Viral Customer Acquisition – Business Model Analysis

Source: Tom Eisenmann – Professor, Harvard Business School

The following post is republished with permission from the author’s blog, Platforms & Networks.

This post is part of a series on business model analysis for entrepreneurs. The first post in the series presents a comprehensive list of issues (available as a downloadable PDF) entrepreneurs should consider when designing a business model. Others delve into specific issues; this one provides an overview of viral customer acquisition dynamics.

A product grows virally when its use spreads through direct, customer-to-customer transmission. Viral growth occurs through four different mechanisms listed below. With the exception of incentives, these mechanisms do not entail any marketing expenditures, so business models that harness strong viral growth can be very attractive.

  • Direct Network Effects. To function properly, some products must be used jointly by two or more parties. These products are said to exhibit direct network effects, because their users interact directly. For example, early versions of Skype required both a call originator and recipient to use Skype software. When one party who already has such a product wishes to interact with another who does not, the first party can contact the second party to suggest that they acquire the product.
  • Word-of-Mouth. Even if they do not enjoy direct network effects, products can spread virally when a happy customer recommends them to another party, as when a satisfied diner suggests a restaurant to a friend.
  • Casual Contact. Like the common cold, some products can spread virally through casual customer-to-customer contact. For example, the free, web-based email service Hotmail grew explosively in 1996 after its founders added a link at the bottom of users’ emails that simply said, “Get your free email at Hotmail.”
  • Incentives. Many companies structure incentives that encourage their existing customers to recruit new customers, for example, MCI’s 1990s “Friends-and-Family” plan, which offered reduced long distance rates for calls between MCI customers in a circle of up to twenty members.

Virality and network effects are often conflated and confused, so the distinction between them warrants clarification. It should be clear from the list of mechanisms above that not all products that spread virally exhibit network effects. Likewise, not all users of products with network effects are acquired through viral, customer-to-customer transmission mechanisms. Specifically:

  • A new user of a product with direct network effects might sign up based on press coverage or advertising, then discover parties with whom they can interact after using the product. This pattern was evident in the rapid growth of MySpace, Second Life, Twitter, and the question-and-answer service Quora.
  • Direct network effects are distinguished from indirect network effects in a two-sided network, in which growth in one side’s user base (e.g., Android phone owners) attracts more users to the other side (e.g., Android application developers), and vice versa. With indirect network effects, the mechanism of attraction is the aggregation of a larger base of users, rather than contact between individual users.

Many startups combine more than one viral mechanism in their go-to-market plan. Dropbox, for example: 1) harnessed a direct network effect when users employed the service to collaborate on documents; 2) benefited from word-of-mouth referrals from loyal customers; 3) acquired customers through casual contact when users emailed links that allowed recipients to download (without installing Dropbox) files stored in the sender’s public folder on Dropbox; and 4) offered a two-way “user-get-user” bonus, that gave both the inviter and recipient an additional 250MB of free storage.

Viral Coefficient

A firm’s viral coefficient is calculated as the number of additional customers subsequently acquired through viral mechanisms for every new customer initially acquired. Startups that rely heavily on viral growth should track their viral coefficient overall and by customer cohort—that is, for each “vintage” of new customers acquired during a given period through different types of marketing program employed by the firm. As shown by the table below, a viral coefficient greater that 1.0 yields self-sustaining growth from an initial “seed”—that is, a batch of new customers acquired in period 1. In the table, we assume that a seed group of 1,000 new customers each purchase one unit of a firm’s product in year 1. These seed customers do not repurchase the product, but through viral means, they attract some additional customers who purchase in year 2, who in turn attract some more customers in year 3, and so forth.


Number of New Customers


Viral Coefficient

Year 1

Year 2

Year 3

Year 4

Year 5



















When modeling viral growth dynamics for customer relationships that have a multi-year life, it is important to be specific about whether the viral coefficient should only be applied in year 1, or in each year. In some contexts, new customers are likely to quickly exhaust word-of-mouth recommendations or other viral mechanisms (e.g., opportunities to leverage “member-get-member” bonuses).

David Skok of Matrix Partners discusses viral coefficients in depth in this post, and Adam Penenberg’s book Viral Loop provides many examples of viral customer acquisition.

Tom Eisenmann is the Howard H. Stevenson Professor of Business Administration in the Entrepreneurial Management Unit at the Harvard Business School.  You can find this post on Tom’s blog called Platforms and Networks

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The following post is republished with permission from the author’s blog, Platforms & Networks.