Why did my website traffic drop (or increase)?
Diagnosing a sudden drop off in traffic, or even an unexpected surge, can be daunting. When you open up Google Analytics, you’re certain the answer is in there somewhere, but how do you know where to check first? While you sift through the kajillion metrics, dimensions, filters, segments and date ranges in front of you, you’re reminded that website traffic is down and people are demanding answers!
Take. A. Deep. Breath.
Here’s a handy website traffic analysis decision tree to help make sure you’re asking the right questions at the right times.
Please note: This image is LARGE. I’m not talking about file size. I’m talking about SIZE size. Open on mobile at your own risk. Once you select the image below, you’ll want to make it bigger by clicking on the full-screen icon in the upper right-hand corner.
How to use this decision tree
Answer the first yes-or-no question. Go to the next question based on your answer. Continue until you hit a dead end, which should be a green box.
That’s it. It’s that simple to use.
About this traffic analysis decision tree
Have you ever played a game of 20 Questions? Someone thinks of a person, place or thing and the other person has twenty yes-or-no questions to ask in order to guess it. A good first question might be, “Is it a thing?”. It’s a smart question because no matter what the answer is, it eliminates a significant number of possibilities.
An answer of “yes” eliminates all people and places. An answer of “no” eliminates all things.
A bad first question would be, “Is it a fidget spinner?”
When it comes to investigating a sharp drop off or uptick in traffic, you should approach it the same way. You wouldn’t want to check your Google rankings without first making sure the issue is specific to organic search issue, right?
Begin with what you know and try to back the problem into a corner with questions designed to narrow the field each time. That is the objective of this decision tree.
Caveats and feedback
- I’m not a data scientist or a machine learning programmer. I created this using my own logic and drew from my own experiences.
- This was created using abductive reasoning, meaning it’s not foolproof and points to a probable conclusion, not an absolute.
- This is industry-agnostic. If you created one of these for traffic analysis on your own site, it might be a little different.
- This decision tree assumes there is a single culprit causing the dramatic shift in traffic. Many times, there is more than one factor causing the swing, so keep that in mind when using this.
If you have any feedback on any errors or areas of improvement, leave a comment below.