Meaningful Graphs in Web Analytics
Have you ever used Google Analytics (or any other tool) to compare your metrics to the previous month's or any other past timeframe?
Did you then realize, that the graphs shown didn't really tell you a story?
So here's a tip – which in case you haven't already discovered this yourself – is so simple, that you'll probably bang your head against the desk.
This tip probably applies to most Web Analytic Tools out there. As i have no access to any other tools, i'll use Google Analytics here for the examples.
Google Analytics has a nice date-picker which lets you select the timeframe used to display its verious reports. The timeframe is usually defined by a start- and end-date. In order to let you compare metrics to the past, there can even be two start- and end-dates.
Let's assume you want to look at your dashboard, and compare your statistics for April to the ones from March (the previous Month).
By default, you'll define a date range by either clicking on the month name (April) which automatically inserts 04/01/2008 and 04/30/2008 into the date fields or you fill in the date fields by hand (or even use the fency Timeline slider or what have you).
You then repeat this step for the timeframe in the past you want to compare your metrics to, click on Apply and you're done defining your timeframes and your report is being displayed.
The defined timeframe as seen in the little calendar view
In your dashboard, you'll see all kinds of graphs and probably also one for Absolute Unique Visitors which might look just like the image below:
As you can see in the graph to the left, we have a nice pattern, as the data used for this example is from a B2B Site which has an audience browsing the site mainly during weekdays.
The blue line represents April's trend and the green line represents visitors in March. You'll notice, the pattern is somehow repeated in the green line for March.
What the graph to the left fails to communicate however is the data you might probably be interested in most: The difference in quantity between the two months. Did April have more unique visitors or March?
The graph simply fails because there is too much noise and the pattern isn't aligned.
If your traffic doesn't show any patterns and you have the same amount of traffic every day, please stop reading, as all i say here doesn't apply to you.
The Hand Tuned, Meaningful Beauty
Now here's the simple trick. Let's align the patterns by adjusting our timeframes in a way, that makes sense for our scenario: Instead of picking whole months, we need to align our timeframes by a specific Weekday, which will be the same day for the current and past timeframe. We'll also make sure, both timeframes have the same length.
Adjusted timeframes with similar lengths and the start days aligned at a Monday.
We're looking at four weeks each.
Look at the graph to the left. Isn't this a beauty? Get the story this picture is telling you? Huge difference. We can still immediately identify the patterns from the previous graph, but can also see a meaningful, comparable representation of quantities.
I believe everybody will now be able to immediately spot the decline in March's visitors which lasts from the middle of week three until mid of week four.
This trick is so simple, i wish google had "Align by Weekday" build right in to Google Analytics already.
I hope this was news to at least some of you and that this little nugget will be helpful to you as you are crunching your numbers.
PS: If you're into Web Analytics and don't read him already, do yourself a favor and have a look at Avinash's Blog.