More than 50 million businesses make use of Google Analytics, which clearly indicates most business leaders make use of the data in the tool to review their campaigns. If your data is not accurate, it would have an impact on your decision-making. Here are some common causes for misunderstanding the data or incorrect data in your performance reports:
Most marketers consider bounce rate as a negative parameter. A high bounce rate is perceived as lack of interest in the content of the website. Google defines bounce as a "single page session on your site", which means that the user exits your site after viewing just 1 page. This part is understood well by most business leaders but many of them seem to share a common misunderstanding - bounce rate is calculated from total page views.
Consider an example where the total number of page views is 10,000,000 in a month and the bounce rate is 50%, have you concluded that there were 5,000,000 exits with just 1 page view? If you answer is yes, you are overestimating bounces.
Bounce rate is calculated from unique visits, also called sessions, which is totally different from page views. Consider the same example. If the total number of visits or sessions is 6,000,000, then your bounce rate of 50% indicates that you had only 3,000,000 bounces and not 5,000,000.
Bounce rate = Number of visits with 1 page views (also called bounces) / Number of unique visits (also called sessions)
Bringing the denominator on the right hand side of the equation to the left hand side, you will get Number of Bounces = Bounce rate * Number of sessions
From the example figures:
Number of Bounces = 6,000,000 * (50%) = 6,000,000 * (50/100) = 3,000,000
Very often, this number is overestimated and it can have an impact on how you measure the impact of your content on new visitors. On a side note, any bounce rate below 30% is excellent and you must be worried if your website is clocking a figure over 70%.
2. Total Visitors and page views:
The total number of visitors and page views in the Analytics report for a large contingent of websites are overestimated because of the lack of filters for in-house traffic and spam bots.
If the internal traffic, i.e the people who visit your website from your own company, is not filtered out, it adds weigth to the number of visitors and page views but have no impact on conversion in most cases.
The other important issue to address is the fake traffic sent to your website. These are the fake hits sent by spamming bots to your website or landing pages. You can make use of the ‘Bot filtering’ feature in Google Analytics to filter the fake traffic but this will not be 100% efficient. The other techniques that can help you fight this type of fake traffic is by making use of your server log and blocking IPs and referrers.
Finally, there are bots that don’t visit your website but make use of your property ID in the tracking code to send fake traffic to your analytics account directly. To prevent damage from them, you can make use of the Google Tag Manager to hide the property ID.
The spam referrers, ghost traffic and internal traffic are going to inflate your number of total visitors and page views.
3. Unique Page views:
4. Referral Traffic:
Have you ever noticed the spike in referral traffic from one of your social media like facebook appear just after you have launched a campaign? This is because you have not added the UTM parameters in the URLs for your campaigns. UTM parameters let Google match the traffic with the right source. When you’ve not added the right UTM parameters to your URL, the traffic that must have been assigned to your ad campaign in facebook instead gets assigned to the referral traffic from facebook.
This problem is not limited to facebook and your incorrectly configured URLs in campaigns such as email, social media and websites are also going to cause referral spikes in wrong sources.
5. Average Time on Page:
Google Analytics takes into account the exits from the page when it calculates the average time on page. It calculates time on page as the difference between the time stamps on hits.
Time on Page = Time when the user entered the next page - Time when the user entered the page for which the metric is being calculated.
If you have no custom events set up, when there is an exit from the page, Google wouldn’t be able to calculate the time the user spent on that particular page.
Example: In case you have a blog and the user reads the entire article and leaves the page after a long time without visiting another page in your website, Google Analytics still wouldn’t be recording the time the visitor spent on the page.