July 2010


In the early days of RSS I spent some little time scouring the web and a little less time trialling a series of different RSS readers. Few have come as close to ‘home’ for me as SharpReader. clip_image001Simplicity itself SharpReader’s display is crystal clear; the updates arrive at regular, definable intervals; it is devoid of the advertising noise typically associated with RSS aggregators and the application size is low so it doesn’t hit your PC performance unlike leaving a web-based aggregator open throughout the day.

Subscribing and unsubscribing to feeds is a simple right and left click. By supporting OMPL (Outline Processor Markup Language) SharpReader makes your reading easily transferrable between aggregators or devices for those with a wandering eye or work location.

Some of my favourite features:
• Drag and drop feed subscription enabling you to consume the output of simple syndication or complex Boolean search algorithms for the Jonny’s of the world
• Grouping – to easily follow similar resources or discussions
• Manageable pop ups
• Sits in the system tray so I can grab it when I snack on news
• Images appear quickly within the same interface

@mattwarder

Diesel isn't afraid to let its fans do the talking, and the influencing, with its Be Stupid campaign.

The t’interweb has changed the way we engage with brands and the things we buy by integrating our friends and networks into the decision process. I realise this isn’t anything new, heck I’ve even written about it before.

But it’s worth a revisit this week. The reason being that Gartner have released a report to quantify exactly how they think this process is taking place.

Drawing parallels with Edelman’s own thinking on the topology of influence, the report explains that 74% of people can be categorised as connectors, mavens or salesmen when it comes to recommending things to friends.

Whatever lexis we use to group people, the fact remains that now, more than ever, our group of friends are the advocates we listen to and trust to the highest level. This is obviously facilitated perfectly by the online world – explained here very well by Paul Adams of Google – leading Gartner to quite rightly/obviously claim that companies need to engage with people on social networks. I’d hope to be preaching to the converted in flagging that fact, but Gartner believe this is ‘a critical but underutilised aspect of the marketing process’.

Critical it definitely is, earlier in the year, we here at Edelman conducted a study which looked at social entertainment (the annual Edelman Trust in Entertainment survey). The research from across the UK and US showed how for the first time social networks have emerged as a source of entertainment in their own right. With more than 70% of 18-34 year olds putting social networks alongside the likes of TV and film as a tool to keep them entertained, the window of opportunity for purchase recommendations is greater than ever.

However, anyone who has the keys to a marketing budget will quite rightly want this ‘influence’ converted into some kind of ROI which makes the following stat from the research pretty interesting – 37% of those who view social networks as an entertainment source, spent more on entertainment this year than last year.

If any brands need further convincing of the importance of influence and engagement over reach, they should take a look at these results from the World Cup which sort of say it all.

Click here for more information on Edelman’s annual ‘Trust in Entertainment’ survey.

@AJGriffiths

Borges' Bookshelf

If you haven't read the short-story by Borges. Do. It's awesome.

A timely post perhaps – what with yesterday’s revelations about the war in Afghanistan.  Without doubt the Wikileaks story marks a significant milestone in terms of the use of data and transparency and there are interesting debates to be had around the role of information, state security and the freedom of the press that more intelligent people than I will have.

I wanted to post about the bulging shelves of Wikipedia (like a wing of the Library of Babel) because while dancing around the South Bank on Sunday – I was struck by how the ‘trusted’ source of info has become far more ingrained in culture than any encyclopaedia before it.

On Sunday I participated as an audience member in Domini Public – presented in the UK buy the Gate Theatre, but ‘staged’ outside the National.  (Incidentally I did this alone as a friend couldn’t get out of bed) The concept of the ‘play’ is aces.  The audience wear headphones, and are guided by an authoritative yet kindly voice, around the play space.  At first the instructions are simple enough: “stand to the right if you were born North of the river”.  As the play progresses, however, the audience is divided into three factions: police, prisoner, red cross.  What follows is a simple, yet insightful, comment on civil unrest, law and order and the role of the individual within society – all delivered with a wry smile.  Alas, Sunday was the last London performance – but should you ever come across the company behind this production do check it out.

So what does Wikipedia have to do with all this?  Well, during the warm-up section the entire cast/audience were grouped at one side of the space.  We then had to take four steps forward, if we could answer yes to the following questions:

  • Have you finished a book in the last week?
  • Have you seen a concert in the last seven days?
  • Have you been to the cinema this week?
  • Have you referenced Wikipedia this week?

Watching the  70 or so audience/cast members play out their answers to these questions, was really quite interesting.  I wouldn’t go as far as saying we displayed a microcosm of society (it was the Southbank on a Sunday, not a census), but what followed was quite startling.  I’d say the audience were aged between 16 and 60, though, so a good mix of ‘consumers’.  I’ve put in brackets below the rough percentage of who in the audience/cast moved.

  • Have you finished a book in the last week? (15 per cent)
  • Have you seen a concert in the last seven days? (10 per cent)
  • Have you been to the cinema this week? (20 per cent)
  • Have you referenced Wikipedia this week? (85 per cent)

So why did this interest me?  Well, it was a clear illustration that online media, entertainment and  information are all now a central part of our once analogue culture.  The immediacy, ease and (in many cases) low cost of social entertainment encourages us to use the internet as a more frequent source form of  entertainment and information than traditional mediums.  On Sunday, as a crowd of people, of mixed  backgrounds and ages, surged forward four steps – broadcasting their association with Wikipedia – it made a profound statement about the place of Wikipedia, and the internet, within the future of our society.  I’m not very good at maths but if 15 per cent of people are reading a book, but 85 are reading online – you can see how this is going to continue to snowball.

In case you’re interested, I took 12 steps…

@LukeMackay

In a scene reminiscent of the 90s cult classic ‘Lawnmower Man’, it is understood that Jonny Bentwood – pioneer of the celebrated Tweetlevel online popularity contest – has been consumed by his own Twitter algorithm; in the process becoming the first human being to become physically socially networked.twitter all

Concerns were first raised when semi-digital manifestations of Bentwood appeared on computer screens thoughout the company, during prolonged physical absence from his desk alongside the Edelman Technology team – concerns that were confirmed when he disappeared altogether and became wholly digital.

The first unexplained phenomenon was the appearance of a series of seemingly motivational – yet slightly unsettling – Twitter related posters (right).  These were followed by ghostly bangings from the server room, alongside muffled, digital screams about ‘influence’, ‘popularity’, ‘engagement’ and ‘trust’. White-noise and repeated yells of the word ‘retweet’ have also been heard in what is being dubbed a ‘polterzeitgeist haunting’ by experts.

It is now feared that Jonny has used his integration into the Twitter portal to access other online territories, with the Edelman server under attack, and traditional media lists being erased from client files to be replaced by an audio file of someone reading out complex and often nonsensical algorithms and laughing maniacally.

"At this stage we’re at a loss as to what to do," commented a spokesperson, "this is entirely without precedent and we’re unsure whether to eradicate the threat; monitor and analyse it; or whether this is in fact an innovative route to influencers not seen before, which we can exploit and use to bypass newly evolving platforms being used by early adopters. Harnessing Jonny’s new digital access and power has the potential to put us so far ahead of the curve we may well rebound back onto ourselves and BECOME the curve."

It is understood several Helpdesk tickets have been raised, and allocated to a member of the IT team to handle; although an unnamed IT member said that they were unsure as to how to tackle this threat other than "turning something off and on again and hoping for the best"

Zynga, the fast-growing maker of Facebook games like FarmVille and Mafia Wars, has been called by the New York Times “the hottest start-up to emerge from Silicon Valley since Twitter and, before that, Facebook.” This week, its CEO, Mark Pincus, is profiled in the story, the second in two weeks, highlighting the company’s recent success (though not without its fair share of controversy).  Among other things, the article profiles Pincus as a fearless entrepreneur and visionary aiming to build an online entertainment empire as important to the internet “as Google is to search.”

