A response to Clipboard Health’s President, Bo Lu
‘Real Problems We Tackle Everyday: Pricing #1’.
“Your task is to maximize the company’s net revenue (the difference between the amount riders pay and the amount Lyft pays out to drivers) for this route in Toledo for the next 12 months. Let’s assume that you cannot charge riders more than the prevailing rate.
The core question is: how much more or less do you pay drivers per trip (by changing Lyft’s take)? Your goal is to maximize net revenue for the next 12 months on this route.”
In this case study, I developed a model to optimize Lyft’s revenue by adjusting the company’s take per ride, considering factors like match rate, driver and rider churn rates, and the number of active users. I proposed a reduction in Lyft’s take from $6 to $4.55 per ride, which would increase the match rate from 60% to approximately 84%, thereby maximizing net revenue and improving overall marketplace health.
My gut said that a middle point between $6 & $3 will be the optimal Lyft share to drive revenue while addressing the other levers of the business i.e. churn, MAU, etc. Here’s how I approached the case study to address that hypothesis…
Given that 60 out of 100 rides are matched, the current net revenue per month is $6 * 60 = $360.
When Lyft’s take was reduced from $6 to $3, the match rate increased from 60% to 93%. This suggests that for every $1 reduction in Lyft’s take, the match rate increases by about 16.5%. The relationship between Lyft’s take and the match rate is not linear but for the sake of the study let’s say it is. More rides happened at $3 but this resulted in Lyft taking home $279 opposed to $360 per month. More drivers and active riders were present but we gave too much $$ to get there. We can find a middle price that brings a heroically consistent match rate across 12 months. This rate could potentially shift down churn (need more data due to rapid fluctuations) and build rider/driver base.
So, if Lyft’s take decreases by, $1.45 (from $6 to $4.55), the match rate increases by about 24% (from 60% to 84%).
Revenue from riders = $25 * 84 = $2,100 |
Cost for drivers = $20.45 * 84 = $1717.8 |
$2,100 — $1,717 = $382.2 Monthly Net Revenue
Alternatively, 4.55 * 84 = $382.2 Monthly Net Revenue
With more money for Drivers, we can increase number of active drivers which creates more matched rides. This leads to lower rider churn and increases number of active users that feeds back into match rate. We’ll find a new synchronicity with these price levers.
