On a daily basis, my inbox is flooded with emails. There is so much infor­ma­tion to be processed and so much data to look at that it can at times feel over­whelm­ing. I’d wager this phe­nom­e­non is not one I’m fac­ing alone.

Every day, mar­keters are get­ting more and more infor­ma­tion about what their cus­tomers are doing. As a result, cus­tomer ana­lyt­ics and pre­dic­tive ana­lyt­ics have become increas­ingly impor­tant. You’re col­lect­ing data from dig­i­tal mar­ket­ing, social media, call cen­ters, mobile apps, and more. But how do you take that data and make it action­able? How do you use the infor­ma­tion on what your cus­tomers have done in the past to gauge what they may do in the future? The answer to these ques­tions is sim­ple: you use statistics.

Pre­vi­ously, I talked about cus­tomer ana­lyt­ics and its impor­tance to your busi­ness. Pre­dic­tive ana­lyt­ics is a huge piece of the cus­tomer ana­lyt­ics puz­zle. Under­stand­ing what our cus­tomers are likely to do is at least as impor­tant as what they have done or pur­chased in the past. To have a truly robust under­stand­ing of your clients and their needs, pre­dic­tive ana­lyt­ics needs to be part of your toolset.

Pre­dic­tive ana­lyt­ics has many busi­ness appli­ca­tions. Ulti­mately, this sci­ence helps you under­stand what your cus­tomers are likely to do based on what they’ve done in the past. Many com­pa­nies shy away from this sci­ence because it seems like such an over­whelm­ing con­cept. Isn’t using sta­tis­tics to pre­dict behav­ior ter­ri­bly futur­is­tic? Not as much as you’d think. Pre­dic­tive ana­lyt­ics is some­thing you likely encounter every day.

For instance, your credit score is cal­cu­lated using pre­dic­tive ana­lyt­ics. Based on a set of data points regard­ing your pay­ment his­tory, loan appli­ca­tions, employ­ment his­tory, and more, finan­cial insti­tu­tions cal­cu­late your credit wor­thi­ness. In their eyes, your credit score is a pre­dic­tive indi­ca­tor that uses sta­tis­tics to deter­mine how likely you are to repay a loan. This sci­ence has been used for years. That said, pre­dic­tive ana­lyt­ics is grow­ing in both pop­u­lar­ity and accu­racy. Plus, new tools on the mar­ket make it much more acces­si­ble to the aver­age business.

Still, it can be tough to under­stand what goes into the cal­cu­la­tions behind this sci­ence. Pre­dic­tive ana­lyt­ics would be noth­ing with­out statistics—a word many find daunt­ing. Not to fear. Sta­tis­tics can be eas­ily acces­si­ble and inter­est­ing when explained well. In fact, my col­league John Bates can even use sta­tis­tics to explain why there is a cor­re­la­tion between poi­son and knit­ting. How much more fun could sci­ence be?

You may be ask­ing your­self how this could pos­si­bly be fun. In fact, you could even be ask­ing your­self what a cor­re­la­tion is. Don’t worry. I’ll make this fun by giv­ing you a job we’ve all always wanted. Imag­ine you are a detec­tive. Your job is to solve a mys­tery. This mys­tery may be some­thing as per­ti­nent to your busi­ness as find­ing out whether mobile app users are more loyal. To solve that mys­tery, you’ve got a set of data points (which is kind of what facts and clues really are anyway).

The set of data points we’re going to look at encom­passes all the facts you have as a detec­tive. These facts can include infor­ma­tion about Web behav­iors, mobile inter­ac­tions, call cen­ter expe­ri­ence, point of sale infor­ma­tion, social media activ­ity, and more. This set of data points is what orga­ni­za­tions refer to as Big Data.

The prob­lem for most detec­tives is that Big Data is just what it sounds like: it’s a ton of data. A huge data set can be incred­i­bly hard to sift through and under­stand. In fact, Deloitte pub­lished an arti­cle ear­lier this year stat­ing that in a sur­vey of 100 CIOs, Big Data was expected to be one of the biggest tech­no­log­i­cal dis­rup­tors of 2013. Most com­pa­nies sim­ply don’t know how to han­dle the vol­umes of data they now have access to. So how are you going to sort through that and solve your mys­tery in a fash­ion that won’t take years? That’s right: sta­tis­tics! Using sta­tis­tics to power pre­dic­tive ana­lyt­ics will make your detec­tive work successful.

A great exam­ple of this is The Gen­eral Auto­mo­bile Insur­ance Ser­vices. By ana­lyz­ing its Web traf­fic, this enter­prise found that users tended to stop the appli­ca­tion process when they landed on a page request­ing their VIN num­ber. The Gen­eral used this infor­ma­tion to cre­ate a pro­gram that pre­pop­u­lates the VIN num­ber based on other infor­ma­tion, result­ing in an increase in con­ver­sions. They used sta­tis­tic and cus­tomer data to pre­dict how a change in their inter­face could increase cus­tomer sat­is­fac­tion and sales.

Still not sure you need sta­tis­tics and pre­dic­tive ana­lyt­ics? Con­sider it this way: sta­tis­tics let you deter­mine the rela­tion­ship between cer­tain data points. With­out this, you’re mak­ing edu­cated guesses at best. Sta­tis­tics works like a lever, allow­ing you to work with much larger data sets than you nor­mally could man­age the same way a lever allows you to lift much heav­ier loads than you, as a mere mor­tal, would nor­mally be able.

If you’re not uti­liz­ing Big Data, sta­tis­tics, and pre­dic­tive ana­lyt­ics, you can’t get to the bot­tom of your busi­ness ques­tions, cre­ate effec­tive strate­gies, or accu­rately mar­ket to your customers.

If you’re look­ing to learn more on pre­dic­tive ana­lyt­ics or sta­tis­tics, check out this great pre­dic­tive ana­lyt­ics read­ing list at For­rester. And, of course, keep stop­ping by to read more on my blog.

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