Metcalfe’s Law states that the power of a network is proportional to the square of the number of users connected to that network. It was originally calculated in the 1980s by George Glider regarding telecommunications networks and later attributed to Robert Metcalfe. It has been referenced extensively for social networking sites during the web 2.0 movement of the early 2000s.
It is considered to be a heuristic or metaphor, not a technically accurate model, but it does illustrate the significance of community adoption, in the value of user adoption and what is otherwise called “networking effects”.
When developing a business model with a dependence on social interaction, it is important to consider this dynamic carefully. If you are an early provider of a service, networking effects can have a profoundly positive effect on your business. Conversely however, if you are a late entrant, this will be an exponentially negative effect and will cause significant difficulty with trying to penetrate your target market.
I had my first opportunity to think through this dilemma in 2006, while trying to get traction with an online dating site. It was a truly great site, with excellent features, design, and user experience, and spent a lot of time and money doing so. Just one problem – I wasn’t getting the adoption that i was hoping for.
With online dating, networking effects are particularly compounded by the need for someone to pay money to join. Why would someone join your site for any amount of money, when just for a little bit more money, they can have access to exponentially more attractive members at the other website that already has traction? This lead to trying a freemium model and even purely free for a while, trying to overcome this hurdle and achieve critical mass, so that I could eventually return to a paid model. The problem? Many others were encountering the same problem and using the same tactic as me to overcome it, so there was little value here.
In retrospect what I should have done was address the networking effects dilemma head on. Rather than spend my advertising budget on building a national audience, I really should have focused it one one target market. Since people search for attractive people to contact in an immediate geographical area before deciding to spend money, I would have been able to accelerate adoption and achieved higher conversion rates, had I focused on increasing the number of members in one immediate area first. And based upon the success of that one target city, I could have expanded more efficiently into one market at a time from there.
There were a couple lessons learned here. First, if you’re a bootstrap company and are not first to market, avoid a business model that depends on networking effects; it is truly a tough paradox to overcome without resources. Second, if you’re in such a market or have reasonable resources to overcome this initial challenge, look at how you can divide and conquer by appealing to specific segments one at a time. In retrospect, I believe that really would have been the key. And solving the underlying problem rather than trying to distract away from it with something such as a freemium model would have been a lot more successful.
Like every dynamic, network effects can be a powerful force, if channeled properly. Look for opportunities to leverage networking effects if you are a relatively new or viral idea that people might want to socially identify with. The litmus test when applying networking effects to your business model should be to ask yourself if there is something inherently viral about your product. You can validate your chances early by doing simple tests such as posting content and doing a few social experiments via Twitter and StumbleUpon.