My editor let me know the first copies of Social Media Analytics have arrived at the main office in Manhattan ( last Wednesday night) and review copies are just being sent out now.  I’m having a book signing in Manhattan at the offices of @PeekYou (whose advisory board I’m part of) on August 18th, from 6-8 pm – information is on the sidebar of every page on this site).  So, I’m excited – and can’t wait to touch my first printed copy.  Wow!   A year of work, now materialized.

Anyway, I meant to write about something I observed this week – eliminating noise in Social Media Monitoring and Analytics and came up an interesting idea that reminded me of what I saw in Collective Intellect CI:Insight earlier this year, when I still had access.  Collective Intellect has a visual interface that allows an analyst to cluster information using the semantic filter builder that I feel is one of finest interfaces I’ve seen.  NetBase, follows with similar functionality but it’s not visual (yet).  The semantic filter builder is captured in the movie below between 0:57 and 1:45 seconds into the video with additional dimensions which are custom created for clients (similar to the intent information OpenAmplify provides)  at 2:12 to 2:14 into the video.

Using Social Media Analtics to Identify Customers Concerned with Pricing from Collective Intellect on Vimeo.

The fundamental point was to disambiguate the choices we face when working with these systems – but it also shows me that there are back doors part of the same functionality, via Word Clouds, in platforms that support it such as Radian6.

I did a simple query that included (“New York City” OR NYC) and I wanted to see how much noise I’d get by sculpting my query and then regenerating the results – till I got what I wanted.

Got rid of “miss?—Can” , “owner”, “tomorrow”,  etc, each time getting closer to signals I wanted.

And actually, learning about patterns was helpful as there was all kinds of things I don’t know about my city – but you could do the same for anything, and query, and just keep sculpting till you have what you want.

It isn’t nearly what Collective Intellect is offering, but it is a way to segment a query, sculpt it, that might be better than looking only at a river of news, or verbatim results, which is much harder for the average mind to work with.

I was also thinking of a recent post by Gary Angel on Analyst vs. Implementer and another on Two Tiered Segmentation and decided that Social Media Analytics, similar to Web Analytics, could support the same segmentation that exists for Databases, as Gary Angel points out, but it would need a lot of tagging and metadata around each social mention which is currently not usually done – in other words, it would be very manual and tedious to replicate what tagging schemes exist in site Analytics into Social Media, but might be worth while, if were done (and someone was willing to pay for it – which is usually not the case).

For example, I rewrote what was provided in the second post against a possible use case to point out that a whole range of typical meta-data items that should be captured: 

• Functional Taxonomy: (examine mentions to decide what someone is doing – supported using OpenAmplify)
• Taxonomy: The hierarchical level a user it at (ie: stage 1, stage 2, stage 3, stage 4 of a particular journey or issue)
• Product Taxonomy: The product/family the mention concerns (e.g. TVs/LCD/ModelX, drug Y, disease Z, etc)
• Topic Taxonomy: A topic coding of what the mention or content pertains to (e.g. International Affairs/Middle East/Egypt/Revolution, shopping, illness, disease, etc – this take a lot of work to do manually, but may be the best way)
• Audience: The visitor falls into what segment?  (e.g. All, engineers, consumers, health-care providers, professionals, etc. – text mining of profiles may provide much of this information)
• Sales-Stage: The place in the sales stage the content is direct to (e.g. Early, Middle, Late)
• Page Components: The modules an online mention contains (e.g. videos, images, reviews, etc.)
• Component Classification: The value or status of the online mention (e.g. Overall Review Rating is High or Low, Price is Discounted or List, Availability is Out-of-Stock)
• Content Cardinality: The percentage on topic a mention is for a subject of the query
• Content Source: The publisher, source, author of the content (e.g. Columnist X, Database Y, blogger Z)
• Publish Date & Days since Changed: The recency and freshness of the content

This is a very partial list, my adaption of what was in Gary Angel’s post – but my point being we have two options to get this data tagged

1. Do it our selves manually (ie: adding tagging in Radian6, etc)  very time consuming and tedious, esp for large projects
2. Use robots to do it via platforms like Crimson Hexagon / Glide Intelligence, at el.

So, in theory, you could train Crimson Hexagon to pull data out and segment it along the lines of the above, once you translate what those  categorizations mean in terms of online listening and map it to the actual verbatim languages – but its a lot of work, no matter how you cut it… it probably can’t be done well in a MarCom environment because no one today would pay for this kind of work and there is hardly any in those situation that has right DNA to do it – but I’ll argue this is how we get value out of Social Media that becomes actionable in a new, and much more timely way.

