It's no secret that accurate website analytics are critical to growing your business online, and Google Analytics is often the first port of call for insights.
But is Google Analytics data accurate? Can you fully trust the figures given? Here is a detailed explanation.
How accurate is Google Analytics? A data-driven answer
When set up correctly, Google Analytics (Universal Analytics and Google Analytics 4) is moderately accurate for tracking global traffic. That being said: Google Analytics does not accurately display European traffic.
RespectivelyGDPR Provisions, websites using GA products must display a cookie consent banner. This consent is required to collect third-party cookies, a tracking mechanism to identify users across web properties.
Google Analytics (GA) cannot process data about the user's visit if they have rejected cookies. In such cases, your analytical reports will be incomplete.
Cookie rejection refers to visitors rejecting or blocking cookies from ever being collected by a particular website (or in your browser). It immediately affects the accuracy of all the metrics in Google Analytics.
Google Analytics is not accurate where cookie consent tracking is required by law. Most consumers do not like annoying cookie banners orhave privacy concerns— and chose to decline tracking.
This leaves companies with incomplete data, which in turn leads to:
- Lower traffic numbersas it does not collect 100% of visitor data.
- Loss of website optimization features.You are unable to make data-backed decisions due to inconsistent reporting
For the above reasons, many companies are now considering cookieless website tracking apps that do not require a consent screen.
Why is Google Analytics not accurate? 6 causes and solutions
A high rejection rate for cookie banners is the main reason for inaccurate reporting by Google Analytics. Additionally, your account settings may affect the accuracy of Google Analytics.
If your analytics data seems shaky, check out these six Google Analytics accuracy issues.
You must obtain consent for the collection of cookies.
To comply with the GDPR, you must display a cookie consent screen to all European users. In addition, other jurisdictions and industries require similar measures for the collection of user data.
This is a nuisance for many businesses, as rejecting cookies undermines their remarketing capabilities. Therefore, some try to help maximize cookie acceptance rates.dark patterns. For example: hide the option to opt out or make the texts too small.
Unfortunately, not everyone treats users with respect. TOjoint studyby German and American researchers found that only 11% of US websites (out of a sample of more than 5,000) use GDPR-compliant cookie banners.
As a result, many users are unaware of the background data collection to which they have (or have not) consented. OtherAnalysis of 200,000 cookiesfound that 70% of third-party marketing cookies transfer user data outside of the EU, a practice that violates the GDPR.
Of course, data regulators and activities are behind this problem. In April 2022, Google was forced to do sointroduce a "reject all" button for cookieson all its products (this was probably contributed to by a fine of 150 million euros). While noyb has sentmore than 220 complaintsagainst individual websites with deceptive cookie consent banners.
remove that? If you mess up the cookie consent mechanism, you could get yourself into legal trouble. Do not use sneaky banners as there are better ways to collect website traffic statistics.
Solution: Try Matomo's GDPR-compliant analysis
Fill in the gaps in your traffic analysis with Matomo, a fully GDPR compliant product that doesn't rely on third-party cookies to track web visitors. Based on its design, this was confirmed by the French data protection authority (CNIL).Matomo can be used to collect data without tracking consent.
This is possible with MatomoTrack website users without asking for cookie consent. And when you do, we'll deliver a compact, compliant, and non-disruptive cookie banner design.
Your Google tag is not embedded correctly
Google Tag (gtag.js) is a web tracking script that sends data to Google Analytics, Google Ads, and Google Marketing Platform.
A corrupt installation of gtag.js can cause two accuracy issues:
- Duplicate Page Tracking
- Missing script installation
Is there a way to tell if you are affected?
Yes. You may have duplicate scripts installed if you have a very low bounce rate (below 15-20%) on most pages of your website. The above can happen if you use a WordPress GA plugin and also embed gtag.js directly into your website code.
A tell-tale sign that some pages are missing a hyphen is low or no access statistics. Google draws your attention to this with a banner:
Solution: Use available troubleshooting tools
To useGoogle Analytics DebuggerExtension to analyze pages with low bounce rates. Use the search bar to find duplicate code tracking items.
Alternatively you can useGoogle Tag Assistantto diagnose snippet installation and to troubleshoot individual pages.
