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The 5 Mistakes Marketers and Webmasters Make When Analyzing Site Data

Posted on 5.05.2016

:: By Larry Alton, @LarryAlton3 ::


The world of online marketing is as exciting as it is challenging. Just a decade ago, breaking into the digital world with a website was somewhat difficult and intimidating—now, business owners have vast libraries of free tools they can use to build, launch, and monitor their sites to achieve long-term growth.

Google Analytics alone provides in-depth data streams, for free, to millions of webmasters and users around the world—but having access to all this data doesn’t necessarily mean you’re using it correctly.

The Objective Nature of Data

There’s an inherent flaw in the way most of us think about data, and it all starts from a real truth about data—it’s objective. It’s a numerical, logical measure of something, and it can’t be argued or directly manipulated. This much is true. When Google tells you your site had 1,000 visitors this past month, the number is accurate. However, the way you perceive the number, or use it in a practical context, is subjective.

According to datapine, “data analysis is only as good as the questions you asked yourself before you’ve even started analyzing.” How you look at the data and what you do with it can completely compromise the otherwise “objective” nature of Web analytics.

The Biggest Mistakes You Can Make

With that in mind, let’s look at some of the most frequently made (and most significant) mistakes in Web data analysis:

1. Having a forgone conclusion.

One of the simplest, yet most commonly made mistakes in analytics is going in with your mind already made up. For example, you might assume that your site is doing well and that your engagement numbers are increasing. With this forgone conclusion in mind, you’ll start looking at the data differently. You might see correlations and trends that aren’t really there, or you might make excuses for why certain areas of your data show an opposite trend. Remain as objective as possible by asking neutral questions at the outset, and keeping yourself from reaching a conclusion until you’ve seen the data from a neutral position.

2. Looking at a fraction of the data.

No matter what tools you use, you’ll have access to tons of data—probably far more than what you’ll ever use. You don’t have to look at every piece of data in your stream (even if you could, it would probably only confuse you), but you can’t rely on tiny patches of data to form your conclusions and dictate the direction of your campaign. If you can, draw data from multiple sources, and try to “zoom out” to look at the big picture. Look at how related metrics interact with your targeted metrics, and get a sense for how all these areas relate to each other.

3. Going in with the wrong questions.

The questions you ask of your data are what will lead you to your conclusions. If you ask the wrong questions from the beginning, you’re going to be led to an incorrect answer, or you’ll miss out on some of the most important insights you need to find. The questions that “matter” the most will vary, depending on your business, your goals, and your past, but you need to be as thorough as possible, and keep your focus on the improvements that you want to make. For example, you could ask: “How many people visited my site?” This interesting, but not particularly useful. Or you could ask: “How much did my web traffic grow?” This is much more specific. This is a highly simplified example; your questions will range into many different realms and will come with many degrees of specificity.

4. Assuming correlation means causation.

This is a subtle distinction, and one that’s difficult to pick up on. Just because two trends or items are linked doesn’t mean that one has caused the other. For example, you may have started a new marketing strategy, and your traffic may have increased, but this doesn’t necessitate that your marketing strategy caused this increase. This isn’t the best example, since it’s more than likely that there’s at least some causational link here, but it’s important to remember that correlation is a different relationship altogether.

5. Forgetting that not all insights are practical.

This is important as a practical element to your data analysis. It’s exciting, fun, and enlightening to gather new insights and form conclusions about your website and key demographics, but not all of these insights are valuable; conclusions are only significant if they lead you to make some meaningful change to your current strategy.

These aren’t the only mistakes you can make when gathering and interpreting your web data, but they are some of the most important to watch out for. Human biases are notoriously hard to compensate for, so you’ll only drive yourself crazy if you strive for perfection. Instead of trying to make your process perfect, try instead to make it better, even if only slightly, by compensating for these common and impactful mistakes.


Larry Alton is a professional blogger, writer and researcher who contributes to a number of reputable online media outlets and news sources. In addition to journalism, technical writing and in-depth research, he’s also active in his community and spends weekends volunteering with a local non-profit literacy organization and rock climbing. Follow him on Twitter and LinkedIn.

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