Taylor Davidson · Modeling Viral Loops

Articles on modeling viral customer acquisition loops
by Taylor Davidson · 23 Jan 2009

Recently I created a short financial model for a friend to help him understand how his newest startup “works” and estimate the basic operational and financial metrics behind his business.

After we went over the basic information about the product and business (a standard part of my initial data request before creating a startup financial model), I realized the crucial part in creating the financial model was going to be understanding the customer acquisition and engagement cycle. I started digging into viral marketing, viral loops and viral expansion loops to understand how to translate people’s behavior and usage of his product into a series of assumptions and equations.

Even though the estimates will undoubtedly be wrong, breaking down the operations into a series of equations forces one to take a very tactical look at product development choices and business strategies.

Maybe someday I’ll release a version of my viral loop customer acquisition and engagement model. Until then, I figured it might be valuable to share a bit of the research I found most interesting and valuable.

Yes, there are a lot of links to Andrew Chen, and as you read you’ll figure out why…

Andrew Chen, What’s your viral loop? Understanding the engine of adoption: A viral loop is…

…The steps a user goes through between entering the site to inviting the next set of new users.

… Ultimately, viral loops are like induction proofs in that you are jumping to a steady state situation in which your viral widgets/emails/messages are already out there, and you are optimizing some set of steps that users have to jump through. Then, once you get this right, then you are figuring out how to build “on-ramps” into your viral loop so that you bootstrap the entire process.

Fast Company, Ning’s Infinite Ambition:

“Incorporating virality into the functionality of the product.”
Ning grows because each new user begets more users. Every time someone sets up a social network, he has no choice but to invite friends, family, colleagues, and like-minded strangers to sign on as well. The company calculates that each person signed up for a Ning group is worth, on average, 2 people, compounded daily: On day two, that individual brings in 4 group members and on day three, 8; within a week, she has brought in 128 people. Which is how Ning has been able to grow at a daily average of more than .4% and add 500 new groups a day, doubling roughly every 137 days.

… “double viral loop,” which spreads two ways, because every network creator is a user and any user can become a network creator.

Andrew Chen: Viral marketing is not a marketing strategy:

Viral marketing is not a marketing strategy
Successful viral products don’t have viral marketing bolted on once the product has been developed. It’s not a marketing strategy. Instead, it’s designed into the product from the very beginning as part of the fundamental architecture of the experience.

Viral marketing is not a product feature
No single feature determines the virality of the product – instead, it’s part of a viral loop that connects a disparate set of functions into a cohesive motivation for the user to tell their friends. If the fundamental product doesn’t drive a viral motivation from its users, then it’s very hard to force it.

Viral marketing is a fundamental product design discipline
Instead of:

We have product X, how do we virally spread it?

… we ask:

We have viral loop X, what’s the right product to put into it?

The skillset for effective viral marketing
Because of the above issues, “viral marketing” is not really something that ought to be in the domain of soft-skill folks like PR, advertising, and marketing people. Nor is it in the world of hardcore technical folks that can architect systems but not consumer interactions.

Instead, it’s something that needs to bridge both soft and hard skills. You need an interesting combination of skills, including:

  1. Understanding the motivations behind user behaviors
  2. Understanding and exploiting the technical loopholes to create viral loops

I think that the fundamental compartmentalization of these two skillsets is what ultimately drives huge companies being worse at viral products than startups.

Eric Ries, Engagement loops: beyond viral:
On synthetic notifications:

The most blunt instrument is to simply reach out and contact your customers on a regular basis. …true ROI of a synthetic notification has to balance ROI, customer fatigue, and the engagement effects of the campaign itself.

On organic notifications:

… the mechanics of sending users notifications when new friends of theirs join the site is a great organic re-engagement tactic. From the point of view of the existing customer, it goes beyond reminding them that the site exists; it also provides social validation of their choice to become a member in the first place.

The ultimate form of engagement is when the company doesn’t have to do anything explicit to make it happen

Connecting engagement and viral loops:

The two loops are intimately connected, in a figure-eight pattern. Customers exit the viral loop and become part of the engagement loop. As your engagement improves, it becomes easier and easier to get customers to reenter the viral loop process and bring even more friends in. And as in all dynamic systems, there’s no way to optimize a sub-part without sub-optimizing the whole. If you’re focused on viral loops without measuring the effect of your changes on other parts of your business (of which engagement is just one), you’re at risk of missing the truly big opportunities.

Todd Stephens, Viral Expansion Loop.

Tony Wright, Value or Viral?

It’s easier to build a great business on top of an existing viral engine than it is to build virality into an existing business”
At the time, I found myself nodding. … It turns out that viral loops are HARD.

But, as I think about it, I can name something that’s a LOT harder, and that’s building a product that people really want.

Andrew Chen: Freemium business model case study: AdultFriendFinder ARPU, churn, and conversion rates:

Visitors -> Members: 6-15%
Members -> Subs: 10-22%
Subs -> Renewing Sub: ~80%
Revenue per member: $0.48-$0.95

And most recently, the latest from Andrew Chen on Freemium models, How to create a profitable Freemium startup (spreadsheet model included!). Worth a deep look…