It was gratifying for me to see the Salesforce announcement about the latest iteration of its SMB service desk product, Desk.com because it is so in-line with my thinking as well as my book, Solve for the Customer (I know, it’s a shameless plug). While I happily acknowledge that I advise the company from time to time, there is no causal relationship between the book and product, but sometimes, correlation is just fine. This is one of those times when correlation yields validation in both directions.
Of course there’s a press release and you can find it at Salesforce.com because it is not my intention to regurgitate it here. I prefer to focus on one new function that draws my interest and shows the parallels I mentioned, Desk.com Customer Health Monitor. Billed as a category-first among service providers, the monitor does what I’ve been advocating with minor exceptions. It tracks metrics about customers that a vendor thinks are important and reports on them thus providing alerts that help to prevent churn or attrition.
FYI, Zuora, another company I advise recently bought FrontLeaf to do much the same from a different angle. This idea is gaining traction.
This approach amounts to managing by exception. A small company can’t afford the labor or even subscribe to the systems involved in constant customer outreach and this tactic focuses on what evidence shows are customers that need an intervention, perhaps by a customer success manager. All good.
Now for some nits that need to be worked out—not in the product but methodologically. The big, and for many, hidden issue is knowing what you don’t know i.e. how does a business know what things to measure? An obvious example in the press release is what happens when a customer calls support twice in a month. Is this a sign of trouble or frustration and possibly a churn signal? It could be and the point of an alert is to call for further investigation, which leads to interrogating other metrics to triangulate the situation.
For example, new customers getting up to speed will likely call in more than established customers so it’s best to correlate frequency with other factors like seniority and possibly also products in use—did the customer just install the latest upgrade?
There are many iterations of all this and the simple point is that any company will first want to identify all of the situations that need monitoring and develop accurate metrics for them. I call the situations Moments of Truth, things that both vendor and customer care about and that must be addressed, moments of truth. So we must know our moments of truth before the rest of this makes sense.
We can safely assume we know some of our Moments of Truth but that’s no longer enough. We need to know all of them or we’ll be missing things we can help with and that’s bad because successfully negotiated Moments of Truth lead to bonding which leads to customer advocacy. We really can’t have too much bonding so we need processes that find all of the Moments of Truth and instruments them via tools like the health monitor.
Discovering Moments of Truth is likely a task for a future product release and probably other products like community and analytics. Using our brains to find the low hanging fruit will do just fine for now but suffice it to say there’s more to be done.
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.