December 27, 2012 at 09:00 AM EST
JCPenny and the Dangers of Selective Data
Image from: JCPenney Rebrands / brandsunderconstruction.com JCPenny has been undergoing some huge changes in the past year – they’ve launched not one, not two, but three new attempts to rebrand the store. The most recent move was to launch a “store-in-a-store” program similar to Target. The Wall Street consensus, as well as their earnings, has [...]

JCPenney Rebranding

Image from: JCPenney Rebrands / brandsunderconstruction.com

JCPenny has been undergoing some huge changes in the past year – they’ve launched not one, not two, but three new attempts to rebrand the store.  The most recent move was to launch a “store-in-a-store” program similar to Target.  The Wall Street consensus, as well as their earnings, has said that this strategy isn’t work.But I wanted to take a look at the Compete data to see if there was anything there that might point to a rebound in consumer sentiment.

My method for this analysis was to look at the Compete Rank (Compete’s ranking of top traffic websites).  First, let’s look at how JC Penny’s ranking has changed over time (a really cool feature of Compete PRO by the way):

Compete Rank of JCPenny.com

This chart actually supports the idea that they are back on an upswing.  After moving to a 2 year low ranking of #69, they’ve moved back up to #33.  And they’ve rebounded very quickly although falling short of the #28 ranking they had at the same time last year.  So this is good news for JCP right?

I decided to next look at the Compete Rank for two competitors alongside JCP – Kohl’s and Macys (granting that Macys really targets a different, more affluent shopper).

Compete Rank between JCPenny, Kohls and Macys

And what this shows is a different picture – namely that all the retailers in the group are affected by the same level of seasonality.  So what was happening in JCP was not unique.  As the saying goes “A rising tide lifts all boats”, and it also works in the other direction as well.

All this is to say that when you look at website performance data, make sure you take into account the competitive context and other factors that may be affecting your data (in this case, seasonality).  You might, for example, look at a group of marketing campaigns, see that one outperformed, and then jump to the conclusion that it must be something about that campaign rather than considering other outside forces that may have affected the performance.

How are you separating your marketing performance from seasonal and other factors?  Let us know in the comments!

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