Discover 3 Free Trial - Retail Quick Wins from Adobe Consulting

Over the com­ing weeks, the retail indus­try experts in Adobe Con­sult­ing will share a series of analy­sis quick wins for retail­ers, using Adobe Dis­cover 3. For a lim­ited time, Adobe Site­Cat­a­lyst 15 clients can inquire with their account team and ask to take part in a free trial of Adobe Dis­cover. We’ve made it eas­ier than ever to try Dis­cover, and we’re show­ing some great Dis­cover analy­sis oppor­tu­ni­ties spe­cific to the retail indus­try. For more infor­ma­tion and to request trial access, con­tact your account man­ager or account executive.

Adobe Dis­cover — Retail Quick Win #3
Simul­ta­ne­ous Seg­men­ta­tion with Table Builder

One of our favorite fea­tures of Dis­cover 3.0 is Table Builder, which allows users to build highly flex­i­ble pivot tables that can incor­po­rate mul­ti­ple data dimen­sions, mul­ti­ple seg­ments, mul­ti­ple met­rics, and your desired tem­po­ral gran­u­lar­ity all in one analy­sis or report.

Addi­tion­ally, even though this fea­ture looks and feels much like a tra­di­tional pivot table, the user still has the abil­ity to “drill any­where” to per­form deeper dives or ad-hoc analy­ses within the defined Table Builder report.

One of the many valu­able ben­e­fits of the Table Builder fea­ture in Dis­cover is the abil­ity to eval­u­ate high level key met­rics (e.g. bounce rate, con­ver­sion rate, etc.) or met­rics for a par­tic­u­lar data dimension(s), such as prod­ucts pur­chased, across mul­ti­ple seg­ments at the same time. In the two exam­ples below, we’ll look at how the use of simul­ta­ne­ous seg­men­ta­tion can pro­vide instant insight into the visit share and per­for­mance of mul­ti­ple vis­i­tor seg­ments to your site, as well as the land­ing page per­for­mance of your mar­ket­ing chan­nels for new vs. return visitors.

Exam­in­ing Site Traf­fic by Vis­i­tor Status

Whether it’s to inform per­son­al­iza­tion efforts or just gain a bet­ter under­stand­ing of who is vis­it­ing your site, we can cre­ate mutu­ally exclu­sive visit-based seg­ments with Table Builder and then eval­u­ate what por­tion of your site traf­fic each seg­ment rep­re­sents as well as how they per­form against your site’s KPI’s (Key Per­for­mance Indi­ca­tors). Con­sider these poten­tial seg­ments as a start­ing point, avail­able to many retail and travel clients using a com­bi­na­tion of out-of-the-box vari­ables as well as some com­mon cus­tom variables:

  • New vis­i­tors that register
  • New vis­i­tors that do not register
  • Return vis­i­tors that sign-in and have purchased
  • Return vis­i­tors that sign-in and have not purchased
  • Return vis­i­tors that do not sign-in and have purchased
  • Return vis­i­tors that do not sign-in and have not purchased

The screen­shot above shows 3 KPI’s (vis­its, rev­enue per visit (RPV), and con­ver­sion rate), by day for our 6 seg­ments of interest.

Note the con­ver­sion rate of seg­ment #5: Return vis­i­tors that are reg­is­tered on the site but have never made a pur­chase. While their con­ver­sion rate is already 5X the total site con­ver­sion rate, we might con­sider pre­sent­ing tar­geted pro­mo­tions to these vis­i­tors at key check­points dur­ing their visit (e.g. on prod­uct details pages or the shop­ping cart).

Look­ing at what prod­ucts these vis­i­tors are brows­ing and buy­ing (by drilling down within these seg­ments – right-click on the seg­ment name) might help us refine such a strat­egy even more effec­tively and pro­vides ample oppor­tu­nity for test­ing. Addi­tion­ally, we might use this data as a track­ing mech­a­nism to mon­i­tor the suc­cess of efforts we have in place to encour­age vis­i­tors to log into the site.

