A crit­i­cal step in the test­ing and opti­miza­tion process is fil­ter­ing your reports by cus­tomer char­ac­ter­is­tics, uncov­er­ing how your tests per­form in smaller group­ings or seg­ments of your vis­i­tors. A test­ing tool that does not pro­vide this fea­ture does not allow you to inter­pret your test results or tar­get fully and accu­rately.  You will not clearly see how your con­tent is per­form­ing in dif­fer­ent sub-sections of your cus­tomers.  You are also com­pletely inca­pable of qual­i­fy­ing your test results based on other influ­enc­ing fac­tors within your test group, mak­ing it impos­si­ble to tar­get rel­e­vant con­tent.  It is sur­pris­ing to hear that there are test­ing tools out there, right now, that do not pro­vide this option; be sure to inquire and eval­u­ate these capa­bil­i­ties before choos­ing to imple­ment a new prod­uct.  Let me share the impact of not hav­ing this capability.

Here’s a sim­ple A/B test exam­ple that clearly under­lines the issues:  I’m an online cloth­ing retailer.  Let’s say I’m test­ing my home page mast­head ban­ner ad.  One option has a “buy one, get one free” offer on shirts, and the other offers “free ship­ping on orders over $50”.  Let’s take it one step fur­ther and say that I only want to test these offers within the cus­tomer seg­ment of Cal­i­for­nia res­i­dents.  I tar­get the test to Cal­i­for­nia res­i­dents.  After reach­ing sig­nif­i­cance, I find that 85% of Cal­i­for­ni­ans pre­fer the “buy one get one free” offer.  It might seem log­i­cal to then tar­get the “buy one, get one free” offer to Cal­i­for­ni­ans, and call it a day.

BUT………..what if we fil­ter these test results by the seg­ment of gen­der. Or age. Or cat­e­gory affin­ity. We just might find that Cal­i­for­nia women in their 30s actu­ally pre­fer the “free ship­ping on orders over $50”.  If we had tar­geted the “Buy one, get one free” ad to all Cal­i­for­ni­ans based upon the unfil­tered results, Cal­i­for­nia women would not be receiv­ing their pre­ferred, most rel­e­vant ad!!!  Even if I ran an addi­tional test tar­geted to Cal­i­for­nia women, I could not fully eval­u­ate my orig­i­nal test results based on these new findings.

I can’t stress enough how impor­tant it is to eval­u­ate your results com­pletely, based on the crit­i­cal seg­ments or sub­di­vi­sions of your dig­i­tal pop­u­la­tion.  There is no other way to inter­pret your results fully, from every angle and every influ­enc­ing fac­tor, with­out this capa­bil­ity.  When shop­ping for an effec­tive test­ing and tar­get­ing tool that will be an inte­gral, long-term invest­ment for the growth of your opti­miza­tion pro­gram, a tool that does not pro­vide seg­ment fil­ter­ing in their results is not worth your time or eval­u­a­tion.   You need a tool that will adapt and grow with the chang­ing needs and influ­ences of your cus­tomers over the long-term, not a tool that gives you mis­lead­ing results.

Adobe Test&Target has seg­ment fil­ter­ing capa­bil­i­ties in its reports, with built-in seg­ments right out of the box.  Seg­ment fil­ters can also be cus­tomized based upon any influ­enc­ing fac­tor that defines a seg­ment in your pop­u­la­tion.  New seg­ments and seg­ment fil­ters can also be gen­er­ated based upon any cus­tomer data or attribute cap­tured in mboxes, as well as from 3rd party data inte­gra­tion.  In addi­tion, being an open plat­form, with Site­Cat­a­lyst inte­gra­tions and built in 3rd party data aggre­ga­tion, user pro­files can undergo an enrich­ment process, bring­ing a sophis­ti­ca­tion to the seg­men­ta­tion and tar­get­ing process.  With all of this at your fin­ger­tips, no influ­enc­ing fac­tor will be over­looked, and you can feel secure that there won’t be sub­stan­tial por­tions of your vis­i­tors who are left receiv­ing the wrong con­tent at the wrong time.