Benjamin Lauzier: preference

Transcript context

…Is there a metric you recommend people specifically look at to understand liquidity? I think the metric that I like the most is a predictor of liquidity. So your liquidity might be, it's typically a measure of demand utilization. It might be looking for something on there, maybe how many of those searches with intent actually turn into a transaction. So, it's your field rate of your intentful demand typically, and that's really indicative of the net output of your marketplace. And so, that gives you a sense of the health of your marketplace, but it can be influenced by a whole bunch of different factors. So, if you think about for Thumbtack, it can be influenced by if there's a snowstorm out there, if the competition is bidding, there's a whole bunch of exogenous factors that come into play. And the metric that I think is slightly more actionable is a little harder to define, but so much more helpful in my opinion. It's what I call a market health metric, and this is basically think of your proxy that is the best predictor of your liquidity. So, I'll use the example of Lyft. You have your liquidity is your demand utilization, it's how many app opens turn into a ride, and what predicts this? What will predict you and deciding to book a ride? For Lyft and for Uber, it was ETAs. So, we knew that if the closest driver was at least two minutes away from you or closer, then we had hit a ceiling, you were more than X person likely to convert and book a ride. If it's more than two minutes, if it's five, then maybe you check at Uber, maybe you walk, maybe you take the bus. If it's two minutes, it doesn't make a big enough difference, you're just going to book the ride anyway. So find this threshold, find this predictor that tends to plateau that correlates strongly with retention but with also the transaction happening, and that's the metric that you can predict. That's a metric that's so much more actionable for teams to work against. If you're a supply team now you can think of, "Okay, I'm adding 100 supplies into the platform. I want to know if it's actually reducing ETAs in this case," or I can look at correlations like this and limiting some of the effect of those exhaustive factors that I mentioned. Awesome. So, essentially watch fill rate is the term used that a lot of people love, which is just people with intent converting. So, the Airbnb example is exactly the way we did Airbnb is we looked at people that are searching with dates as intentful users, and then how many of them convert to a booking. So, that's basically what you're trying to get to, and your point here is that's kind of the output metric. That's what you want to move, but in order to move it, there's something that is the biggest lever to moving fill rate. And in your experience, and I've seen this exact same thing, it's usually amount of supply. Do you have enough good supply? And so, in the case of Lyft is do you have enough cars? Do you have enough homes, do you have enough plumbers on Thumbtack? And that's usually where you can actually impact fill rate. Sweet.…

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