I’ve written previously about the important role social networks play in the success of any startup, but especially startups based in smaller towns and rural communities that need connections to grow their product and reach a substantial market.  A new study from Queen Mary University of London suggests that one’s network is so powerful to an entrepreneur that the value of their startup can be assessed by the strength of it.

The researchers assessed data from over 41,000 companies over a 25 year period to explore the relationship between the company and their employees.  The data revealed that when startups gain a central position within these social networks, it seems to correlate with long-term economic success.

Predicting success

The team then flipped this on its head to develop a prediction algorithm to try and predict whether a startup would succeed or not.  When tested on real-life startups, it was found to be around three times as accurate as methods used by many venture capital firms today.

Each startup achieved a ranking based upon their ‘closeness centrality’ value, which is the average distance between the companies in the network.  The data suggests a strong link between the ranking and the long-term success of the business.

The approach was validated using highly successful startups, including Uber and Facebook, and most of them rapidly attained central positions in their networks.

“The people within a company, be it investors, employees or advisors, bring with them experience from other firms on effective strategies, know-how on cutting-edge technologies, and their own personal contacts,” the researchers say. “Global networks are the backbone through which knowledge is gained and shared, and this information can be used to build predictive intelligence on the future economic performance of young companies.”

The researchers hope that their work will place a renewed emphasis on networking and human capital when organizations seek to support entrepreneurs, while also providing investors with a different approach to gauge the value of a startup.