I will start this post of with a few announcements:
1. I am starting to contribute to a new column for Click Z about Convergence Analytics and my first post is scheduled to be published on July 29th, 2013 and I am working on it now. I already have some ideas that haven’t been expressed anywhere else, so stay tuned.
2. My latest thoughts about the subject of Influence and Influencers is driven by research connected with the course I teach at Rutgers University for the Mason Gross School of the Arts online (MGO).
Last semester in the section focusing on influence I gave students an assignment to find influentials aligned to their major areas; I spent over a 100 hours trying to work with the data, merge and clean it (to the extent I could clean it) and figure out how to make those choices useful and actionable.
My goal in this project was take the data I got – add additional metadata and insights to it, then reflect it back to the students. All students who took the course in this or previous semesters (or in the future) will get the research, and I’ll also release a condensed version of it here.
I urged students to use Followerwonk to set up a few queries to bring of influencer twitter accounts – including Geo-located influencers for the tri-state area.
With a total of 52 student submissions (51 from Spring 2013 and 1 from this Summer semester class I’m teaching) I complied a list of each major area based on the composition of my class (below), putting an online version of the list into Klout (below). I believe you will be able to see my lists if you are logged into Klout, otherwise, they will just bring up the log in screen of Klout.
I also considered Facebook in this set of lists, but incorporated that information separately.
I came up with this approach to crowd-source the selection of influence because I wanted to stack the odds in favor of my students being able to find the best influencers and approach them (esp those graduating) – I hoped to give them a “book of Influence” and someday, I just might.
But I found the work just too much. I needed more time, time I just didn’t have … but I wanted to keep my promise and release something – so I put out the first cut of the information with an emerging approach that I briefly go over below.
Major Pages Liked using Facebook Graph Search (source: WebMetricsGuru INC)
Computer Science http://klout.com/#/webmetricsguru/list/332295 Dance http://klout.com/#/webmetricsguru/list/332296 Sports/Wellness http://klout.com/#/webmetricsguru/list/332297 Food http://klout.com/#/webmetricsguru/list/332312 Fashion/Beauty http://klout.com/#/webmetricsguru/list/332311 MARCOM http://klout.com/#/webmetricsguru/list/332313 Music http://klout.com/#/webmetricsguru/list/332314 Theater http://klout.com/#/webmetricsguru/list/332317 TRAVEL http://klout.com/#/webmetricsguru/list/332318 Visual Arts http://klout.com/#/webmetricsguru/list/332319 Web/Graphic Design http://klout.com/#/webmetricsguru/list/332321 Writing http://klout.com/#/webmetricsguru/list/332322
Students also picked a specific influencer they would pitch personally – here’s that list http://klout.com/#/webmetricsguru/list/332323.
After spending a bit of time working on the #mgartr13 project(often fruitlessly, this Spring and early Summer) I finally got my hands around the data enough that I developed a point of view (POV) about this subject that was different from what I expected, and different somewhat from what I started with.
- I decided that the social media influence technologies are still too immature to be as useful as they need to be, but that many free tools, triangulated together could provide better information than most paid tools, regardless of price.
- I believe the right methodology to approach influence and influentials are much more important than any of the influence tools that currently exist today. For most people, including agencies, the free tools I choose are more than enough, esp with the type of approach I’m evolving. The biggest investment you have to make is to devote enough time and consistency to the influence search.
- The tools I chose (below) can be substituted or extended as we become aware of new and better ones, or should any of these cease to work.
Klout- Easy, very familiar to most people, has gotten better and has a “topic analysis”, includes data from many social properties.
PeerReach.com – Similar to Klout, has interesting visualizations and its own list, breaks down influence by geography.
NeoFormix.com – a Data Analysis / Text Analysis blog that has some nifty tools such as Spot and Tweet Topic Explorer.
Bluenod.com – Community visualization of influencers of any twitter account.
Advanced Twitter Search – use Twitter API to geo-locate relevant conversations nearby your location and find Twitter accounts you can friend.
Here’s an example based on the Visual Arts list:
(with the top 4 influencers audiences compared to each other)
Focusing on the top influencer in the list I used the Tweet Topic Explorer to see the entirety of the latest set of tweets from a text analytics perspective that was actually pretty darn good for free tool (and better than most paid tools I’ve seen in this space). The point would be to “see everything” as much as you could – taking a bird eyes view of the people or businesses you want to approach.
Tweet Topic Explorer was used with MuseumModernArt (MOMA) and I saw the “rainroom” and it looked interesting so I used the text analytics to dig in deeper. I was totally unaware that the MOMA rainroom was going on, and in fact, will be closing in a few weeks.
I wanted to know more about the Rainroom, so I used another tool called “Spot”.
While I would like to make it over to MOMA’s rainroom before it closes – with an average 8 hour wait – I think I’ll pass – willing to get “all wet” outside the museum ha!!
I also wanted to encourage my students to explore the “community” around any influencer they wanted to connect to – and for that I found Bluenod.com has a pretty neat tool.
While Followerwonk is a great tool (now part of MOZ Analytics) the part of the tool I wanted to hone in on wasn’t really free, and rather than stay with this tool, I went back to the source itself, Twitter, for what I consider to be a better approach if your willing to put the time into it.
Twitter Advanced Search with Geo-Location can be used to cull a list of topical localized influentials – you just have to spend the time to regularly collect the data, qualify it for yourself, either use keywords or just look at the interactions of people with the influentials – there in turn will be the people who can, hopefully be approached to pitch an idea (but you have to be careful about that).
Here’s part of the Visual Arts Influence List:
Klout List – http://klout.com/#/webmetricsguru/list/332319
|Visual Arts||name||Klout URL||Market Research||Account||Topic Word Cloud (last 1500 Tweets)||Spot Intelligence – Neformix||Community of this Influencer|
|91||MoMA The Museum Of Modern Art||http://klout.com/user/MuseumModernArt||http://peerreach.com/MuseumModernArt||MuseumModernArt||http://tweettopicexplorer.neoformix.com/#n=MuseumModernArt||http://neoformix.com/spot/#/MuseumModernArt||http://bluenod.com/user/MuseumModernArt|
This approach is still evolving, and I also want to get some feedback to see how this “visual” approach will work with those who are primarily artists – though students can come from all areas at Rutgers, including programmers and computer science.
Using Facebook Graph Search
I used the magazines that students read and found from the Influence assignment to develop an approach that could help them find jobs by exploiting the possibilities that Graph Search opened up this year. Here’s an short excerpt of the list:
Among the ways I used Graph Search are with queries like this:
|Fashion Designers working in NYC who like Glamour Magazine||https://www.facebook.com/search/26815555478/likers/110684922292220/job/108424279189115/employer-location/employees-2/present/intersect|
In some cases, where it made sense and there was enough data, I used an influencer on the list and combined it with a topical magazine and a location.
|Targeted to Pitch Influencer Cathy Horynand live in New York, NY||https://www.facebook.com/search/108424279189115/residents/present/108254102529145/likers/intersect|
Here’s a few people on that Graph Search result
This is a long post and rather than get into the Web Journal part – I’ll end it here by saying that the “triangulation”with when it is combined and used with someone who is motivated, who is willing to put focus and effort into data collection will result in superior results . This is my belief.
WebMetricsGuru Social Intelligence
It’s this “Lens” approach that uses tools that don’t necessarily all line up in their outputs (given similar or the same input) that none the less, with the right practice, provide superior results – that is an aspect of the WMG SI (WebMetricsGuru Social Intelligence) that I have been evolving, together with a few strategic partners – though the selection of this list and the particular triangulation I have done comes entirely from me.