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Viral coefficient: What it does and does NOT measure

It’s easy to overemphasize viral user acquisition
I’ve recently been receiving a deluge of e-mail related to viral marketing – in particular, people sometimes represent it as a "magic bullet" to solving their startup’s problems. In fact, I’d argue that it’s merely one of many steps required to create a long-lasting, value-generating web property.

After all, the last thing you want to be is a fad, a one-hit wonder, or many of the other terms that are out there for rapidly spreading idea that quickly burnout.

Viral coefficient is only one metric of many
If anything, my overall point is to emphasize a hypothesis-driven, data-centric view of both the internal and external factors of your business. You need to build a sufficiently fine-grained model to expose the different levers available to you to optimize, verify, and repeat. If you care about pageviews, for example, but are only measuring the virality of your product, then you are missing out on all the other contributing variables in your business.

To summarize – metrics like the viral coefficient give you understanding of:

  • For every user coming into your site, how many friends do they bring?

However, they don’t give you an understanding of:

  • How long will it take for you to saturate the entire network of users?
  • Do your customers love your product? Does it stimulate other positive emotions?
  • Is your product sticky? Does it generate a lot of pageviews?
  • Where does your traffic monetize well? And what methods of monetization work best?
  • When and how does your product fit into the lives of your customers?
  • Is your market big enough? Can your startup grow to be a billion+ business?
  • etc.

The point is, if you’re trying to create an equation for revenue, where the virality is one part of the business – don’t be lazy. Build models and conduct experiments left and right on your product, so that you are always iterating.

Summarizing my undergrad degree in one statement
For some, this might seem like a disaster – an overflow of data. That’s absolutely a danger, and this is exactly when you have to start making smart choices to understand what variables are important to measure and which ones aren’t. Folks like engineers, statisticians, physicists, and other applied sciences will relate to this thought.

My undergrad degree was in a variation of Applied Math, and when describing to my friends what I learned, I often say I didn’t learn much beyond one phrase:

All mathematical models of the world are flaws, yet useful in their own way

Any mathy types that want to swap notes on viral marketing and user retention, feel free to shoot me an -mail ;-) I’m always curious to see how other people are modeling their traffic.

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