In his recent blog post, “My Cus­tomer Ana­lyt­ics Epiphany,” my col­league Ben Gaines described what cus­tomer ana­lyt­ics is and how it’s dif­fer­ent from dig­i­tal ana­lyt­ics. Adam Jenk­ins, another Adobe blog­ger, also wrote a great piece on the impor­tance of cus­tomer ana­lyt­ics to dig­i­tal marketing.

Cus­tomer ana­lyt­ics is a hot topic around here because it’s a hot topic for you.

As mar­keters mature their dig­i­tal activ­i­ties, they’re find­ing more sources of infor­ma­tion about how cus­tomers inter­act with their brands.  There’s data from web­sites, mobile apps, call cen­ters, social media, and more.  By com­bin­ing all this and look­ing at peo­ple as full cus­tomers rather than frag­mented inter­ac­tions, mar­keters can have much more mean­ing­ful con­ver­sa­tions with them.

That’s what cus­tomer ana­lyt­ics is all about.  The prob­lem is there’s a huge gap between the poten­tial of cus­tomer ana­lyt­ics and mak­ing it action­able from a skills stand­point.  Tra­di­tional data sci­ence is usu­ally looked to for solu­tions.  But the field as we know it is fail­ing to bridge the gap in some impor­tant ways.

For one thing, tra­di­tional data sci­ence tools are com­plex enough that you need an impos­si­bly smart, flex­i­ble, and business-savvy indi­vid­ual to run them and gen­er­ate the insights you need about your cus­tomer.  In my opin­ion, Rob Bear­den, the CEO of Hor­ton­works, nailed it when he said, “[Finding] truly qual­i­fied data sci­en­tists … may be the biggest imbal­ance of sup­ply and demand I’ve ever seen…. The tal­ent pool is, at best, prob­a­bly 20 per­cent of the demand.”

Enter­prises seek­ing to hire their way out of this prob­lem have a pretty tough row to hoe.

Sec­ond, if you’re lucky enough to have found the data sci­ence pro­fes­sional you need, get­ting cus­tomer insights to your marketers—the folks tasked with cre­at­ing a great con­ver­sa­tion with your customers—is nearly impos­si­ble.  Mak­ing that data live on your dig­i­tal prop­erty is harder yet.

I’m sure by now that you’ve heard about the Gart­ner report stat­ing that the typ­i­cal CMO will spend more on tech­nol­ogy than the CIO by 2017.

We think that’s true.  It’s sim­i­lar to the trend we saw 10 years ago when mar­keters were starved for infor­ma­tion about how cus­tomers were nav­i­gat­ing their web­sites.  Adobe solved the prob­lem by democ­ra­tiz­ing access to that infor­ma­tion.  This enabled a new depth of under­stand­ing about how cus­tomers inter­act through dig­i­tal prop­er­ties and opened up a new world for mar­keters who wanted to use that under­stand­ing to make life bet­ter for their customers.

Now, our cus­tomers are look­ing for solu­tions that allow them to extend that under­stand­ing beyond the IT orga­ni­za­tion, dis­sem­i­nate insight through­out the enter­prise, make that data live on their dig­i­tal prop­er­ties (or in any cus­tomer inter­ac­tion), and do it at huge orga­ni­za­tional scale.

That’s what cus­tomer ana­lyt­ics means to us.

We’re pretty good at allow­ing enter­prises to self-serve cus­tomer infor­ma­tion and mak­ing that data live on our cus­tomers’ prop­er­ties.  How­ever, we’re still inno­vat­ing aggres­sively in this area, adding pre­dic­tive ana­lyt­ics capa­bil­i­ties, mak­ing it eas­ier than ever to share insights through­out the enter­prise, and more closely tying cus­tomer ana­lyt­ics to the rest of the Adobe Mar­ket­ing Cloud.  Many of our most sophis­ti­cated cus­tomers are already using these capac­i­ties to delight their customers.

Our goal is to enable dig­i­tal ana­lysts to pro­duce the insights that cur­rently require a data sci­en­tist to pro­duce.  We also want to dra­mat­i­cally improve on the tools data sci­en­tists them­selves have to choose from.  By doing so, we hope to give CMOs exact­ing con­trol over how they inter­act with their cus­tomers and pro­vide fan­tas­tic expe­ri­ences with their brands.

Stay tuned.