While Zynga will cite profits and player numbers as success criteria, it is another recent trend Zynga is pioneering that has caught my attention; advertising through social gaming. Zynga came under fire recently for allowing advertisements into its games. Some ads, for example, signed up players for subscriptions to costly text-messaging services. This caused a PR headache for the company with TechCrunch, the technology blog, calling the practice “ScamVille,” after some users filed a class-action lawsuit.

But with 211 million players every month, according to AppData.com, Zynga is perhaps well on its way to making social gaming as important to the internet as anything else thanks to a new partnership with an American food manufacturer, (also covered in the New York Times recently).  Cascadian Farm, an organic farm in the U.S. and subsidiary of General Mills, is using one of Zynga’s more popular games, Farmville, to reach a growing customer segment through advertising. Instead of your bog standard click-through ads a la GoogleAd Words however, the Cascadian Farms content will be integrated into the gaming experience.  

In Farmville, you participate, create, build and manage your own farm. You gain experience points by visiting your friends’ farms and lending a virtual hand. From next week, players in the U.S. will be able to purchase (using farm bucks) and plant, an organic blueberry crop from Cascadian Farm.  In doing so, FarmVille users will learn about organic farming and green living through standard game play, and at the same time, earn additional points to grow fruits and vegetables or raise animals on their virtual farms. Cascadian Farm executives said in a New York Times article that they hope that the company can expand its food niche and make itself better known by increasing awareness among FarmVille’s audience – that’s 221 million players a month. Users will also be able to access a $1 off coupon.

It will be curious to see just how successful Cascadian Farm is on Farmville. Will the strategy work to attract and educate potential customers through participation and content or will it back fire just like the imbedded ads? While integration in game play gives the user unique exposure to content in an experiential manner, will users see through the stunt and reject it as advertising or is this campaign just clever enough to work?   
 

@jacqui_cooper

imageWho are the analysts who use Twitter the most effectively?

In my regular call for analysts to be more competitive with each other in this mock top trumps scenario, it easy to forget that I don’t care who is number 1. What is crucial is:

who are the analysts that important for my topic area?

If semi-conductors or telecoms is your game then talking with Jeremiah Owyang will be interesting but not a good use of time. The way I would use this list is to focus on those analysts that are important by category. Nevertheless kudos must be given to those analysts who have excelled – they have understand that business models are changing, conversations in the online space are moving at a pace and enabling connections that were previously impossible. These analysts understand that if they are not part of the conversation, then the analysts that are influencing (and probably gaining the revenue) are their competitors.

Using SageCircle’s excellent database combined with a good number of my own I have used TweetLevel to understand who are the most important analysts on Twitter.

The majority of the top 500 names are included on this Twitter list.

The tool I have used to compile this league table (also now being used by MTV in their apprentice style reality show) compiles twitter data from over 30 sources and feeds the data through an algorithm to rank an individual according to four weightings:

  1. Popularity (i.e. How many people follow you)
  2. Influence (i.e. What you say is interesting, relevant and many people listen)
  3. Engaged (i.e. You actively participate within your community)
  4. Trusted (i.e. People believe what you say)

Of course, the explanation above is a simplified definition of a complex algorithm(the full methodology of this is shown at the bottom of this post).

 

imageRanking Criteria The primary ranking metric is influence. However, it is interesting to see that when we analyse the same 1,000names by influence we get a completely different top 10 list.

Findings Once again a huge hat tip and pat on the back to Jeremiah Owyang who leads the way by a country mile. Everyone should take note how he uses Twitter to engage with his community and provide real value. For the first time I have now seen a larger number of brands in the top five than individuals.

eMarketer, Forrester, EConsultancy and Altimeter Group all fair extremely well. We should therefore recognise other people who punch well above their weight scoring exceptionally highly even though they are only in smaller firms.

Common complaints Whenever these lists are published, there are several points that always get raised which I will address now…

  1. This twitter account is not from an analyst. The argument as to whom is an analyst or a consultant is becoming largely moot. In my opinion if someone is independent and directly influences technology procurement then they are an analyst – I for one therefore see Vinnie Mirchandani as an analyst (as well as all the enterprise irregulars and Stowe Boyd who mailed me to tell me he is one too).  I know this will cause a huge amount of disagreement but as an outsider looking in this is the way I see the market. This is not to say that some analysts have different strengths over others, it is more a case that I think as an AR pro, I need to monitor the lot of you.
  2. The twitter handle is written by multiple authors. Some twitter accounts have several analysts writing them whereas others do not. The merits of a single twitter account author is something that I personally favour as this allows me to understand the tone of author without having to understand the many personalities that are associated with it. Regardless, for this table, my view has not been to argue this but merely to present the data.
  3. It is irrelevant showing all the tweeters as I am only interested in a specific topic– bingo, that is exactly right. My suggestion to all AR pros is to identify which of your analysts are on this and only look at those. If you would like to understand who is important for a particular micro-topic area, please let me know.
  4. Hey – you have forgotten to include this list. Please let me know the name and if I will include it as an edit.

Top Analyst Tweeters (ranked by Influence on TweetLevel)
These names are included on this Twitter List

