The beauty of the Web is that everything is measurable. But while there’s no shortage of data to analyze and digest, much of it looks at what happened in the past. The problem with historic data is that we are often compelled to simply look at what was successful in the past and copy it exactly, hoping to replicate the success. This is what I call the rear-view mirror problem, and for those of you looking to increase the social traction and virality of your content it’s not the best approach.
Here’s a typical scenario. You’re browsing through your Omniture or Google Analytics data for the previous month and you notice that - unexpectedly - an image slideshow of cute kittens sleeping next to cute puppies generated huge traffic. Not only was cats and puppies great in terms of uniques and page views, but it also generated some pretty significant sharing activity via social networks.
At this point the natural reaction would be one (or all) of the following:
a) This is amazing. We clearly need to produce more image slideshows.
b) This is amazing. We clearly need to produce more kittens and puppies stories
c) We need to product Cute Kittens and Cute Puppies: The Sequel by the end of the week
As humans our instinct is to – once we think we’ve found a winning formula – replicate it exactly without further question.
And sometimes this approach can work – to a point. Kittens and Puppies: The Sequel could be a huge hit, but by the time we reach Kittens and Puppies Part 9: They’re Back And This Time They’re Hungry the likelihood is that the traffic driving and shareability potential will have diminished considerably.
As an editor or creative person the biggest problem with Web data is that it simply tells you what was popular yesterday, or a week ago, or last month. If you’re lucky and have access to social media listening software, you’ll also be able to get a more real-time view into what your target audience is currently discussing and what topics are trending. This can be useful, but it doesn’t solve the problem that most Web data only tells us the “what” and not the “why”?
Why did so many people in your specific target audience feel compelled to share Kittens and Puppies? Which specific emotions were triggered – and by what – to drive sharing behavior?
In order to answer these questions we have to move away from the historical view of data, and move into a more predictive mode. What if we could analyze the Kittens and Puppies slideshow and extract some of the elements, narratives and themes that activate sharing behavior in your specific audience? And then what if we could take that data and use it to inspire new products, features and content that may not be a carbon copy of Kittens and Puppies but that includes certain core elements that we think drive sharing?
One of our core offerings at Bashki Generation is to help brands do just this. We’re eliminating some of the guesswork from social content sharing by working with clients to build a unique Sharing Index that is tailored to their specific brand, content and audience. This index consists of a framework that predicts what combination of elements will be required in a new feature to trigger sharing behavior. We then take that predictive data and use it to inform our creative development process, ensuring that all our ideas have a grounding in something tangible.
Understanding what provokes a user to share content is going to become more-and-more important for brands, especially as social discovery and personal recommendations drive much stronger engagement than other channels (i.e. organic Google search). We really are entering a new era of The Sharing Economy and – in this rapidly changing sphere – understanding the science of sharing will become increasingly important in the war for user attention.