1. Productivity (which they say is by far the most important)
2. Compliance, regulatory issues and concern over inappropriate messaging
3. Bandwidth usage (a distant third)

The question of productivity is nothing new. Telephones and “personal” calls or worse “personal long distance” calls caused the same productivity and cost concerns for companies in the mid 20th Century. Businesses had no ability to restrict access  so, in the absence of tech tools, they exerted professional discipline on their employees. It was expected that people would stay focused and stay off the phone on personal business. And, in some firms phone bills were spot checked after the fact. Unfamiliar long distance calls were questioned.

Compliance issues do hold a special concern, posting inappropriate content or corporate secrets to blogs, Twitter, Linkedin or Facebook is an issue. But the sharing of confidential information is nothing new. Inappropriate memos and insider trading have been with us forever and there are laws and consequences for such behavior.

So if bandwidth is not an issue, why are CIOs playing Kindergarten Cop?   Given other hugely important issues such as Security, shouldn’t CIOs be focusing more time on that and leave the issues of professionalism up to HR and management?

I was awe struck at how ineffective the CIOs were at restricting access to the Internet – people are simply using their mobile digital devices for Internet access – untraceable, available, personal. If ever there was a reason for HR to step in and set parameters for personal professional behavior, mobile access is it. Attempting to control employee Internet access is a total waste of precious CIO productivity.

SMBs whether selling B-to-B or B-to-C find themselves confronting two voracious converging online trends:

1. Intense flattening of our markets: regional enterprises go online to find they are now competing with other regional enterprises around the nation – and the world. Price wars ensue, in a race to the bottom.
2. Ever increasing customer expectations: first it was web 1.0, then web 2.0 filled with engagement mechanisms and video. Mobile is on the horizon, if not already here. With each iteration, SMBs find themselves having to spend more to keep up. Even though programming development costs are going down, the average cost of a full-featured web site has not changed much since 1995 – it costs to stay in the game.

An environment this chaotic is unforgiving. SMBs find themselves in the challenging position of having to out smart the web to succeed. Our new offering will help them do that. Stay Tuned!

Reading the article was like watching a bunch of accountants put a dollar value on “good will” in a business valuation. These three academic stooges created a “model” of course. And used LOTS of servers to store LOTS of Tweets on three movies launched on the same day.

They used Twitter’s “advanced” search feature that is “capable” of measuring whether a Tweet is good or bad based on emoticons vs . OMG!  Even our academics had to admit this is not scientific. I hung in and read on…

Their “model” was a “bit” more sophisticated. REALLY? Their model adjusted for “language” issues, as in when people are talking vacuum cleaners and Tweet “it sucks!” that would be a good Tweet. Language is nuanced and notoriously tough to measure in any quantitative fashion, and it certainly can’t be done on a broad scale (thus Twitter’s silly emoticon measurement).  How much more sophisticated could these professors’ model be beyond emoticon measurement? Not much.

They were able to successfully “predict” the biggest grossing movie – but only in retrospect, which is not really predicting is it?

Beyond the obvious problems, they’re data was purely relational, there was no way of knowing how many Tweets make a winner, just that one movie got more “positive” Tweets than the others. But it could have easily been a statistically insignificant number. And here is the kicker: the true predictive information was available within 24 hours of the first weekend release, not in Tweets, but in the box office receipts. DUH.

This only proved one thing: there are enough Tweets about movies to give three professors enough data to write something utterly inane and enough ignorant editors at the WSJ to approve the article.

For other large industries this model is hardly applicable.  Products do not launch at the same time. Not everyone who Tweets is your customer. There maybe a whole lot more people who are your customers and have very strong “predictive” feelings who NEVER Tweet.

For the rest of us, in businesses and industries outside mass appeal – that would be the vast majority of companies and small businesses – this exercise is TOTALLY useless.  We are trees falling in a Tweetless forest.  Turning to Tweets as a predictive measure is a waste of time. Meaningful interactions with our customers and prospects via the medium THEY prefer a far better use of our time.