    Account Influence Popularity Engagement Trust
1 jowyang 76.6 72.8 54.8 67.6
2 eMarketer 74.6 67.9 44.4 69
3 Econsultancy 69 65 39.7 61.1
4 forrester 68.5 69.2 48 57.5
5 Gartenberg 66.1 58.4 63.8 54
6 augieray 65.6 56.1 59.3 52.5
7 rwang0 65.3 58 53.2 52.5
8 bfeld 64.6 61.8 51.1 53.6
9 monkchips 63.2 58.2 62.8 47.8
10 stoweboyd 61.1 61.2 56.1 46
11 charleneli 60.6 68.8 55.3 42.2
12 TomRaftery 60.4 56.4 60.8 43.2
13 VanessaAlvarez1 60.4 50.5 60.4 46.7
14 ekolsky 60.3 49.9 71.3 44.6
15 Gartner_inc 60.1 66.3 39.5 46.2
16 hharteveldt 59.9 52.9 56.2 46.7
17 DT 59.7 46.7 60.4 49.7
18 dhinchcliffe 59.5 59.4 46.5 46.3
19 CurtMonash 58.6 50 62.4 42.7
20 antallan 58.4 43.9 78.3 38.7
21 mgualtieri 58.2 53 46.5 45.8
22 bobegan 58.1 46.3 67.2 41.8
23 ITSinsider 57.9 55.7 59.7 40.4
24 pgreenbe 57.9 52.7 45.5 42.7
25 storageio 57.7 47.2 70.1 40.7
26 Ross 57.5 61.2 40.1 41.5
27 mfauscette 57 55.9 10.6 43.7
28 richi 56.9 53.2 56.4 40.8
29 Enderle 56.8 57.7 52.1 40.8
30 debs 56.3 56.4 58.1 35.9
31 SethGrimes 56 48.7 62 40.8
32 bmichelson 55.8 47.8 57 39.7
33 drnatalie 55.8 57.6 50.6 35.2
34 maggiefox 55.4 58 47.5 35.8
35 rmogull 55.2 49.2 50.9 41.7
36 jbernoff 55.1 59.8 50.2 36.1
37 stiennon 55.1 60.9 50.7 35.1
38 cote 55 52.8 48.6 34.5
39 wood83 54.8 48.1 56.6 39.7
40 dealarchitect 54.8 45.9 63.7 36.9
41 jesus_hoyos 54.8 55.2 46.4 35.8
42 sig 54.6 39.3 60.4 48.1
43 rossrubin 54.6 48.3 65.3 36.5
44 JEBworks 54.4 48 53 38.7
45 cselland 54.3 47.9 48.4 38.8
46 skemsley 54 48 45.7 40.4
47 jeffnolan 53.9 48.9 44.6 39.2
48 mkrigsman 53.9 56.2 46.9 33.1
49 jyarmis 53.6 50.4 53.8 34.6
50 steven_noble 53.5 47.7 57.4 34.1
51 PhoCusWright 53.5 52.1 35.7 39.3
52 jameskobielus 53.2 48.5 39.2 35.6
53 sogrady 53.1 48.8 60.5 32.1
54 InFullBloomUS 53.1 48.3 46.1 39.1
55 gyehuda 52.9 50.3 46.3 37.2
56 blairplez 52.9 44.4 57.5 37.9
57 ZoliErdos 52.8 45.4 63.1 30.9
58 ccmehil 52.8 46.7 64.7 29.6
59 lehawes 52.7 46 53.5 36.6
60 AndreaDiMaio 52.4 46.3 49.8 43.5
61 jevon 52 48.6 60.4 29.5
62 NeilRaden 51.6 46.4 67.7 27.9
63 jberkowitz 51.4 50 37.6 36.1
64 Dana_Gardner 51.4 49.5 7 41.1
65 merv 51.2 40 54.3 35.3
66 dbmoore 51.1 46.3 39.6 38.1
67 dvellante 50.9 45.5 49.9 28.7
68 dankeldsen 50.8 53.4 53.7 26.7
69 bkwalker 50.6 48 37.6 34.2
70 cswolf 50.6 46 53.9 34.5
71 ggheorghiu 50.4 44.1 46.1 31.9
72 Claudia_Imhoff 50.4 47.8 49.8 28.8
73 johnrrymer 50.4 47 44.4 33.4
74 graemethickins 50.3 51.5 40.2 30.4
75 AndiMann 50.3 47.5 55.1 30.4
76 slmader 50.2 44.9 45.7 35.3
77 abnerg 50.1 45.1 52 33.5
78 juliebhunt 49.9 39.1 44.2 34.6
79 MaribelLopez 49.8 45 49.2 32.5
80 Jake_Hird 49.8 44.5 35.9 37
81 tomcummings 49.5 47.8 56 26.7
82 Mark_Mulligan 49.4 50.4 49.1 25.9
83 nenshad 49.4 47.4 47.3 28.1
84 SteffWatson 49.3 39.9 55.9 26.9
85 john_chilmark 49.3 48.5 43 29.8
86 passion4process 49 44.1 50.4 35
87 tebbo 48.9 42.1 54.1 30.7
88 idarose 48.9 47.5 52.6 26.3
89 stephenodonnell 48.9 60 50.3 26.9
90 stephenmann 48.8 43.5 55.3 27.3
91 vendorprisey 48.7 47.4 49.6 26.4
92 Wikibon 48.6 50.6 45.6 28.3
93 jonno 48.4 44.2 53 24.1
94 markmadsen 48.4 44.7 54.2 27.7
95 denschaal 48.4 40.4 45.5 33.8
96 ghaff 48.3 42.3 52.8 30
97 samirb 48.1 47.5 55 25.9
98 Hitwise_UK 48 58.1 44.1 26.7
99 stevemann 48 46.7 53.7 25.7
100 randygiusto 47.9 44 55 26.9
101 clivel_98 47.9 41.9 53.8 29.4
102 JimLundy 47.8 46.7 49.4 24.4
103 bitterer 47.6 46.4 44 27.4
104 nate_elliott 47.4 49.7 45.1 26.3
105 Hitwise_US 47.3 59.1 36.4 28.9
106 timbo2002 47.2 43.1 47.5 27.8
107 billtrippe 47.1 46.5 48.6 25.4
108 znh 47.1 46.6 44.5 25.6
109 janovum 47.1 39.2 55.3 27.7
110 KarenYetter 46.8 60.2 11 25.5
111 andreascon 46.6 45.9 39.6 36.4
112 kasthomas 46.6 60.1 32.9 28.8
113 Dale_Vile 46.5 42.9 53.8 23.3
114 atmanes 46.5 44.2 34.7 30.7
115 krestivo 46.5 44.9 49.3 24.8
116 jhammond 46.4 44 44.2 25.4
117 nyuhanna 46.4 52.1 9.9 30.6
118 megheuer 46.4 48.6 45.3 23.9
119 caostheory 46.4 43.3 0 39.4
120 fiberguy 46.2 41 51.1 24.6
121 rreitsma 46.2 42.9 38.5 35.7
122 stylishidea 46.2 46.9 41.3 24.9
123 rshevlin 46.1 44.5 66.4 23.3
124 TonyBaer 46.1 41.2 48 27.6
125 fscavo 46.1 45 49.9 25.5
126 carterlusher 46 40.8 43.9 28.5
127 iglazer 46 42.1 45.1 24.5
128 johnlovett 46 47.8 52.8 30.3
129 jblock 45.9 50.8 40.7 28.2
130 jclarey 45.9 52.7 45 24.4
131 stevedupe 45.7 46.4 40.3 28.6
132 gilliatt 45.6 48.8 50.6 23.6
133 christineptran 45.6 42.8 44.5 24.7
134 Staten7 45.3 44.2 35.8 29.9
135 adamhleach 45.3 38.6 52.5 24.8
136 802dotchris 45.2 42.4 48.3 24.7
137 visionmobile 45.1 46.2 42.5 32.8
138 neilwd 45.1 41.5 38.2 25.7
139 arj 45 44.7 42.4 29.3
140 YankeeGroup 45 48.2 28.3 33.1
141 btemkin 44.9 46.8 31 37.1
142 vtri 44.9 43.8 44.7 22.5
143 vgtero 44.9 42.7 38.7 23.8
144 adriaanbloem 44.7 39.5 52.2 26.8
145 Horses4Sources 44.6 41.8 36.3 37.5
146 ianfogg42 44.5 42.5 36.8 23.9
147 maslett 44.5 41 43.5 24.3
148 cmswatch 44.4 52.3 33.3 28.5
149 jonathaneunice 44.2 46.2 56.2 22.5
150 JeffreyBreen 44.2 43.3 44.5 23.3
151 FreeformCentral 44.1 41.1 39.1 33.6
152 AlanWebber 44.1 46.7 39.1 24.5
153 rgruia 43.9 46.6 42.8 22.3
154 nselby 43.7 45 50.2 22.1
155 cwood 43.6 38 45.6 21.5
156 paulhamerman 43.6 43.1 30 28.6
157 KeithHumphreys 43.6 41.5 42 28.9
158 cmooreforrester 43.5 46.1 36 25.3
159 IDC 43.4 56.5 5.5 32.3
160 jcorsello 43.4 46.4 35.2 22.2
161 Zak_Kirchner 43.4 38.6 47.8 21.1
162 jamet123 43.3 44.7 34.1 22.3
163 hyounpark_AG 43.3 41.6 46.1 23.2
164 RayLucchesi 43.2 42.5 55.2 27.7
165 robinbloor 43.2 52.5 36 22.7
166 apoorv 43.1 40.2 49.3 22.9
167 jgptec 42.9 38 33.7 23.4
168 storageswiss 42.8 44.1 37.3 27.2
169 darranclem 42.8 39.5 41.5 23.6
170 McGeeSmith 42.8 38.3 38.8 30.5
171 seuj 42.7 32 64.3 20.1
172 pjtec 42.4 39.9 37.2 23.5
173 bradholtz 42.3 40.9 37.6 21.7
174 pnjarich 42.3 36 44.7 23.8
175 bevelson 42.3 43.2 38 24.1
176 biz_mobility 42.3 34.1 30 27.7
177 Neovise 42.3 38.6 38.6 26.9
178 NigelFenwick 42.3 46.4 44.8 21.6
179 jalvear 42.2 39.3 49.9 20.3
180 SarahBurnett 42.2 45.2 45.7 21.6
181 Teresacottam 42.1 38.1 47.9 29.6
