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.
It is very hard to pinpoint a disruptive innovation and the moment it hits the market and I have said this many times before. It’s easy to know that you use this or that technology today and couldn’t imagine living or working without it but, really, can’t you imagine the time before you started using this stuff? Social media might be a good example since most of today’s users still kind of remember what life was like before. Today is one of those days, I think.
Today Ayasdi came out of start-up stealth-mode and announced itself and you can see an article about it here in the New York Times.
So what is it in a nutshell? It’s only the first really new and different way to analyze big data since we started collecting it. Ayasdi uses something called topological data analysis and here’s one place where it’s different. Rather than type a query or ask for a report from a big data set, Ayasdi just looks at the whole data set and tells you where the interesting clusters of data are — clusters in places you may not have thought about.
So that means you no longer have to more or less know what you are looking for to use analytics, you simply need to know that you want to understand the interesting clusters. That’s a disruptive moment if you ask me — presuming it works as well as the early hype says it does. To me this sounds more like advanced data mining than business intelligence but I am not an analytics guru so this is simply speculation.
So what’s it good for? Well, if you’ve collected a lot of data about a molecule you think might have beneficial pharmaceutical properties, rather than performing a lot of screening tests, you might first examine the data topology and then investigate where the data says there are interesting relationships. And, yes, substitute customer for molecule in the above and more interesting things happen.
As with any disruption, it’s hard to think of what the world will be like in the aftermath, but if this works as advertised a few years hence we might all be scratching our heads trying to recall what life was like before.
Forecasting and pipeline management don’t get nearly the attention they deserve and that doesn’t make sense. Of all the parts of CRM, the forecast is one of the few things many companies still leave to manual systems, i.e. spreadsheets. Even sales compensation has a higher place in heaven as companies like Xactly have blazed a trail away from spreadsheets to a system with a database and analytics, with excellent results. You’d think that sales people would be willing to invest as much in the forecast as they do in counting their commissions.
Part of the challenge with forecasting and pipeline management is that some professionals might resent conventional forecasting systems for the same reasons they like compensation systems. Confused? You shouldn’t be. Both types of system reduce uncertainty to certainty as much as possible. But while that’s a good thing when you are counting existing money (your commissions), it’s a problem when figuring out the future because the future is anything but certain.
That’s why last week’s Cloud 9 Analytics user meeting was so important. At their third annual user conference, CEO Jim Burleigh, talked about the importance of understanding the probabilities when forecasting. It’s no coincidence that Cloud 9 now boasts a forecasting user interface that uses probabilities but also acts like a sales manager.
If you’ve spent any part of your career in sales then you know there are deals and there are DEALS. Some deals are like racehorses, they practically sprint from first call to closure while others plod along and maybe even stop. That’s an extreme situation and it’s easy to spot the real winner. But consider two deals at a 90 percent completion stage. They might look the same numerically but each took a different path to that 90 percent mark. One might have taken twice as long, one might not have enough money budgeted, one may be run by a C-level officer on the customer side the other might be managed by a director.
These differences in the history of the deal add up and a seasoned sales pro knows they are important. But conventional pipeline and forecasting tools (e.g. spreadsheets) make no use of history, which might help explain why only nine percent of organizations we’ve surveyed have a 0.9 correlation between the forecast and reality. The rest? Foregtaboutit. When it comes to forecasting these deals, the sales pro might favor one over the other for reasons that add up to gut instinct. So, it’s no surprise that the pros create three flavors of forecast — the best case, worst case and the most probable.
The genius of Cloud 9 today is that they’ve found a way to take the best of what analytics can do to track history and spot trends and combined it with a forecasting user interface that enables a professional to apply common sense to arrive at best, worst and most probable scenarios. Some people call it gut instinct and I suppose that’s as good a term as any, but really, it’s not gut — it’s applied intelligence and experience that just happen to be hard to put into words. At any rate, the new forecasting UI is straightforward and looks easy to use and it will remind professionals of their beloved spreadsheets, but with a lot more intelligence behind it.
Getting sales people to put aside the pure spreadsheet approach and go with something with more rigor behind it may still be a challenge. But Cloud 9 has demonstrated that it both understands the challenge in all its dimensions and that it can turn its knowledge into very serviceable product. Like the compensation managers before them, Cloud 9 has replaced the spreadsheet with something that makes more sense, is easier to use and should result in better results all around.