It should be noted that the name of the cus­tomer has been changed to pro­tect my income. All mar­ket­ing tools are assumed inef­fec­tive until proven oth­er­wise in a court of the laws of mathematics.

Okay, that was a bit of a nerdy state­ment, but nec­es­sary. I do pro­tect my clients’ pri­vacy when con­sul­ta­tions are needed, and thus I pro­tect my income. I also take the approach that some­thing in a clients’ mar­ket­ing machine is wrong, that some­where in their mar­ket­ing plan there is a but­ler hold­ing a can­dle­stick in the library … with Col. Mustard.

On one par­tic­u­lar after­noon, I got the call that a large global tech­nol­ogy com­pany (whom we shall refer to as Big Tech) had an issue. This par­tic­u­lar com­pany had taken a loss in sales in one of their Asian mar­kets. The year-over-year sales on the small and medium busi­ness web­site had declined by 40 per­cent. The top-level exec­u­tives wanted answers and pos­si­bly a rec­om­men­da­tion on how to fix the issue. At times, this job can make one feel a lit­tle like a detec­tive, or a super­hero. In this instance, I didn’t slide down a pole hid­den behind a book­case into my secret lair and climb into my Stat­mo­bile. Nope, much more exciting—I fired up my laptop.

This was within a year or two of the 2008 down­turn when a lot of busi­nesses’ sales had slowed. Big Tech had seen a slow in sales, much the same as every­one else. One com­pany can­not change global eco­nomic con­di­tions; that’s out of their con­trol. What the client wanted wasn’t nec­es­sar­ily a super fix, but rather to estab­lish what was within their con­trol and what was not. The waters can some­times get a bit muddy with­out sup­port­able facts and that means numbers.

After a few inter­views with Big Tech’s exec­u­tives and sales peo­ple, it became appar­ent that the loss was being blamed on the U.S. dollar’s poor per­for­mance at that time as well as the econ­omy. The thought process in place that sup­ported this the­ory was that if the dol­lar was trad­ing badly against the yen, U.S. com­pa­nies would be doing poorly abroad. I was not opposed to the idea, but I was not con­vinced either. With most large clients, such as Big Tech, you often get lit­tle guid­ance as to where to look and how to approach a ques­tion. For ana­lysts like myself, this is a god­send. We will gen­er­ally have an open, green field to begin our study. So there I was with all of Big Tech’s raw Inter­net data and carte blanche to do what I had to do to get some answers.

The first hur­dle one must over­come when approach­ing a big chunk of data like the one that Big Tech gave me was decid­ing where to begin. Is it bet­ter to start with acqui­si­tions? Do you look at the site and how cus­tomers are inter­act­ing with it, or do you get right to where the meat meets the metal and dive into the cart check-out data? Time to do some data min­ing and correlating.

Using data min­ing tech­niques that are now built into Adobe Ana­lyt­ics Pre­mium, I was able to find a strong cor­re­la­tion between email recip­i­ents who landed on a gen­eral prod­uct page and those who landed on a spe­cific prod­uct page. For instance, if the cus­tomer clicked on a wid­get that was adver­tised in an email blast and landed on the prod­uct page, they most likely would pur­chase said prod­uct. Those who landed on a gen­eral page and were forced to look for the adver­tised prod­uct more or less just gave up. At least that is the infer­ence that could be drawn from the data. Using pre­dic­tive mod­el­ing tech­niques, we were able to test the the­ory offline and it looked like that dog would hunt.

By sim­ply chang­ing the URLs in the email blasts so that all the redi­rects pointed toward the prod­ucts being adver­tised, we were able to see an imme­di­ate impact. This sim­ple “fix” landed Big Tech an overnight rev­enue increase of $150,000 to as much as $500,000 in weekly incre­men­tal rev­enue. This was a huge win for Big Tech because they finally had got­ten a han­dle on what they could con­trol instead of feel­ing vic­tim­ized by the things that they could not. The job was still not com­plete, however.

I went back and decided to look at the exchange rate the­ory to see if there was any cre­dence to the idea that a poorly per­form­ing dol­lar was a fac­tor. In ana­lyt­ics, one never dis­counts a the­ory; I’ve seen some pretty crazy cor­re­la­tions that turned out to be spot on (remem­ber knit­ting and poi­son?). I still approach each the­ory with a grain of salt, though. I brought the exchange rate between the dol­lar and the yen into the equa­tion (lit­er­ally) and found next to zero cor­re­la­tion between sales and exchange rate. Another win. In addi­tion to email rec­om­men­da­tions, senior man­age­ment was able to dis­prove a preva­lent the­ory and gain some account­abil­ity within their organization.

Did Big Tech close the gap on their 40 per­cent decline? No, not overnight. At the time, com­pet­ing prod­ucts were hit­ting the mar­ket, so its sales would have been impacted any­way. The econ­omy was not in as favor­able a con­di­tion as it had been either. Both of these issues were con­tribut­ing fac­tors that were out of Big Tech’s direct con­trol. But the com­pany was able to iden­tify what it could con­trol and max­i­mize its efforts and resources accord­ingly. I could dust off my britches and call it a day on this case.

The thing to remem­ber is that you do not have to be a Big Data super­hero if you have the right tools. Tools such as Adobe Ana­lyt­ics Pre­mium allow you to do the dead reck­on­ing you need to do to get your mar­ket­ing back on course. Spend­ing more time answer­ing ques­tions and not being over­whelmed with manip­u­lat­ing Big Data is what ana­lyt­ics is about. That, and get­ting to travel to exotic places like Tokyo … on a con­fer­ence call.