182 jeffmann 42.1 45.4 37.3 21.4
183 mikeferguson1 42.1 39.4 38.4 25.8
184 pfersht 42 44.4 39 25.9
185 PhilippBohn 42 34 46.2 29.3
186 OvumICT 42 41.6 39.8 27.7
187 SimonBramfitt 41.9 37.5 46.4 24.2
188 rogermud 41.9 42.5 9.5 22
189 dnm54 41.9 37.6 38.7 23.3
190 GigaOMPro 41.8 45.7 32 24.6
191 gilbane 41.8 48.7 36.9 23.8
192 Thomas_Husson 41.7 44.8 37.2 26.6
193 jhurwitz 41.7 50 36.3 22.4
194 lcecere 41.7 44.3 36.2 21.8
195 IDCInsights 41.7 50 30.6 23.8
196 bsdunlap 41.6 38 36.5 26.4
197 StefanRied 41.5 43.7 34 23.3
198 michaelwolf 41.5 41 41.3 24
199 omriduek 41.5 38.8 50.1 21.7
200 the451group 41.5 50.1 23.5 28.2
201 bfinucane 41.4 46.6 51.7 21.4
202 s_crawford 41.4 43 48.4 21
203 iSuppli 41.3 46.1 5.5 25
204 rbkarel 41.3 41.7 41.8 24.7
205 EricaDriver 41.2 44.3 44.3 21.1
206 sjschuchartCA 41.2 35.7 57.1 22.3
207 keith_rng 41 40.7 44.6 23
208 cuttertweets 41 44.9 34.8 20.9
209 gwoodill 41 49.6 43.4 23.6
210 jesstsai 41 39.3 36 22.8
211 gleganza 41 42.5 44.2 21.1
212 Montejam 40.9 40.7 45.4 20.7
213 TonyByrne 40.9 47.2 38.7 21.9
214 siriusdecisions 40.9 46.5 38.9 22
215 joestanhope 40.7 40.2 42.1 23.6
216 joeltweet 40.6 53.7 43.4 22.1
217 jimholincheck 40.6 44.3 31.8 22.2
218 gilbanesf 40.5 46.5 39 22.7
219 ajbowles 40.2 38.6 40.9 23.5
220 cmendler 40.2 43.8 47.4 20.5
221 BurtonGroupIT 40.1 46.5 31.6 21.6
222 mosterman 39.9 41.3 31.6 21.2
223 lisarowan 39.9 43.3 33 21.9
224 pragkirk 39.8 43 42.5 20.8
225 etravelproject 39.8 44.7 34.3 20.2
226 SeanCor 39.7 48.1 46.9 22.1
227 glennodonnell 39.7 42.9 44.1 20.9
228 NickLippis 39.7 42.8 27.6 23.2
229 timehhh 39.7 41.3 40.5 25.2
230 nielr1 39.7 39.4 43.9 20.7
231 shigginski 39.6 35.4 43.3 22.3
232 pund_it 39.6 36.8 38.8 22.7
233 theresaregli 39.5 42.4 40.5 22.3
234 Jossgillet 39.4 33.5 45.1 19
235 allenweiner 39.4 42.1 31.7 19.1
236 davidcard 39.4 44.6 39 21
237 btudor 39.3 39.3 48.9 19.6
238 s_v_g 39.3 38.6 47.6 19
239 ventanaresearch 39.3 43.8 30.7 20.3
240 bojan_simic 39.3 37.3 38.1 28.6
241 BobWarfield 39.2 42.4 41.1 20.3
242 CurrentAnalysis 39.1 44.4 7.3 21.6
243 bryanyeager 39.1 36.7 34.2 21.7
244 Mark_Goldberg 39 38.6 35.7 21.5
245 mark_koenig 39 39.5 44.8 19
246 pattyinboothbay 38.9 44.7 34.2 19.7
247 martinatherton 38.9 37.9 44.1 19.2
248 gilbaneboston 38.9 48.5 40.7 20.8
249 jpmorgenthal 38.9 42.8 41.2 20.3
250 douglas_quinby 38.8 44.1 41.2 20.2
251 sureshvittal 38.8 45.9 42.9 20.9
252 lauraramos 38.8 51.7 33.3 22.6
253 iangjacobs 38.7 40.8 35.9 23.1
254 weckerson 38.7 42.4 36.3 21
255 Celent_Research 38.7 43.2 31.4 25.2
256 erainge 38.5 42.2 36.1 22.7
257 Josh_Bersin 38.4 52.3 29.8 27.9
258 ARC_Advisory 38.4 44.5 28.3 19.7
259 stessacohen 38.3 41.2 52.1 19.6
260 jjegher 38.3 40.5 43 22.9
261 ripcitylyman 38.3 37.8 29.2 23.4
262 rschmelzer 38.3 39.8 34.8 19.8
263 ravenzachary 38.3 42.9 32.3 19.6
264 Bersin 38.1 50.9 28.6 22.7
265 rayval 38.1 46.7 35.8 20.3
266 MattRosoff 38 43 30.4 22.2
267 thinkovation 38 39.7 47.7 19.3
268 punirajah 38 35.5 37.4 18.7
269 GartnerTedF 37.9 37.6 33.6 28.9
270 ironick 37.9 38.5 46.1 19.1
271 JeffPR 37.8 43.9 32.8 19.3
272 lauriemccabe 37.8 40.7 38 19.2
273 sliewehr 37.7 38.1 49.8 19.2
274 mbrandenburg 37.7 38.5 50.4 19.2
275 TomGrantForr 37.6 42.7 30 19.5
276 BarryRabkin 37.6 41.1 44.3 19.1
277 TKspeaks 37.4 52.9 34.3 22.6
278 dxmitche 37.4 42.4 32.9 19.2
279 LinusGreg 37.3 44.9 32.8 19.6
280 philww 37.3 44 34.1 22.4
281 smulpuru 37.3 45.8 25.8 21.6
282 Heuristocrat 37.2 40.1 27.8 18.2
283 bmirabito 37.2 47.9 33.6 20.1
284 paulfroberts 37.2 42.1 34.4 19.2
285 heidishey 37.2 37 44.2 19.2
286 alexkwiatkowski 37.1 37.1 40.9 20.4
287 fjeronimo 37.1 38.9 27.9 23.7
288 imlazar 37.1 40.9 42.2 18.7
289 chinamartens451 37.1 34.8 27.4 19.5
290 Gabeuk 37 40 39.6 18.9
291 srepps 36.9 40.3 36 30.7
292 ChristianKane 36.9 36.3 40.9 19
293 EliseOlding 36.9 38 35.7 21
294 Lciarlone 36.8 41 38.4 18.5
295 aschmitt 36.8 36.6 33.6 18.5
296 msbrumfield 36.8 39.1 34.2 20
297 ca_bshimmin 36.7 41.2 31.1 18.8
298 GaryatWikibon 36.7 39.3 38.9 18.4
299 jeunice 36.7 38.7 35.5 18.5
300 VLI 36.6 33.6 40.4 20.9
301 oliviepaul 36.5 33 41.2 16
302 rwhiteley0 36.5 40.5 37.8 20.9
303 smwat 36.5 34.4 38 21.3
304 waband 36.4 43.4 36.7 23.5
305 brian_riggs 36.3 40.7 38.1 18.9
306 MobileAberdeen 36.3 40.1 36.5 19.1
307 bobblakley 36.2 36.2 48.9 20.3
308 alyswoodward 36.2 37.9 48.2 18.4
309 spendmatters 36.2 38.5 30.1 19.3
310 D_Hong 36.1 40.2 34.1 18.8
311 DortchOnIT 36 38.9 33.9 19.2
312 NewsShark 36 43.9 8.4 20.3
313 nealhannon 35.9 39.5 35.3 18.1
314 erichknipp 35.9 38.4 38.8 18.4
315 Hitwise_AP 35.8 50.1 27.8 20.2
316 Gartnergreg 35.8 40.5 35.4 18.8
317 adamjura 35.7 36.3 43.6 18.3
318 cdhowe 35.7 34.4 29.9 19.7
319 jaysonsaba 35.7 38.6 32.2 21.4
320 ema_research 35.6 43.4 7.3 22.1
321 MarkRaskino 35.4 40.6 30.6 18.9
322 dschatsky 35.3 34.8 32.8 17.6
323 minicooper 35.3 44.5 37.9 19.5
324 NormTravelTech 35.3 42.9 29 18.7
325 toppundit 35.3 40.3 29.1 18.7
326 dkusnetzky 35.3 40.5 40 18.7
327 katehanaghan 35.3 37.6 47.6 17.8
328 scottn7 35.2 37.7 30.8 17.6
329 tjkeitt 35.2 38.5 34.6 18.8
330 mlees 35.2 38.8 38.2 17.7
331 ForresterJobs 35.2 49 33.9 21.7
332 Frost_Sullivan 35.1 43.1 5.5 19
333 ABI_4G 35.1 38.1 5.9 21
334 esganalysttmac 35.1 41.7 44.2 18.7
335 Shawn_McCarthy 35.1 39.5 35.7 18
336 Nemertes 35 39.2 8.4 19.3
337 dfrankland 35 43.6 37.3 19.2
338 LockTD 35 37.8 32.9 17.8
339 lauradidio 35 36.6 24.5 18.3
340 jim_techclarity 34.9 39.2 35.8 18.3
341 gcolony 34.9 56.7 27 22.7
342 nora_freedman 34.9 39.8 46.5 18.1
343 ekmurphy 34.8 34.3 42.8 16.6
344 Rick345 34.7 38.1 31.7 18.2
345 jonathanbrowne 34.7 41.8 35.5 18.2
346 jblin 34.7 36.8 26.2 17
347 tim_walters 34.6 39.1 32.3 24.4
348 CXPOeilExpert 34.6 37.2 7 17.4
349 Burgess_Gary 34.6 34.9 37.7 18.8
350 mlevitt 34.6 40 25.6 17.5
351 jmcquivey 34.5 45 38.1 20.1
352 nengelbert 34.5 40.2 30.8 17.8
353 jensbutler 34.4 35.4 33.5 17.7
354 DenisPombriant 34.4 43.2 29 19
355 infotrends 34.3 37.4 30.8 17.6
