BUSINESS-10 Harvard University – You’re the pricing product manager for Lyft’s ride-scheduling feature, and you’re launching a new city
QUESTION
You’re the pricing product manager for Lyft’s ride-scheduling feature, and you’re launching a new city –Toledo. The prevailing rate that people are used to paying for rides from the airport to downtown (either direction, one way) is $25. The prevailing wage that drivers are used to earning for this trip is $19.
You launch with exactly this price: $25 per ride charged to the rider, $19 per ride paid to the driver. It turns out only 60 or so of every 100 rides requested are finding a driver at this price. (While there is more than one route to think about in Toledo, for the sake of this exercise you can focus on this one route.)
Regarding the Drivers:
Customer acquisition cost (CAC) of a new working driver is between $400 – $600. CAC is sensitive to the rate of acquisition since channels are only so deep. At the prevailing wage, drivers have a 5% monthly churn rate and complete 100 rides / month.
Regarding the Riders:
CAC of a new rider is $10 to $20 (similar to driver CAC it’s sensitive to the rate of acquisition, since existing marketing channels are only so deep). Each rider requests 1 ride / month on average. Churn is interesting: riders who don’t experience a “failed to find driver” event churn at 10% monthly, but riders who experience one or more “failed to find driver” events churn at 33% monthly.
You’ve run one pricing experiment so far: when you reduced Lyft’s take from $6/ride to $3/ride across the board for a few weeks, match rates rose nearly instantly from 60% to roughly 93%.
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. How much more or less do you pay drivers per trip (by changing Lyft’s take)?
ANSWER
Maximizing Net Revenue for Lyft in Toledo: Optimizing Driver Payouts
Introduction
As the pricing product manager for Lyft’s ride-scheduling feature in Toledo, my primary goal is to maximize the company’s net revenue over the next 12 months. To achieve this objective, it is essential to find the optimal balance between rider demand, driver availability, and pricing strategies. By analyzing the prevailing rates, driver and rider acquisition costs, churn rates, and the impact of pricing experiments, we can determine the most effective approach to increase match rates while maximizing net revenue.
Analyzing the Current Situation
Currently, the prevailing rate for rides from the airport to downtown Toledo is $25, with drivers earning $19 per ride. However, only 60 out of every 100 ride requests are successfully matched with a driver at this price point. This low match rate indicates a mismatch between rider demand and driver availability.
Considering Driver Economics
To attract and retain drivers, we need to address their economics. The prevailing wage for this trip is $19, but with a churn rate of 5% per month, drivers complete an average of 100 rides per month. The customer acquisition cost (CAC) for a new driver ranges from $400 to $600. Therefore, it is crucial to ensure that driver earnings are attractive enough to offset CAC and reduce churn.
Analyzing Rider Behavior
Riders, on average, request one ride per month and have a CAC of $10 to $20. Churn rates differ based on the rider’s experience with failed driver matches. Riders who do not encounter “failed to find driver” events have a monthly churn rate of 10%, while those who experience such events have a higher churn rate of 33%. This indicates that improving match rates and minimizing failed driver matches is essential to retain riders and reduce churn.
Impact of Pricing Experiment
A previous pricing experiment revealed that reducing Lyft’s take from $6 to $3 per ride resulted in an increase in match rates from 60% to approximately 93%. This suggests that adjusting Lyft’s take can have a significant impact on driver availability and overall match rates.
Optimizing Net Revenue
To maximize net revenue, we need to find the right balance between rider demand, driver availability, and pricing. Since we cannot charge riders more than the prevailing rate, we must focus on adjusting driver payouts (Lyft’s take) to incentivize driver participation while maintaining profitability.
Based on the findings, increasing driver payouts per trip can be a strategic move to improve match rates and increase net revenue. By offering drivers a higher payout, such as $21 per ride, we can attract and retain more drivers, reducing the churn rate and ensuring a higher availability of drivers for ride requests.
Conclusion
In conclusion, to maximize net revenue for Lyft in Toledo, it is crucial to optimize driver payouts while considering rider demand, driver economics, and churn rates. By offering drivers a higher payout per trip, such as $21, we can increase driver participation and improve match rates. This, in turn, leads to increased rider satisfaction, reduced churn, and ultimately maximizes Lyft’s net revenue. It is essential to continuously monitor and evaluate the impact of pricing strategies and make adjustments as needed to maintain a balance between driver earnings and company profitability.
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