Six Cutting-Edge Applications of AI Marketing Tools

Machines are tak­ing over more mar­ket­ing deci­sions every month for an ever-grow­ing num­ber of busi­ness­es. This has led many con­cerned peo­ple to ask what may become of the dig­i­tal mar­ket­ing indus­try with­in the next five years. Should mar­keters start prepar­ing for a very dif­fer­ent future?

The truth is com­plex but nowhere near as grim as some would have you believe. Toward the end of my com­pan­ion arti­cle to this piece —in which I explore sev­er­al arti­fi­cial intel­li­gence (AI) trends that will define dig­i­tal mar­ket­ing in the near future—I men­tion that over the next few years, humans will move from being mere­ly oper­a­tors of dig­i­tal mar­ket­ing tools to being cura­tors, and even col­lab­o­ra­tors with machines.

This is already the case in many busi­ness­es. The lat­est gen­er­a­tion of AI mar­ket­ing tools is already mak­ing an impact on cus­tomer pro­fil­ing, ad tar­get­ing, and con­tent test­ing and opti­mi­sa­tion. Here I explore some of the main areas where machines are trans­form­ing mar­ket­ing, and how AI tools can make mar­ket­ing eas­i­er for your busi­ness.

Con­tent gen­er­a­tion

Main­tain­ing a blog of curat­ed con­tent and com­men­tary was once a full-time job. Not so now that machine-aid­ed web design is rapid­ly being replaced by machine-dri­ven design. For exam­ple, there are com­pa­nies pro­vid­ing “AI web design­ers” that per­form mul­ti­vari­ate test­ing on mil­lions of ver­sions of every page until they arrive at the top-per­form­ing ver­sion and using nat­ur­al lan­guage gen­er­a­tion (NLG) to turn a spread­sheet of data into a com­plete­ly orig­i­nal arti­cle, right on the spot. This great­ly lev­els the play­ing field, pro­vid­ing one-per­son start-ups with the same iter­a­tive design and con­tent gen­er­a­tion pow­er used by For­tune 500 enter­pris­es.

Gen­er­a­tive design

AI-dri­ven design goes far beyond con­tent cre­ation. Today’s gen­er­a­tive design algo­rithms can start with a human-spec­i­fied list of goals and con­straints, then explore mul­ti­di­men­sion­al solu­tion sets to arrive not only at new con­tent, but at entire new cam­paigns and strate­gies that human mar­keters might nev­er have con­sid­ered. Some have called this a kind of arti­fi­cial accel­er­at­ed evo­lu­tion, test­ing and redesign­ing mil­lions of pos­si­ble cam­paigns, quite lit­er­al­ly overnight.

Con­tent tar­get­ing

Key­word-based tar­get­ing is begin­ning to die out. The lat­est con­tent tar­get­ing algo­rithms scan pop­u­lar con­tent based on con­text, sen­ti­ment, and seman­tics (just as a human mar­keter would) and rec­om­mend con­text-rel­e­vant ads tar­get­ed at those audi­ences most like­ly to respond and take action. This offers an obvi­ous advan­tage over tra­di­tion­al “dumb” key­word tar­get­ing, which val­ues ad space based sole­ly on clicks and impres­sions.

Rec­om­men­da­tions and con­tent cura­tion

Whether you’re using AI to gen­er­ate your con­tent or curat­ing it your­self, the lat­est gen­er­a­tion of ana­lyt­ics has become adept at analysing vis­i­tor behav­iour, learn­ing from real-time feed­back, and serv­ing audi­ences with tai­lored sug­ges­tions that improve over time. The days of “you might also like …” are rapid­ly com­ing to an end, as sug­ges­tions based on clicks and pur­chase pat­terns are being replaced by smarter and more ratio­nal rec­om­men­da­tions, derived from a robust under­stand­ing of each visitor’s actu­al needs and inter­ests.

Cus­tomer seg­men­ta­tion

As you sup­ple­ment your first-par­ty cus­tomer data with sec­ond-par­ty data from your part­ners, as well as third-par­ty data pur­chased from com­pa­nies like Nielsen or Axiom, seg­ment dis­cov­ery algo­rithms make it easy to expand your address­able audi­ence by auto­mat­i­cal­ly gen­er­at­ing pre­dic­tions about cus­tomer seg­ments that will grow in val­ue over time. As you zero in on these new seg­ments, looka­like mod­el­ling algo­rithms can find new audi­ences whose traits resem­ble those of your high­est-val­ue cus­tomers. Although these peo­ple may nev­er have heard of your brand, you can begin to shift your adver­tis­ing spend toward them.

Pre­dic­tive ana­lyt­ics

Under­stand­ing your cus­tomers’ cur­rent needs and desires is only the begin­ning. Pre­dic­tive ana­lyt­ic algo­rithms analyse your users’ data—behavioural, sub­mit­ted, and otherwise—to dis­cov­er insights and pre­dict their future behav­iour. In oth­er words, you’ll not only know why your cus­tomer got in touch last time, but why they’re most like­ly to reach out to you next time. This lets you allo­cate resources much more intel­li­gent­ly, and deliv­er enhanced per­son­al­i­sa­tion that greets each cus­tomer with exact­ly the con­tent they’re look­ing for, before they’ve even asked you for it.

As I men­tioned in the com­pan­ion arti­cle, the idea of dig­i­tal mar­ket­ing AI is not to replace peo­ple with robots, but to “remove the robot from the per­son,” as Aviva’s CFO, Tom Stod­dard, superbly expressed it in a recent inter­view. The more of the grunt work mar­keters can hand over to the capa­ble hands of machines, the more they’ll be free to tell cre­ative, orig­i­nal sto­ries about their brands. To find out how Adobe is build­ing AI into our cloud plat­form, and enabling many of the capa­bil­i­ties men­tioned above through Adobe Sen­sei, read more here.

One Response to Six Cutting-Edge Applications of AI Marketing Tools

  1. Charley says:

    Of course, what a mag­nif­i­cent blog and illu­mi­nat­ing posts, I sure­ly will book­mark your blog.Have an awsome
    day!

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