Archive for the ‘Influencer Marketing’ Category

Social Influencer Analysis Smackdown: Klout vs PeerIndex vs Kred

Measuring influence is hard.  It’s more than just Twitter followers, reTweets, and mentions – influence has many dimensions.

Yet at least 3 companies have thrown their hats into the ring to measure influence: Klout, Kred, and PeerIndex.

The Social Influence Smackdown

While researching these companies, I asked myself, “How different are the results? Is this easy? Or is it hard? And if these three companies got into a fistfight, who would win?”

Today I’ll look at how these three companies stack up on some influencer identification tasks:

Heat 1: How Influential is Matt Gratt?

I started with the easiest question: How influential am I?  (The answer is ‘Not Really, At All.’)

Let’s see what our players came up with:

Klout: 51/100

PeerIndex: 61/100

Kred: 705/1000

Outreach Level: 6 of 12

 It appears that I’m most influential on PeerIndex.  I believe these scales are logarithmic (not absolutely certain – doesn’t seem to be in the documentation), not linear, so with a little math, we can make an apples to apples comparison:

Klout: .85

PeerIndex: .89

Kred: .95

Heat 2: How Influential is Aziz Ansari?

Aziz Ansari is a famous and incredibly funny actor and comedian, best known for playing Tom Haverford on Parks & Recreation, Randy in Funny People, and appearing in Flight of the Conchords, I Love You, Man, and many other entertaining programs.  He is also really, really good at Twitter.


85 of 100



74 of 100



Influence: 988 of 1,000

Outreach Level: 6 of 12

Now this ranking begs the question, “What does it mean to be influential?”  For example, if Aziz Ansari tweets he has a new comedy special, I’ll buy it immediately. However, if Aziz (for some strange reason) decides to start a display advertising platform, I would not be moved by his endorsement.

Heat 3: Who are the Most Influential People in Business Intelligence?

In this heat, I’m using a business case:  Who are the most influential people in business intelligence? If I ran marketing at a BI company, who should I build relationships with?


Klout produces a list of ten influencers.  In a quick analysis of the ten influencers, 4 were vendors (either marketers at vendors or corporate accounts), 2 were consultants, 1 was an analyst, and 3 seemed to have no relevance to business intelligence.  So in that search, 3 useful results were obtained.


PeerIndex doesn’t have topic pages – they have these ‘coming soon’ pages.



To get a Kred page, I had to search on the hashtag #BusinessIntelligence.  Then I got this page back.  Using the same rubric as before, I see two analysts and eight others who appear to have no relevance to business intelligence.

Note I didn’t use any of the companies’ paid products in this test – I only used their free versions.  I understand their paid versions are more robust, particularly at influencer identification.

As you can see, while this tool category is rapidly developing, we’re still far away from reliably sourcing influencers from social media data alone. 

Thanks for reading- What’s your favorite social media influence assessment tool? How do you use it?


The Problem with Influence Scoring

Jeremy Porter has a post on Journalistics today about influence scoring and the challenges associated with it.  Jeremy’s post does a nice job of pointing out some of the challenges with trying to use influence scores like Klout, PageRank, etc..  Most notably, when looked at them by themselves, they’re not particularly useful because, unlike a search engine that includes both relevance and influence/trust in its algorithm, there’s no contextual relevance.  So Justin Bieber may have a Klout score of 95, but if I’m selling fly fishing equipment,  the guy with a klout score of 20 who only writes about fly fishing and who is very active in a number of fly fishing community sites is much more important to me.

I don’t think this problem is unique to klout…this is a very difficult problem to solve.  Frankly though, given the changed face of media, I’m not convinced it’s even a good idea to rely on fine-grained scores like this at all.  Knowing that one influencer has a score of 64 while another has a score of 78 might be useful in a world where a relatively small set of traditional outlets have significant reach (and you’re going to be extremely high touch with a small number of outlets), but when you have a completely fragmented landscape, you just don’t need to be this fine-grained.  It’s a bit of a dirty word, but frankly in a world where everyone is an influencer and where links and social mentions drive search performance, the biggest issue is scale – like it or not, you have to build a lot of relationships in order to move the needle for the business and spammy approaches just don’t work.  So the challenge is this – how do I build REAL relationships with LOTS of people without hiring an army of people to do it?  When you rely on these fine-grained scores, inevitably you get caught in the discussion of  “is this person really more influential than this person in my niche.”  It’s a total time suck and it really shouldn’t impact how you engage.

Given that you need to engage with a lot of people in order to have an impact, I think you’re better off thinking in terms of broad groupings – i.e., a person’s level of influence is either high, medium, or low.  Then you can focus your efforts on the thing that really matters – developing the processes and tools that will allow you to engage with more people (in a real, relationship-oriented manner).  Specifically, you need to reduce the time required to: 1) find out when influencers are talking about the topics you care about (so you can engage), 2) keep track of the conversations you’re having with influencers (so your conversations are more meaningful and relevant), and 3) engage with more people in less time without sacrificing personalization and relevance.

So, given this, you’re still left with the challenge of developing a methodology for classifying people into the “high/medium/low” influence categories as a starting point.   I think the details for this are probably best covered in another post, but at a high-level I think there are three things you look at:

  • Are they relevant?  (using tools like listorious, alltop, google searches, monitoring, etc)
  • What percentile do they fall into for some of the key engagement and reach metrics? (e.g., average comments, uniques, retweets)
  • Who’s in their network (i.e., do they have relationships with some of the known influencers in the space)?

All of this info is available, the key is developing a way to quickly aggregate it and leverage it to classify people.  I’ll cover this in a follow-up post.

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