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The dramatic influence of the internet on how businesses operate and the emergence of a handful of gigantic, digitally enabled corporations have led to breathless pronouncements regarding the importance of a peculiar new class of monopolies built on digital platforms. These platforms, it is argued, fuel network effects that lead inexorably to winner-take-all marketplaces.1 This perspective is invariably coupled with infectious optimism and investment euphoria regarding the extraordinary scale and strength of network-effects businesses.
In theory, the key attribute of a network-effects business is its momentum-driven flywheel. Every new participant increases the value of the network to existing participants, attracts more new users, and makes the prospect of a successful competitive attack ever more remote — thereby bolstering the relative attractiveness of the business. The imagined innate indomitability of network effects stems at least in part from the breathtaking strength of notable platform businesses like Facebook’s social network or Microsoft Corp.’s Windows operating system.
The problem is that not all platform businesses exhibit network effects. Moreover, even a cursory survey of the landscape does not support the oft-repeated assertion that such effects are “likely to strengthen a market’s winner-take-all tendency.”2 For every Facebook and Microsoft, there are literally hundreds of network-effects businesses operating in crowded sectors or in sectors where it is not clear that anyone will ever turn a profit. Take, for example, the once hot peer-to-peer lending space, which after more than a decade has attracted dozens of aspiring entrepreneurs and spawned a billion-dollar IPO but nevertheless has largely been a bust. The first mover in U.S. P2P lending, San Francisco-based Prosper Marketplace Inc., continues to struggle to achieve consistent profitability, and the billion-dollar IPO of San Francisco-based Lendingclub Corp. quickly ended in tears for investors.3
Nor are digital platforms necessarily better businesses than the analog versions that they displace. Analog malls had the benefit of their shoppers being many miles away from competing malls, and the benefit of their retail tenants being committed to long-term leases. On the internet, platform competitors are only a click away, and companies regularly and dynamically optimize their customer reach across competing platforms and directly via their own sites.
It is not that marketplace businesses built on e-commerce platforms do not have advantages or that they cannot thrive. Rather, it is that the mere existence of network effects tells entrepreneurs and investors relatively little about the attractiveness of a particular business. For example, Uber Technologies Inc. and Airbnb Inc., the global leaders in the ride-hailing and short-term lodging marketplaces, respectively, both benefit from network effects. However, other characteristics of those industries make it likely that Airbnb will enjoy dramatically stronger results than Uber will ever achieve.
Why Airbnb Is Better Than Uber
Three key structural attributes drive the value of network effects in the digital domain. The first is the minimum market share at which the network can achieve financial breakeven. The second is the nature and durability of the customer relationships spawned by the network. And the third is the extent to which the data generated by the network facilitates product and pricing optimization.
These should sound familiar. They are updated versions of the same core competitive advantages that have long underpinned the best business franchises: economies of scale, customer captivity, and learning. And they are as relevant to today’s digital platforms as they were — and continue to be — to analog ones. Comparing Uber and Airbnb along these dimensions highlights both their profound relevance and Airbnb’s inherent advantages.
Minimum viable market share: Two attributes determine the minimum viable scale of a network: product/service complexity and fixed-cost requirements. The former regulates the extent to which additional network participants enhance the product, and the latter controls basic break-even economics. With regard to these two attributes, Uber and Airbnb could not be more different.
In any given city, the financial viability of both companies is a function of local density — of drivers on the platform on the one hand and available property inventory on the other. A key distinction between Uber’s and Airbnb’s respective marketplaces, however, is the level of intrinsic product complexity and the resulting marketplace liquidity required to establish a competitive service. In ride-hailing, other than price, the ability to deliver a car within three to five minutes dominates all other customer considerations. Having so many drivers that cars arrive sooner than that is not useful: Riders often can’t get to the car much faster. In the short-term lodging market, there are multiple customer considerations that ensure that the value of higher incremental density in local listings does not top out in the same way. Indeed, more listings attract more travelers and drive higher occupancy rates, reinforcing the value of relative network scale.4
Turning to fixed costs, although both Uber and Airbnb are predominantly variable-cost businesses, the relative fixed-cost requirements of Airbnb are far greater. Both have similar technology and overhead costs, but users of ride-hailing services primarily use those services in a single city. In contrast, customers of short-term lodging services use those services in many different locales. As a result, people choosing platforms on which to list their primary residences are not just concerned about the density of listings in their home cities — they are also concerned about the density of listings in other popular destination cities. Thus, the collective fixed costs associated with a national or global network of local market leadership positions (which in turn benefit from spreading the central fixed overhead) become a significant obstacle to new entrants.