I don’t want to over-complicate things, as people often want quick results with data (I do the same thing sometimes) but I think the “rough’em tough’em” approach to social media mostly fails where it could succeed, sometimes, in web Analytics and search Analytics.   The difference is that most of the data can be easily tagged and worked with if you set up is right with Web Analytics (though the setup might take a lot of work) and Search Analytics – but that’s almost impossible to do well, off the bat with Social Media Analytics.

Some data is structured, more or less, such as “likes” and “votes”, but the rest largely isn’t – anyone that thinks the “Wild West”, roll up your sleeves, quick and dirty approach works well in Social Media Analytics, such as you could get away with it using site and search Analytics,  is probably a PR based person that really value or understand data Analytics.

Kevin Hillstrom has an interesting post, or set of 2 posts on why not to hire him  (mine that data), but I think it’s actually meant to illustrate why they should (hire him); Kevin also wrote a testimonial for my book and I’m honored.

Almost two years ago I wrote about Tweetlevel, a Twitter Analytics platform Edelman created; it got a lot of buzz at the time, but I had not heard much about it since then.  About two months ago, when I was last in London, I met with Jonny Bentwood of Edelman at the Royal Society, who I was unaware of, at the time, connected to Tweetlevel;  Jonny let me know yesterday that a new update to Tweetlevel is now available, and it has some added intelligence to boot.

I usually resist the idea of trying a platform the way the developers would like me to and go directly to the source, my own experience with it, trying it out, seeing how it actually works for me, and how it might work for you.

Right away I can see the improvements from two years ago.

INFLUENCERS:  Tweetlevel now displays Influencers on any subject and ranks them and it seems to do in a way that makes sense - I looked for Influencers for Social Media Analytics as I know the subject and the area, and the results look good, at least, from first glance.

The  donut diagram and related links are pretty darn interesting, when you think about it, as it’s the top content shared on Twitter around a particular keyword phrase.

Certain phrases such as “socialanalytics” and “metricsnerds” might be slang like and important to know about when doing paid and organic search, as well as tracking conversations in Social Media using Listening Platforms.    The shared links give us a way to target the  content shared, itself.  Also, the top 85 Twitter Accounts associated with “Social Media Analytics” are listed at the bottom of the Tweetlevel readout.

I ran Tweetlevel.com on my own Twitter handle – WebMetricsGuru; you can run it with anyone’s account, if they have one.

There’s also some customized advice from Tweetlevel.com which reproduced below:

Your Influence score - You are a Twitter superstar. In your segment, you have a huge number of followers who find what you are saying interesting. As Spiderman said, “with great power comes great responsibility”. Carry on tweeting and sharing your opinions – people like what you have to say. If you score goes down though, it is because you are not engaging with your community but merely broadcasting your opinions – solve this by responding to individuals.

Your Popularity score - Your popularity score is excellent but can easily get better. This number is solely based on how many followers you have. Many Twitter measurement tools purely rank people according to this metric, however just because someone is popular doesn’t mean they are influential. To increase your popularity you will need to follow more people, post regular and interesting content, time your posts to peak times, follow trends and add hashtags to make it easier for people to find your tweets.

Your Engagement score - Your engagement score is OK but could be better. You understand that even though influence is important, to many people how you engage is what counts. You don’t need to be movie star to score high in this critical category as it is your participation within niche communities that count. Take more time talking to individuals, make your posts easier to find by including hashtags and enjoy the conversation.

Your Trust score - Your trust score is pretty good but could be better. The Edelman Trust Barometer states that 77% of people refused to buy products or services from a company they distrusted. It is trust that makes someone act – for this reason alone, having a high trust score is considered by many to be more important than any other category. Trust can be measured by the number of times someone is happy to associate what you have said through them – in other words, how often you are retweeted. To increase your trust score you will need to create more interesting and informative posts that will give your followers a reason to retweet what you have said.

Someone wrote a post on the correct grammar for Social Media, such as when or if to use “ReTweet” or “retweet”.  And Hubspot released findings that said the more you blog, the more traffic there will be (but I’m not sure that’s what we really want, is it?).

See some stats from Hubspot below:

And the Wired Danger Room has a post on Geo Location and all the data a company called TruePosition has on us.

Finally, students can now buy used textbooks on the Kindle for a large discount.

According to Gizmodo

Students can customize their textbook rental period (anywhere from 30 to 360 days) and from the looks of it, a semester-long rental is significantly cheaper than buying the book.

…. Amazon says it’ll have “tens of thousands of textbooks” available for renting, all of which will use Kindle features like Whispersync, highlights, notes and the ability to use it on any Kindle app (iPhone, Android, etc.).