This is Part 2 of “Everything You Always Wanted to Know about Unit Economics but were Afraid to Ask”.  

This part covers the Unit Economics of Two-sided Marketplaces.  If you need an introduction (or refresher) on Unit Economics, please read Part 1 first.

Definition

Firstly, let’s define two-sided marketplaces:  these are businesses that make money by connecting Supply with Demand.  Examples are Wonolo, Uber, eBay, and Airbnb.

Sometimes, which side is “Supply” versus “Demand” can be counterintuitive.  To make it simple, Demand is normally the side that pays the money.  The marketplace business collects that money, takes out its slice, and passes the rest of the money to the Supply side.

At Wonolo, our “Supply” is our Wonoloers (the workers who do the job) and our “Demand” is our customers (companies that pay money to have the work done).

For Uber’s core business, riders are the “Demand”, and “Supply” are the drivers and their cars.  For Airbnb, “Demand” are guests and “Supply” are hosts with properties.

Unit Economics

Unit Economics get tricky in two-sided marketplaces because you have to consider both Demand and Supply.

To start with, you have two acquisition funnels, two CACs, two LTVs, two Break-even Points, etc.  You also have marketplace effects to consider.

This provides for a number of possible approaches to understanding Unit Economics:

  • model Supply and Demand in isolation,
  • model the Demand side as your “unit”, and consider Supply-side costs as variable costs,
  • model your Supply side as your “unit”, and consider Demand-side costs as variable costs, or
  • combine Supply and Demand and model at the individual interaction level – e.g. your unit is a job (Wonolo), ride (Uber), or stay (Airbnb).

In practice, you will often end up doing all of the above to provide multiple perspectives to investors.

However, for simplicity, my strong recommendation is that you start with modeling your Demand side as your “unit”.  I find that this is the most intuitive for most cases.  It most closely resembles the modeling of Unit Economics for simpler SaaS businesses, which are what most investors are familiar with.

Extra Layers of Complexity

In addition to considering how to model Supply and Demand, many marketplace businesses have additional complexities which make their Unit Economics even harder to model.

These complexities are not unique to marketplaces but are seen in many of them:

  • Variable spend: unlike a simple SaaS business, where customers sign-up and pay a fixed amount each month (MRR), marketplace users can have highly variable spend month-to-month. This has large impacts on LTV and break-even, and can make averages misleading.
  • Variable pricing: often the fee that customers pay is negotiated and/or variable, meaning margin is variable.
  • Seasonality: if a business is seasonal, it means the Break-even Point depends on when in the year a customer signs-up. This makes it hard to compare cohorts.
  • Ambiguous churn: for a simple SaaS business, customers sign up and then pay every month until they explicitly cancel their service. In contrast, many marketplaces only make money when supply and demand transact. Either or both sides can go dormant at any time and then come back at any time. This makes modeling churn hard and churn is a big determinant for LTV.

There are various ways to cut through these complexities, including comparing cohorts of like customers, and seasonal adjustments, but I’ll save those for a potential Part 3.

Marketplace Example: Wonolo

Since Wonolo is a business I’m intimately familiar with, I’m going to use it as my example.  However, the concepts here will be very similar for any two-sided marketplace.

Note: the numbers I’m using here are for illustrative purposes only, and to make for simple math. Several details that have a smaller impact on the numbers are omitted for simplicity.

Demand-side

Per the above, we’re going to model our Demand-side first.

Demand-side CAC

At Wonolo, our demand side is our customers. A typical customer is a logistics company with a warehouse needing Wonoloers (workers) to work on their production line.

The primary components of our Demand-side CAC are marketing and sales.

For illustrative purposes, let’s say the marketing component is $1,000 and the sales component is $4,000, making a total Demand-side CAC of $5,000.  (Remember that these are averages and can vary widely.)

Jobs

Our unit of interaction in our marketplace is the job. e.g. an 8 hour shift in a warehouse.

Length of shift and the hourly pay rate vary but, on average, let’s say a shift pays $100 and the average fee to our customer is 50%.

So, for each job, we charge the customer $150. We pass $100 straight on to the Wonoloer who did the job, leaving us with $50.*

Wonolo Job Economics

Combining this with the CAC above, we can see that it will take the customer using Wonolo for 100 jobs to pay back our Demand-side CAC [$5,000 / $50].  This gives us some sense, but not a full picture, since it doesn’t include the time dimension or the Supply side.

*Staffing companies typically lead with their Gross Revenue, which includes the wages to be paid to the worker. I think this is a misleading “vanity metric” because the wages are simply passed through. At Wonolo, we lead with Net Revenue.

Demand-side Break-even Point

So, we know that it takes 100 jobs to recoup our Demand-side CAC.  But, we don’t know how long that takes.  

To calculate that, we have to know how frequently our customers use our service – frequency of interaction (also referred to as “frequency of transaction”).

Let’s say that, on average, our customers post 2 jobs (shifts) per day.  We therefore now know that we recoup our Demand Side CAC after 50 days usage [100 / 2 = 50].

We also know that we hit our CAC-Doubling Point at twice this – 100 days.

Customer Lifetime & LTV

The next question we need to ask is whether we keep customers for at least 50 days. If we don’t, then we lose them before we pay back our Demand-side CAC, and our Unit Economics are negative.

Good news: we keep an average customer for 500 days.  Easily long enough to recoup our Demand-side CAC.

