We need data for @matchFWD.

A lot.

We need data to help us suggest the best  talent matches for an open job opportunity to our connectors.

We’re only 2 developers, and I spend too much time not coding so we need help.

There’s currently 2 API that buzzed trying to evaluate influence, reach, reputation and authority of Twitter users : Klout and PeerIndex.

I am not really interested by most metrics right now, but I want to know what topics a user tweet about. Lucky for me both API have such a method so I ran a small manual test on Twitter users I know and follow well enough to judge the results

Here are the results…

Ben Yoskovitz (byosko)

• Klout : ”Entrepreneurship”,”social media”,”Human Resources”
• PeerIndex : “private equity”,”venture capital”,”finance”,”marketing”,”montreal”

GeorgesDuverger

• Klout : “Montreal”,”Yul”,”Developers”
• PeerIndex : “free software”,”realtime web”,”google chrome”,”science”,”api”

Carl Mercier (cmercier)

• Klout : “Montreal”,”Met”,”technology”
• PeerIndex : “databases”,”data management”,”digital media”,”ipad”,”iphone”

DavidDesjardins

• Klout : nothing….
• PeerIndex : “cycling”,”andy schleck”,”bessette”,”bicycle sharing system”,”cervlo”

Martin Dufort (mdufort)

• Klout : “iPhone”,”Apple”,”Google”
• PeerIndex : “iphone”,”cloud clients”,”embedded linux”,”apple tv”,”nexus”

Seb Provencher (sebprovencher)

• Klout : ”"Montreal”,”Marketing”,”Quebec”
• PeerIndex : “marketing”,”mass media”,”realtime web”,”digital media”,”bing”

Alex Dao (alexdao)

• Klout : “Montreal”,”Retailers”,”Regions”
• PeerIndex : “montreal”,”realtime web”,”google search”,”marketing”,”villemarie montreal”

Myself

• Klout : “Quebec”,”Techno”,”Francophone”
• PeerIndex : “mass media”,”collaboration”,”marketing”,”community websites”,”criticism of facebook”

Klout seems to limit the number of topics to three and is much more generic. If I had to classify my users in large bucket, that could be interesting but it wouldn’t make it easier to identify specifically everyone in my twitter feed.

PeerIndex results are much more precise and diverse. The downside, I would guess without testing, is that they are probably much more volatile. The topics you rank for this week, won’t be next week topics… So how do you deal with this… You weight all tags you attach to your users ?

What am I going to use ? Most probably both. Now let’s it Stackoverflow API and GitHub API to find great developers…

Do you use these ? How ?

Related articles

• Who needs Whuffie – What’s your PeerIndex at Le Web? (techcrunch.com)
• Real vs Faux ‘Influence’ and why Klout Matters (enterpriseirregulars.com)
• Klout Gets $8.5M to Create the Page Rank of the Social Web (gigaom.com)
• Your Klout and PeerIndex Are Something, But Not Everything (scrapsofmygeeklife.com)

Had (yet another) idea couple weeks ago and sent an email to my friend Duncan Moore to validate. Here’s the mail :

As more and more brands, agencies, producer and individuals disseminate content on 3rd party sites (Youtube,Vimeo, Scribd, Slideshare, Digg, Reddit, Facebook, etc.), it gets more complex to track all that content and measure the buzz (comments, links, bookmarks, RT, diggs, etc.) it generates.

Per piece tracking gets rapidly overwhelming and Google Analytics is of little use, as the often-accompanying link back to the website is only a minor indicator of the effectiveness of the content. Traditional ORM solution might help for the buzz around it, but won’t tell you much about views and other data and will most likely received multiple hits for the whole conversation/campaign (the delicious link, the multiple retweets, the 5 diggs, the blog post, etc.) without really helping with analyzing that particular campaign.

It might be as simple as entering all the youtube video your brand has and monitoring the number of views they generate, number of favs, comments, number of link back and so on.

From a development point of view, it could start as being fairly simple as it’s mostly about aggregating data and displaying it in a comprehensive dashboard view.

http://www.youtube.com/watch?v=zC3k4IARSrY

NDLR:  I hope it’s obvious I am joking when I say they stole something … at least from me

When trying to identify early influencers and pool of knowledge around a certain topic or industry, finding early poster/commenter is one simple trick often used. Not very complex, but it often leads to other links and users and help shape how the information started to spread. Backtweets is great to see who shared a link on Twitter (despite the limited timeframe available), but Delicious gives a very different type of results and the ability to research much older content to see who picked up on it first.

