What happens when everyone is talking?

Over the last five years, the level of conversation on the larger social networks has increased exponentially, and when you  combine it with all of the other user-generated channels, that’s a lot of content being created on a daily basis.  But what does that mean to the quality of insights that can derived from the larger conversation ?  Is there more noise on a percentage basis or is there more insight as a result of these increases?

There was interesting point raised at the end of a recent article in the Economist that discussed the value of social media buzz, and the diminishing returns that come from an ever increasing volume of posts.

“Most commentary on social media ignores an obvious truth—that the value of things is largely determined by their rarity. The more people tweet, the less attention people will pay to any individual tweet. The more people “friend” even passing acquaintances, the less meaning such connections have. As communication grows ever easier, the important thing is detecting whispers of useful information in a howling hurricane of noise. ”

While the value of each individual tweet will inevitably be reduced by the sheer volume of tweeting, the high volume of created content actually can help to improve overall insights.  Based on our work with clients, we notice that general trends and insights are in fact better and more substantial when the volume of conversations go up.  But as the article states, “Everyone will need better filters—editors, analysts, middle managers and so on—to help them extract meaning from the blizzard of buzz.”

Filtering conversations based on purchase intent and measurable business value can allow companies to get a better handle on what their customers think of their services and products.  In fact, it could be said that the promise of using social media for business intelligence has not been fully realized because there is still not enough volume to do everything that we hope for.  The time for companies to start working and testing their data filters is now, because we’re not too far away from the day where everyone really is talking.  And then there’s going to be a lot of data to sift through, and less time to experiment.


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