Lyft vs. Uber and a quick breakdown of Lyft’s S-1

Lyft just published its S-1, throwing the hat into the ring. Thanks to their invigorated interest in a future public offering, we now have for the first time such a rich trove of publicly available data to compare the ride-sharing juggernauts head-to-head. As an investor in the mobility space, I crunched the numbers, and here is my take on the newly publicly available data. Please note that we are using all publicly available data (Lyft S-1, Uber data from WSJ, Bloomberg and TechCrunch, please see the sources at the bottom for more details) to do a quick analysis. This is by no means an exhaustive analysis, but a quick comparison in terms of some of the key metrics. The big question remains — how will public markets value ride hailing startups?

Revenue growth: Revenue growth is slowing down rapidly for both companies. It’s important to note that Uber sold its operations to Yandex and Grab in 1Q 18. Given its sheer size ($3B rev in 4Q 18), Uber is still growing at a reasonable rate around 25%. As a comp, Salesforce grew almost the same rate when it was at a similar size during the quarter ending in May 2018. Meanwhile, Lyft puts its YoY revenue growth north of 90%.

Take rates: Surprising to see that Lyft has improved its take rate significantly over the last few quarters, whereas Uber’s take rate has stabilized around 20–25%. I wouldn’t expect Lyft to push this further as higher rakes could endanger sustainability as Bill Gurley lays out nicely here:

Cash burn: A good metric for cash generation capabilities is FCF/Sales, but we are using Adj. EBITDA/Sales because of simplicity and lack of data. Both companies are far from being cash flow positive and will keep hemorrhaging money in the short run. Well, the warning is in Lyft’s S-1 for a reason: “we may not be able to achieve or maintain profitability in the future”.

Keep in mind other miscellaneous topics that might materially impact the companies:

  • Lyft is a more of a pure-play than Uber, as Uber is active in many other verticals beyond ride hailing, such as food delivery and freight business, among others.
  • Lyft has a dual-class share structure; whereas Uber doesn’t seem to have one.
  • Uber is quite active abroad (rumors on Careem acquisition) so there might be some other risks to consider (currency, among others); Lyft, on the other hand, is operating in US and Canada.

A Quick Breakdown of Lyft’s S-1 — Cohorts, payback and growth

Lyft shares some very rough rider cohort data per years. I wish they had provided this data in a more granular format (quarterly) along with a detailed driver cohort data. Let’s not forget that Lyft is operating a marketplace. S-1 covers the demand dynamics in some details, yet very little is being mentioned on the supply side. Achilles heel for ride hailing startups is driver retention, and there is unfortunately no info on that in Lyft’s S-1. S&M costs give us a hint, though.

Payback per cohort: If we connect the dots by looking S&M costs per cohort and contribution margins over time, we get the chart below. In order to be profitable at a cohort level, customers need to stay on the platform at least for 3 years. This is eye-opening from a cash-burn perspective. Bad news is that none of the cohorts are at a point where they are profitable yet; the good news here is that payback period is going down over time.

Let’s take a quick look at the growth vectors. If we lay out the funnel, it would look like as follows: Active Rider (growth trending downwards) > rides per active rider (flat) > revenue per ride (trending upwards)

Active rider growth has slowed meaningfully and getting close to zero. This could reflect the maturity of the market, and showing that the company is at a point where acquiring new customers or reactivating the old ones is becoming really hard.

Rides per active rider has been flat at roughly 3 rides per active rider per month.

  • Lyft boosted revenues per ride significantly over time. The meaningful increase in 2Q 18 is a result of “increased service fees and commissions”.

Given the charts above, it would be fair to say that the growth has increasingly been fueled by the boost in revenues per ride. The chart below also shows us that the impact of unique rider growth is decreasing over time as the user growth slows down.

Revenue per ride is a function of take rate and the booking. As Lyft already has a high take rate, I’m skeptical on how much further they can push the revenues per ride. Considering the intense competition, I wouldn’t expect the active rider number to re-accelerate, either. This shows that the number of rides per active rider might be the most relevant choice for further growth. It is clear that the traditional ride hailing business is coming to a mature level, and Lyft’s alternative offerings (transit, shared bike and e-scooters) could play a key role to increase rides per active rider and boost the revenues. Yes, this could be the reason why you keep getting discounts on Lyft, too. Obviously, development of robotaxis can help ride hailing companies keep all of the bookings, too.

If you’re working on an early stage startup active in any of our key areas of interest, please feel free to reach out at I’d love to hear from you!

UPDATE: After Uber’s S-1 was released, I’ve updated the analysis, but instead of publishing a new article, I’ve decided to share it via a series of tweets here-

If you love digging into data as much as I do, here are my detailed calculations:


UPDATE: Here is an update with public numbers from Uber-

Principal @BMWiVentures. Proud @DukeU, @UNC (yes, both!) and @vcic alumnus. Engineer with top-tier investment banking and VC experience. Views are mine.

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