The expansionary social web consists of a myriad array of digital social products, jostling for fractions of our finite time and attention. In this competitive environment, user preferences shift fast and fads fade even faster. Yesterday was Draw Something. Today's Vine. It is the drive to become The Next Big Thing that is spurring bursts of entrepreneurial endeavors in the marketspace.
To build a runway for a successful product take-off, traits like innovation, market intuition, leadership competency, growth strategy, team agility, and funding matter. But for social products, success is largely defined by user growth. Higher growth means higher commercialization potential and higher business valuation.

A social product basically comprises of an online community of users interacting with one another and also, creating and consuming contents. Its growth is the quintessential emergent process, characterized by uncertainty and punctuated by discontinuity. Startups face an ever-present likelihood of Taleb's Black Swan lurking around the corner. Oftentimes, a successful social product with runaway growth can be attributed to a convoluted mixture of impeccable launch timing, affective product use experience, subjective gut feeling, satisficing features, and/or a healthy dosage of luck. Despite such unpredictability, dissecting the dynamics of users growth is not a frivolity as it could lead to better insights on reaching critical mass of users.

Carl Shapiro and Hal Varian depict the messy, emergent patterns of users growth as phases of an evolutionary loop in their Lock-in Cycle[1]. A new product starts with user awareness in the Cycle. A new entrant must push aside barriers of skepticism and overcome the status quo inertia to get the bandwagon rolling. Product concept and features must be sufficiently compelling to motivate people to talk about it (online and offline) and eventually, spark viral awareness.

Adoption barriers must be eliminated as much as possible to spur take-up. That's why social products are free to use, a tactic to eliminate cost barrier.

Products like Mailbox, Pinterest, and Quora used velvet rope launch tactic. During a closed beta phase, invited early adopters help to stir interests and create wants for the product, in addition to populating the network with contents, setting tone for the budding community, providing functionality feedback, and load testing infrastructure. Some of these early adopters may eventually become lead-users in the network.

Next is the sampling phase. As an experience good, its values can only be derived through utilization. People must be convinced enough to act on the awareness and start using the product to receive usage satisfaction (or dissatisfaction).

First experiential impression matters. On-boarding process helps to connect the new user to interesting contents and to other users on the network. The recommended contents inform a new user on what to expect from the network and also what are expected from his contributions. Expectation management is essential to ignite adoption.

However, initial growth pattern is not a reliable indicator of a product's long term success. Instagram reached the 1 million users mark in just three months after its October 2010 launch. But for Pinterest, three months after its launch in March 2010, the visual social bookmarking site has only 3,000 users. Nine months later, the site has less than 10,000 users. Today, Pinterest can be considered a successful venture, at least in terms of users acquisition. Pinterest eventually took only 9 months to grow from 50,000 to 17 million monthly unique users whereas Twitter took 22 months, Facebook 16 months, and YouTube 12 months. The company is now valued at $2.5 billion after closing its latest financing round in February 2013.

From the try-out phase, startup needs to quickly move on to entrench the new users by making the product progressively useful and addictive. Each user of a social product benefits significantly from others' use of the same product. Specifically, the marginal social benefit of one more person joining the “network” is greater than the marginal private benefit. More users beget more user-generated/-curated content, more social linkages, more opportunities for purposeful and serendipitous discovery, and hence will lead to greater usefulness to users. Additionally, the number of social connections a user has and his/her interaction intensity with these connections tends to increase switching costs and fortify resilience within the network.

The next phase is lock-in where users form usage habits. A user community as a collective whole is now characteristically different than its users as fragmented individuals. Each community has unique emergent properties, which are hard for other networks to emulate. Over time, user growth can trigger a positive inflection point and enables a product to "achieve the demand side economies of scale generated by network effects.”[2] That emergent properties probably the most effective form of lock-in. Twitter and Facebook are two examples of online communities that have achieved critical mass of lock-in users.

The Lock-in Cycle is an infinite loop - from awareness to sampling to entrenchment to lock-in, repeat. It doesn't assume a final destination or a state of nirvana. Each user goes through the Cycle multiple times. As users operate in an open system, they will constantly expose to new products and many will try them out. They will decide, again and again, either to switch or spend more time on the new products or remain loyal to the existing ones. At any point in time, different users are at different phases in the Cycle connecting and interacting with one another on a same platform. At any point in time, a startup must juggle multiple tactics concurrently to accommodate all the permutations of user predispositions. And that juggling act is more art than science.

[1] The Lock-in Cycle appears in Chapter 5 of Information Rules - A Strategic Guide to the Network Economy by Carl Shapiro & Hal Varian (1998). The discussion on the Cycle in this article is loosely based on that chapter.
[2] On page 14 of Information Rules.

Note: I originally posted this article on Forbes blog.