3 Steps to Quickly Diagnosing Problems (& Successes) in Analytics

If your boss is anything like mine, you don’t just hand over reports and call it a week/month/quarter/etc. When traffic jumps or drops, you’re asked to explain not only what happened, but why it happened. For any online marketer, this means a deep dive into your data. But how do you extract meaning from the numbers quickly?

1. Consider the Source

I find traffic source to be the most common driving force behind changes in performance you can’t immediately identify. This is the case because when elements that a company has direct, transparent control of (such as altering page designs or mark up) change they often document those changes. Meanwhile traffic sources can shift without any explicit change in strategy. The first step is to start looking at the sources of your traffic, and to compare them to past results. If you’re using Google Analytics, there is a handy date-picker that allows you to compare two chunks of time:

2. Analyze Your Overall Mix

Word of mouth, an offline campaign, or some other variable might cause a surge in direct traffic, which might have positively impacted your conversion rates. Or you might have driven a lot more traffic through SEO or referrals because of ranking shifts or a mention of your company driving lots of visitors.

You can check this easily enough by looking at your traffic sources, and comparing them month-over-month (and then comparing date ranges):

3. Identify Who or What is Sending You Traffic

Additionally, referring sites and specific keywords can send you unexpected traffic. For instance, last month on the WordStream blog Ken had a post go “hot” on Sphinn. While we anticipated some degree of traffic, we saw more than we expected. The same could be applied to a specific keyword; you might have a keyword jump a couple spots without actively building links or doing any on-page SEO, which might affect your organic traffic numbers.

So how do you drill down to figure out what caused the change?
First, make sure you have the appropriate dates selected. If you’re analyzing month-over-month, see the screenshot above and be sure to grab the date range that makes the most sense. Next, drill down to the area in question. In this case, referring sites:

Next, you’ll want to drill down even further to see the specific sites that saw a change. Once you have the date selected, you can then use the comparison feature to see precisely which sites had the biggest uptick in referred traffic:

In the screenshot above, we see that by selecting the comparison option, we can instantly see that a site (in this instance Sphinn) has sent more than 700% more traffic than usual. Obviously we’ve likely identified a major driving force in our traffic spike.

You can also apply this methodology to identifying a number of other metrics, as well. For instance, if you want to analyze goals or user engagement metrics, you simply select a different view:

Why is this Type of Analysis Important?

While you could certainly do more in-depth analysis than that outlined above, I think this is a really good way for small business owners and small marketing departments to get a quick handle on the way that their numbers are changing, and the reasons for the changes.

Tom Demers is the Director of Marketing at WordStream, a keyword analysis and organization software provider for PPC and SEO. You can get in touch with Tom by following him on Twitter, checking out the WordStream Internet Marketing Blog where he’s a frequent poster, or by sending him an Email at tdemers at wordstream dot com.


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