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Who Are The Top 25+ Market Research Influencers On Twitter?

Ray Poynter details a recent attempt to understand influence and influencers within the market research industry via a variety of social media analytical tools.

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Editor’s Note: Before I’m accused of navel gazing or self aggrandizement for posting this let me explain why I think understanding the potential of social influence is important for researchers.

First we have the more pragmatic marketing angle: if you are tasked with helping to grow a business then knowing the dynamics of how to utilize all available channels to grow brand awareness is important. And if you want to shape the perception that goes with that awareness then understanding how to maximize influence is also vital; my belief is that influence is the “carrier wave” of brand perception. It’s both a by product and a component.

Second, as researchers we need to understand this topic because it is an important part of brand tracking, A&U, mix modelling, and a host of other research-related practice areas. Social media will only continue to be an evolving disruptive force in marketing, so the more we can get a handle on how it works and can be harnessed for marketing impact, the better we can do our jobs.

Since GreenBook’s mission is to help research organizations grow their businesses and their understanding of emerging tools, using our own industry as a test case makes good sense. In the case of influence tracking we do that annually via our Summer version of the GRIT report via survey-based data collection. Since we’re in that midst of that right now, when Ray suggested an experiment in July to compare new approaches to understanding influence within our industry (at least within the Twittersphere) I thought it was a great idea, and today Ray has unveiled his findings.

This is a snapshot of one defined time period (when I happened to be on vacation, much to my chagrin!) using 4 different approaches to measure relative influence within Twitter. In this post Ray gives a meta analysis of the key findings and links to a longer and deeper exploration of each tool used and it’s findings.  It’s a great primer on how marketers and researchers can use these (and other) tools to understand the role and applications of social influence. It’s good stuff; enjoy!


By Ray Poynter

Back in July I asked ‘Who are the most influential market research people on Twitter?’ After some banter we narrowed the question to the #MRX tag and mid-July I asked for nominations. Jeffrey Henning prepared a special version of his #MRX tweeted links report, and we have had input from ColourText, Texifter, and NodeXL.

You can read the full report by clicking here , and the full report includes several links back to much fuller and interactive information form some of the people who have made this report possible.

But here is a meta-analysis of the findings. To produce the list I tabulated who made the top ten of at least one of the lists, counted how often they made the top ten, and ranked them by that. So this meta list is a follows:


Account Score
euromonitor 5
lennyism 5
mramrx 5
raypoynter 5
researchlive 5
jhenning 4
thomasjohne 4
ipsosmori 3
kristofdewulf 3
tomderuyck 3
darrenmarknoyce 2
djsresearch 2
gavinspavin 2
lovestats 2
1sue3 1
colinstrong 1
edward04 1
effectiveresrch 1
erica_dfirst 1
joelrubinson 1
jonpuleston 1
lrwonline 1
mdmktingsource 1
tomewing 1
tomhcanderson 1
tweetmrs 1
visioncritical 1


A five means the account was identified as ‘influential’ or widely linked or widely reacted to or linked to popular links by most of the routes used in the report. A 1 means the account made one of the top ten lists.

Of course, this does not mean these 27 are the most influential, nor does it mean the people at the top are the most influential, and it does not mean that influence exists in the way it is often assumed to (see this great TED talk by Sinan Aral on this topic).

Read the full report by clicking here.

I’d like to give my thanks to NodeXL, Jeffrey Henning, Texifter, and ColourText for helping produce this report, and to @lennyism for his support in getting the idea off the ground and for helping share the results.

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15 responses to “Who Are The Top 25+ Market Research Influencers On Twitter?

  1. Dear Lenny, Ray,

    Thanks for sharing this. I don’t understand what is the value of this post though – except for self-promotion (a prime objective of Social Media, not the least for people in this list), there’s no way that this list is somehow helpful understanding who is “influential” in Market Research. What are you measuring Ray? Messages that contain the #MRX tag? So the only thing we learn from this, is the distribution of people using the #MRX tag. Who are they? People who are smart enough to know that they will turn up in posts like yours, or are self-referential (thanks Insites, VC).

    There’s no way that this has to do anything with influence. It’s a bit tiring to see some of the so claimed ‘challengers’ of traditional MR coming up with these really unscientific posts. We can do better as an industry.

  2. Hi Rich
    I think the value is in the report and beyond that the reports produced by the contributors such as ColourText.

    The question arose from one of my clients asking a) who should they follow on Twitter, and if they were trying to get a message out, who should they try to get on-side. So, the context for the question was influence on Twitter in the context of MR – not influence per se.

    The problem with a concept like influence is that there is no agreement what it is (and as I point out in the report, little agreement as to whether it really exists). For example, is having a lot of follower influential? Is being retweeted influential, is being replied to or favourited influential?

    Yes, as Lenny said in his introduction, this topic is a bit self-indulgent. But if somebody wants to know the parameters of what is generally understood to be influential, this report is going to show several different approaches, and show the consequences of making different decisions. If somebody wants to know how to improve their scores (as opposed to knowing what the scores mean) there is advice in the report and in the articles supporting it.

