Suc­cess­ful tar­get­ing is about get­ting per­sonal and really matchmaking—identifying the right con­tent for the right user while gen­er­at­ing the high­est returns. It’s impor­tant to make sure you’re learn­ing every­thing pos­si­ble about your vis­i­tors, using that knowl­edge effec­tively, and giv­ing users the best pos­si­ble expe­ri­ence. This requires con­sid­er­ing three essen­tial topics:

Data, and lots of it.

For any tar­get­ing solu­tion to be effec­tive, you need rich data—it’s the raw mate­r­ial for pow­er­ing cre­ative expe­ri­ences and mak­ing smart deci­sions. In addi­tion to user infor­ma­tion col­lected through online inter­ac­tions, data from cus­tomer rela­tion­ship man­age­ment (CRM) soft­ware and other enter­prise sys­tems can enrich user pro­files and enable bet­ter pre­dic­tive deci­sions about a cus­tomer. Ide­ally, any tar­get­ing sys­tem you adopt will incor­po­rate data from sev­eral key sys­tems to enable more tai­lored and rel­e­vant tar­get­ing, with­out com­pli­cated and time-consuming data integrations.

Robust rules-based and auto­mated behav­ioral tar­get­ing tools.

To make the best deci­sions and dis­play the right con­tent to the right user at the right time, many dif­fer­ent attrib­utes must be con­sid­ered: What ZIP code is the user from? When did they last visit the site? How many times have they vis­ited? Have they pur­chased pre­vi­ously? What did they pur­chase? Did they aban­don their online shop­ping cart and come back to the site later, or make the pur­chase in a store? The more attrib­utes that can be taken into con­sid­er­a­tion together (not in iso­la­tion), the more accu­rate tar­get­ing is likely to be.

Mar­keters need a sys­tem that will allow them to explic­itly express their intent through rules, as well as make auto­mated pre­dic­tive deci­sions, to achieve desired out­comes. In recent blogs, we’ve explored how using machine-learning algo­rithms and auto­mated behav­ioral tar­get­ing can help over­come the lim­i­ta­tions of sim­ple rules-based tar­get­ing sys­tems to improve deci­sion mak­ing. Auto­mated sys­tems also enable your efforts to scale beyond what is pos­si­ble with a man­ual sys­tem. When mar­keters want to write their own rules, look for a solu­tion that makes the process easy with a flex­i­ble and com­pre­hen­sive tar­get­ing query language.

Real-time results.

The abil­ity to mea­sure the results of the test or tar­get­ing cam­paign in real-time is cru­cial. This first allows you to ensure every­thing is run­ning cor­rectly when you first launch the cam­paign, and later enables your team to fully under­stand what hap­pened and how each unique seg­ment inter­acted with the changes made. Data should show up in minutes—not hours or days—so you can quickly make deci­sions. Look for sim­ple views to under­stand how the cam­paign affected engage­ment, con­ver­sion, and rev­enue. Be care­ful of sim­pli­fied engagement—someone inter­act­ing with any part of the page should not be con­sid­ered suc­cess.  Instead, track clicks, page views, and time on the site. Finally, make sure you can eas­ily view how dif­fer­ent defined seg­ments per­formed. This way you can ensure you are select­ing the best result per user group, rather than just one win­ner for your whole dig­i­tal experience. 

We just touched on a few key things to think about as you look to get the most out of your tar­get­ing efforts. In part II of our dis­cus­sion about “mak­ing it per­sonal,” we’ll explore how to use pro­file scripts in solu­tions like Adobe Tar­get to “tag” vis­i­tors with cer­tain attrib­utes for tar­get­ing and seg­men­ta­tion with­out requir­ing addi­tional integration.

Part II of Mak­ing it Per­sonal will run on Wednes­day, June 5.

 

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