Authen­ti­cat­ing helps match vis­i­tors to cus­tomers which should help improve site per­son­al­iza­tion efforts. These vis­i­tor seg­ments could eas­ily be expanded to include other attrib­utes such as loy­alty pro­gram membership.

Mon­i­tor­ing Land­ing Page Bounce Rates by Mar­ket­ing Chan­nel for New and Return Visitors

Under­stand­ing land­ing page per­for­mance is a crit­i­cal input in plan­ning and eval­u­at­ing mar­ket­ing cam­paigns. One of my favorite analy­ses in Dis­cover is to look at sev­eral key met­rics, bounce rate and con­ver­sion rate in par­tic­u­lar, across 3 dimen­sions: mar­ket­ing chan­nel, entry page type, and vis­i­tor type (new vs. return). I like to incor­po­rate the vis­i­tor type dimen­sion because it helps me to keep in mind cus­tomer acqui­si­tion as an objec­tive of some of my cam­paigns. Note that hav­ing a solid page type vari­able in place is cru­cial for this analysis.

Con­sider the table below, which I’ve set up to include 4 seg­ments: New Vis­i­tors from Nat­ural Search, Return Vis­i­tors from Nat­ural Search, New Vis­i­tors from Paid Search, Return Vis­i­tors from Paid Search.

There’s a lot to take in here but I’ll point out a few things:

  1. Note the dif­fer­ence in con­ver­sion rate between the seg­ments when land­ing vis­i­tors on prod­uct details pages (Row 2). Return Paid Search vis­i­tors have a 46% higher con­ver­sion rate than the next clos­est seg­ment (yel­low square). What makes Dis­cover so pow­er­ful is that we can now break down the Prod­uct Detail row by another dimen­sion such as Entry Page Cat­e­gory to get deeper insight into which types of prod­ucts are con­vert­ing so well for this seg­ment. That knowl­edge could help us test adjust­ing the paid search spend on cer­tain categories.
  2. While rel­a­tively few vis­its from paid and nat­ural search land on a Search Results page, these vis­its (blue squares) have a rel­a­tively low bounce rate and con­vert very well for vis­i­tors famil­iar with your site or brand (as opposed to new vis­i­tors). Again, drilling down into this entry page type across other dimen­sions will tell us what the prod­uct set was for these search land­ing pages as well as what exter­nal search key­words were dri­ving to these land­ing pages. For this par­tic­u­lar dataset, we found that the nat­ural search vis­i­tors were try­ing to find retail store locations.
  3. With respect to cat­e­gory land­ing pages (red square, Row 4), you can see that the stick­i­ness and con­ver­sion rate of these land­ing pages dif­fers sig­nif­i­cantly between paid and nat­ural search. Again, there’s oppor­tu­nity here to drill down and under­stand which spe­cific cat­e­gory land­ing pages are dri­ving the results we want.

Final Note

Site­Cat­a­lyst offers the abil­ity for any user to relate one com­merce dimen­sion to another and to apply a seg­ment to any given report. Using Dis­cover offers the power ana­lyst the abil­ity to ana­lyze mul­ti­ple seg­ments simul­ta­ne­ously within Table Builder while being able to drill down any­where within the data with­out limit. From my expe­ri­ence work­ing on the client side and now as a con­sul­tant with Adobe, I’ve seen first-hand how ana­lysts quickly adopt Dis­cover as their go-to tool for answer­ing the hard ques­tions and find­ing those oppor­tu­ni­ties to drive incre­men­tal value within their organizations.

 

Matt Gilli­gan is a con­sul­tant in Adobe Con­sult­ing, focused on dig­i­tal strat­egy, ana­lyt­ics & opti­miza­tion for retail & travel clients. He tweets at @gilliganmatt.

If you’re an online or cross-channel retailer using Adobe Site­Cat­a­lyst 15, you should try these Retail Quick Wins in Adobe Dis­cover. We’ve made it eas­ier than ever to expe­ri­ence a free trial of Dis­cover. For more infor­ma­tion and to request trial access, con­tact your account man­ager or account executive.

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