Know Your Tools
When looking at your web analytics reports, it is important to know what you’re looking at. What may initially look like errors in the data, could just be the result of not understanding how the report or the individual metrics are defined. Consultants, who frequently work with multiple analytics packages, must be especially careful. Here’s a short example of what I mean:
A client came to me with 2 reports. One was the product SKU report for a particular SKU. The other report was that same report, but segmeted by source.
In the report that was segmented by source, all of the summary data was considerably higher than on the non-segmented report. The client didn’t know which one was correct — why was the data changing when he segmented the report? (And when you see two different sets of data you think should be the same, you wonder if either is correct.)
Open up your own Google Analytics ecommerce report and follow along.
First go to the product SKU report by choosing Ecommerce -> Product Performance -> Product SKUs from the left sidebar menu in the reporting interface.
You’ll see a list of all SKUs that sold during your selected time period. Click on one to see a summary screen for just that SKU. It will look something like this:
Now use the pulldown menu to segment by source. Most of you will see the summary numbers jump up, like my example here:
In the example images, the Quantity went from 545 to 715, just from segmenting. And Product Revenue jumped from $14,385.33 to $19,585.61.
When Google Analytics displays the segmented report, it is pulling Quantity, Product Revenue, etc from the Transaction Level. That is, the Quantity is now the total number of items purchased in all transactions that included your selected SKU. Likewise Product Revenue is the total revenue for all transactions that included that SKU — not just the revenue generated by that product.
If you go straight to the segmented report, you might not even notice that the data is different, and you could be making decisions based on the wrong information.
It is not that the data presented is wrong, it’s not. But it may not be the data you are expecting, which can be just as bad.
Although my example is in Google Analytics, it’s important to consider regardless of your analytics package. Make sure you know how a report is defined and if you find something that doesn’t seem quite right, be careful of your assumptions and don’t always believe what the report tells you about itself.