Delicious

I am (still…) an heavy user of delicious. By clicking on the number of persons who shared a link in delicious, you access the bookmark history page. On top of this page,the ID of the first user to share this link is displayed and the date when he shared it. It also gives you a basic timeline to look at to see when the url popularity took off. Despite a clear drop in popularity, there’s quite a bit to be said about the usefulness of the data in delicious. Something to try…

Note : Because Delicious development has been pretty stale (cough) lately, I find myself using Diigo more and more. My favourite feature is the ability to create private groups to share links with clients and partners on specific topics and projects. Main problem is that I still search for links in delicious and the auto-tagging feature is not as relevant as there is less links shared on Diigo…

I’ve also recently found out about a new startup in Montreal called Wajam offering the same kind of bookmarking tool, but haven’t had the opportunity to test it yet.

Some voices are questioning the real value and possible ROI of these platforms, sometimes in opposition with free solutions. And it totally make sense when monthly fees easily range in the 500-1000$/month for the main solutions with a decent set-up.

One of these, entititled “The problems with Social Media Monitoring technologies“, seems to have decent web findability as it was sent to me a couple times by friends and clients in the past weeks. I’ll take a moment to go over these particular points one by one.

1. The technology is fairly stupid. It either don’t do what it says on the tin or do it quite badly. It is supposed to be simple: based on keyword configuration, the software scrolls the social web and collect mentions of your keyword(s). It then supposes to analyse themes and influence ranking and sentiment etc, which it does with limited accuracy . For example, will pick up any header, ad sense or footer mentions of your keywords even if it’s in the totally irrelevant context. If your brand name is pretty generic you are in deep sh*t. Hours of configuration and exclusions awaiting you.

You have the same problem when you search with Google. Building up good search queries takes time, analysis, trial & error and a good understanding of the brand, product and industry . Nobody said social media monitoring would be easy or obvious. The web is getting noisier everyday and good queries are more valuable than ever.

Unreliable data. The most important thing to understand is that the software simply provides you with piles of data. Before you can extract anything meaningful from this data you have to go through hours and hours of spam filtering which can be very tedious if you are dealing with 1000s of mentions every week/month. In some occasions I had over 50% irrelevant data coming through my dashboard. Additionally, the spiders cannot access all social spaces and sometimes the most important conversations are blocked.

There’s a clear need to be able to classified results by social engagement, source, language, sentiment and geo-demographics.  There’s spam in Google results as well obviously and you need good tools and techniques to cut through the noise.

Sentiment analysis is flawed. Again, this is part of the limitation of the technology. The software analyses keywords, not human emotions and, on average, the software gets it wrong 30% of the data because human emotions are subtle and complex and not easily categorised by software – we are not there yet.

I agree. And one of the reason is that we are usually measuring the message instead of the effect. Sentiment analysis should be mainly used as an extra filter. 60-70% reliability is better than nothing and probablu much higher for tweets.

Good monitoring tools allows for user to overwrite the results of sentiment analysis. Results needs to be reviewed, but it’s still much faster than evaluating everything manually.

Region specific data: for global brands, social media have very strong global element as well as clear regional bent (forums, blogs, networks etc). This is tricky especially if you are working with a regional client (e.g Huggies UK). Problem is for the software it’s not about where you are but which domain are you using. So reliable geo / regional analysis is, in many cases imposible to carry or not complete so need to be complemented with manual search.

True, Twitter localization is only based on what the user enters as location in but will soon be changed by the new API opt-in feature.
The ability to drill down the query with localization-related keyword helps obviously.

Influence analysis is flawed. Well, the concept of influenced is flawed so of course technologies of measuring it are flawed as well. Similar to sentiment, the technology is just not as clever as they want you to believe. It is based either on bogus metrics or just irrelevant, obsolete ranks .

Agrred, most implementation are. That’s why I always ask before evaluating or using. Sysomos has their own Postrank-style ranking. Social Mention (free) as added PostRank to their results.

If I were to design a full platform, I’d be using Postrank to rank results.

Time consuming. Because all of the above, the reality is that while thess companies provide you with piles of data and funky visualisations the profound unreliability of the software means you have to sit for hours and days and configure the dashboard, refine the data, correct the scores, filter the spam, get rid of irrelevant data AND THEN, AND ONLY THEN you can start making some meaningful analysis.

