While your website’s analytics should be one of your most powerful tools, it can also be a hindrance when misused.
A simple analytics mistake leads to fixing things that aren’t broken or – even worse – making a mess of a well-performing site.
That’s why we’re going to look at 5 common mistakes that people make when analyzing their site’s performance and solutions to avoid those costly errors.
1. Undefined or Unclear Goals
How can you define your site’s shortcomings if you don’t understand its successes?
A lot of people just look at their analytics and expect it flash “SUCCESS” or “FAIL” automatically.
But success in analytics isn’t universal. A success for one site can be a failure for another.
That’s why you need clear goals to determine which data matters to you. Without this basic definition, you’re just spinning in a whirlwind of meaningless information.
Your goal could be a simple as a form submission or it could be more nuanced like visitor engagement. Both of these goals require different metrics to determine whether you’ve been successful or failed to meet your goals.
One thing’s for sure – if you don’t have clear goals, you’ll have no way to measure how your site’s performing.
Create goals for your overall site and any pages you’d like to watch. Then determine which measurements will help you define the success or failure of each goal.
You can also dig up some historical data if you’re trying to improve an existing metric so you can measure the degree of change over time.
2. Mixing Up Sessions and Pageviews
The distinction between sessions (or “visits” in some analytics) and pageviews is crucial to understanding your data.
- Sessions / Visits: this is counted when a person visits your site. When they arrive, their session begins. It ends when they’re inactive or away from your site for a specific set of time – usually 30 minutes.
- Pageview: This is counted when someone visits a particular page of your site. The same person can trigger dozens of pageviews in a single session, but will only trigger one session in this time.
Keep this data in mind when you’re interpreting this data – if you mix them up, you’ll end up with tragically inaccurate interpretations of your site’s health.
3. Examining Data in Tunnel Vision
Another major mistake people make is acting on data without considering the bigger picture.
Every little piece of data doesn’t warrant an action. If one or two visitors don’t click your CTA – you don’t necessarily need to change the CTA!
That’s just not enough visitors to draw any conclusions about your CTA.
Another example is when people see a sudden negative change on their site and react to it without looking at the context.
For example, maybe your site’s bounce rate jumps up 5 – 10% for a day or two. Have you made any changes to that page during in that timeframe? If not, you might wait it out to see if it goes back down. It could just be a short-term anomaly.
Make sure you’re looking at the bigger picture – if something looks troubling, for example, look at your data in historical context so you can see if you’ve spotted a trend or just an anomaly.
Similarly, make sure you collect enough data before acting on your analytics. If you only get a handful of visitors per day, you’ll probably want to wait a couple of weeks before drawing any conclusions from your data.
4. Confusing Correlation and Causation
A good marketer creates a narrative based on the data. How do people behave on the site? Are they doing what you want them to? Why or why not?
As you dive into the story of your visitors, you’ll start comparing different reports and several metrics.
Just keep in mind – when you’re asking questions like “Why are they doing this?” or “Why aren’t they visiting this page?” you run the risk of attributing false causes to your data.
Let’s look at an example:
You’ve seen an uptick in conversions on your Request a Consultation Landing Page last week, so you look into your analytics for a cause.
You see that more people have visited your latest blog than the previous weeks.
You might be tempted to say: “well, clearly the increase in blog traffic has caused the uptick in conversions”.
What you’ve overlooked, however, is that both pages are getting more visitors because your site was mentioned on a popular website.
Both the increase in conversions and the increase in blog traffic were effects of the referral. You were attributing causality where it didn’t belong.
When you’re looking for cause and effect, make sure your story is airtight.
In the above example, you might look at how many people were traveling directly from the blog to the landing page. If you don’t see any, then you can likely rule out a causal link between the two observations.
5. Not Using Traffic Filters
You should want uncontaminated data in your analytics. That’s why you should block out traffic that might give you inaccurate results.
Contaminating traffic includes:
- Internal IP addresses. These are your personal IP address and your company IP addresses.
- Any spam referrals of visits. These are fake sessions that link back to spammy sites. You will usually see them under your referral traffic. They often show tons of visits form obscure sites with no engagement whatsoever.
Both of these kinds of traffic will certainly sully your data.
For example, someone inside your company is likely to peruse the site much differently than someone who arrives organically. Your colleagues can end up falsely inflating your engagement metrics.
Spam referrals, on the other hand, inflate your bounce rate and decrease your engagement metrics. You’ll also see inaccurate traffic metrics on your site.
Use filters to block your internal IP address. Also implement a strategy to block analytics spam altogether so you can have the most accurate data possible.
Analytics data is a powerful tool in the marketer’s toolbox, but misuse of the tool devour time and energy.
You should always know what you’re looking for in your analytics – whether it be the number of conversions or the change in bounce rate over a few months – so you don’t get bogged down by irrelevant metrics.
Once you have the data, make sure you’re looking at the bigger picture before implementing a change. While it’s tempting to alter your site every time someone behaves in an undesired manner, your work will never end if you react to every anomaly in your analytics.