It may be that in some small markets, the fixed operating costs won’t sustain more than one or two ride-hailing services. But in larger metropolitan areas, multiple robust offerings are always available, with viability achievable at market shares of less than 20%. This effectively translates to a permanent pool of five or more Uber competitors, severely limiting achievable returns.
Conversely, the greater fixed-cost needs in short-term lodging mean that Airbnb competitors can break even only at far higher market shares. It is not a coincidence that while Uber may face competition from dozens of local and regional ride-hailing services, Airbnb has far fewer direct competitors of size in any given market — and the serious ones have generally attempted a more global footprint. To help spread the fixed-cost requirements, Airbnb’s primary competitors have become part of larger international travel companies. For example, Homeaway Inc. (which has half as many listing as Airbnb) was acquired by Expedia Inc., and FlipKey Inc. (with one-third as many listings as Airbnb) was purchased by TripAdvisor Inc.
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Customer captivity: The nature and durability of customer relationships determine the speed at which share can move (or shift among competitors) in a particular marketplace. When combined with minimum viable market share, the level of customer captivity enables a potential new entrant to quickly calculate how long it can expect to lose money before achieving a break-even market share. So, for instance, in an industry where customer loyalty limits annual share movement to a couple of points and break-even market share is 20%, an insurgent can expect at least a decade of losses before establishing viability.
By enhancing the ability to easily search out, compare, and switch between sellers, the internet has set the bar far higher for businesses to articulate truly compelling reasons for customers to stay put. What’s more, customers and business partners operating in an environment characterized by swift technological change are generally wary of long-term commitments. Nonetheless, robust captivity is still achievable when service quality, breadth of offering, the verification of nuanced counterparty credentials, and/or integrating critical data seamlessly into buying processes are central to the ultimate decision to transact.
Unfortunately, even the best-run ride-hailing company will struggle to encourage loyalty among drivers and riders. Already, more than two-thirds of drivers overall work with two or more services.5 Moreover, although a minority of riders currently use multiple ride-hailing apps, that percentage is growing fast and varies widely by geography and demographic.6 Among my MBA students in New York City, it is greater than 90%.
There is a difference between an individual’s willingness to entrust short-term rentals of his or her home to multiple companies and a professional driver’s willingness to drive for multiple ride services. And, as we’ve discussed, for the customer, price and speed are the overwhelming factors influencing the decision about which ride service to use for a short trip, but many other factors play a large role in the decision to a stay in a stranger’s house. More broadly, a platform’s verification capabilities — that is, its ability to reassure both parties in a transaction by providing detailed information about with whom they are doing business and what they are getting into — is a much more critical factor in reaching a decision about a short-term rental than it is in choosing which service to use for a brief ride. Homeowners want to know who will be staying in their homes, and guests want to know the experiences of others who have used those homes. A single good experience will make customers much less likely to take a chance with an alternative platform that seems to offer a comparable or, maybe, a slightly better proposition.
When Airbnb establishes a leadership position in a market, competitors are at a disadvantage in terms of inventory availability. However, the same is not true for Uber’s competitors in the ride-hailing market, because most drivers use multiple apps. Many drivers engage in a regular ritual in which they review the weekly promotional offers available from competing services before deciding which app to favor in the subsequent days. Furthermore, while a driver can easily manage multiple apps in real time, a homeowner (and the vast majority of Airbnb listings come from primary homeowners rather than professionals) most likely will need to enlist a “channel manager” service to do the same — and that often entails incremental expense and definitely involves greater complexity.
The observance of ride-hailing market-share shifts of greater than 5% over a matter of months nationally (and of even more in some localities) suggests a future filled with a steady stream of new competitors for Uber.7 This is not the case for Airbnb in the short-term lodging market, where the time required to recruit and sign up new units significantly slows the potential rate of market-share shifting and the resulting time it would take a new entrant to break even.
Data: Finally, the fortunes of network-effects businesses depend on the value of the data — and the data exhaust — that they can elicit in their respective markets. Some transactions yield data that is proprietary and, when combined with appropriate analytics and technology, is actually probative. Zillow Inc.’s continued dominance in the online real estate marketplace, for instance, is in part a function of its ability to use its unique access to data to continually improve its automated valuation models and its home search and recommendation engines.8 In contrast, P2P lenders discovered that, for most borrowers, their proprietary data yielded little more insight than was readily available elsewhere from sources such as credit scores.