It does take quite a lot of time to set-up and it’s not easy. After a lengthy initial process, the work involved somewhat decrease but  you will still have to filter out some spam.

The analysis will always take time. That’s why many brands work with analysts to help them make sense of it.

Price. This varies significantly but the fact is that you pay just for the data and license fees to use the software. For the level of service you don’t get value for your money.

I’d expect to have access to an API to integrate with client workflow and platforms or to customize views and results, the ability to query on past period and modify queries at any time.

Conclusion

Social Media Monitoring platforms are not be-all end-all solutions, more of a building block of brand’s modern tool set for effective market research, marketing and customer support. They require investment both in time and money and more often than not, some support from people who have done it before and are comfortable using these particular tools.

Related articles by Zemanta

• How to Create a Share of Voice Report (socialmediatoday.com)
• Do Social Media Marketers Dream Of Monitoring Tools? (threeminds.organic.com)
• Collecta releases API to spur development of real-time search apps (venturebeat.com)
• Twitter Works to Improve Location ID Service (marketingpilgrim.com)
• Google Gives Sentiment Analysis a Multistar Rating (mikemoran.com)

Ranking social media users can be seen as an ego trip, but with communication and information channels growing and evolving so fast, it simply make sense to use a somewhat automated way to filter out users based on their influence. While PostRank gives me a nice way to evaluate social engagement around content, many apps are starting to express the need to use a similar filter to evaluate Twitter users by interest and activity.

Getting a score for a user is not so useful in itself, but is (we’ll be) a building block and filter-classifier of many upcoming tools and services.

I haven’t yet found a tool that fully satisify my needs but here’s the best of what I’ve tried so far.

The Tested Ranking Tools

1. Twitterank
2. Twitter Grader
3. Retweetrank
   

The References

To perform a real test, I needed some kind of references, so I used the following profiles:

The Normal Guy

@PhilGo20 : My own profile. I consider myself a fairly active Twitter user, with an ok following (around 560) and a close to 1:1 follower/following ratio. I retweet, been retweeted a couple times and am mentionned a couple times a week in reply.

The VIP

@SebProvencher : Seb is a Praized.com co-founder (Mtl startup) and Product Development Manager. I like his tweets and read all of them (thxs for TweetDeck group feature). He has close to 1500 followers,  but he’s only following 480 persons, giving him what is seen as an excellent follower/following ratio.

The Popular

@Caterina : Caterina is Cofounder of Flickr and Hunch and a  social media startup star. She has over 9000 followers but just over 200 tweets.

The Not-So Active

@PhilipBoum : Philippe is one of the NOFOLO/Percute Technologies guy in Quebec City, involved in the business side of these web services small companies. I don’t know if he’s using an automated follow back script but he has over 1000 followers with only 14 tweets and have not tweetted once in the last month. I am guessing he has a life outside Twitter, and that’s good !

The Spammer

@entrepreneur949: Sorry if you read this and you are not, but by your tweets and your link, if I were to design a spam tool for Twitter, you would be targeted.

The Inactive (used as a reference for the test)

@philippegauvin : I am squatting my own name domain. Only 2 tweets redirecting to my real account. 0 followers / 0 following.

Each reference is pretty self-explanatory, except the difference between Caterina and SebProvencher. I wanted to have both because I think they have different type of influence. To me most of SebProvencher influence comes from his activity on Twitter while Caterina’s comes from who she is. Without being very active, she has an important following. Praized is not know on the street while Flickr is (pick your street).

The Results (on August 27th 2009)

Retweetrank (0-100 percentile)

• PhilGo20 : 91.4
• SebProvencher : 98.59
  
• Caterina : 98.6
  
• PhilipBoum : 0:0
  
• PhilippeGauvin : 0.0
  
• Entrepreneur949 : 0.0
  

Twitter Grader (0-100 I suspect it’s a percentile)

• PhilGo20 : 95.7
  
• SebProvencher : 99.7
  
• Caterina : 99.4
  
• PhilipBoum : 96
  
• PhilippeGauvin : 25
  
• Entrepreneur949 : 89
  

Twitterank (not normalized, 0-200+)

• PhilGo20 : 16.93
  
• SebProvencher : 94.97
  
• Caterina : 32.17
  
• PhilipBoum : 0
  
• PhilippeGauvin : 0
  
• Entrepreneur949 : 0

First, despite the usefulness of TwitterGrader and some well thought features (I like the tag cloud), it is no yet usable in my sense as it rank inactive and spam account way to high.

