Platform business models are booming—becoming bigger and more powerful than ever. Just consider that a few tweets from the president caused Amazon’s market capitalization to fall by about $40 billion, or that Russian influencers were able to reach 126 million people through Facebook. At OpenMatters, we spend a lot of time studying network orchestration—business models where companies facilitate relationships and interactions, rather than serving up all the products, services, and pieces of content themselves. Think Facebook, Uber, Pinterest, Alibaba, Airbnb, and the myriad “unicorns” that are being showered in investor dollars. These companies are groundbreaking, leveraging networks effects and near-zero scaling cost to trounce competition or define new markets. However, not all platform plays work—the business model alone isn’t sufficient for success. There are lots of things that can make a platform succeed or fail, of course, but an increasingly central aspect of a successful platform strategy is machine learning.
A Platform Strategy Won’t Work Unless You’re Good at Machine Learning
Capability with machine learning is what distinguishes a good platform from a bad one. Platform companies exist on a continuum of curation. On one end are “wild west” companies that merely aggregate everything served up by the network, or using simple rules like up-voting to elevate content. These systems put a great deal of responsibility on their users to identify the content that they want. Unfortunately, they are too basic to deal with varied users and use cases, and they are also easily manipulated. On the other end are companies that use machine learning to review and organize the data, services, or products that flow in, and serve them up to their customers in a customized way. Those companies that curate, rather than simply pass along, offer users more value.