The Market Health Metric
Find the leading proxy that predicts liquidity, then find the threshold where it plateaus.
- Difficulty
- Advanced
- Time to result
- ~months to results
- Steps
- 5
- Confidence
- 95%
Liquidity — the fill rate of intentful demand — is the true output metric of a marketplace, but it is polluted by exogenous factors (weather, competitor bidding) and too laggy to steer with. Lauzier's answer is a market health metric: a leading proxy that is the best predictor of liquidity, with an identified threshold beyond which conversion plateaus. Teams then work directly against the proxy.
Origin
Developed by Benjamin Lauzier across Lyft and Thumbtack and taught in his Reforge marketplace growth course. The Lyft/Uber example uses driver ETA as the predictor; the Airbnb parallel (searchers with dates as intentful demand) is offered by Lenny Rachitsky from his Airbnb experience.
Core principles
- 01Liquidity is how marketplaces win — it is a direct multiplier on marketplace efficiency and the ultimate engagement loop.
- 02Define liquidity as the fill rate of intentful demand, not raw transaction volume.
- 03The output metric is too noisy to act on; find an actionable predictor upstream of it.
- 04A good predictor plateaus — past a threshold, more of it buys you nothing.
- 05The predictor lets supply teams attribute their work: add 100 units of supply, watch the proxy move.
How to run it
- 1
Define intentful demand
Decide which user actions signal genuine buying intent rather than browsing. Lyft: opening the app intending to book a ride. Airbnb: searching with dates. Thumbtack: a search with a real project behind it.
Pro tip Be strict — inflating the intent definition inflates the denominator and hides the real fill rate.
- 2
Measure liquidity as fill rate
Compute the percentage of intentful demand that converts into a transaction. This is the net output of the marketplace and the number the whole company ultimately exists to raise.
Pro tip Picture it as a Venn diagram: what supply wants to sell overlapping what demand wants to buy — liquidity is the overlap.
Watch out This metric is influenced by snowstorms, competitor bidding and other exogenous factors, so it is a scoreboard, not a steering wheel.
- 3
Hunt for the predictor
Find the variable that best predicts whether an intentful user transacts. At Lyft and Uber it was ETA — the distance of the closest driver. Correlate candidate proxies against both conversion and retention.
Pro tip Good candidates are things supply-side teams can directly influence: wait time, choice count, response time, coverage.
- 4
Find the plateau threshold
Identify where the predictor stops paying. At Lyft, a closest driver 3 minutes away or nearer hit the ceiling: at 2 minutes conversion barely improved, at 5 minutes riders defected to Uber, walked, or took the bus.
Pro tip The threshold turns a vague 'lower is better' into a concrete operational target per market.
Watch out Chasing the metric past the plateau wastes supply investment for zero conversion gain.
- 5
Make the proxy the supply team's operating metric
Hand the threshold to supply teams as their goal, per market and per category. They can now reason causally: adding 100 units of supply in this market should pull the proxy under threshold, and the effect is measurable without waiting on noisy output metrics.
Pro tip Drive the proxy up in every small market and every category you operate in — that becomes the company focus.
In the wild
Lyft defined liquidity as app-opens-with-intent converting into rides, then found ETA was the predictor. Below three minutes to the closest driver, conversion plateaued — two minutes was no better than three. Above three, riders started checking Uber, walking, or taking the bus.
→ Supply teams got an actionable, per-market target (keep closest-driver ETA under three minutes) that insulated them from exogenous noise in the raw fill-rate number.
Airbnb treated searchers who entered dates as intentful demand and measured what share of them converted to a booking. Supply density (enough good homes in the searched market) was the lever that moved it.
→ The same output-plus-predictor structure: fill rate as the scoreboard, supply density as the actionable lever.
Common mistakes
Steering on the output metric alone
Fill rate is influenced by snowstorms, competitor bidding and countless exogenous factors. Teams that manage it directly cannot tell whether their work moved the number or the weather did.
Failing to build an actionable playbook against liquidity
Most teams agree liquidity matters, then have no answer to 'so what do I do about it on Monday'. Without a predictor and a threshold, liquidity stays a slogan.
Is it for you?
Best for
Post-PMF marketplace product and growth leaders who need a supply-side operating metric per market.
Not ideal for
Pre-PMF companies, where marketplace dynamics are a distraction from the core exchange of value.
From the transcript
“liquidity is how marketplac win right it's it's this measure of your ability to match buyers and sellers efficiently”
“it's what I call like a market Health metric and this is basically think of your proxy that is the best predictor of your liquidity”
“and for Uber it was etas uh so we knew that if we had uh if the closest driver was at least three minutes away…”
“find this sort of like threshold find this sort of predictor that tends to Plateau that correlates strongly with retention”
From the episode
How marketplaces win: Liquidity, growth levers, quality, and more
Benjamin Lauzier (Lyft, Thumbtack, Reforge)