Real Problems We Tackle: Pricing #2 My Take on this: Part I

Real Problems We Tackle: Pricing #2 My Take on this: Part I

New Question of the Day Scenario 2

(Clipboard’s marketplace has mechanics similar to Lyft's ride-scheduling business, in that our users also “book a transaction in the future.” So we use the Lyft analogy below to leverage the understanding many people have about their business when describing a very real challenge for us.)

Pretend you’re the pricing product manager for Lyft’s ride-scheduling feature, and you’re launching a new city like Toledo, Ohio.

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.

So 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 of so of every 100 rides requested are finding a driver at this price.

(While there is more than route to think about in Toledo, for the sake of this exercise you can focus on this one route.)

Here’s your current unit economics for each side:

Drivers: customer acquisition cost (CAC) of a new working driver is roughly $500. When paid the current rate drivers have a 20% monthly churn rate and do roughly 100 rides / month.
Riders: CAC on new riders is on the order of $10 to $20 (but sensitive to the rate of new rider acquisition, since existing marketing channels are only so deep at a particular CAC). 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%.

Let’s assume you can’t charge riders more, and you’re tasked with maximizing the company’s total net revenue (the difference between the amount riders pay and the amount Lyft pays out to drivers) for Toledo for the first 12 months after launch.

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 revenue for the next 12 months on this route.

This scenario is very similar to some of the marketplace pricing and liquidity problems the Clipboard operations and product team works on day to day. We’re dive deep into the numbers, and we have a bias toward action. We have real fun finding answers to these sorts of questions. :)

My take on this Part 1 (Full Answer)

Firstly, please bear in mind that there is no right or wrong answer and there is no one solution for this: 

To maximize the company's total net revenue for the first 12 months after launch in Toledo, you can try increasing the amount that drivers are paid per trip. This will increase the match rate, as more drivers will be willing to accept rides at a higher price.

Here are some steps you can follow to determine the optimal price for drivers:

  1. Calculate the current net revenue per ride: Subtract the current amount paid to drivers ($19) from the amount charged to riders ($25) to determine the current net revenue per ride ($25 - $19 = $6). This is the amount of money that the company is making on each ride.
  2. Determine the impact of increasing driver pay on net revenue: For each increment that you increase driver pay, calculate the corresponding decrease in net revenue. For example, if you increase driver pay by $1, net revenue will decrease by $1. This will help you understand the trade-off between increasing driver pay and reducing net revenue.
  3. Calculate the impact of increasing driver pay on match rate: Using the data from the previous pricing experiment, estimate the increase in match rate that will result from increasing driver pay. For example, if you increase driver pay by $1, the match rate may increase by a certain percentage (e.g. 5%). This will help you understand how much the match rate is likely to improve as you increase driver pay.
  4. Determine the optimal driver pay: Based on the data you have collected, determine the driver pay that will result in the maximum net revenue over the next 12 months. This may involve some trial and error, as you will need to find the balance between increasing the match rate and decreasing net revenue. To do this, you can create a spreadsheet or use a calculator to input different driver pay scenarios and see how they impact net revenue.
It's also important to consider other factors that may impact net revenue, such as the cost of customer acquisition and churn rates for both riders and drivers. For example, if increasing driver pay results in a significant decrease in rider churn, it may still be worth it even if it reduces net revenue in the short term.

Here are some additional factors to consider when determining the optimal driver pay:

Customer acquisition cost (CAC): The CAC for new riders is on the order of $10 to $20, but this may be sensitive to the rate of new rider acquisition, as existing marketing channels may be limited at a particular CAC. You will need to consider the impact of increasing driver pay on CAC and how it may affect net revenue.

Churn rates: Riders who don't experience a "failed to find driver" event churn at a rate of 10% monthly, while riders who experience one or more "failed to find driver" events churn at a rate of 33% monthly. Similarly, drivers have a 20% monthly churn rate. You will need to consider the impact of increasing driver pay on churn rates and how it may affect net revenue.

I hope this helps! Feel Free to connect with me on LinkedIn, let me know if you need further help?

A small tip and treat:
to solve any such riddles, read further.

Marketplace pricing and liquidity problems refer to challenges that arise when there is an imbalance between the supply and demand for goods or services in a marketplace. This can result in a lack of liquidity (i.e. the inability to buy or sell goods or services easily), which can lead to inefficiencies and other negative consequences for buyers, sellers, and the marketplace as a whole.

To define and solve marketplace pricing and liquidity problems, it is important to:

  1. Understand the root cause of the problem: The first step in solving any problem is to understand what is causing it. In the case of a marketplace pricing and liquidity problem, this may involve analyzing data on supply and demand, as well as factors that may be impacting the balance between the two.
  2. Identify potential solutions: Once you have a clear understanding of the problem, you can begin to identify potential solutions. This may involve analyzing the costs and benefits of different options and considering the impact on different stakeholders (e.g. buyers, sellers, and the marketplace).
  3. Test and evaluate potential solutions: After identifying potential solutions, it is important to test and evaluate them to determine their effectiveness. This may involve conducting pilot studies or experiments to see how the solution performs in a real-world setting.
  4. Implement the chosen solution: Once you have identified the most effective solution, you can implement it and monitor its impact on the marketplace. This may involve making changes to pricing, marketing, or other aspects of the marketplace to address the underlying problem.
  5. Continuously monitor and optimize: It is important to continuously monitor the performance of the chosen solution and make adjustments as needed to optimize its effectiveness. This may involve gathering and analyzing data on supply and demand, as well as other factors that may be impacting the balance between the two.
Data Science Project for Price Optimization:
Read my previous take on this riddle
Ajmal Muhammad 可汗

I am Open-Source Advocate, Cloud Consultant, I have experience in Digital Transformation, Security, Data Analytics, ML/AI, PMO, Product Managment focused on Growth Strategies and enhanced customer experience and Experience design. I’m passionate about creating usable digital products. I have worked with incredibly talented people across different companies. Skilled in Entrepreneurship, Startup, Open Source, Digital Transformation, Cloud, Security, Data Analytics, AI/ML Consulting, Investment Valuation, Seed Capital, Board of Directors and Advisory. Strong business growth professional with a Postgraduate Diploma focused on International Business from University of Cambridge. |► Connect with me on | linkedin

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