example from my experience: uberHOP was a point-to-point (a.k.a UV express) service we launched in Manila. We needed high occupancy so route selection was critical. <a href="https://t.co/dfGdDlfE1j">pic.twitter.com/dfGdDlfE1j</a></p>— TJ Palanca (@tjpalanca) <a href="https://twitter.com/tjpalanca/status/1438812960692977668?ref_src=twsrc%5Etfw">September 17, 2021</a></blockquote>