Segments in 5 Minutes [Analysis with Insight]

It’s quite com­mon that a cus­tomer start­ing to use Omni­ture Insight will tell us that one of the key busi­ness rea­sons for adding Insight to their tool set is for its power of Segmentation.

In Omni­ture Insight, Seg­ments are sim­ply user-defined, re-usable selec­tions of data. They’re quick to use and pow­er­ful in analy­sis when you want to seg­ment and explore a group of vis­i­tors, cus­tomers, trans­ac­tions, vis­its, or other sim­i­lar level of data.

Let’s imag­ine that I work for a small group of retail stores and use Omni­ture Insight, loaded with my web­site data, but also with in-store trans­ac­tion data and cus­tomer demo­graphic data.

I have a Key Busi­ness Require­ment (KBR) this year of increas­ing over­all rev­enue per transaction.

As a strate­gic step toward that KBR, I am eager to under­stand female cus­tomers who pur­chase casual wom­ens’ cloth­ing and who have higher house­hold incomes. Tac­ti­cally, I’d like to develop ways to increase the num­ber of items (and the asso­ci­ated rev­enue related to those items) in each of their purchases.

I’ll start my Insight analy­sis by open­ing a table to dis­play Trans­ac­tions, Cus­tomers, and Avg. Dol­lars per Trans­ac­tion by Depart­ment. Sort­ing by Trans­ac­tions by Depart­ment, I see that my top depart­ments (for all cus­tomers, and all of their trans­ac­tions) are Wom­ens’ Casual, Kitchen and Acces­sories, Girls’, etc.:

Next, I’ll add a Seg­ment visu­al­iza­tion. Since I’m eager to ana­lyze cus­tomers who fit my selec­tion, I want to cre­ate a customer-level segment.

I’ll select trans­ac­tions that included Wom­ens’ Casual. While I’m at it, I’m also going to add tables for Gen­der (and select Females) and Income Descrip­tions (and select the upper income segments.)

In my cus­tomer seg­ment visu­al­iza­tion, I sim­ply select “Add Seg­ment”, then des­e­lect in the other tables. For con­ve­nience, I rename the cus­tomer seg­ment to “Upper Income Females, Casual.”

Then, I sim­ply select the cus­tomer seg­ment that I cre­ated. As a result, what I’ll see in my analy­sis work­space will be the data related to this selec­tion of cus­tomers (female cus­tomers in upper income ranges who pur­chased casual wom­ens’ shoes.)

Look­ing at the Depart­ments that appear in the most trans­ac­tions for these cus­tomers, I dis­cover that their trans­ac­tions fre­quently include kitchen and acces­sories items, wom­ens’ dress clothes, beauty and fra­grance items, etc.

Sort­ing the Depart­ments for these cus­tomers by Aver­age Dol­lars per Trans­ac­tion, though, I dis­cover some­what sur­pris­ing results: the top depart­ments by dol­lars per trans­ac­tion include boys’ items, mens’ ath­letic items, wom­ens’ ath­letic items, and chil­drens’ ath­letic items. Per­haps there’s an oppor­tu­nity to try to do some­thing I haven’t done before and cross-promote wom­ens’ casual items with chil­drens’ ath­letic or boys’ items.

I can use this seg­ment to con­tinue to dive deeper into other infor­ma­tion — spe­cific items, average time between pur­chases, dis­counts or coupons most fre­quently used, etc.

Clos­ing the loop from this analy­sis back to my tac­ti­cal objec­tives (and ulti­mately my KBR of increas­ing over­all rev­enue per trans­ac­tion), I might decide to select a group of these cus­tomers (that live a cer­tain dis­tance from a store, that have chil­dren in the house­hold, that meet other fac­tors that I dis­cover through analy­sis) and send them a spe­cific coupon offer invit­ing them to a spe­cial dis­count when they pur­chase casual wom­ens’ items and kids’ items in the same transaction.

The best part of this whole exer­cise: With my data in Omni­ture Insight, I com­pleted this entire analy­sis well within 5 min­utes, and I’m on to my next hypoth­e­sis, my next analy­sis, and my next dis­cov­ery to impact my business.

Have a ques­tion about any­thing related to Omni­ture Insight? Do you have any tips or best prac­tices related to Omni­ture Insight you want to share? If so, please leave a com­ment here or send me an e-mail at mhal­brook [at] omni­ture [dot] com and I will do my best to answer it here on the blog so every­one can learn. (If you pre­fer, I won’t use your name or com­pany name.) You can also fol­low me on Twit­ter @Michael­Hal­brook.

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