356 darrenbibby 34.3 38.5 26.3 19.2
357 AgingTech 34.3 44 26.1 20.9
358 mbkmbk 34.2 38.7 27.5 19.4
359 katiesmillie 34.2 37.6 33 17.2
360 kmcpartland 33.9 35.7 30.8 17
361 dmavrakis 33.9 36.6 38.7 17.1
362 billnagel 33.9 33.5 35.3 16.9
363 Chris_Gaun 33.9 43.5 27.5 19.7
364 REdwards 33.9 37.9 24.9 19.1
365 ferrusi 33.8 36.9 35.1 17.3
366 rob_bamforth 33.8 35.6 35 17.1
367 edwardsjonat 33.7 35.5 40.8 17.6
368 EveGr 33.7 35.1 28.3 18.5
369 epopova 33.6 35.8 30 18.4
370 benwoony 33.5 36.2 25.6 27.3
371 jtsd 33.5 30.3 30.9 17.2
372 cee_m_bee 33.5 33.6 35 15.8
373 analystnick 33.5 40.3 32.7 17.8
374 infonetics 33.5 44.9 25.4 22.2
375 NPDFrazier 33.4 42 36.9 19.3
376 royillsley 33.4 39.1 29.4 17.3
377 hkisker 33.4 38.7 25.7 17.5
378 prashanthrai 33.3 33.8 29.2 16.2
379 suds 33.3 36.8 33.5 16.5
380 nickpatience 33.3 38.9 33.5 17.7
381 Telesperience 33.3 36.1 27.7 20.5
382 IDCBrasil 33.2 41.3 2.9 23.3
383 chris_townsend_ 33.2 40.4 44 18.3
384 azornes 33.2 38.5 4.8 17.6
385 stor2 33.2 39 30.5 17.1
386 CRozwell 33.2 42.6 29.5 18
387 InterpretLLC 33.2 30.2 31 23.5
388 howett 33.2 31.6 46 16.4
389 TheNakedChief 33.2 34.5 33.9 18.3
390 nigelwallis 33.1 33.3 34.3 16
391 logisticsviewpt 33.1 40.9 32.2 20
392 brucemr 33 33.5 29.6 16.3
393 TECtweets 33 37.6 26.5 16.9
394 demartek 33 40.3 31.8 17.6
395 nickster2407 33 37.5 34.5 17.2
396 rnantel 32.9 38.3 7 17
397 passingnotes 32.8 37 36.7 16.5
398 peter_w_ryan 32.8 30.2 40.2 15.7
399 KSterneckert 32.7 39.8 32.6 17
400 jcapachin 32.7 38.3 30.8 17.2
401 gregbuzek 32.6 33.4 27.9 17.5
402 carldoty 32.6 38.1 30 17.1
403 AMR_Research 32.5 51.2 26 19.5
404 TGView 32.4 36.7 27.2 16.9
405 acfrank 32.4 37.9 27.8 16.7
406 Senformation 32.4 37.4 36.2 16.8
407 jarrodgingras 32.3 41.8 35.6 17.5
408 fgilbane 32.3 39.9 35.6 17.4
409 EquaTerra 32.2 40.9 6.6 20.4
410 euroLAN 32.2 37.2 31.5 16.3
411 TedSchadler 32.1 42.5 26.4 19.8
412 carriejohnson 32.1 37.4 36.7 17.7
413 Payment_Expert 32.1 46.4 27.6 18.2
414 joltsik 32 40.2 25.7 20.4
415 PHassey 31.9 34.4 31.1 16.1
416 e2theb 31.8 29.3 35.5 16.3
417 infotrends_mps 31.8 37.3 33 16.5
418 kreidy 31.7 40.2 41.4 17.6
419 mtauschek 31.7 33.3 36.1 16.1
420 heyshiv 31.6 33 48.8 16.2
421 securityjeff 31.6 35.6 35.7 16.3
422 jboye 31.6 39.6 29.6 17.9
423 Craw 31.5 40.3 30.1 19.4
424 ePubDr 31.5 33.7 27.1 16.1
425 EverestGroup 31.5 39.3 24.5 16.9
426 zkerravala 31.5 38.6 23.1 16.9
427 pmcginnis 31.3 38.2 8.4 15.6
428 SA_Update 31.2 40.3 3.3 17.5
429 PJHines 31.2 39.6 27.4 16.8
430 Aberdeen_CMTG 31.2 46.6 27.2 17.9
431 PikeResearch 31.2 40.2 8.4 20
432 diteb 31.2 38.5 37.6 16.4
433 lauwhitehouse 31.2 39.2 31.4 20.2
434 npdgroup 31.2 49.4 4.4 19.5
435 cloudpundit 31 37.6 28.1 18.3
436 strothkamp 31 36.8 27.2 21.7
437 Andrew_Boyd 30.9 42.2 34.1 16.9
438 AndyBrownSA 30.8 30.5 38.2 15.1
439 kevinnolanuk 30.8 38.7 28 17.9
440 mizkonary 30.8 35.6 30.8 16
441 100Gigabit 30.8 41.5 26.9 16.7
442 mobilesimmo 30.8 35.5 26.8 16.1
443 markhrobinson 30.7 38.5 24.5 16.1
444 nsturgill 30.6 33.1 23.6 20.1
445 mgrey 30.5 36.5 21.5 16.2
446 DaveMario 30.4 37.5 34.6 16.7
447 twehmeier 30.4 31.2 49.4 15.3
448 hokun 30.4 36.1 37.5 16.3
449 wif 30.4 35.4 28.7 20.5
450 CompassIntel 30.4 38.1 24.4 16.1
451 ABINextGen 30.3 30.7 30.6 15
452 stavvmc 30.3 31.8 36.7 15.2
453 idccanada 30.3 43.3 8.4 16.9
454 tamarabarber 30.2 38.5 36 17
455 juniperresearch 30.1 38.3 25.2 16.9
456 matteastwood 30 37.3 23.2 16.5
457 melanieturek 29.9 36.4 33.6 16.4
458 dlevitas 29.9 36.7 28.7 15.9
459 benwood 29.9 42.4 25.2 17.3
460 Eval_Source 29.8 35.9 29.4 15.5
461 drjimmys 29.7 33.1 7.7 14.3
462 instantBP 29.6 35.1 26.6 15.3
463 DougWilliamsMHD 29.6 31.7 33.7 15.9
464 markpmcdonald 29.6 38.1 4.8 16.3
465 jmp_katz 29.4 38.2 35.1 16.4
466 jctec 29.3 32.1 29.8 14.6
467 bseitz 29.3 28.7 39 14.4
468 JoshSMartin 29.3 27 29.8 14
469 jfrey80 29.2 38.8 25.5 16.5
470 dconnor 29.2 38.5 32.9 16.1
471 markbowker 29.1 39.1 24.7 16.3
472 Nathan_Safran 29.1 33.2 35.8 15
473 RachelDines 29.1 39.3 33.4 16.4
474 thickernell 29 34.9 24.3 15.5
475 MattyHatton 29 37.2 35.5 15.9
476 paulbudde 29 34.7 20.8 15.6
477 algillen 28.9 36.1 29.5 17.7
478 fgens 28.9 41.5 26.5 16.9
479 vvittore 28.8 28.9 37 13.3
480 TechMarketView 28.8 34.4 5.9 14.8
481 zmcgeary 28.8 38.7 46.6 15.7
482 FillMurphy 28.7 34 22.3 15.1
483 Madtarquin 28.7 37.6 37.2 15.6
484 andreasantonop 28.7 37.5 39.5 15.6
485 jamystewart 28.7 39.9 27.2 16.3
486 Canalys 28.5 38 5.1 15.7
487 mgilpin 28.5 38 31.1 16.4
488 lisabradner 28.5 41.3 27.7 16.5
489 michaelgreene 28.5 37.9 28.8 16.3
490 ABIresearch 28.4 39.2 2.9 19.9
491 jgownder 28.4 34.4 28.2 17
492 msmvnj 28.4 36.3 27.3 15.7
493 dtwing 28.3 34.3 30.7 15.1
494 JulieAsk 28.3 40.2 16.3 16.6
495 mbarbagallo 28.3 36.4 27.8 16.9
496 KeithKTsang 28.2 34.6 34.6 14.9
497 Lluis_Altes 28.2 32.1 5.9 14.2
498 ChrisOber 28.2 34.4 27.8 15.1
499 treerao 28.2 31.3 42.5 15.3
500 nikkibaird 28.1 36.1 23.4 15.6
501 mHingley 28.1 36.7 6.6 14.8
502 PaulBrown_SA 28.1 29.6 30.7 12.9
503 IDC_EMEA 28.1 31.2 54 15
504 brightfly 28 36.5 5.5 14.9
505 annenielsen 28 35.8 26.5 14.7
506 mgerush 28 37.5 28.2 15.7
507 reastman 27.9 31.3 6.6 13
508 DarylPlummer 27.9 41.4 32.4 17.3
509 adriandrury 27.9 31.7 26.4 14.4
510 G_ant 27.9 33.8 35.2 15.1
511 RogerKay 27.9 38.3 26.1 16
512 elizabethstark 27.9 35.2 30.6 15.3
513 richwatson 27.9 38.6 29.7 16
514 dougwashburn 27.9 38.5 31.5 16.2
515 rstormonth 27.8 32.6 24.9 14.4
516 lightplay 27.8 31.4 33.3 14.7
517 debswee 27.8 32.4 31.4 14.7
518 MKRasmussen 27.8 40.9 5.1 16.3
519 chrischute 27.8 32.3 26.6 14.6
520 PeteCunningham 27.8 36.7 36.6 15.8
521 ghalimi 27.8 36.3 0 15.9
522 anshublog 27.7 32.7 33.2 14.6
523 stephatkins 27.7 31.7 26.5 13.9
524 Frost_Events 27.7 36.4 24.3 14.6
525 leedoyleidc 27.7 37.6 18.3 15.5
526 galvar60 27.6 36 4.8 15.1
527 dreeves1 27.6 31.6 29.2 14.8
528 rkoplowitz 27.6 39.6 27.4 16.4
529 naveenhegde1 27.5 30.2 22.6 13.7
530 gcreese 27.5 37.6 19.3 16
531 TheTowerGroup 27.5 36.3 22.8 15.8
