Building a Financial Model for a Two-Sided Marketplace
Two-sided marketplaces are tricky. You have two different customers, two streams of revenue, and these two segments can have different factors that affect their performance, like conversion, or growth.
As an intern on the Software Business Accelerator team at the Center for the Creation of Economic Wealth, I am working on our financial models to help determine where the startup can be in 3 years, and the factors we can focus on to help the startup reach its goals. The product is a site similar to Airbnb, but focused on events. I tried templates for other financial models, but each one I found either had too much, or too little, pieces for me to work with. So, I made my own.
This is my two-sided business model, focusing on a shared economy platform with multiple streams of additional revenue.
I started with a breakdown of each revenue stream:
- Featured Listings (Think of “Promoted Tweets”)
- Advertising Revenue (Not my first choice to add ads, but it’s not my product)
- Strategic Partnerships (Tours, experiences, excursions)
Once I had these outlined, I was able to break down the most important part for each item. For example, for featured listings, it’s important to take into account how many you’re starting with, the growth rate, and the price per month for a featured listing.
To begin, I decided to focus on the two most important parts to this equation — the renters and the listers (the others were fairly simple to put together). On the renter side, elements of this model include site traffic, conversion rate, active users, growth rates, average revenue per account (ARPA), and average frequency per account (ARFA), which is how many times per month the customer will book on our site, and of course, the commission off of each booking. From this, I was able to easy calculate monthly revenue, and apply month-over-month growth using the conversion and growth rates.
Connecting this with the venue side, I assessed the frequency of bookings per venue as the (number of active users * AFPA) / number of venues. Assuming an ARPA of $250, and commission of 7.5% each, for the lister and renter, I was able to calculate the projected revenue for the month.
Initially when developing the projection, I was calculating number of matches * ARPA * commission, but this was much less accurate for a few reasons: I wasn’t able to change individual growth rates for the renters and listers, so I was assuming that the growth rate for both of them was 10% month-over-month. With the new model, I can work with the growth rates separately, and show listers growing more slowly than renters, or vice versa. Additionally, I can isolate specific items in each, like the frequency each venue is booked, or the conversion rates for our users. I even included the rate of the rates. Does the growth rate slow over time? Does our active user conversion rate increase?
The rest of the model follows the same general idea: Outline the assumptions, plug them in, solve the equation. It became easier as I went along to add additional streams after I understood the basic concepts.
The model isn’t perfect, and it won’t work for every situation, but I think I’ve set myself up with a pretty good template for any two-sided marketplace scenario I find myself in, and something that’s easily modifiable for any future financial projection projects I may have.
There are still a few more things I need to add. For example, the current model only calculates revenue. I am working on implementing a cost sheet, profit projections, and tying in factors like marketing costs, and cost of user acquisition, but for now, if you want to modify it for your use, or just mess around with the numbers, you can download it here (Link will be updated as I add more “features”).