How AI is turning data into value

As mar­keters, our pri­ma­ry goal is to encour­age peo­ple to buy our company’s prod­ucts or ser­vices through the effec­tive and tar­get­ed promotion.

What makes mar­ket­ing con­tent effec­tive? For cus­tomers, cre­ativ­i­ty and rel­e­vance are what count the most. Par­tic­u­lar­ly on dig­i­tal chan­nels where there is so much noise to cut through, peo­ple only have time for con­tent that is:

1. Beau­ti­ful and enter­tain­ing, as well as
2. Per­son­alised to meet a gen­uine and per­son­al need

Cre­ativ­i­ty and rel­e­vance have long been the top focus for adver­tis­ers but it takes time to devel­op and roll out great con­tent, and mar­ket­ing teams are still stuck devot­ing much of their ener­gy to ret­ro­spec­tive admin tasks like report­ing. Man­age­ment is a cru­cial part of every marketer’s job, but if you’re con­stant­ly check­ing to ensure exist­ing process­es are run­ning smooth­ly, that leaves lit­tle time for try­ing new things or push­ing the envelope.

This is why the buzz around Arti­fi­cial Intel­li­gence (AI) has reached a fever pitch in recent months. The rea­son admin takes so long and many brands are adapt­ing to dig­i­tal plat­forms more slow­ly than they’d like is that they are sim­ply col­lect­ing more data than they can man­age. Just think – a dig­i­tal cam­paign tar­get­ing tens of mil­lions of peo­ple, in dozens of mar­kets, cre­ates more data in a day than an entire year’s worth of cam­paigns would have cre­at­ed just a decade ago. Where do you even begin look­ing for answers in these results, much less uncov­er new insights to inform your future activity?

Ana­lyt­ics is a process. Action is the goal

One of AI’s biggest sell­ing points is that it allows brands to spend less time look­ing at the past and more time gain­ing a step on the future. Most ana­lyt­ics solu­tions (even recent ones) were only designed to col­late and sum­marise facts. They com­bine data on cus­tomers, con­tent per­for­mance, third-par­ty sources, and so on into a more digestible for­mat that serves a mar­ket­ing team’s report­ing needs. This is huge­ly valu­able for dig­i­tal adver­tis­ers, but they are still lim­it­ed to ask­ing the same ques­tions they’ve always asked, just on a larg­er scale.

Real inno­va­tion hap­pens when you uncov­er insights you didn’t even think to explore, and that’s what AI offers. It allows mar­keters to under­stand why a par­tic­u­lar data set or com­bi­na­tion of data sets is inter­est­ing, which can have much broad­er impli­ca­tions than what that data says on the sur­face. It can take months or some­times years to uncov­er pat­terns that reveal it’s time for a change, where­as AI algo­rithms can help mar­keters do the job in seconds.

That’s why near­ly 85% of exec­u­tives believe AI will allow their com­pa­ny to devel­op or sus­tain a com­pet­i­tive advan­tage, accord­ing to one MIT Sloan study. Our own research rein­forces this point – when asked what their biggest pri­or­i­ty will be in the next three years, near­ly 40% of mar­keters and agen­cies said it was to “deliv­er per­son­alised expe­ri­ences in real-time”, some­thing that can only be achieved at scale with AI.

Of course, it’s not all just the­o­ry. At the risk of over­do­ing it on the facts and fig­ures, Capgem­i­ni recent­ly found that three-quar­ters of organ­i­sa­tions cur­rent­ly using AI have already seen cus­tomer sat­is­fac­tion rise by more than 10%.

A pre­scrip­tion for cus­tomer engagement

To under­stand how AI adds val­ue to data, we first need to under­stand the dif­fer­ent forms of ana­lyt­ics brands cur­rent­ly rely on, of which there are four:

• Descrip­tive ana­lyt­ics – This is basic data analy­sis allow­ing brands to report on what’s already hap­pened, with no added value.
• Diag­nos­tic ana­lyt­ics – Tak­ing things a step fur­ther, this involves select­ing and sort­ing data to under­stand what hap­pened on a deep­er level.
• Pre­dic­tive ana­lyt­ics – Things are now get­ting more inter­est­ing, with brands start­ing to make edu­cat­ed guess­es about the future based on his­tor­i­cal pat­terns in their data. This is as far as most of today’s ana­lyt­ics pro­grammes go.
• Pre­scrip­tive ana­lyt­ics – This is the holy grail, where­by users com­bine present, past and real-time data to help them make the best pos­si­ble deci­sions about how to serve a spe­cif­ic cus­tomer in a spe­cif­ic scenario

The next ques­tion many mar­keters will ask them­selves is “Well, I didn’t study data sci­ence, or any sci­ence for that mat­ter, so how am I sup­posed to under­stand and use advanced data ana­lyt­ics technologies?”

It’s a valid ques­tion, but the beau­ty of AI is that the tech­nol­o­gy has matured and become more acces­si­ble. You don’t actu­al­ly need to know how the tech­nol­o­gy works or be able to devel­op an algo­rithm from scratch. You just need a clear idea of what you want to achieve.

At Adobe, we’ve devel­oped pur­pose-built AI solu­tions to help mar­keters with every­thing from intel­li­gent tar­get­ing to cre­ative con­tent cre­ation. We under­stand that dig­i­tal adver­tis­ers need a leg up to stay on top of change, and believe that even novices or occa­sion­al users should be able to get use­ful, action­able insights from their data eas­i­ly and on their own.

That’s when ana­lyt­ics is at its best, when every­one is empow­ered to find the infor­ma­tion they need when they need it and act on it in time. It still falls to mar­keters to define clear objec­tives, whether it’s to raise aware­ness for a new prod­uct, sup­port a sales cam­paign, or seg­ment cus­tomers more pre­cise­ly. From there, they can deter­mine how AI can help them to do the job better.

We are still in the ear­ly days of AI-fuelled ana­lyt­ics, but com­pa­nies from SKY UK to Time­Out are already start­ing to reap the ben­e­fits and they’re just get­ting start­ed. Click here to see how we’re help­ing cus­tomers get more from their data with Adobe Sensei.

And read our report, AI-pow­ered ana­lyt­ics: new insights, bet­ter busi­ness results, for a deep­er dive on how Sen­sei can fuel bet­ter deci­sion-mak­ing in your own organisation.