AirBnB, Uber and the Unicorn growth delusion
AirBnB and Uber are now entering into their seventh year of operations and, although the financial details are sketchy, it is perhaps worth spending a little bit of time putting these two growth stories into context.
AirBnb and Uber are what we call two sided exchanges. They sit in the middle between buyer and seller. Two sided exchanges are notoriously difficult to create at scale. But, theoretically at least, once you have achieved ignition, profitability should be relatively easy moving forward.
Industry commentary suggests it is significantly easier to scale at speed today that what it was 15-20 years ago.
So the question we will be exploring here is: Has the worldwide explosion in internet users over the past 20 years made it cheaper or more expensive to build an exchange network at scale on the network today than it was back in the 1990's?
To do this we will explore how this mobile generation of two sided exchanges shapes up against the old dot com era equivalents and a couple of start-ups that sprouted in the "fallow" period in between these two tech bubbles.
In this first illustration we have mapped Uber and AirBnB against the surviving Dot Com travel sites Expedia and Priceline and a couple of post Dot Com crash entrants in Orbitz and Kayak.
The size of the bubble is a relative indicator of market capitalisation during the 7th year of operations. The horizontal axis indicates relative profitability (ie. Operating margin) and the vertical relative ability to generate revenue.
A quick look at this chart suggests both Priceline and Expedia were more efficient in their 7th year of operations than either Uber or AirBnB at this time.
However Uber appears to be generating more revenue.
But is it? Let's play a little thought experiment by adjusting the raw figures for inflation and factoring in the efficiency of the models based on the size of the potential market (i.e. total number of internet subscribers at the time of their 7th year of operations). Once we do this quick calculation we discover that both Expedia and Priceline were not only more efficient at a comparable stage of the business lifecycle they were also generating comparatively more revenue once we take into account the size of the potential market.
The result brings into question the popular idea it is easier today to build a two sided exchange on the internet at scale quickly - if you are willing to invest heavily in funding future growth - simply because more people are using the internet than ever before.
And this raises questions about how we perceive the value of network effects within the context of monetizing a two sided exchange. How much does our idea about the potential size of the available market, its potential expansion and the potential ability to acquire future market share shape our price expectations (i.e. the market capitalisation of the company)
To explore this question let's focus on the journey of Priceline from the heights of the Dot Com Boom, its subsequent collapse and its valuation today.
Again we begin with the raw data estimates.
Then adjust them for inflation and factor in the relative efficiency of the models based on the size of the potential market (i.e. total number of internet subscribers at the time of their 7th year of operations).
And then finish it off by adjusting the market cap for inflation and factoring in the relative efficiency of the models.
As you can see the expectations for Priceline during the Dot Com Boom - even compared to today's Unicorn valuations - was off the charts. Then these expectations collapsed spectacularly before we have the recovery and growth story that is Priceline today.
Is this the journey ahead for both Uber and AirBnB? Only time will tell.
But the numbers help to visualise how the narrative - in this case the sharing economy, a herd of Unicorns and the value of network effects / previously the eBusiness, the Dot Com Boom and the value of network effects - distort our hopes and expectations for the relative efficiency of these two sided exchange business models. Particularly when measured against the realities of doing business on the network.
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