We can also now calculate our Customer Lifetime Value (LTV).  2 shifts per day, at $50 per shift, for 500 days, is an average LTV of $50,000 [2 x $50 x 500].

Supply Side

So far, we haven’t considered the Supply side – in Wonolo’s case, our Wonoloers (workers).

In this approach, we’re going to model the Supply-side costs as Variable Costs incurred in delivering our service to our Demand side (customers).

How much Supply is needed?

The first question to answer is, how much Supply do we need to satisfy our Demand?

In Wonolo’s case – and since the “unit” we’re modeling is our Customer – the question is, how many Wonoloers do we need to do the work one Customer needs done?

The first answer might be to say we need one Wonoloer per shift.  However, Wonoloers work multiple times for the same customer, so we don’t need nearly that many.

It turns out that, on average, a Wonoloer works for a given customer 10 times.  So we only need 1 Wonoloer for every 10 jobs that our Customer needs done.

Supply-side CAC

Now we need to consider how much it costs us to acquire a Wonoloer.

There are two primary elements here:  marketing cost (to get them to download the Wonolo app) and onboarding costs (to get them ready to work).

These costs add up to around $50 to find a Wonoloer and get them ready to do their first job.  i.e. our Supply-side CAC is $50.

Impact of Supply-side CAC on Demand Side

Now we know how much Supply we need to satisfy a customer’s demand, and we know how much that Supply costs, we can put it all together.

For convenience, let’s look at a year. 

Over the course of a year, a customer will use us for about 730 jobs on average [2 jobs per day for ~365 days]. 

So, on average, we’ll need 73 Wonoloers to meet that demand [730 / 10], and it’ll cost us $3,650 to acquire them [73 x $50].

Let’s look at how that impacts our overall unit economics calculations.

One approach is to look at the individual job level.  It costs $3,650 to get the supply for our customer’s 730 jobs per year.  So, per job, it costs us $5 to acquire the needed supply (Wonoloers) [$3,650 / 730 = $5].

Remember that we receive about $50 per job as our fee; so this means we’re spending 10% of that on Supply Acquisition.

So, rather than receiving the full $50 per job to pay back our Demand-side CAC, we’re netting $45 per job.  This pushes back our Break-even Point by a corresponding amount.  It takes us 111 days to hit breakeven on our $5,000 Demand-side CAC, once we’ve taken account of our Supply side costs [$5,000 / $45 = 111].

Given that we’re viewing the Supply side acquisition costs as variable costs, we can also say that our Contribution Margin of our staffing business is $45 per job and our Contribution Margin Ratio is 1.11 [$50 / ($50 – $5)].  

Marketplace Dynamics

So, good news:  it looks as if our business has positive unit economics!  We break-even on a per customer basis after 111 days on average, even taking into account the cost of finding the needed Supply (Wonoloers).

However, because we’re looking only at one customer in isolation, this entirely misses the fact that Wonolo is a marketplace.  These marketplace dynamics actually make the unit economics significantly better.  This is one way in which marketplaces can be extremely powerful.

Remember we said that, on average, a Wonoloer works for a given Customer 10 times?  That’s true but Wonoloers work at more than one Customer!  It turns out that Wonoloers work on average for 10 different Customers during their lifetime on Wonolo.

Therefore, we share our Supply-side acquisition costs across multiple Customers. 

This means we can divide the $5 per job that we spend by 10, meaning we only really spend 50 cents on average acquiring the needed Supply for one job [$5 / 10].

This means our Contribution Margin is actually $49.50 per job.

These marketplace dynamics exist in most marketplaces – arguably, that’s the point of the marketplace. Uber drivers don’t just drive one person, eBay sellers don’t just sell to one buyer, Airbnb hosts don’t just have one guest.  The ability to “sell” the same Supply to multiple Demand is what makes a marketplace powerful.

Ignored Components

As I said at the outset, I’ve ignored some components of Wonolo’s unit economics that would be included in any full accounting. These would include items such as:

  • customer account management,
  • customer and Wonoloer support, and
  • payment network and banking fees to collect payments from customers and pay Wonoloers.

Negative Unit Economics and The Big Gamble

Everything I’ve described above discusses Unit Economics in a sober and rational way; which is often not the way Silicon Valley works.  

There are several high-profile cases where companies have continued to grow aggressively despite clearly having negative unit economics.  The poster-child arguably being Uber.  At the time of writing (July 2020), despite its huge scale (meaning you’d expect its contribution margins would start to cover its fixed costs), Uber has never made a profit.  Will it ever? We’ll see.

To continue to grow despite having negative unit economics, businesses have to continue to raise bigger and bigger gobs of money.  Remember: negative unit economics means the more money you spend, the more money you lose.  Uber raised a total of $24.5B, including over $9B in their Series G alone.

Investors in Uber are continuing to make a big gamble that there will only be one or two winners, and the winners will be those that capture as much of the market as possible, even if it is done at a massive loss.  The thesis is that, once they’ve “won”, they’ll be able to keep out competition and control pricing to the extent that they can become profitable in the long-term.*

Investors are also gambling that Uber will be able to continue to raise money to cover the growing losses.  Fortunately for them, their market timing was good and they were able to continue to raise bigger and bigger rounds at higher and higher valuations before IPO.  If they were trying to raise that Series G today, it would likely be a very different story.

*This thesis is itself based on a perhaps flawed understanding of the strength of Uber’s network effects, but that’s a topic for another day…

 

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