Retweetrank does a pretty good job, but it’s very harsh if you haven’t tweetted in the last month. I would think PhilipBoum deserves better than 0. Having read the way they rank users, it seems to be only looking at the last month of activity. There is pros and cons to that approach in my mind. It does a very good job at filtering out Spam account though. What I am wondering is this : Could only one spam supporter account  retweeting the main account be sufficient to put Retweetrank at wrong ? Have not tested it yet. Also, it does not really separate significantly me, Caterina, and SebProvencher. That’s why you cannot be using a percentile as an actual rank.

TwitterRank does a good job too, but also harsh on PhilipBoum account. Maybe that’s the way it should be, but maybe the guy took a month off Twitter ! On the other hand, this is the only tool, that seems to normalize the value (not using the percentile as a rank) and the only one that shows the difference between Caterina and SebProvencher’s influence.

Conclusion

I think TwitterRank is doing the best job by far here and their simple API is also a big plus. There’s is going to be a large number of ranking tool for social media users coming up in the next months, so it’s futile to bet on the future of this service but the need for it is obvious to me. Mash it !

I was reading yesterday about “United Breaks Guitar Part 2” and I got interested in United Airlines reaction to the whole chain of event.

My first reaction when I heard about the video lat month was that they should have (assuming it wasn’t fixed before the videos) offered him 2 open tickets to any destination valid for 1 year. And a replacement guitar. Why ? Well simply because an open ticket is the kind of stuff that most of the population dream about and public opinion would have gone : “Wow, that’s cool ! They did a mistake but they’ve fixed it properly !”Just a replacement wouldn’t cut it at this point obviously.

Chances are he might have had accepted while some critics would have scream he’s a social media sellout. In the end the story would have died down but would be refered as “Customer Complaint Through Social Media 101″ course. The video would have lived forever and be the standout element of the story.

Well, United Airlines offered a replacement guitar to Dave Carroll which understandably he did not want and gave to charity. Too little, too late … With all the fame he got from his first video, it would take much more than a replacement guitar to close the story. To make matter worse, United added that …

“‘While we mutually agree this should have been fixed much sooner, Dave’s excellent video provides us with something we can use for training purposes to ensure that all customers receive better service for us,’ spokeswoman Robin Urbanski told the (Chicago Sun Times).”

Really ?

So today when I read about the video sequel, I thought that there might have been a better way for United Airlines to do damage control (in fact there’s many…).

My idea ? Fight fire with Fire. Answer with another viral video. United should have hired a hip young music video producer to shoot a cool and funny ‘We are sorry, we make a mistake and will try to avoid suck miss-up in the future’ excuse video with a little tongue-in-cheek humour for good measure. A couple United Execs and VPs singing bad choirs or trying the guitar would have done the trick.

Such a video, if well done and there is a high number of people who could have done something amazing, could have become nearly as popular as the original one. Great PR coup. And most of all, this would have greatly close the gap between the customer (Dave Carroll) and the brand (United Airlines).

Customer way of expressing complaints is evolving, so should Brand answers and tactics to keep customer happy. Obviously, it should be fixed way before, but in reality, we all know many more episodes of that kind will happen in the next months and I will be curious to see which brand will be the first to take it to the next level.

Now producing a video to cover up the Domino Incident would have had been way more challenging They probably took the right decision by going “corpo” with their excuses…

There’s now a gazillion way to do it, from free mash-up techniques like the one described in my previous post to uber-expensive solutions from media company.

Before evaluating the solutions, one would need some parameters to do so. Here’s a short list half-mine, half-borrowed

• Efficient filtering of queries based on language, country, source, date, topic
• Depth of coverage
• Real-time monitoring
• Duplicate elimination
• Smart Sentiment analysis (learning with time)
• Ability to modify sentiment analysis
• Sentiment plus (point in time, trend over time, compared with competitor, overlay with another issue etc.)
• Ability to associate timeline with events
• Identification, ranking and monitoring of influencers on multiple networks
• Ranking based on “social popularity” or social engagement (PostRank)
• Comparison to competitive information
• Identification of entity and events within the conversation
• Easy, dynamic ability to chart and graph analysis of queries
• Self-service set up of queries
• Multiple User Management
• Backward trending (and not just for 30 days!)
• Real-time threshold monitoring
• Integration with CRM system
• Analysis services by experts

I tend to put an heavy emphasis on the last point as so far from my experience, monitoring tools are only used as a first step to decipher the whole conversation and identify influencers while the real value lies in the results of the analysis services offered afterward. Despite great progress in technology, we still need a human with social science skills to make some sense out of all this.