532 LFrankKenney 27.5 34.1 34.5 15
533 wjmalik 27.4 32.8 35.4 14.7
534 susankevorkian 27.4 35.1 22.7 14.4
535 instat 27.4 40.5 5.1 16.2
536 DortchAtFocus 27.3 28.3 31.9 15.2
537 CushingAnderson 27.3 37.7 32.5 15.4
538 billtancer 27.3 38.9 36.5 16.2
539 rwildeman 27.2 36.9 21.8 15.4
540 smcleish 27.2 37.5 27.4 15.7
541 kimknickle 27.2 32.7 26.6 14.4
542 jonathansteel 27.2 37.2 28.7 15.3
543 zapthink 27.2 32.2 17.7 15.1
544 RichBohn 27.2 40.6 9.2 14.9
545 IDCLatin 27.2 36.9 5.5 15.2
546 diane_clarkson 27.1 40.2 23.6 16.1
547 geoffblaber 27.1 37.8 32.7 15.7
548 dpotterton 27.1 34.1 5.1 14.4
549 NinaLytton 27 29.9 30.2 13.4
550 wfz 26.9 32.1 6.2 13.7
551 jamiemlewis 26.9 37.6 33.1 15.5
552 Richard_Fouts 26.9 35.9 24.2 15
553 JonCrotty 26.9 32.6 8.4 13.5
554 itstorage 26.8 37.5 32.2 15.4
555 pyramidresearch 26.8 39.6 5.9 15.7
556 jimlmurph 26.7 40 23.4 15.7
557 tonyiams 26.7 32.7 24.2 16.1
558 davidmsmith 26.6 38.6 27 15.7
559 HW_Support 26.5 33.1 25.8 14.7
560 DavidJWest 26.5 37.2 5.1 15.4
561 bgassman 26.5 39.8 27.8 16.1
562 domdupuis 26.5 28.1 26.8 13.4
563 ITAnalysis 26.5 29 23.5 13.3
564 gcoimbra 26.4 29.7 28.3 13.7
565 jonarber 26.4 27.3 38.5 13.5
566 whita 26.4 34.6 40.4 15
567 acschmidt 26.3 32 24.7 15.6
568 Informatm 26.3 35.6 19.6 15.4
569 SaaSpro 26.2 37.7 22.3 15.4
570 barryparr 26.2 38.7 27.1 15.5
571 rsilvalondon 26.1 30.6 2.6 13.9
572 davecapp 26.1 34.1 24.3 14.5
573 tombitt 26.1 40.5 33.2 16.4
574 wmcneill 26.1 36.5 22.9 14.6
575 versace57 26 28.8 30.4 13.3
576 MWDAdvisors 26 34.6 25 14.4
577 jenbelissent 26 37.5 26.4 15.3
578 wkernochan 26 28.6 50.1 13.9
579 JoeGalvin 26 36.6 24.1 14.9
580 tcorbo 26 35.7 24.7 14.6
581 srog 26 37.4 35.5 14.8
582 tarzey 25.9 34.9 41.7 14.5
583 jeffheynen 25.9 35.1 26.9 14.6
584 cborovick 25.9 36.6 23.4 14.7
585 poneillforr 25.9 30.6 22.4 13.9
586 P_Wannemacher 25.9 30.3 28.8 13.5
587 emilynaglegreen 25.9 37.1 25.8 15.7
588 jbozman 25.9 34.3 5.5 14.2
589 tracresearch 25.9 33.1 28.7 14
590 paula_rosenblum 25.8 33.4 41.7 14.6
591 AiteGroup 25.8 35.8 27.7 15
592 prussom 25.7 35.6 22.4 15.5
593 bpmfocus 25.7 38.4 23.8 15.6
594 sebastiancoss 25.6 33.5 21.4 17.2
595 IDEASCP 25.6 26.3 16.7 14.7
596 CCSInsight 25.6 38.7 23 15.3
597 josiesephton 25.6 32.4 26.9 14
598 vernonxt 25.5 35.8 18.8 14.8
599 mdecastro 25.4 34.7 25.4 14.2
600 jeffkagan 25.4 41.2 26.6 14.7
601 LizBoehm 25.4 32.3 25.8 14.3
602 aaronmcpherson 25.4 34.4 26.9 14.3
603 danbarlev 25.3 29.1 27.5 12.7
604 TheEbizWizard 25.3 33.9 28.5 14.1
605 gphifer 25.3 38.7 4.8 14.9
606 daniel_krauss 25.2 30.8 22.1 13.2
607 lcannell 25.2 32.7 27.9 13.8
608 christinaylee 25.2 31 35.5 13.6
609 SW_Support 25.2 32.3 25.7 13.8
610 hwdewing 25.2 30.5 23.1 15.3
611 pete_lacey 25.2 29 25.9 13
612 jamesbrehm 25.2 34.6 21.6 14.1
613 MWardley 25.1 33.3 23.9 14
614 SaaSEurope 25.1 34.3 24.6 14.5
615 alexcullen 25.1 38.4 20 15.6
616 tobybell 25.1 36.6 45.5 14.7
617 dianemyers 25.1 34.4 24.1 14.1
618 ABI_MobileNW 25 33 20.4 14.3
619 BLheureux 25 30.3 28.3 16.8
620 clayryder 25 25.5 18.4 12.4
621 miravperry 25 34.5 28.1 14
622 red_gillen 24.9 30.6 22.2 15
623 WiseGreyOwl 24.9 34.2 28.1 14.3
624 angie_p 24.9 33.1 30.8 13.9
625 IDCPortugal 24.9 27.8 20.2 12.8
626 shauncollins 24.9 35.7 22.3 14
627 Aberdeen_HCM 24.9 40 20.9 15.2
628 DouglasHayward 24.8 31.4 21.8 13.3
629 creativestrat 24.8 29.9 15.9 13.5
630 karenmassey 24.8 35 28.3 14.2
631 MaryJTurner 24.8 33.8 24.4 14.1
632 RJWitty4Gartner 24.8 34 22.3 14.2
633 tjennings 24.8 36.3 32.9 14.6
634 Bajarin 24.7 34.8 3.7 14.3
635 coverby 24.7 34.5 32.6 14.4
636 pallega 24.6 30.3 28.6 13.7
637 duncanwjones 24.6 28.5 27.7 13.4
638 adelesage 24.5 35.8 25.1 14.4
639 Tiffani_Bova 24.5 36.4 21.1 14.5
640 Whiterope 24.4 28.8 18.7 14.4
641 cchristiansen 24.3 35.6 5.9 13.8
642 rdkugelvr 24.3 31.4 22.6 13.1
643 skingstone 24.3 35 29.1 14.5
644 dfloyer 24.3 31.5 4 13.5
645 marksdriver 24.3 35.3 3.7 14
646 melindaballou 24.2 31 23.1 13.3
647 ltabb 24.2 36.9 15.9 14.9
648 emmaking_escp 24.2 34.4 31.7 13.8
649 simonrob451 24.2 31.4 27.3 13.6
650 CurlyGuyBry 24.2 30.6 33.1 13.6
651 DanVesset 24.2 33.3 22.7 13.9
652 rambadri 24.1 29 32.7 13
653 Staggmacey 24.1 34.2 21.4 14.2
654 tim_shepherd 24.1 37.1 16.3 14.4
655 fjhr 24.1 34.1 3.7 13.8
656 EinatShimoni 24.1 32.8 26.5 13.3
657 bobbymuhlhausen 24.1 32.3 29 13.3
658 bswtelecom 24.1 35.8 20.9 14.1
659 DaveBoulanger 24 40.3 31 14
660 VicGart 23.9 32.7 21.7 13.6
661 bolarotibi 23.9 30.9 27.4 12.9
662 AnalysysMason 23.9 33.5 19.6 13.9
663 danolds 23.9 30.3 18.5 12.9
664 sbalaouras 23.9 40.2 21.4 15.2
665 ChrisFletcher 23.9 36.5 21.2 14.1
666 lesliehand 23.9 31.9 23.2 13.5
667 ShivBakhshi 23.8 36.3 2.6 14.2
668 wzhou 23.8 31.2 23.5 12.9
669 dlogiudice 23.8 30.7 22.2 12.8
670 StephenB_TG 23.7 30.1 22.7 13.1
671 PierManenti 23.7 29.8 23.5 12.6
672 pdebeasi 23.7 36.2 19 14.1
673 DellOroGroup 23.7 41.8 19.3 15.7
674 Bloor_Research 23.6 33.9 0 14.2
675 SimonYates 23.6 36.3 24.1 14.5
676 MiriamKutcher 23.5 34.6 26.2 13.7
677 JackieFenn 23.4 37.9 2.9 14.5
678 cjones1405 23.4 35.1 17.5 13.7
679 Stuart_Robinson 23.4 28.9 21.6 13.9
680 DaveEckert 23.4 34.6 24.5 13.8
681 chrismines 23.4 34.6 36.8 14.1
682 Experton 23.4 33.8 6.6 13
683 IDCconferences 23.4 29.4 18.9 12.7
684 scottpezza 23.3 30.5 25.2 17.7
685 Quocirca 23.3 33.2 20.7 13.7
686 komarah 23.3 38.5 18.5 14.4
687 julie_craig 23.3 31.5 18.2 13.5
688 marylaplante 23.3 31.3 4.4 13.1
689 lizherbert 23.3 39.8 17.4 15.1
690 jackzimmerman 23.2 32.7 19.1 13
691 monkchipsconf 23.2 29.1 24.9 12.5
692 ramonkrikken 23.2 25.8 38.1 12.4
693 LaurenEcks 23.2 29.8 24.8 12.3
694 KimballBrown 23.1 32.5 31.3 12.9
695 TPI_News 23.1 35.9 22 13.9
696 s2_williams 23 27.2 19.1 11.8
697 OvumButlerBI 23 36 14 14.2
698 zStorageAnalyst 23 30.2 31.2 13.3
699 illuminata_inc 23 33.7 23.3 14.2
700 Display_Market 23 29.6 21.3 12.6
701 tomaustin 23 33.9 24.4 13.7
702 tpohlmann 22.9 33.6 27.3 13.6
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Algorithm and Methodology tmp9CD

