For some time now, predictive analytics has been hailed as the “next big thing.” A quick Google search for “predictive analytics” shows that everyone from Forbes to The Wall Street Journal and beyond have written about how predictive analytics is going to “transform business” and “turn analytics on its head” and even “make BI obsolete”.
However, despite years of optimism, the analytics market is still dominated by visualization and business intelligence software such as Tableau, Qlik, and Birst. If predictive analytics is the next best thing, why isn’t everyone using it?
Predictive analytics examines data and tells you what it is likely to happen in the future. It promises to give you the power to “predict the future of your business” and to “know what will happen next.” And current technology is capable of making thousands, even millions, of predictions each second. Sounds pretty darn impressive.
But the dirty secret is that much of the automated predictive analytics technology on offer simply isn’t very useful. Why? Knowing what’s going to happen next is nice, but if you don’t know why, you won’t know what to do about it, and it will be of little value.
Consider the simple case of customer churn. With predictive analytics, you will know which customers are likely to take their business elsewhere. But if you don’t know why they are leaving, you won’t know the right actions to take to ensure loyalty.
If, for instance, a customer is leaving because her product keeps failing — but you don’t know that or really anything about why she’s leaving — this story will play out in one of two common scenarios.
One scenario is you guess what might make her stay. Maybe you offer her a longer contract and a free add-on. And you will fail at keeping her because you haven’t addressed her pain point, and you will fail at keeping some substantial percentage of all the other customers who are leaving for greener pastures, and your investment in predictive analytics technology will not have resulted in an acceptable reduction in churn.
Or, you could do some work. You can pick up where your predictive analytics technology left off, digging through the data — manually, frequently via a business intelligence dashboard — to determine what churning customers have in common, why they’re leaving and therefore what interventions might be the most effective. This requires expertise and attention that most businesses can’t afford, and time that customers aren’t going to give you.
Predictions are clearly not enough on their own. So what’s the solution?
Emcien gives you the ‘why’ with every prediction, so you (or your systems) know what to do about each and every future threat or opportunity.
Going back to the churn example, if you knew the reason why your customer was leaving (faulty product) you could intervene in a meaningful way, offering her a replacement product, effectively keeping her as a customer, and successfully reducing churn across your customer base.
No more guessing. No more manual analysis work. And you will unlock the true potential of predictive analytics that everyone is talking about, but that few have actually harnessed.