"A good team is greater than the sum of its parts"
"About a month ago I had one of those really intense heart in mouth moments. I was really pretty nervous." These were the words of my Saberr co-founder, Sam Mead, who was about to stand on stage at a regional final for the Microsoft Imagine Cup, an app development competition. It was a big moment for us because we were about to put the reputation of our company on the line. We had used our new team dynamic software to make a prediction as to which team would win the event, in fact we'd gone so far as to rank all the teams.
With an employment history at Outward Bound (a global outdoor school with team building at its core), an engineering degree and a fascination with people, it's only natural that I wanted to build a business around behavioural economics. The kick of inspiration came from research conducted by Noam Wasserman of Harvard Business School and the Kauffman Foundation stating that 83% of startups fail and that 65% of those failures are due to poor team dynamics, not product/market fit or lack of resources or even a bad idea. So the question was, how to quantify team dynamics?
Initially we focused our analysis on two primary areas: behaviour (for this we used our own psychometric analysis, similar to Belbin) and resonance (a measure of the quality or the interpersonal relationship between two people). The key thing is that we didn't try to quantify a single person, instead we always try to quantify the relationship between people. This negates many of the issues with existing psychometric tests. It’s a bit like a football team - on a day-to-day basis it’s quite hard to predict the performance of an individual player but it’s easier to predict the performance of the team as a whole.
We took inspiration and insight from the wealth of data available from online dating and combined it with sophisticated mathematics and well researched psychology to build a short question set in order for us to quantify team dynamics. Our questions are not the ordinary either, a typical example being “Do you like horror movies?”
It turned out that our first attempts were suprisingly accurate. Our predictions at the Imagine Cup were spot on; we even got the ranking correct. This came as a big relief to Sam, I am assured! We’ve tested our algorithms at several other events, primarily through SETsquared, including the XING business planning competition at the University of Surrey and we currently have over 98% accuracy rate in predicting high performing teams.
What this points to is that skills and experience, the two key indicators we currently use to assess a candidate’s quality and judge future performance, come well behind that of team dynamics. The question we keep asking ourselves is, can we really predict business success through numbers? And if so, how easy is it to engineer teams for even greater success?