    And, of course, it showed the influence of GreenBook in getting you to read something that you are not a fan of 🙂

  3. Hi Ray,

    Valiant effort but something is off here. You say that this was in response to a question of whom to follow when trying to get a message out. There are some obvious hits here, like yourself, Sue, Lenny, Edward, Jeff, Annie and the Dutch boys. Though the two Toms seem way too far down the list.
    What seems off is the position of those with followers below 300, or who are following far more than they are being followed. @darrenmarknoyce @erica_dfirst @gavinspavin and @effectiveresrch. Nothing against them, but this hardly shows influence.
    And where on earth is @QuirksMR, @ResearchRocks, @KristinLuck and my own @QRC?
    (I would have written this even if I was on the list – honest).
    This reminds me of a bad conjoint study. If the outcome makes no sense, there’s a spanner in the works somewhere.

    Research Arts

  4. Hi Frankie, well I certainly follow you (in the sense I read what you say and Tweet, not have just clicked ‘follow’).

    In terms of your comments I would highlight 3 things. 1) This looked at one specific time period. That was to make the task straightforward, but it has made the findings less useful – if we had not done it when Lenny was on vacation his scores would have been much higher.

    2) Some of the systems seem to use ratios, so somebody who does not tweet much or who does not have many followers can get a high score if they get a large number of interactions compared with their base.

    3) Some people score well just because a they tend to retweet popular stuff. This does not make them influential, but probably would make them good people to follow.

    The final point is to query whether the meta analysis is as good as any of the five separate analyses? I suspect that if somebody has not specific question in mind the meta analysis is the place to start. But, if you have a real question, choose the tool that best meets the needs of the question.

    For example if you go to the NodeXL analysis ( you will see that QRCA is one of the top hashtags in G2, and is the 5th highest mentioned account in G2, just behind TomHCAnderson.

    1. Great dialogue folks! To echo a few points, while this may seem self aggrandizing because we’re in the list, this was just an easy test to run because we know this space and could use our own awareness of the market to gutcheck the findings. The goal here to my mind was to try to understand how various tools measure influence and to share that with the industry as a prompt for researchers to begin to pay attention to this idea and make measurement more scientific. And of course to also show those not using social media that it is does potentially yield some specific business value.

      Also as Ray pointed out, this was a snapshot in a small window of time. Many of the usual suspects are not listed because they simply were not active. Jeffrey probably came a bit closer to addressing this issue since we have years of historical data via his #MRX Top 10 to compare against. The rest just used a very defined data range.

      Unlike Ray I do think influence is real and measurable, but I'm not sure that we proved that here, which is an interesting finding in itself. Effectively we have 4 approaches that generated 4 different results, although there was some commonality to them, hence Ray's meta analysis (similar to how polling aggregators do it).

      I wrote a bit about this a few years ago in this post:

      If I had to write a formula for measuring influence it would be something like this: [[C (content) x S (sharing) = R (reach)] x [T (trust)]= I (influence)]]. And then we’d need to tie influence into some other measurable data point like sales or clicks or conversions.

      Like all research, this may have been flawed but it brings up some really interesting questions for future exploration and we'll continue to follow-up and see what we can figure out in the future.

  5. Thanks Lenny and Ray.
    Yes, this was a good chance to compare the results of these approaches to what many of us know intuitively about the #MRX world. And it does point out how far we have to go when it comes to SM research.
    One thing for sure – both of you have worked extremely hard for years to earn the influence you have. I’m a qual at heart so I’ll just say what the two of you contribute to MRX is beyond measure…. 😉

  6. Great effort into a very difficult but important measurement topic. Couple of points:
    -Being influential on twitter may or may not translate to anything tangible or what we may think it means Twitter does = real world.
    -#mrx gang, from my perspective, tend to be oriented towards more traditional type researchers. Feels very New England-y. That might be perception though.
    -use of #mrx tag may vary. I tend to use it when I think the topic is broad. But if tweeting about data, I may use #newmr or #bigdata
    – some very influential and relevant entities don’t tweet to #mrx, like Forrester, IDC, Gartner, etc. Always wondered why Euromonitor did.
    -could we use this analysis to improve the relevancy and awareness of mrx as a field? How about pairing #mobile and #bigdata as a practice to encourage collaboration x-discipline?

  7. As is the case with most “social listening analysis”, here is yet another case where the subsequent discussion was far more interesting than the analysis

    I find the ‘trust’ issue brought up by Lenny interesting, especially as it relates to the “influential and relevant” entities brought up by Michael Louca, one of which right now is being sued for unethical/pay-for-play behavior.

  8. I don’t often agree with Tom, but I do on this point, a discussion of a paper or post is often more interesting and illuminating than the post itself.

    Perhaps the purpose of most posts should be to elicit discussion?

  9. In case you were wondering re Tom’s comment, it was Gartner,

    I know in the past Forrester has received some criticism about relationship with Microsoft; Gosh-that was 10 years ago.

    Dirty little secret that exists everywhere to some extent. nice article here;

    The other ‘wild card’ with twitter is all the fake accounts. I think twitter has a little bit of an issue that they need to figure out-if their “Big Data” is trusted.

    Also, I used to think Twitter was just self promotion with limited real life use, but the last 2 years or so, I have really grown to appreciate it uniqueness and value. In a lot of ways, its one of the last few unfiltered forums on the web. Sadly, I do see this slowly changing, as the web trolls end up ruining everything. But Twitter is ofter my first stop for breaking news stories-like Boston Marathon. And is really incredible source of information that is just under the radar. I use it all it for stock market info. Lot of the info is crap, but twitter is big enough that it impacts market.

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