A Study of More Than 250 Platforms Reveals Why Most Fail

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Platforms have become one of the most important business models of the 21st century. The problem is that platforms fail at an alarming rate.  By identifying the sources of failure, managers can avoid the obvious mistakes. To understand why and how platforms fail, we tried to identify as many failed American platforms as possible over the last twenty years that competed with the 43 successful platforms. The 209 failures allowed us to extract some general lessons about why platforms struggle. In general, platforms fail for four reasons: (1) mispricing on one side of the market, (2) failure to develop trust with users and partners, (3) prematurely dismissing the competition, and (4) entering too late.

Platforms have become one of the most important business models of the 21 century. In our newly-published book, we divide all platforms into two types:  Innovation platforms enable third-party firms to add complementary products and services to a core product or technology. Prominent examples include Google Android and Apple iPhone operating systems as well as Amazon Web Services. The other type, transaction platforms, enable the exchange of information, goods, or services. Examples include Amazon Marketplace, Airbnb, or Uber.

Five of the six most valuable firms in the world are built around these types of platforms.  In our analysis of data going back 20 years, we also identified 43 publicly-listed platform companies in the Forbes Global 2000. These platforms generated the same level of annual revenues (about $4.5 billion) as their non-platform counterparts, but used half the number of employees. They also had twice the operating profits and much higher market values and growth rates.

However, creating a successful platform business is not so easy. What we call “platformania” has resembled a land grab, where companies feel they have to be the first mover to secure a new territory, exploit network effects, and raise barriers to entry.  Uber’s frenetic efforts to conquer every city in the world and Airbnb’s desire to enable room sharing on a global scale are the two most obvious recent examples.

The problem is that platforms fail at an alarming rate.  By identifying the sources of failure, managers can avoid the obvious mistakes.

To understand why and how platforms fail, we tried to identify as many failed American platforms as possible over the last twenty years that competed with the 43 successful platforms. The 209 failures allowed us to extract some general lessons about why platforms struggle.

The average life of the failed platforms is only 4.9 years. Many gig economy platforms collapsed within 2-3 years because they did not have enough users or funding. Given the need for deep pockets, it should not be surprising that standalone firms tended to have shorter lives than those that were acquired or launched as part of a larger firm or consortium of firms.  Standalone firms had an average duration of only 3.7 years. Acquired firms, which generally had stronger balance sheets, were capable of fighting longer (averaged 7.4 years), while firms that were part of larger entities were just average in length of survival.

We grouped the most common mistakes into four categories: (1) mispricing on one side of the market, (2) failure to develop trust with users and partners, (3) prematurely dismissing the competition, and (4) entering too late.

Researchers have extensively studied pricing decisions, yet managers still get them wrong. A platform often requires underwriting one side of the market to encourage the other side to participate.  But knowing which side should get charged and which side should get subsidized may be the single most important strategic decision for any platform.

Firms may have to throw commonsense pricing out the window when two or more platforms are racing to create a network effect. For example, Sidecar pioneered the peer-to-peer ridesharing model before Uber and Lyft, but it never became a household name.

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Yves Mulkers

Yves Mulkers is the founder of 7wData and a widely followed voice in the data and AI community. He curates the 7wData and AI Beat newsletters, reaching hundreds of thousands of data and AI professionals, and writes on data strategy, analytics, AI, and the evolving data ecosystem.