Following – Twitter lists the number of people each user follows. The tendency for most celebrities is to only follow a few individuals. The more people that someone follows, there is an increased likelihood of them actively participating in conversations with the community instead of simply broadcasting to it. Following ranges were determined (i.e. more than 20, more than 30, etc.) and each range was assigned a number (0 to 30) that was used as part of the algorithm. Note: Twitter opened its API to TweetLevel so that data could be sourced easily and quickly to benefit the user.

Followers – Twitter lists the number of people that follow each user. Like subscribing to a feed, this is a clear indication of ‘popularity’ as it requires someone to actively request participation. Even though TweetLevel has a ranking of people based upon popularity, it is influence, engagement and trust that is more important. Due to the nature of logarithmic ranges, a change in the number of people that follow someone, such as from 500 – 1000, will give a far higher change in score than a move from 180K – 200K. Following ranges were determined (i.e. more than 20, more than 30, etc.) and each range was assigned a number (0 to 30) that was used as part of the algorithm. Note: Twitter opened its API to TweetLevel so that data could be sourced easily and quickly to benefit the user.

Since the initial creation of TweetLevel, we have now been able to incorporate Twitter Lists into this part of the algorithm. Someone’s follower score will increase depending upon the number of times a user is included in a list, the number of people who follow that list and the authority of those people.

