No Two Unicorns are the Same
Four Tests for Evaluating a Digital Business Model
The recent spate of IPOs mean different things to different people. For Silicon Valley types, the splashy payouts represent a culmination of years of business building, and peak returns. For sober Wall St types, the cratering in the stock price of unicorns like Lyft, Uber and Peloton represents a long overdue reckoning for the VC-backed insanity in tech markets.
The main critique focuses on the absence of profitability accompanying market entry. While one could easily point to the once common understanding that growth-oriented companies need time to come by profits, this answer is hardly satisfactory for investors seeking value for their funds and clients.
Traditional media have therefore been quick to paint companies like WeWork, Revolut, Airbnb and Stripe in a similar category to their now-public cousins — inefficient, cash-burning monsters, too enamored with their own myth-making to be concerned with petty details like profits.
This picture is partial at best. While it may be the case that VC-backed tech is bubbly, the truth is that the long-term success (or otherwise) of the unicorns will be driven by the strengths and weaknesses of their underlying business models.
Four key tests help frame analysis of digital business models: unit/interaction costs, scaling/acquisition costs, market sizing, and product stickiness.
Unit/interaction costs
Successful business models must drive margin as measured by the unit/core interaction cost. If not, the business is in big trouble. The imbalance between what WeWork generates in revenue from the subscription model and what it spends on leases and real estate investments is unsustainable when evaluated against its existing asset base. In other words, independent of scaling costs, the company will still lose money. The same is true for Uber and Lyft which lose money on every single ride. While other initiatives at these companies offer potential profit, the lack of profitability in the core is a major challenge.
Other tech disruptors, like Casper, Airbnb, and Peloton, make money on every core interaction. Airbnb incurs labor costs of precisely zero for facilitating a rental, in stark contrast to driver costs in the Uber/Lyft model. Companies like Airbnb pass the first test — their fundamental business model is sound, and held steady state, (i.e. without extensive scaling investment) they have a clear path to profits.
Scaling / Acquisition costs
Growing a critical mass in a user base requires heavy investment in acquisition and sales. When there is underlying profit in the core transaction, companies can effectively plan to offset these scaling/acquisition costs and secure profitability on a longer time horizon. However, if costly acquisition spending is the primary driver of revenue growth (as with some subscription meal kits and the 2000 tech bubble generation like Pets.com) the model is likely doomed to fail.
Overhead costs will also see rapid and unpredictable growth when scaling in tight time horizons. Investors used to expect initial losses in order to grow business. Now it seems expectations have shifted to quick generation of both scale and profitability — an unrealistic combination. It may be that the speed of scaling tech “hares” confuses timeline expectations when compared to traditional “tortoises”, but client advisors should understand that 12-month profitability targets for new ventures are not realistic for genuinely disruptive, rapidly scaling companies. Instead, companies with strong underlying profits should be prepared to offset scaling/acquisition costs in the same fashion as tech giants like Google, Facebook, and Amazon.
Market sizing
Most Wall Street commentators are still struggling to articulate appropriate market sizing for tech companies. Google, when classified as an ad company, looks wildly overvalued relative to the size of the ad market. However, it appears to be fairly — or even undervalued in relation to the marketing market at large (inclusive of brand building, awareness, inventory optimization, store design, geolocation etc.)
Similarly, some media have compared Peloton to luxury home products, like thousand-dollar coffee machines or televisions. This market definition limits the potential consumer base to those in households making north of $150K annually — a small market. However, when looking at monthly fees for buying a Peloton bike and subscription with Affirm, the overall Peloton package is $58 a month — equivalent to the exact cost of the average gym membership in America. Peloton’s sales and marketing strategy suggest they are strongly pushing the $58 per month offering, not the fixed cost of a $1200 bike. The North American gym subscription market is vastly larger than the market for $1000k+ coffee machines. As with Googles extensive marketing services, Peloton’s subscription revenues broaden its business model — further supporting by the 46% unit margin Peloton receives for each bike. Wall St. analysts are starting to wake up to the potential value of this business model and have largely upgraded Peloton to “buy”
Stickiness
Stickiness remains the most voodoo and challenging part of a digital business model to evaluate. For commercial products, like Datadog, Salesforce or AWS, evaluating product-market fit is easier owing to stable pipelines and long-term contracts.
For B2C products, and especially multi-sided platforms, things are much harder. I’ve previously written about the incredible power of network effects — here it is sufficient to say that large multi-sided platforms create powerful demand and supply side effects that generate enormous pull for suppliers and consumers to participate. The power of platforms is continuously misunderstood and under-appreciated.
In many respects, the evaluating digital stickiness can be likened to the trouble previous generations had with evaluating brands. Like brands, sticky digital businesses have high mind-share, are difficult to compete against generate demand in ways that aren’t obviously utilitarian or rational, and monetize in unexpected ways. Google, Facebook and Amazon, along with pre-Azure Microsoft, have been written off only to monetize and grow value explosively by monetizing across multiple dimension in their platform businesses.
Uber, though dilutive in its core interaction, is a highly sticky platform. For years, the Economist has been saying Uber can be attacked on price, which misses the fact that doing so means either setting lower driver fees (subsequently damaging the supply side of the platform) or accepting even lower unit margin. Uber’s commanding competitive position — most every business traveler in America instinctively opens the app when leaving the airport — lends itself to multiple potential pathways to profitability. These pathways include:
· Integrated business/personal travel (Play to attack Airbnb/Trip Advisor)
· Banking and payments (as recently announced)
· Logistics (A large, complex, and highly inefficient market as a whole)
· Light mobility (Scooters/bicycles — which are profitable to operate on a unit-level, thanks to the lack of labor costs)
Should one or more of these bets pay-off, Uber could have a path to profits.
Ignore the labels
In conclusion, lumping digital unicorns into a single amorphous category is as poor a piece of value analysis as it is to evaluate banks, oil companies or retailers as one. In all cases, the strength of the underlying business model, and the ability to execute against that business model, will drive financial performance. Not all unicorns are the same. Both VC hotshots looking for fast money, and Wall St. analysts looking for an easy short, should bear this statement in mind
