February 2011 Archives

Since 2006, I've been learning about social media analysis—as a business, a set of technologies, and a set of business practices. If you read the blog, you've seen some of what I've figured out. Along the way, my professional interest in the information value of social media activity collided with some of my other interests, which has led to a rough draft of a strategy that I'm modestly calling Omniscience.

It's too early to publish the whole framework, but I want to share a few foundational thoughts that are shaping the way I look at things. I find myself referring back to these every day, whether the topic is business, current events, or long-term futures. As you read through the individual elements, think about how they interact.

1. Everything is connected.
   A drought in China, floods in Australia, turmoil in the Middle East—which could affect economies in the US and Europe? Right. All of them. Cause-and-effect relationships circle the globe, and they don't respect the arbitrary domains of knowledge that we create. Energy, climate, economy, politics—they're all connected, and so is everything else.

2. Everything is uncertain.
   Any useful prediction has an element of uncertainty that we like to ignore. It's easier that way, and anyway, uncertainty is interpreted as weakness. A better approach is to embrace the uncertainty—evaluate it, and consider the possibility of unexpected outcomes. Do you bet everything that you're right?

3. Think and, not or.
   I see so many topics framed as false choices when the right answer is probably all of the above. Explore with and, focus with or, and never stop exploring.

4. Only one future.
   We like focus, but focus projected into the future is tunnel vision. We have only the one planet. Everyone's predictions—on technology innovation, business growth, sovereign debt, energy supply, climate, demographics—have to play out in the same world. Everything is connected and uncertain, so predictions interact, even (especially?) when we would prefer to deal with one topic at a time.
   

Update: the rest of the framework is up.

One of the first people to see the Omniscience framework suggested that I read The Black Swan, just in time for the Arab Spring uprisings that threaten so much of what had been described as "stability." Perfect timing.

Fred Wilson posted a simple request yesterday and got an amazing response. He wrote that he was experiencing Blogger's Block, and he asked his readers to suggest topics. The response? Over 360 comments (so far), suggesting and discussing more topics than Fred will be able to write about in a year. Obviously, people—a lot of them—are interested in Fred's opinions. I think this is great, and every blog ought to do it.

As I see it, asking the question invites two main risks: you might get no response, or people might suggest topics you don't want to write about. As for the former, I've invited responses that never came. While it's not fun, it doesn't leave a scar. No response means that the post didn't land with your readers, so they're unlikely to remember any of it.

If your audience is interested in something you don't want to write about, that probably tells you something you need to know.

Why are we here?
I sometimes wonder what people really want or expect from this blog. It accomplished its original goal a long time ago, and now it's a blend of what I'm finding around social media analysis and other topics I find interesting. The list posts tend to draw a lot of traffic, while the thought posts (where I think the real value should be) mostly don't.

Increasingly, I'm working on new topics that might surprise you if you think I'm interested primarily in marketing. I haven't yet worked out how much of that I should include.

Now for the scary part
I think every blogger could benefit from hearing what readers want more of. So, what do you want to see from this blog? Don't leave me listening to crickets.

And if you have your own blog? Tag, you're it.

Photo by WSDOT.

If you're a software developer (I'm not), you might find it easy to throw together a new application. You can handle pulling in data, moving it around, and displaying results—the normal software stuff. If you're analyzing social media content, though, the core functions of text analytics might be something you'd rather not learn. There's an API for that.

These services—some free, most commercial—allow you to skip the R&D and plug text analytics into your system. In a market crowded with me-too platforms, it might be the step that gets you off the entry-level rung of the ladder.

• Aiaioo Labs
  
• AlchemyAPI (Orchestr8)
  
• Calais
  
• Chatterbox
  
• Clarabridge
  
• ConveyAPI (Converseon)
  
• Dhiti
  
• Evri
  
• Expert System
  
• Extractiv
  
• Intellexere
  
• NetBase
  
• Open Amplify
  
• Repustate
  
• salsaDev
  
• Saplo
  
• Semantic Engines
  
• Semantria
  
• Semiocast
  
• Sentimetrix
  
• Sysomos
  
• Texifter
  
• TextWise
  
• ToneAPI (Lymbix)
  
• Tweetfeel (Conversition)
  

More posts in the "Build or Buy?" series:

• Building Blocks of Social Media Analysis
  
• Professional-Strength Social Media Aggregators
  
• The Rise of the "Influence" Peddlers

Visualization by Thomas Jenkins.

Analytics as a service: AaaS? Wouldn't want to hazard a pronunciation on that one.

Way back when MTV played music videos, I was on the radio. On the creative side, we got excited about the ratings—especially when we topped the market. When I was on the sales side, I learned about how the business uses those numbers. In the course of writing a review of Social Media Metrics, Jim Sterne's excellent overview, I thought of some similarities in how easy it is to focus on the least useful numbers.

One number for the public, many for the pros
When the radio ratings came out, everyone knew that 12+ ratings were for newspaper articles. Somebody had to have "the most popular" station. Anyone using ratings in the business—whether their job was sales, promotions, or programming—knew to look into the breakouts.

Instead of looking at overall audience size, we wanted to know how we ranked with important demographic groups—and not just rating, but time spent listening and cumulative audience size. We looked at specific dayparts (times of the day) to see how the programming held up, and we were very interested in how we did against stations with similar formats. The 12+ number that always made it into the local paper never came up in the inside discussions.

Drill into the social media numbers
Social media generates some feel-good numbers, too. Friends and followers numbers might not mean much, but they're hard not to notice. So the web analytics angle is decently complicated, and a lot of people are working out how to find something meaningful in influence, traffic, and engagement. Those aren't the numbers I want to pick on.

I've been paying attention to the listening business since 2006, and I've heard a couple (hundred) explanations of what people are doing there. There's a lot of variety out there, and tools with significant number-crunching heft. But the enduring argument about sentiment analysis suggests that we're still not over the mood ring.

Whatever tool you use, the first number it gives you—followers, post count, overall sentiment—is candy for the boss. The useful insights are deeper in the numbers, in the filters and cross-tabs that you use to slice and dice the data. Look at topics within sentiment within demographics on a subtopic deep dive. Compare with competitors and ask the why questions suggested by sudden changes. Then you might find something you can act on.

Ratings books were printed on paper then, too. Let's just ignore that, mmkay?

Photo by D'Arcy Norman.