Mer­chan­dis­ing vari­ables are fun­da­men­tal to any suc­cess­ful retail web ana­lyt­ics imple­men­ta­tion. Adam Greco has a great post about the tech­ni­cal details of how to set Mer­chan­dis­ing vari­ables (with great follow-up com­ments from our own Kevin Willeitner).

For this post, I wanted to back-up and give an overview of why mer­chan­dis­ing vari­ables are so impor­tant to retail mer­chants, and what insights you can get for site optimization.

Mer­chan­dis­ing vari­ables can be drive impor­tant insights into your cus­tomers’ behav­ior. They can help answer key ques­tions that you can­not get from any other inter­nal report­ing tools. For example:

  • How much rev­enue is being dri­ven by inter­nal search vs. tra­di­tional brows­ing (or other methods)?
  • What web cat­e­gories are dri­ving the most rev­enue? Top cat­e­gories for a given prod­uct (if that prod­uct is shown in mul­ti­ple categories)?
  • What site fea­tures are cus­tomers using (e.g. zoom, alt views, size fil­ters, refinements)?

The chal­lenge with tra­di­tional vari­able attri­bu­tion is that a cus­tomer may have mul­ti­ple prod­ucts in an order, with mul­ti­ple influ­ences for each prod­uct. So what fea­tures get credit for a given order? Mer­chan­dis­ing vari­ables give us the answer: Give credit at the prod­uct level to the fea­tures and func­tion­al­ity that drove a cus­tomer to each product.

Let’s look at an exam­ple: A cus­tomer views and pur­chases 4 prod­ucts on your site. For each prod­uct they pur­chased them from 4 dif­fer­ent web cat­e­gories, and used mul­ti­ple dif­fer­ent site fea­tures for each prod­uct. How do you allo­cate the $135 order total? Mer­chan­dis­ing vari­ables allo­cate the rev­enue at the prod­uct level, so each cat­e­gory gets the credit for their product.

For this exam­ple, I cre­ated a sam­ple “matrix” of site fea­tures they used for each prod­uct (which are cus­tom for every imple­men­ta­tion). Each of these columns in green rep­re­sents a pos­si­ble mer­chan­dis­ing vari­able that is set when the cus­tomer views each prod­uct. An ana­lyst can now run these reports indi­vid­u­ally and under­stand how that spe­cific fea­ture con­tributed to site rev­enue. How much rev­enue is dri­ven by zoom or alt views? What is the most pop­u­lar find­ing method?

For exam­ple, an ana­lyst can run the “Web Cat­e­gory” report and see the rev­enue break­down for prod­ucts pur­chased from those cat­e­gories. The site mer­chants that cre­ated these cat­e­gories use this infor­ma­tion to under­stand cat­e­gory per­for­mance, and opti­mize their them for max­i­mum revenue.

Addi­tion­ally, an ana­lyst can use this report to under­stand the prod­uct con­ver­sion rate (look-to-buy ratio), to under­stand cus­tomer inter­ac­tion with prod­ucts in that cat­e­gory. In this case, the “Spring Looks” cat­e­gory has a lower con­ver­sion rate than the oth­ers, so the mer­chant may want to review the prod­uct selec­tion in that cat­e­gory. Also, an ana­lyst can look at a spe­cific prod­uct (or prod­uct clas­si­fi­ca­tion) and break down by the web cat­e­gory to see where cus­tomers are buy­ing a spe­cific product.

Another report in this exam­ple would be the “zoom” report: How much rev­enue is directly dri­ven from cus­tomers who used zoom before pur­chas­ing a prod­uct? What types of prod­ucts use zoom more than oth­ers? In the exam­ple below, an ana­lyst could break down the Sweaters Prod­uct Fam­ily (a clas­si­fi­ca­tion of prod­ucts) by Zoom Usage to see how that fea­ture is used for Sweaters.

Com­mon uses for mer­chan­dis­ing vari­ables include any sit­u­a­tion where you want to track rev­enue for product-specific fea­tures, such as web cat­e­gories, prod­uct find­ing meth­ods, search key­words, zoom and alt view track­ing, size fil­ter­ing and refinements.

To sum­ma­rize, here is what Mer­chan­dis­ing vari­ables pro­vide for the retail web analyst:

  • Abil­ity to iso­late “Direct” prod­uct rev­enue dri­ven by the fea­tures and func­tion­al­ity on the site
  • Abil­ity to “break down” a mer­chan­dis­ing evar to see the prod­ucts that con­tributed to the rev­enue for that feature
  • Inversely, “break down” a prod­uct to see what fea­tures drove the demand for that product


Mer­chan­dis­ing vari­able imple­men­ta­tions can be a lit­tle tricky, so be sure to con­sult with Adobe Con­sult­ing if you plan to take advan­tage of this fun­da­men­tal fea­ture of SiteCatalyst.

In a future post, I will list some tips for imple­ment­ing mer­chan­dis­ing vari­ables, and caveats for report­ing and analysis.


I would love to hear your feed­back or ques­tions. Thanks for reading.