Five Ways To Improve Online Influence Measures
Five Ways To Improve Online Influence Measures
Five Ways To Improve Online Influence Measures

Regular readers of my posts here and at BrandSavant know that I have, on occasion, raised questions about various measures of online influence. No, they are not perfect, and in some cases are downright meaningless. Lately, though, I’ve been hearing that still, small voice in my head: “Don’t bring me problems, Webster – bring me solutions.” There are some positive aspects of these measures (Klout’s focus on topics of influence comes to mind), and while the easiest stance to take would simply be to dismiss these measures out of hand, I think it’s more productive to (in the words of former President Bill Clinton) “mend it, don’t end it.”

With that in mind, here are five ideas, ranging from minor tweaks to major overhauls, for improving online influence measurement. These aren’t the only five, I’ll grant you, but that’s what the comments are for, right?

1. It’s All Semantics

Under construction
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Some of my issues with online influence measures are largely due to semantics. I don’t believe, for instance, that Klout is the “standard for influence,” as their corporate tagline proclaims. That doesn’t mean that there isn’t a “there there.” Klout does a very good job measuring the dissemination of messages on Twitter, and differentially modeling the impact upon those messages of various messengers. Klout does not, to my mind, measure “online influence,” but they do measure the effectiveness of individuals at propagating messages on Twitter, by topic. True, there isn’t much of a sexy tagline in that previous sentence, but many of my objections to these various measures would evaporate if they only said what they did, and did what they say they do.

2. Incorporate Web Analytics

This (along with #3, below) would begin to address what I call the “Seth Godin” problem. The number of people more influential on the social web than Seth Godin can probably be counted on just a few hands, but his Klout is 69 and his PeerIndex is only 29 (mine is 45). Seth Godin is not “good at Twitter.” He is, however, a pretty fair blogger, wouldn’t you say? Again, if these various measures are going to purport to gauge online influence, then either their grasp must begin to equal their reach, or they need to adjust their reach (see #1.) Incorporating Godin’s true reach, by factoring in his blog readership, would surely vault his online influence scores towards the top, where they arguably belong.

3. Deeper Blog Insights

Along with measuring the impact of, say, Seth Godin’s actual blog analytics, it would also be interesting and productive to measure the impact of Seth’s blogging on other blogs. It’s one thing to link to his blog, which certainly implies some kind of influence (either positive or negative); it’s another, however, to write a post about Godin’s blog. This very post, in fact, has spilled a lot of E-Ink about Godin, and it could fairly be argued that this in itself is indicative of Godin’s true clout on the web. Not that Chris Brogan, for instance, needs any added online influence, but one of the truest measures of his impact on the social web is not the quantity of retweets that he manages to generate, but the number of people who spend time writing posts in response to Brogan. Retweets are effortless. If, however, I take the time to write a careful (or careless, for that matter) response to another blog post, this is surely a sign of deeper engagement, and influence.

4. Better Text Analytics

Speaking of retweets (which, as I have written before, are not a proxy for trust,) there is altogether too much weight placed upon retweets in these various measures, and not enough attention paid to the character of those retweets. For instance, note the difference between these three hypothetical tweets:

A. RT @AmberCadabra: The Three Teams You Need To Organize And Scale Social Media.

B. Just read @AmberCadabra’s take on the HR restructuring social engagement requires. Is your organization ready? (link.)

C. Wow – we are in the process of setting our HR strategy for 2011, and I just discovered @AmberCadabra’s excellent article. I laughed, I cried, it became a part of me. It was better than Cats. I’ll read it again and again. It was the feel-good post of the winter.

You get the point (and yes, I may have exceeded 140 characters with “C.”) I think text analytics are at the point where the qualitative difference between “A” and “B/C” is readily apparent, and the difference between B and C is certainly within our grasp.

5. Why Not Ask?

The most problematic assumption that online measures of influence make is the assumption of motive. If I retweet someone, why did I do this? Did I do this because they are influential? Because I disagree? Because I am playing “spot the loony?” Mining unstructured data alone will never give you the answer. There are, however, simpler inputs. One might be to incorporate a user-generated influence input to the algorithm – Digg-ing people, in other words. If I see a list of users on one of these services and I disagree with how these users are ranked, I might certainly “vote” some of them up or down to correct this perceived affront. If enough people do this, voting could become a viable input to (but not a replacement for) the various algorithms these services use.

One could also incorporate a polling mechanism. Ask enough people to name the three most influential people in their topic area of interest, and you’ll get a pretty convincing proxy for influence within those topic areas. Again, this wouldn’t negate the behavioral measures, but augment them.

These literally took me a half-hour to brainstorm, so I know you have more ideas. Why not share them in the comments?

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About the Author

Tom Webster
Tom Webster is Vice President of Strategy for Edison Research, sole provider of U.S. National Election exit polling data for all major news networks. Webster has 20 years of experience in market and opinion research, with a particular emphasis on consumer behavior and the adoption of new media and technology. He is the principal author of a number of widely-cited research studies, including Twitter Usage In America, The Social Habit, and The Podcast Consumer Revealed, and is co-author of the Edison Research/Arbitron Internet and Multimedia Research Series, now in its 18th iteration. Reach him on Twitter at Webby2001, or on his blog at BrandSavant.

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