The Shape of The Network in Platform Competition
In Why Some Platforms Thrive and Others Don’t, researchers Feng Zhu and Marco Iansiti try to extract some general lessons from the specific challenges that Metuan — ‘a giant player in online-to-offline services food delivery, movie ticketing, and travel booking’ — caused for the then-market-leader in ride hailing across China, Didi:
Why hadn’t Didi’s immense scale shut down its competition for ride services in China? Why wasn’t this a winner-take-all market, as many analysts had predicted? Moreover, why do some platform businesses — such as Alibaba, Facebook, and Airbnb — flourish, while Uber, Didi, and Meituan, among others, hemorrhage cash? What enables digital platforms to fight off competition and grow profits?
To answer those questions, you need to understand the networks a platform is embedded in. The factors affecting the growth and sustainability of platform firms (and digital operating models generally) differ from those of traditional firms. Let’s start with the fact that on many digital networks the cost of serving an additional user is negligible, which makes a business inherently easier to scale up. And because much of a network-based firm’s operational complexity is outsourced to the service providers on the platform or handled by software, bottlenecks to value creation and growth usually aren’t tied to human or organizational factors — another important departure from traditional models. Ultimately, in a digital network business, the employees don’t deliver the product or service — they just design and oversee an automated, algorithm-driven operation. Lasting competitive advantage hinges more on the interplay between the platform and the network it orchestrates and less on internal, firm-level factors. In other words, in the digitally connected economy the long-term success of a product or service depends heavily on the health, defensibility, and dominance of the ecosystem in which it operates.
The researchers explore key characteristics of the networks that comprise platform ecosystems: Network Effects, Network Clustering, Disintermediation, Multi-Homing, and Network Bridging.
Network Effects — It’s well-understood that as more people join Facebook, that in turn leads to more friends being attracted to Facebook, and so on. That’s the ‘direct’, same-side network effect.
Facebook also leverages cross-side (“indirect”) network effects, in which two different groups of participants — users and app developers — attract each other. Uber can similarly mine cross-side effects, because more drivers attract more riders, and vice versa.
The impact of the strength of network effects is less well known:
When network effects are strong, the value provided by a platform continues to rise sharply with the number of participants. For example, as the number of users on Facebook increases, so does the amount and variety of interesting and relevant content.
Platforms companies can design features to intentionally strengthen network effects. The authors describe how Amazon’s review system started as a same-side attraction, but as more reviews were created — and attracted even more visitors — it attracted more sellers of books. Similar effects occurred with other sorts of recommendations across the platform:
While not usually recognized as a network effect per se, learning effects operate a lot like same-side effects and can increase barriers to entry.
Learning is the second engine in all platform ecosystems, and people will stay with platforms that help them learn quickly and regularly.
Network clustering — Uber is a network of cities while Airbnb is a global network (excluding a user’s city):
Now let’s compare Uber’s market with Airbnb’s. Travelers don’t care much about the number of Airbnb hosts in their home cities; instead, they care about how many there are in the cities they plan to visit. Hence, the network more or less is one large cluster. Any real challenger to Airbnb would have to enter the market on a global scale — building brand awareness around the world to attract critical masses of travelers and hosts. So breaking into Airbnb’s market becomes much more costly.
Disintermediation — Homejoy collapsed when people sidestepped the service to directly work with home cleaners. eBay’s EachNet was the market leading ecommerce platform, but it closed after Taobao offered a competitive commerce solution that did not charge transaction fees, and allowed buyers and sellers to haggle on prices directly. Taobao instead built a revenue model on advertising and sales of storefront software.
Multi-Homing — The ‘cost’ to users of using both Uber and Lyft is negligible, so users compare times and prices and drivers decrease their idle time. This leads to price warfare, and perhaps explains a great deal of the losses in core ride-hailing.
Network Bridging — The most successful platforms use the data gathered in one network to diversify into other, adjacent networks:
When platform owners connect with multiple networks, they can build important synergies. Alibaba successfully bridged its payment platform, Alipay, with its e-commerce platforms Taobao and Tmall, providing a much-needed service to both buyers and sellers and fostering trust between them. Alibaba has also taken advantage of transaction and user data from Taobao and Tmall to launch new offerings through its financial services arm, Ant Financial — including a credit-rating system for merchants and consumers. And information from that rating system allowed Ant Financial to issue short-term consumer and merchant loans with very low default rates. With those loans, consumers can purchase more products on Alibaba’s e-commerce platforms, and Alibaba’s merchants can fund more inventory. These networks mutually reinforce one another’s market positions, helping each network sustain its scale. Indeed, even after the rival platform Tencent offered a competing digital wallet service, WeChat Pay, through its app WeChat, Alipay remained attractive to consumers and merchants because of its tight bridging with Alibaba and Ant Financial’s other services.
Alibaba moved in this way from commerce to financial services, and Amazon moved from retail into entertainment, consumer electronics, and freight.
The researchers conclude that it will be difficult for either Uber or Didi to dominate the ride-hailing market in China (and for Uber or Lyft in the US). With locally clustered networks they are effectively waging a separate war in each city, and multi-homing seems intractable. As the authors point out, the eventual emergence of self-driving taxis may completely destroy the ephemeral success they have enjoyed. In sum, the bottom line:
Network properties are trumping platform scale.