The 4 Big Holes in Your Viral Marketing Campaign

Viral marketing exploits network effects like contagion. . .

Viral marketing exploits network effects like contagion. . .

Viral marketing is one of the most sought after engines of growth.  Its potential to drive explosive increases in the number of customers at little or no cost is irresistibly appealing, so  there has been tremendous interest in understanding virality in marketing and product development, particularly regarding the social web. Unfortunately, efforts to build mathematical models for the business community have neither succeeded in reflecting reality nor offering insights into which factors matter most in achieving viral growth. Two of the commonly used models for viral growth have serious shortcomings.  For example:

In The Lean Startup, Eric Ries defines the viral coefficient as “how many new customers will use the product as a consequence of each new customer who signs up” and declares that a viral coefficient greater than 1 will lead to exponential growth while a viral coefficient less than 1 leads to little growth at all. However, his treatment of the viral coefficient makes no mention of a timescale. Is it the number of new customers an existing customer brings in immediately upon signing up? Or within a day? Or within the entire time that they are using the product?

Viral marketing relies on leveraging a customer's existing networks to reach other marketing goals, like purchases.

Viral marketing relies on leveraging a customer’s existing networks to reach other marketing goals, like purchases.

At, David Skok introduces the concept of a “cycle time” — the total time it takes to try a product and share it with friends. In doing so, he correctly notes the importance of a timescale as a factor in achieving viral growth. In fact, he declares it to be even more important than the viral coefficient. He first models the accumulation of users in a spreadsheet and then, with help from Kevin Lawler, derives a formula for viral growth:


where C(t) represents the number of customers at time t, K represents the viral coefficient, and ct represents the cycle time.

This model depends on the following four assumptions, all of which are faulty:

  1. The market is infinite.
  2. There is no churn in the customer base — once a customer, always a customer.
  3. Customers send invites shortly after trying the product, if at all, and never again.
  4. Every customer has the same cycle time and the cycles all happen in unison.

Problem 1: Market Size is, in fact, Finite

Since viral growth can be so explosive, the market for a product can become saturated very quickly. As the market becomes saturated, fewer potential customers will respond to invitations, effectively reducing the “viral coefficient” (as it is defined by Ries and Skok). Since market saturation could occur in a matter of days or weeks, we cannot ignore the effect of a finite market size.

Problem 2: Customer Churn is a Big Deal

Neither Ries’ nor Skok’s models account for churn in customers — the rate at which customers stop using the product. Eric Ries treats the concept of churn in his discussion of another engine of growth which he calls  “Sticky Marketing” and suggests that startups concentrate on only one engine of growth at a time. Even though the advice for startups to focus on one engine of growth at a time is, or at least may be, sound, it does not justify leaving this very real effect out of the equation. . .

Read the rest of the problems and Valerie’s better model for Viral Growth on the Data Community DC Blog


This post was originally published on the Data Community DC Blog and is presented here with the author’s permission.

Our Google+ page