The Strategy Question at the Center of Lyft’s IPO

Executive Summary

Lyft released the filing for its public offering today, and in the section on risk factors it emphasized network effects Just how big of a competitive moat do those network effects create? Lyft’s localized network is more vulnerable to competition than a global network like the one that powers Google’s search business. And Lyft’s business is vulnerable to multi-homing, where users and drivers keep multiple apps on their phone. From a competitive standpoint, the market for autonomous vehicles looks more like the market for search engines than the one for ride-sharing. Driving is a bit different in different places, of course, but an autonomous ride in Arizona makes for a safer ride in New York; network effects in this market are likely to be strong and stable. Lyft and its main competitor Uber are left to compete fiercely with little hope of ever locking in impenetrable network effects. All the while they have to find the money and the talent to compete in self-driving vehicles, which is more likely to exhibit winner-take-all dynamics.

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Lyft released the filing for its public offering today, and though many of the headlines focus on how quickly it’s growing (it now has 18.6 million active riders), on how much money it’s losing (nearly $1 billion last year), or on its dual-class share structure, it raises an important question about competitive strategy. In the filing’s section on risk factors it notes that, “Network effects among the drivers and riders on our platform are important to our success,” and that “if we are not able to continue developing our… network effects our business could be adversely affected.”

Just how big of a competitive moat do those network effects create?

That is the strategy question at the center of the Lyft IPO, and it’s one with broader significance. Uber and Airbnb have also filed confidentially to go public and so investors will have several chances to weigh the competitive positions of the so-called “sharing economy” giants. And they’ll have to confront the fact that all networks are not created equal.

Network effects occur when the addition of a new user increases the value of the offering for other users, and the success of companies like Google, Facebook, and WhatsApp have cemented network effects as an essential element of internet strategy. They’re one of the reasons venture-backed startups place an emphasis on growth; the more users you add, the better the product or service gets.

But there are different types of network effects and different types of networks. And the properties of ride-sharing networks aren’t as advantageous as the ones in search and social media.

In their January/February 2019 HBR article “Why Some Platforms Thrive and Others Don’t,” Feng Zhu and Marco Iansiti of Harvard Business School describe five properties of networks that determine platforms’ success; I’ll mention just two of them.

The first is clustering:

“The more a network is fragmented into local clusters—and the more isolated those clusters are from one another—the more vulnerable a business is to challenges. Consider Uber. Drivers in Boston care mostly about the number of riders in Boston, and riders in Boston care mostly about drivers in Boston. Except for frequent travelers, no one in Boston cares much about the number of drivers and riders in, say, San Francisco. This makes it easy for another ride-sharing service to reach critical mass in a local market and take off through a differentiated offer such as a lower price. Indeed, in addition to its rival Lyft at the national level, Uber confronts a number of local threats. For example, in New York City, Juno and Via, as well as local taxi companies, are giving it competition. Didi [the Chinese ride-sharing company] likewise faces a number of strong contenders in multiple cities.”

Lyft’s localized network is more vulnerable to competition than a global network like the one that powers Google’s search business. (Howard Yu of IMD Business School has made a related argument about the importance of local knowledge in localized networks like ride-sharing.) Airbnb, notably, is in better shape on this score. Vacationers and business travelers don’t primarily care about rooms for rent where they live; Airbnb’s network is far less localized.

The second network property relevant to ride-sharing is multi-homing. Here’s how Zhu and Iansiti describe it:

“Multi-homing happens when users or service providers (network “nodes”) form ties with multiple platforms (or “hubs”) at the same time. This generally occurs when the cost of adopting an additional platform is low. In the ride-hailing industry, many drivers and riders use both, say, Lyft and Uber—riders to compare prices and wait times, and drivers to reduce their idle time. Similarly, merchants often work with multiple group-buying sites, and restaurants with multiple food-delivery platforms. And even app developers, whose costs are not trivial, still find it makes sense to develop products for both iOS and Android systems.

When multi-homing is pervasive on each side of a platform, as it is in ride hailing, it becomes very difficult for a platform to generate a profit from its core business. Uber and Lyft are constantly undercutting each other as they compete for riders and drivers.”

Catherine Tucker of MIT makes this point as well:

“Ride-hailing is characterized by fierce competition and firms burning through obscene amounts of venture capital in an effort to reach scale. However, users can easily install both Lyft and Uber apps on their phone and judge in the moment which is cheaper. Likewise, on the driver side, many drivers have both Lyft and Uber installed, and choose to operate on whichever platform is offering them the more profitable ride.”

Some aspects of Lyft’s business probably do exhibit network effects that aren’t subject to these concerns. Every Lyft ride provides valuable data that helps the company improve its dispatch and routing software, and some of that value transcends geography. When I call a Lyft in Boston, the data from that ride helps Lyft improve in San Francisco, too.

Data feedback loops like that are incredibly powerful. But that’s not necessarily good news for Lyft. In their IPO filing, the company writes, “We believe that in the future, fleets of autonomous vehicles will unlock a new mode of transportation.” Through strategic partnerships, the company claims it has facilitated more than 35,000 rides in autonomous vehicles. But, the filing continues, “If we are unable to efficiently develop our own autonomous vehicle technologies or develop partnerships with other companies to offer autonomous vehicle technologies on our platform in a timely manner, our business, financial condition and results of operations could be adversely affected.”

From a competitive standpoint, the market for autonomous vehicles looks more like the market for search engines than the one for ride-sharing. Driving is a bit different in different places, of course, but an autonomous ride in Arizona makes for a safer ride in New York; network effects in this market are likely to be stronger and more stable than in ride-sharing. Lyft and its main competitor Uber are left to compete fiercely with little hope of ever locking in impenetrable network effects. All the while they have to find the money and the talent to compete in self-driving vehicles, which is more likely to exhibit winner-take-all dynamics.

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