About ten years ago, I wrote a paper that predicted that analytics and social media would converge in CRM. I believed this for two reasons. First, I believed social media was inevitable though I had no idea what form it would take. Facebook was not on my radar and might not have been invented yet, MySpace was something for kids, and Twitter had definitely not been invented yet. But Plaxo and LinkedIn offered tantalizing glimpses of what was possible in business with social networking and I believed something of that ilk would eventually drive CRM.
My second insight was that those social media applications would, of necessity, churn up a lot of data that would be useless unless we pushed it through an analyzer. But if we did the pushing, like making sausage, out the other end would come valuable stuff. What amazed me then and continues to amaze me is the rate at which knowledge is doubling thanks to all the data being churned up by social media.
In 1982 Buckminster Fuller, the futurist, architect and some would say crank, published “Critical Path” a book in which he estimated knowledge doubling like this: Take all human knowledge up to the beginning of the Christian Era and call it one unit. It took 1500 years for that knowledge to double. Knowledge doubled again by 1750, the beginning of the Industrial Revolution, and again by 1900.
Today, knowledge is doubling at a rate of between one and two years and IBM predicts doubling time will be down to 11 hours by 2014.
All this doubling has many practical impacts the most important from my perspective is that those who don’t learn how to manage Big Data and the big information it generates in our own businesses will be toast. Another of those impacts is that our ability to generate new knowledge is so prodigious that it will become increasingly difficult to generate knowledge that is unique to any person or business.
We generate so much data today that it is possible by induction and other processes to infer knowledge that others might have and might think is proprietary. On the other hand, though, failing at analysis will leave much information hidden in a morass of data.
This all points to the need and even urgency attached to developing strategies for dealing with Big Data. Last week at SugarCon 2013 in New York, I gave a talk on the subscription economy, one of my favorite topics. In it I quoted some research from Gartner — by 2015 35% of the Fortune 2000 would derive some of their revenue from subscriptions. And this from Aberdeen — only about twenty percent of companies studied had subscription businesses that appeared viable in that they had high customer satisfaction and renewals and their new contract value (NCV) was a positive number.
So while Gartner might be right in its prognostication, it leaves much unsaid because revenue is not profit — even a bad subscription company can generate revenue while it’s going out of existence because it loses money on every transaction.
So, how does a company become part of that top quintile? Simple. They develop metrics that they derive from customer data about use, payments and sentiment and relentlessly pursue them tying to optimize customer experience and involvement. Needless to say, metrics are made possible by analytics — both the reporting kind and the predictive data modeling kind.
There’s a boom happening in the analytics business these days with companies like GoodData, Totango and Gainsight among many others and I think smart companies — subscription or otherwise — ought to pay attention.
That was my simple message at SugarCon but the not so simple reality is that most companies are not on the bandwagon yet. They still don’t know what to do about Big Data and they aren’t exhibiting the needed curiosity to figure out that it’s time to get on track with subscriptions and analytics.
Another finding that comes to me from long practical experience is that while knowledge might be doubling very quickly, our ability to apply it lags and I wonder and expect that a metric of new knowledge vs. applied knowledge would show a widening gap between what we know and what we do with it.
Very shortly I’ll publish an ebook of interviews with five CMO’s of some fast growing companies. What’s interesting about each of them is how these marketers have embraced Big Data and come up with strategies and metrics that better enable them to understand their businesses. They know that the knowledge doubling that we are all caught up in isn’t some abstract concept, it affects them and it represents one of the great opportunities of this new century.