Uber used a color-coded globe and pricing committee to enter new markets, evaluating GDP, local cab fares, fuel costs, and unemployment to achieve liquidity: enough drivers and riders meeting each other reliably. A playbook tested in Kansas City was adapted for St. Louis, Paris, and Mumbai, though driver supply always lagged behind demand.
Chapters
What were some of those kind of like early challenges when you thought about marketplace expansion? And could you kind of like walk us through, like just, you know, maybe with a few examples of just examples of how nuanced the markets were? Yeah.
So we had a globe in the office with green, yellow, and red pins that we put in there. And the green ones were, we think regulatorily, we're okay here. Yellow, we're not sure. Red, we know we're banned. And We said, okay, let's pick off the green and yellows first and let's see what happens to the reds. And so we really were very intentional about every local market. We had a pricing committee. So we would say, okay, we're going into Kansas City, let's say, because it's green. There's like usually a hole in the regulations which make this mostly legal. And we say, okay, what is the GDP per capita? In Kansas, what can people pay? What are the local cab fares? What is gas cost for a driver? Insurance? What is the average life of a car? What are the— what's the unemployment rate? How many people are working at low-wage jobs where this might be a better opportunity versus not? And then we tried to develop our own thesis about what— how to get to liquidity fastest there. And liquidity for a marketplace Meant that there were enough drivers on one side and enough riders on the other side so that both parties were motivated to be in the marketplace. So you woke up to go to the airport, you had confidence that you can get an Uber ride within 5 minutes. On the driver's side is, could they go out and make enough money so that they'd want to do it again, right? Could they go out and make $50 to, to buy their girlfriend Could they, you know, spend a weekend working and so they could buy their textbooks for this next semester? You know, what are the things that they're trying to achieve and in how long could they achieve them? And we really studied that and, you know, we weren't always right the first time around, but we deployed a pricing model and we recruited so that there was never too much of an imbalance between riders and drivers. Although the biggest problem always and still today with Uber is there's just never enough drivers. There just isn't. The supply side always lagged the demand side, but we took very good care. And as we figured out this model in Kansas City, we wrote a playbook and we gave that to the playbook for the person who was going to launch St. Louis and then for the one who was going to launch Paris and then for one who was going to launch Mumbai. And it evolved over time with nuance so that any new launcher of a city could start with 80% of the problem solved, basically. And that's how we scaled really fast.
