The ori­gin of Big Data, in its present form, is dif­fi­cult to deter­mine. Some attribute the first mod­ern use of the term to John Mashey, a lead sci­en­tist at Sil­i­con Graph­ics, dur­ing the 1990s. Since then, the vol­ume, veloc­ity, and vari­ety of dig­i­tal data has grown so rapidly that it has left exec­u­tives and aca­d­e­mi­cians alike scram­bling to not only cap­ture and mea­sure Big Data but to use the infor­ma­tion con­tained within data sets in a mean­ing­ful way.

Ear­lier this year, the Library of Con­gress announced it has archived over 170 bil­lion tweets. The goal of the pro­gram is to pro­vide researchers with data that depict the cul­tural sig­nif­i­cance of Twit­ter as a means of com­mu­ni­ca­tion and cre­ative expres­sion. While impor­tant, I sug­gest that the real sig­nif­i­cance behind Big Data is how the incred­i­ble vol­ume of struc­tured and unstruc­tured data can drive the eco­nomic engine that sup­ports US and global growth.

Before you ana­lyze Big Data, it’s impor­tant to fig­ure out how the appli­ca­tion of dig­i­tal infor­ma­tion under­pins your mar­ket­ing strate­gies and deliv­ers value to your enter­prise. At Adobe, we have six appli­ca­tions where Big Data ana­lyt­ics pro­vides value to our brand.

1. 360⁰ Cus­tomer Profile

Data dri­ves our abil­ity to under­stand our cus­tomers bet­ter, pro­vid­ing a high-resolution view of their pref­er­ences, val­ues, activ­i­ties, and behav­iors. Through the accu­mu­la­tion and analy­sis of the data, we can deliver rel­e­vancy to our mar­kets. With­out data, mes­sag­ing is less tar­geted and con­ver­sion rates suf­fer. We might as well end up play­ing Led Zeppelin’s Immi­grant Song to an audi­ence of Millenials—“Why is that old dude scream­ing like that?!?”

The view of your cus­tomer becomes clearer as more data sets are infused into the cus­tomer pro­file. Mar­ket­ing exec­u­tives who rely on basic Web data such as site behav­ior and refer­ral sources miss the nuanced behav­ior embed­ded in enriched data sets that include social behav­iors, in-store pur­chases, call cen­ter activ­ity, and loy­alty pro­gram buy­ing behav­ior. Big Data dri­ves value by open­ing our eyes to our cus­tomers. I’ll deep dive into basic and enriched data in a future post.

2. Attri­bu­tion Modeling

Whether it’s a lin­ear, time decay, or last inter­ac­tion model you fol­low, data is the bread to the attri­bu­tion model but­ter. Each touch point along the buyer’s path pro­vides value. You’ve got first touch, con­ver­sion points, aban­don­ment points, last touch, and other inter­ac­tions that reveal where mes­sag­ing is strong and where you need to update your assets. Adobe Ana­lyt­ics was devel­oped to pro­vide this value within the mod­el­ing process. Con­nect­ing sales to engage­ment points leads to far greater mar­ket­ing efficiency—and that can’t be done with­out Big Data.

Who wants to dive into a mar­ket­ing cam­paign with­out insight into the most (and least) effec­tive assets? That prac­tice went out of style in the last cen­tury. Today’s mar­keters have too much infor­ma­tion at their dis­posal to ignore the sig­nals pro­vided by data.

3. Per­son­al­iza­tion

Now that we’ve got data sets that pro­vide a high-resolution view of our cus­tomers, we must use them to cre­ate the most per­son­al­ized, rel­e­vant engage­ments pos­si­ble. I can remem­ber being in high school where some of my teach­ers taught with a broad stroke, but the best ones paid atten­tion to how each kid ingested the sub­ject mat­ter on a per­sonal level. It’s no dif­fer­ent with mar­ket­ing, folks. Lever­ag­ing Big Data means fol­low­ing analy­sis with per­son­al­ized mes­sag­ing that res­onates because it has focused appeal. We can’t get away with a stale, dis­tant approach to our mar­kets; the data that’s out there enables us to craft per­son­al­ized campaigns.

4. Test­ing Enablement

Mar­keters know the value of test­ing. The good news is we’re no longer mired in guess­work when it comes to split test­ing, mar­ket eval­u­a­tion, or prod­uct devel­op­ment. Our cus­tomers ulti­mately tell us which direc­tion to go as we roll out value propo­si­tions. Sure, audi­ence frag­men­ta­tion makes the eval­u­a­tion of test­ing out­comes more chal­leng­ing, but that’s where Big Data pro­vides max­i­mum value. The trans­parency we achieve through Big Data has changed the test­ing envi­ron­ment and enhanced our abil­ity to deliver solu­tions our mar­kets seek.

A word of warn­ing, though. There may be times where test­ing hypothe­ses can be tripped up, even in the face of Big Data. Cor­re­la­tion is not cau­sa­tion. Beware of mak­ing deci­sions based on poorly made assump­tions. Data allows insight, but incom­plete or dis­con­nected met­rics can scut­tle the best-planned roll­out. Ulti­mately, though, whether it’s con­cept, pack­ag­ing, or chan­nel you’re test­ing, the high price of fail­ure dri­ves us to depend on data to shape how we market.

5. Prod­uct Development

Along the prod­uct devel­op­ment con­tin­uum, the mul­ti­ple points where suc­cess or fail­ure is deter­mined have to be sup­ported by data. Solu­tions can be made more valu­able to your cus­tomers when data is used as a devel­op­ment tool. I speak with lead­ers across the globe who have yet to lever­age Big Data as a prod­uct devel­op­ment tool. They’re miss­ing out! Data can push us to deliver tai­lored solu­tions that bet­ter meet exact­ing cus­tomer needs across mul­ti­ple seg­ments in any industry.

Cus­tomer sen­ti­ment is a sig­nif­i­cant met­ric that today is more eas­ily cap­tured through social, search, email, and Web engage­ments. Latent oppor­tu­ni­ties, con­sumer pref­er­ences, and geo­graphic dis­par­ity are all embed­ded in the data we col­lect. As a mar­keter, think hard about the prac­tice of lever­ag­ing Big Data for your prod­uct devel­op­ment efforts—big wins to be had!

6. Fore­cast­ing & Prediction

One of the more influ­en­tial val­ues Big Data con­tributes to enter­prise suc­cess is its impact on fore­cast­ing and pre­dic­tion. Pre­dic­tive capa­bil­i­ties become sharper across busi­ness func­tions when data is used to sup­port expec­ta­tions. The fore­cast­ing lens is clearer because of the abil­ity to seg­ment data sets and ana­lyze end-user behav­ior. Data accu­mu­la­tion pro­vides real-time updates and enables sharper deci­sion mak­ing. For enter­prises using Agile man­age­ment tech­niques, data allows you to adapt to mar­ket changes quickly with less second-guessing.

Whether you base go-to-market deci­sions on your first-party data sources or third-party research, the value derived from data analy­sis is unques­tion­able. Not only does Big Data allow for greater pro­duc­tiv­ity within inter­nal prac­tices, it can pro­vide a bet­ter assess­ment of the com­pet­i­tive envi­ron­ment as well.

Where would we be with­out Big Data? There is too much value to be unlocked for today’s lead­ing enter­prises to ignore. Where do you see the value when it comes to devel­op­ing and mar­ket­ing your offer­ings to your customers?

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