Avtar and I traded ideas about the many other small signals out there that simply aren’t picked up by the analytics programs which are optimized to give us broad trends, patterns, and large-scale shifts in sentiment. Perhaps much of our dark social media really isn’t that dark because we’re just not looking for their signals, or we’re focused in the wrong places. Innovation and insight don’t come from Big Data. They come from Little Data.

The small signals before us

Is it possible that you could somehow organize “small signal sleuthing” to discover a whole new category of passionate customers? Consider:

  • Somebody I didn’t know left me an endorsement for “digital marketing” on LinkedIn. This might be the one and only time I hear from this person, their lone small signal to me that they’re a fan. What if I could determine that in fact this was no idle act — this person only gives out two endorsements per year. Wouldn’t that be meaningful to know?
  • What if a woman among your followers only tweets a few times a month. Her level of tweeting is so obscure that she is invisible on the social analytics radar. But what if I could determine that 25 percent of her tweets were about my company? Isn’t that a “gray signal” that this person cares about my content in an extraordinary way?
  • What if you knew that there is a person who ONLY comments on your blog? That means something, even if they only comment twice a year.

Chances are, these “gray” messages are not weak signals at all. These may be the equivalent of the vast, shy, silent majority virtually screaming their love for us.

If we had a way to scale this process and collect this important gray matter, what would I do with the information? Is there a way to connect the dots and form a more meaningful relationship with these important individuals? I’m convinced these signals from our quiet, yet essential, Gray Social Media audience are beaming to us all the time, but we’re missing them because there’s no easy process to track, quantify, and develop these subtle leads.

How do we measure the relative importance of gray?

Certainly CRM and automated marketing software are evolving in a way that can help us begin to discover these quiet voices, but there are a lot of conditions. A person would have to show themselves as a subscriber to our blog or newsletter before we can set an algorithmic “magnet” that will help us collect other small signals that can bring dark fans into the light.

Likewise, we can possibly communicate with people who visit our sites even when they’re “dark” through retargeted ads. 

But even then, do we know their names? Is there an opportunity for real relationship-building with an IP address? I think a lot of people in the gray space are telling us they’re there. And I’m not sure I’ve seen software that can detect relative intensity or significance of an interaction. For example, the person who mostly tweets about me, but only tweets a few times a month, would be chalked up somewhere merely as an insignificant “mention,” not as an extraordinary sign of commitment from a shy person.

Quiet is not irrelevant!

This might be a business opportunity for somebody out there. Or, are there other ways to detect these folks you’re already using? This is a new idea, so let me hear from you. Have you thought about focusing on the very small signals of the Gray Social Media? Is this the future of social media metrics?

Note: The brilliant data scientist Christopher Penn wrote a wonderful blog post with ideas on how to discover the gray audience. A recommended read: “Gray social media and social monitoring tools

Illustration courtesy Flickr CC and Photomaginerium

Also, thanks to my friend Brooke Ballard who helped clarify my thinking on this topic through fun and lively debates.