I've been writing a bit recently about the shift from linear pipeline to platform business models, in part to help me be more precise in how I define and describe it (to clients), and in part because I think the degree of shift in the dynamics of scalability, ownership, data, and interaction is under-appreciated (particularly in large, incumbent businesses).
I'm reading Andrew McAfee and Erik Brynjolfsson's Machine, Platform, Crowd at the moment, which does a good job summarising some of the big shifts. The a16z podcast above featuring Andrew and Erik is a really good listen, and discusses some of the key ideas in the book. These are positioned in the context of the rebalancing that is needed between the dynamics of mind and machine, product vs platform, core vs crowd. They also discuss a few of the key principles inherent in platform business economics, notably network effects, supply and demand side economies of scale, the changing nature of corporate ownership, the idea of complements, and the red queen phenomena.
To complement this I re-read Chris Smith's excellent two-part post on platform business economics, which set out in simple terms some of the distinctive dynamics involved. As blogging is my way of assimilating and thinking aloud I'm going to summarise some of Chris's points here (largely for my own benefit but as always you're welcome to tag along of-course).
To remind us, platform businesses are essentially orchestrators. They typically have some key characteristics including serving multiple types of customer, facilitating efficient value exchange (between all the constituent players in the ecosystem), and exhibit shared common standards (the terms that ensure the participants can transact easily and fairly). Chris quotes Alex Moazed of Applico who defines two key types of platforms: exchange and maker:
…both platforms that create value and generate revenue by facilitating interactions between consumers and producers. But each platform type has a slightly different core value proposition. An exchange platform creates value primarily by enabling direct exchanges between its consumers and producers. In contrast, a maker platform creates value by enabling its producers to make content and broadcast it out to an audience.
The key thing here is being the facilitator – value being derived from access and interaction (because data). Being a thin layer that enables exchange of value means high leverage but more limited ownership (and as Tom Goodwin has said, that the battle is for the customer interface). The interaction between distinct user types in the platform ecosystem can act as its own market (facilitated by the platform of-course) but this activity strongly affects the other players and markets in the ecosystem, leading to exponential growth through network effects.
The example I used before is Google and Android. Google is the owner of the operating system, and it partners with providers (mobile device manufacturers who serve as the carrier) but it's a win-win relationship (great hardware carrying great software, everybody wins). App developers are the producers (creating apps for Google Play) but again it's a win-win (the more great apps, the more Google Play becomes a differentiator for the OS). Consumers are part of the ecosystem too, but there is a value exchange here too with access to great phones carrying great operating systems and access to a great app store, and money and data flowing the other way. A change in one of these market dynamics positively impacts the other dynamics in the ecosystem e.g. more people buying apps through Google Play doesn't only mean more developers wanting to put their own apps on the platform, but it benefits the OS through more people choosing Android and the devices that carry it.
Chris uses the standard demand curve, which shows the relationship between price and volume, to show how platforms yield network effects.
With a platform business you have to manage multiple demand curves simultaneously – the supply-side and the demand side – and a change in one is tightly linked to changes in the other. A platform like Airbnb for example, needs to have critical mass on both sides if it is to work. But the more accommodation providers are on the platform the more attractive Airbnb becomes to consumers. The more consumers join because of this, the more attractive Airbnb becomes to other accommodation providers, and the better Airbnb gets:
There are two key types of network effects in this context – cross-side and same side. Cross-side is like the scenario above where user dynamics on one side impact user dynamics on the other – for example where realising demand-side economies of scale helps to achieve supply-side economies of scale and vice versa.
Same-side network effects are when user growth on one side of the platform affects other users on that same-side. Chris uses the example of where positive advocacy from great customer experience can enhance user growth, but Chris has a neat table showing how cross-side and same-side network effects can have positive and negative demand curve impact:
Key to the effective orchestration of a platform ecosystem then, is understanding how the demand curves shift in response to each other.
I've liberally lifted from Chris's post here for my own benefit but worth reading the original. He's also written a follow up that deals with growth dynamics which I'll aim to write up in due course.
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