If the above didn't work,Reinstall your analysis script.
Machine learning and blended data are used
Google Analytics 4 (GA4)it relies heavily on machine learning and algorithmic predictions.
By applying Google's advanced machine learning models, the new Analytics can automatically alert you to important trends in your data. [...] For example, it calculates the probability of churn so that you can invest more efficiently in customer retention.
On the surface, the above sounds exciting. In practice, Google's use of predictive algorithms means that you don't see real data.
To offer a cookieless variant of tracking, Google's algorithms close reporting gaps by building models (i.e., data-backed predictions) rather than reporting actual user behavior. As such, your GA4 numbers may not be accurate.
For larger web properties (think sites with over a million users), Google also relies on data sampling, a practice of extrapolating data analysis based on a subset of data rather than the full dataset. . Again, this can lead to reporting inconsistencies, as some numbers (for example, average conversion rates) are overstated or understated.
Solution: Try an alternative website analytics app
Unlike GA4, Matomo's reports consist of100% unsampled data. All of the aggregated reports you see are based on actual user data (not estimates).
Also, you canMigrating from Universal Analytics (UA) to Matomowithout losing access to your historical records. GA4 does not yet have backwards compatibility.
Spam and bot traffic is not filtered
Surprise!42% of all Internet trafficis generated by bots from which27,7 %are bad.
Good bots (also known as crawlers) do basic web "cleanup," such as indexing web pages. Evil bots distribute malware, spam contact forms, hack user accounts, and do other nasty things.
Many of these spam bots are specifically designed for web analytics applications. The goal? It floods your dashboard with fake data in the hope of getting countermeasures from you.
Google Analytics Spam Types:
- Referral spam.Spam bots hijack the referrer shown in your GA referral traffic report to indicate a page view from a random website (which actually didn't happen).
- Event-Spam.Bots generate fake events with free voice input that trick you into visiting their website.
- Ghost-Trafficking-Spam.Malicious parties can also inject fake pageviews that contain URLs that you are supposed to click on.
Apparently, such spam entities distort the actual website analytics numbers.
Solution: Configure bot/spam filters
Google Analytics 4 has automatic bot traffic filtering enabled for all tracked web and app properties.
However, if you use Universal Analytics, you must manually configure the spam filter. First create a new view and then set up a custom filter. Program it to exclude:
- Filter Field: Request URI
- Filtermuster: Bot-Traffic-URL
After setting everything up, validate the results withcheck this filterspecial feature. Then repeat the process for other questionable URLs, hostnames, and IP addresses.
They do not filter internal traffic
Your team(s) spend a lot of time on your website, and their intermittent behavior can hurt your traffic and other website metrics.
To keep your data "employee-free", exclude traffic from:
- Your corporate IP addresses
- Known personal IPs of employees (for remote employees)
If you also have a separate Stage version of your site, you should also filter all traffic originating from that one. Your developers, contractors, and marketing staff spend a lot of time tinkering with your website. This can result in a large discrepancy in average time on page and engagement rates.
Solution: Establish internal traffic filters
google offersinstructionsto exclude internal traffic from your reports using IPv4/IPv6 address filters.
Session times out after 30 minutes
After 30 minutes of inactivity, Google Analytics tracking sessions will start again. Inactivity means that no interaction hits were recorded during that time.
Session timeouts can be a problem for some websites because users often pin a tab to check back later. Because of this, you can count the same user twice or more, leading to skewed reporting.
Solution: Schedule custom timeout sessions
You can code custom cookie timeout sessions using the following code snippets:
- _setSessionCookieTimeout. Set a custom timeout for new session cookies in milliseconds.
- _setVisitorCookieTimeout. Set a custom Google Analytics visitor cookie expiration period in milliseconds.
Thanks to its size and longevity, Google Analytics has some strengths, but the accuracy of its data isn't 100% perfect.
The inability to collect analytics data from users who do not consent to cookie tracking and data sampling for larger web properties can be a deal breaker for your business.
If that's the case, try Matomo - an accurate and GDPR compliant web analytics solution.Start your 21-day free trial now. No credit card needed.
21 days free trial. No credit card needed.