I pore over the reviews of previous visitors before I book a stay in a stranger’s apartment, and an absence of reviews makes it unlikely that I will take the plunge, no matter how nice the pictures. By contrast, Uber driver reviews are not primarily used by riders to select cars (I rarely even note whether a driver’s rating is 4.5 or 4.8) but by the company to manage their fleet quality over time.9 Similarly, what information is more valuable for marketers: The fact that on most days I use Uber to travel from home to office, or the name of the city I am planning to visit and how much money I am planning to spend to stay there? And while feedback on drivers will assist Uber in culling out those who undermine the service and facilitate training those who join, the nuanced picture that emerges from travelers around the world allows Airbnb to direct regular users to the most appropriate venues and help those listing their homes deliver a satisfying experience. The resulting high level of satisfaction among Airbnb users reinforces customer captivity, and the company is able to use the data on satisfaction to encourage both rebooking and referrals.10
Beware the Network Effects Fetish
Uber has built a remarkable business. Today, even in the midst of its well-publicized leadership chaos, it is the clear leader in the U.S. ride-hailing market and the largest player overall globally. However, the structural attributes discussed above suggest that ride-hailing will always be an intensely competitive business in large, local markets. Moreover, in many international markets, Uber is the insurgent, and the network effects it enjoys in the United States provide limited advantage. More broadly, the resilience of Uber’s position hinges on a relentless aggressiveness rather than a structural tendency toward a global winner-take-all (or most) equilibrium.
Airbnb’s network effects, on the other hand, are paired with significant customer captivity. Given the advantages afforded by its global fixed-cost base, the competition that Airbnb faces is less intense than the competition Uber faces. And the data that Airbnb’s leadership position delivers should allow its managers to further entrench and monetize its unique position. Airbnb certainly faces competition globally and locally, but the company seems to be among the minority of network-effect businesses that genuinely have the ability to secure a winner-take-most market position globally.
The lesson for investors and entrepreneurs is to be wary of the fetishization of network effects as an inherently superior form of competitive advantage. The conventional wisdom regarding the winning power of network effects is not justified by either a close study of their structural impact on entry barriers or any empirical evidence of generally increasing market dominance. In fact, all signs suggest quite the opposite — that in the absence of the same characteristics (most notably fixed-cost scale and customer captivity) that have long supported the strongest analog platforms, digital platforms are likely to be significantly harder to build and maintain. It is not a coincidence that the two largest and most enduring purely digital platforms — Google and Amazon — are not primarily network-effects businesses, but instead are companies that benefit from leveraging multiple, complementary sources of competitive advantage.
With the help of other sources of competitive advantage, network-effects businesses can deliver remarkable value to users and riches to entrepreneurs and investors. On their own, however, network effects in a digital context are a peculiarly fragile barrier to entry. Seen in this light, entrepreneurs and investors should treat the identification of network effects as the beginning, not the end, of their analysis. Meanwhile, platform operators should curb any complacent confidence that they may have in their destinies as the conquerors of global markets. Instead, they should redouble their efforts to establish complementary barriers before being displaced by one of what are likely to be many competing platforms.
1.J. Knee, “The Rise of the ‘Matchmakers’ of the Digital Economy,” New York Times, May 20, 2016.
2.G.G. Parker, M.W. Van Alstyne, and S.P. Choudary, “Platform Revolution: How Networked Markets Are Transforming the Economy — and How to Make Them Work for You” (New York: W. W. Norton & Co., 2016), p. 225.
3.G. Morgenson, “Lending Club, a Stock Story That Skimped on the Details,” New York Times, May 13, 2016.
4.SharesPost Company Report, “Airbnb: Unlocking Travel’s Final Frontier,” https://sharespost.com, p. 26: “[W]e observed an interesting trend in the underlying data. As Airbnb was able to increase its listings in a given city, the number of guests per listing also increased.”
5.H. Campbell. “RSG 2017 Survey Results: Driver Earnings, Satisfaction and Demographics,” (n.d.), https://therideshareguy.com.
6.See Pew Research Center, “Shared Collaborative and On Demand: The New Digital Economy,” May 19, 2016, http://www.pewinternet.org. The recent emergence of apps that allow riders to use multiple ridesharing services through one screen will likely accelerate this trend; see D. Gutman, “New app connects you to multiple on-demand ride services through one screen,” Seattle Times (August 31, 2017), www.seattletimes.com.
7.See, for example, L. Hook, “Uber Loses Ground in the U.S. as Rival Lyft Accelerates,” Financial Times (June 18, 2017), https://www.ft.com.
8.F. DiPietro, “Why Data is Powering Growth at Zillow Group Inc.,” Motley Fool, Jan. 13, 2017, www.fool.com.
9.J. Cook, “Uber’s Internal Charts Show How Its Driver-Rating System Actually Works,” Business Insider, Feb. 11, 2015. http://www.businessinsider.com.
10.L. Qian, “How Well Does NPS Predict Rebooking?” Medium, Dec. 10, 2015. www.medium.com.