Common Data Analytics Mistakes

Conceptual image of internet information technology and big data visualizationData analysis demands a lot of time, attention, and energy – essential resources that, once invested, can never come back. In fact, as a marketer, data analysis expert, or entrepreneur, your job is to effectively leverage those important resources to increase your company’s performance.

To improve your analysis results, you’ll need to embrace the fact that you are maybe going to fail, regardless of how well-defined your data analysis plan is. That’s how it works – test, measure, optimize, and repeat.

How nice would it be to predict your future mistakes and not commit them in the first place? How amazing would it be if someone has already failed in your place, doing exactly what you’re currently trying to do? I’m willing to bet that you’d offer a lot just to save yourself from the incoming hustle.

In this post, I provide insights concerning seven of the most common data analytics mistakes that cause marketers, data analysts, and small business owners problems.

Perceiving Low Numbers as a Terrible Result

When you perform a marketing analysis and your results show low numbers, that doesn’t necessarily mean a bad thing. Many marketers and data analysts perceive low numbers as low performance, even though those small numbers can actually indicate an improved performance of the campaign.

For example, certain metrics that show low numbers are actually very good for your business. If your emails witness a decreasing unsubscribe rate, you’re on the right path. If the customer acquisition expenses decrease, your ultimate profitability gets bigger.

The second advantage of having low numbers in your stats is that you’re able to collect relevant feedback on what works and what doesn’t. For example, if 80% of your traffic comes from social media, while 10% of it comes from SEO and another 10% from direct marketing, you can immediately tell what campaign is generating the best results. After you draw a conclusion, you’re ready to scale your social media marketing and keep the results growing.

Confusing Views with Visits

A view and visit are not the same thing, though many marketers aren’t even aware of this fact. These metrics sound similar, yet they’re slightly different. For data analysts and marketers who interact with views and visits metrics, differentiating the two terms is highly critical.

A view or a pageview is counted every time one of your website’s pages is loaded by the browser. The reloads also count. Therefore, if a visitor comes to your website (1 view) and opens another five pages (5 views), and then refreshes three pages (3 more views), you end up counting 9 views for a single visitor.

A visit is counted when a person arrives at your website from an external source. Google Analytics ends a visiting session only after the user becomes inactive for thirty minutes or more. The big difference between a view and visit is that the latter is only counted once per visitor. So, if someone accesses 100 pages from your site, you still got one single visit.

Treating Leads and Marketing Qualified Leads as One Metric

Leads and marketing qualified leads (MQL) are similar yet slightly different terms. If you confuse them, your optimization solutions and reports will become corrupted and misleading.

A lead is the acquisition of personal information from anyone who submits a form through one of your landing pages.

A marketing qualified lead is an “advanced” version of a simple lead, primarily because the person that’s being tracked is much more likely to become a customer. Every company has different metrics for identifying whether a lead is qualified or not, yet the difference is often marked by the prospect’s interest in knowing more about the company (requesting a demo, sending an e-mail back, or showing high interest in emails by opening and clicking them all.)

Analyzing All the Traffic Together

Traffic can come from a lot of sources, and each source is relevant because it shows how your different marketing campaigns are performing.

“Every time you analyze your traffic, ensure that you’re differentiating the main sources (social, search, direct, etc.) and segment your visits based on the device used (desktop, mobile). Of course, if it’s relevant, segmenting the countries and time visits also represent options.” –Ricky Davis, Data Analyst at EssayOnTime.

Bottom point is – never bucket all your traffic together because you’ll get confused and your analysis will probably head nowhere.

Jumping to Quick Conclusion by Confusing Correlation with Causation

If two KPIs grow or decrease at the same time, you shouldn’t immediately jump to conclusions. Many marketers are confusing correlation with causation— a mistake that can cost a lot of time, money, and energy.


In this example, you can see the terms “inbound marketing” and “yoga workout” being analyzed. The graphic shows a significant synchronization between the two terms, yet they are totally unrelated. Whenever you compare two metrics, don’t make a rushed decision based on all the patterns you see. Dig deeper and confirm your suppositions before assuming anything.

Believing that a Longer Visits Equal Better Engagement

If a visitor spends a lot of time on your website, it doesn’t necessarily mean that he’s positively engaging with your content. Yes, in some instances, longer visits can equal better engagement. However, many times, a visitor that opens a lot of pages and searches for a lot of details might have trouble finding the solutions to his problem.

If you can, conduct user testing to identify the real reasons why they spend so much time on your pages. If people can’t find what they’re looking for, they’re likely to never visit your site again.

Not Looking Beyond Numbers

Some data analysts and marketers are only assessing the numbers they get, without putting them in their contexts. Quantitative data is not powerful unless it’s understood. In those instances, whoever performs the data analysis should ask himself “why” instead of “what”.

Falling under the spell of big numbers is a common mistake that so many analysts commit. To avoid it, make sure you always remember that:

  • Those numbers are actually real people
  • If those numbers lack context, there’s no value in them.
  • If you don’t interpret and ask “why” before concluding, you have accomplished nothing.


Data analysis is both a science and an art. You get to be both calculated and creative, and your hard efforts will truly pay off. There’s nothing more satisfying than dealing with a data analysis problem and fixing it after numerous attempts. When you actually get it right, the benefits for you and the company will make a big difference in terms of traffic, leads, sales, and costs saved.

Share this post

Serena Dorf

Serena Dorf

Serena Dorf is an enthusiastic content writer. She is passionate about writing, personal development, psychology, and productivity. In her free time, she is reading classic American literature and learning Swedish. Feel free to connect with her on Twitter at @DorfSerena.

scroll to top