common web analytics mistakesWeb analytics produces an abundance of data that help businesses measure website performance, learn how to attract more visitors and increase sales. However, the shine has worn off the fancy charts and graphs since the early days of web analytics. Organizations seem less enthusiastic about the potential of web analytics and more impatient for analytics that produce meaningful insights.  

Web analytics mistakes remain prevalent for many reasons, causing web analytics programs to fall short of expectations. Causes for disappointments are often due to a combination of factors.

Experienced marketers have learned that taking full advantage of Google Analytics or other web analytics programs requires more than simply blindly accepting data and viewing the resulting graphs and reports. They know that decisions based on flawed data or incorrect conclusions could bring disastrous business consequences.

These are eight of the most common web analytics mistakes.

Incorrect Comparisons. Comparing data from one time period to another can be like comparing apples to oranges. An e-commerce site might compare conversions between January and the previous month and become alarmed by a drop in conversions, forgetting that December purchases spike during the holiday shopping season. Comparing January to January of the previous year will produce information that is more valid.

Focusing on short-term fluctuations. Website traffic can spike or plummet on a given day, week or even due to the weather, news events or a number of other reasons. Short-term fluctuations do not indicate success or failure. Instead, look for long-term trends that span months.

Ignoring goals. “Goals” in Google Analytics can measure conversions that occur on your website and assign a dollar value to different types of conversion. That enables you to quantify the value of digital marketing and compare it to costs marketing efforts. To set up goals, go the Admin tab and find “Goals” in the right-hand column.

Seeking large numbers. Low numbers do not necessarily mean marketing efforts are failing. For instance, one social media platform may produce large numbers of visits yet lead to few conversions, while another refers relatively few visitors but gets high conversions.

Confusing visits and views. In a visit, the website visitor comes to your website from an external URL. The visit ends after the user is inactive for 30 minutes. Google Analytics counts it as one visit even if the web user visits multiple pages on your site. In other words, one visitor can accrue multiple page views during one visit. Google Analytics counts a page view when a page is loaded or reloaded by a browser. If a visitor loads five pages before leaving, they will count as one visit with five views.

Misunderstanding leads. Confusing leads with marketing qualified leads (MQLs) can be a serious error. A lead constitutes anyone who has completed and submitted a form on a landing page. A marketing qualified lead (MQL) is more likely to become a customer. They are more than just curious about your product. Perhaps they have requested a demo or sales consultation. A clear definition for what constitutes an MQL varies between companies. It’s crucial to align sales and marketing to define and classify MQLs.

Confusing data with truth. Rushing to make marketing decisions without first assessing the qualitative value of the data can prompt poor decisions. Identifying trends, rather than reporting numbers for the sake of numbers, is a preferable strategy. Questioning how the data is collected and completing a comprehensive audit can uncover tracking shortcomings. For instance, bots, spiders, website visits by company employees or outside consultants can artificially increase page views.

Not filtering internal addresses. Website visits by company employees, outside sales staff, developers and other third-party consultants can significantly inflate numbers and skew analytics, possibly leading to incorrect conclusions. You can set up filters in Google Analytics to block internal IP addresses by going to Filters under the Admin tab. Click the “New Filter” and name your filter, use a predefined filter to “Exclude” “traffic from the IP addresses,” and add the IP addresses.

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