Updates – How often does someone update what they are doing. This number is purely objective as it scores someone highly no matter what the content of their post (i.e. how relevant is it). Nevertheless it is assumed that if someone posts frequently but has poor content then their ‘followers’ will decrease. Update ranges were determined (i.e. more than 20, more than 30, etc.) and each range was assigned a number (0 to 30) that was used as part of the algorithm. Name Pointing – e.g. @name – How many people engage in conversation with a celebrity or point to their name. The clearest way to establish this is to run a search on the number of people who reference @username in a message. This calculation is based upon a one month period combined with a 24 hour period. The number of times this happens is calculated with each range was assigned a number (0 to 30) – again this was then used as part of the algorithm.

Retweets – Has a tweet caused sufficient interest that it is worth re-submitting by others? Despite a great deal of ‘noise’ (i.e. posts that are not relevant or interesting), when someone sees something that is of high interest, their post can be re-tweeted. The clearest way to establish this is to run a search on the number of people who reference RT @username in a message. This calculation is based upon a one month period combined with a 24 hour period. The number of times this happens is calculated with each range was assigned a number (0 to 50) – again this was then used as part of the algorithm.

Twitalyzer – “This is a unique (and online) tool to evaluate the activity of any Twitter user and report on relative influence, signal-to-noise ratio, generosity, velocity, clout, and other useful measures of success in social media.” This 3rd party tool is a useful method to combine automated metrics dependent upon criteria within posts and publicly available numbers. Where tools such as this are available, we incorporate them into the algorithm to achieve a more confident score. Twitalyzer gives users scores from 0 to 100. Ranges were determined (i.e. more than 20, more than 30, etc.) and each range was assigned a number (0 to 20) that was used as part of the algorithm.

Twitalyzer noise to signal ratioSignal-to-noise ratio is a measure of the tendency for people to pass information, as opposed to anecdote. Signal can be references to other people (defined by the use of “@” followed by text), links to URLs you can visit (defined by the use of “http://” followed by text), hashtags you can explore and participate with (defined by the use of “#” followed by text), retweets of other people, passing along information (defined by the use of “rt”, “r/t/”, “retweet” or “via”). If you take the sum of these four elements and divide that by the number of updates published, you get the “signal to noise” ratio. Twitalyzer gives users scores from 0 to 100. Ranges were determined (i.e. more than 20, more than 30, etc.) and each range was assigned a number (0 to 20) that was used as part of the algorithm.

Twinfluence RankTwinfluence is an automated 3rd party tool that uses APIs to measure influence. For example: “Imagine Twitterer1, who has 10,000 followers – most of which are bots and inactives with no followers of their own. Now imagine Twitterer2, who only has 10 followers – but each of them has 5,000 followers. Who has the most real “influence?” Twitterer2, of course.” As with Twitalyzer, this index uses 3rd party tools to add greater confidence in the overall Twitter score. Similar to the other criteria, ranges were determined (i.e. less than 20, less than 30, etc.) and each range was assigned a number (0 to 20) that was used as part of the algorithm.

Twitter GraderTwitter Grader is the final automated tool to add greater confidence to the final index. This site creates a score by evaluating a twitter profile. Similar to the other criteria, ranges were determined (i.e. less than 20, less than 30, etc.) and each range was assigned a number (0 to 20) that was used as part of the algorithm.

 Involvement Index – The Involvement Index is unique Edelman IP that calculates a score based upon how an individual engages with their community. It is calculated by analysing the content of an individual posts. People who score highest in this category have frequent, relevant, high-quality content that actively involved the twitter community (asking questions, posting links or commenting on discussions) and did not purely consist of broadcasting. Ranges were determined (i.e. less than 20, less than 30, etc.) and each range was assigned a number (0 to 20) that was used as part of the algorithm.

Velocity Index – As more people engage on Twitter, it may become harder to keep activity going. The velocity index measures changes on a regular basis and assigns a score based on increased or decreased participation. Ranges were determined (i.e. less than 20, less than 30, etc.) and each range was assigned a number (0 to 20) that was used as part of the algorithm.

Weighting Each specific variable listed above was given a standard score out of 10. Using a weighting scale I varied the importance of the each metric to establish an individual’s total score.

Weighted for Popularity – the key variable is the number of people someone has following them. There are many online tools that show this such as Twitterholic.

Weighted for Engagement – the key variables are an individual’s participation with the Twitter community (as measured by the Involvement Index), with additional emphasis on the frequency of people name pointing an individual (via @username), the numbers of followers and the signal to noise ratio. Other attributes were included in the final score but were given a lower weighting.

Weighted for Influence – the key variables in this instance is a combination of the number and authority of someone’s followers together with the frequency of people name pointing an individual (via @username) and the how many times and individuals posts are re-tweeted. Other attributes were included in the final score but were given a lower weighting.

Weighted for trust – the best measure of trust is whether an in individual is will to ‘trust’ what someone else has said sufficiently that they are also prepared to have what they tweeted associated with them. The key metric in this instance are a combination of retweets and number of followers. Other attributes were included in the final score but were given a lower weighting. In the true spirit of ‘open sourcing’ this work, I welcome your comments, views and criticisms in how this approach can be as accurate as possible. Whereas I don’t believe for one moment that TweetLevel has found the holy grail of social media measurement, I think it is a good step forward and look forward to discussing this with you.

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Today’s Geeks were born in the world of abundance for them the world of the Long Tail needs no explanation.  They are used to choice,  thrive on choice and rail against without a world without it.   They look for real and trusted descriptions in what they are choosing not just in the long tail of product and services but the parallel long tail of ideas which is rapidly developing.   When this choice is seen to wanting or a particular idea stands out our geeks comment and they comment.  Commenting is the way that authority is shared in the new world.  It links the thought to other communities and ideas it conveys authenticity and engergises the conversation.  There are multiple ways of commentating. Here are just some categories: the troll, the enthusiast, the expert, the mad man, the philosopher, the fan, the bombast and the verbose.  It’s easy to scoff at many of categories in the cold light of day but it’s the variety and enthusiasm of commentators that makes the new world of digital influence go around.   

In the Long Tail this mechanism is brilliantly set out with the example of the sales around an adventure story.  In 1988 Joe Simpson wrote Touching the Void a near death story from the Andes 10 years later a similar book Touching Thin Air became a sensation.  As Chris Andersen observered  thelongtail.com:

“Book sellers began promoting it next to their Into Thin Air displays and sales began to rise… By mid 2004, Touching the Void was outselling into thin air more than two to one.  What happened? Online word of mouth. When Into Thin Air, first came out a few readers wrote reviews on Amazon.com that pointed out the similarities … which they praised effusively.  Other shoppers read these reviews, checked out the older book and added it to their shopping carts … they started recommending the two as a pair.  People, wrote more rhapsodic reviews more sales, more algorithm-fueled recommendations – and a powerful feedback loop kicked in.”

Commentators are very often engaged with a community or conversation it for this reason that such a powerful multiplier kick in.  They have skin in the game and take this responsibility seriously sometimes a commentator may also be an idea starter or curator but very often they see themselves as important simply because of their comments and a commitment to engagement.  Perhaps idea starters and curators are too busy starting and organizing to simply comment.  The huge volume of comment that some online citizen’s generate is actually a sure sign of a true freak geek who is using new technologies to hugely boost their productivity.

Back to Andresen again, “Amplified word of mouth is the manifestation of the third force of the Long tail.  It is not until this third which helps people find what they want in this new super abundance of variety, kicks in that the potential of the Long tail market place is truly unleashed.”

For me what motivates the commentator that is truly intriguing?  An idea starter is motivated by creativity; an amplifier or curator want to share but they also crave a very unstandable status and leadership recognition.  A commentator is I think truly taking part they feel the desire to share and I believe realize that by sharing they get to also find what they want in return at some point.  It is a mutually rewarding activity this may explain why the comments tend to be shorter, pithier less heavily invested but also so prolific as I say in many ways commentators are the true revolutionaries and the missing link.  I would love to hear your thoughts on why commentators comment or why a troll trolls? and possibly explain why a large proportion of comments are also gibberish more I think than in real world conversations?

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