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Network effects are powerful but too often seen as black-or-white: a company either has them or doesn't. In reality, they vary widely in types & strength. I compiled a list of 16 metrics to measure network effects, coauthored with my colleague @dcoolican:
a16z.com/2018/12/13/16-…
a16z.com/2018/12/13/16-…
Measuring network effects begins with the basic definition of a network effect: does your product become more valuable to its users as more people use it? Quantifying the additional value that user n+100 gets over the nth user is tricky, but not impossible.
Products have different types of network effects that mature differently over time. We cover how network effects manifest themselves in metrics related to acquisition, competitors, engagement, marketplace, and economics.
A sampling of metrics in our post: how power user curves (L7 or L30) shift with network effects, how sources of traffic change over time, measuring switching or multi-homing costs, market depth, match time, cohorts for local vs. global network effects, etc.
Each business is unique, so there's no one-size-fits-all set of metrics. In general though, these are good metrics to consider--perhaps as a jumping off point in crafting your own.
Something you'll notice is that most of the metrics here are time-based. That's because network effects are dynamic, and evolve as the product, user base, and competitive landscape changes. We wrote a companion post about the nuances of network effects: a16z.com/2018/12/13/net…
Founders and operators - we'd love to hear your thoughts on how you measure your network effects, key KPIs you track, etc!
And be sure to read @dcoolican's thread on the dynamics and factors that influence network effects!