I have evaluated a few software in this field and was preparing some kind of report for a client when I stumbled on a coveted Forester Report : The Forrester Wave™: Listening Platforms Q1 2009. With my limited resources, I could hardly do better than their evaluation and I was lucky enough to get it for free. And you can get it too, courtesy of Visible Technologies, evaluated in the report.

Only top-tier applications with a minimum of 75% of entreprise-level clients are evaluated in the report. Although they see listening platforms has being in their infancy, I was surprised to see the much-talked about Radian6 platform at the bottom of the ladder. According to Forester’s report, this is due to a lack of

sentiment analysis, NLP, insight generation, and integration and consulting services.

Nielsen BuzzMetrics and Cymfony are given as industry leaders based on

sophisticated sentiment analysis capabilities, strong international coverage, and
multilingual support

for the former and on

comprehensive reporting and analysis capabilities [...] well complemented by
a strategic and consulting services organization that offers advanced support for understanding
sentiment and influence

for the second.

A slightly older but well-done and complimentary report (with pricing info) on 10 leading online conversation monitoring tool can be found on Ryan MacMillan blog.

What do the actual users/customers of these tools have to say about them ? The best place to look for this might be on … a blog. Read the comments following ReadWriteWeb : The Future of Social Media Monitoring post for some great insights.

The conclusion seems to be that no tool yet offers a complete solution and I would be tempted to think that the greatest tool would be a giant mash-up of many smaller tools and technologies out there. More to come…

Have a good read !

DISCLAIMER : Many of the evaluation points mentionned above come from Anna B. at Organic.

Social Media Monitoring is now an essential part of any brand, corp or product marketing plan. Between tools like Radian6, BuzzMetrics, TechRigy, one can also use free tools like Google Alerts to monitor mentions of keywords or brand name in the news. It’s way too late to get any decent readership with a “How to Monitor Your Brand With Google Alerts” but add a SocialMention.com feed to the mix and you just spiced up an old recipe, allowing monitoring of social media platforms. And everybody likes good free online reputation monitoring tools.

Btw, SocialMention is a great social media search engine developed by Jon Cianciullo in Ottawa.

Social Mention monitors 80+ social media properties directly including: Twitter, Facebook, FriendFeed, YouTube, Digg, Google etc.

SocialMention also uses Backtype API to monitor blog comments.

So that’s what this guy did and he created a video tutorial to show how to do it. Super simple.

http://www.youtube.com/watch?v=f9PMLnNLjPk

So what I am adding to this ?

Well, one problem with this method (and with most monitoring services) is that you can rapidly get an avalanche of alerts and you will most likely spend a good amount of time flying through the list to see what could be relevant. On top of that, that does not tell you the reach or range of influence of these results. You’ll have to look it up yourself to see if these links where bookmared on delicious, shared on twitter, if they have comments and so on. Lengthy process.

The magic trick ? PostRank by AideRSS.

PostRank is a scoring system developed by AideRSS to rank any kind of online content, such as RSS feed items, blog posts, articles, or news stories. PostRank is based on social engagement, which refers to how interesting or relevant people have found an item or category to be. Examples of engagement include writing a blog post in response to someone else, bookmarking an article, leaving a comment on a blog, or clicking a link to read a news item.

Available as a GreaseMonkey Script or a FireFox extension, it will basically gives a 0 to 10 ranking score to any item in your Google Reader List and allows you to filter out result based on their PostRank score. Look at your Google Alerts + SocialMention feed in list mode and instantly know which one are worth looking at, based on the social engagement they generated.

That’s it, a cheap and manageable Online Reputation Management tool. Not perfect, but worth trying.

If you need more info about integrating PostRank with Google Reader, look at this.

http://www.youtube.com/watch?v=LLcoNommJXM

EDIT:

Both (mine and Intelligendo) explanation kinda skimp over what you should be monitoring.

Marty Weintraub create a great tutorial on how to do this and how to choose search terms for monitoring