Evan Schuman's StorefrontBacktalk

Take, for example, a company called Klout. Klout generates ratings for various companies (retailers included) about how effective they are in social media. To calculate this, it looks at retweets and mentions on Twitter; comments, wall posts and Likes from Facebook; comments and Likes from LinkedIn; Tips, to-dos and Dones from Foursquare; and comments, reshares and +1s from Google+.

Every analysis metric has to start somewhere, and this seems like a fine place to begin. But what I would much rather see on that list is phrase and concept tracking. If Macy’s tweets something that makes me think I should re-evaluate how long it will be until we replace our bed, I’m likely to ask friends or associates about it. But I’m not likely to retweet the Macy’s message nor even acknowledge that my question is based on a marketing blast.

I’d rather see how many conversations about bed replacement happened after Macy’s message went out. Specifically, I’d like to see a before-and-after stat, as in “For 90 days before the tweet, we saw an average of from 9 to 14 Tweets on the day on that subject. Starting 10 minutes after that tweet, we tracked 8,900 such tweets.” That to me is meaningful, not mere retweets or Likes. Consumers don’t want to feel used, and redistributing marketing messages does that.

The Klout problem is like that very old joke about the guy who notices a stranger standing by a streetlight, carefully examining the street below. He asks the stranger what the problem is and the stranger says, “I lost my car keys.” The guy replies, “Where exactly were you standing when you lost them?” The stranger points about 400 yards away.

“If you lost them over there, why are you looking here?” The stranger replies: “The light is better over here.” Just because the analytical light is good at counting up retweets and Likes, that doesn’t mean those results will help you find your revenue boost or your car keys.