As a proven data analytics specialist, you are expected to know various consumer behavior research tools thoroughly. Of late, Google rolled out the Google Analytics 4 tool, with a promise of ironing out all the seams of its predecessor, Google Analytics. But the results yielded have not been of flawless quality. In fact, GA4 is reported to be a downgrade from GA. Various users on the discussion forums discussed the ambiguity of the data presented by GA4 reports. It did impact the results and overall customer acquisition and retention strategy of the businesses. So, if you have also found GA4 reports giving you wrong data, this post addresses the reasons and may help you work out your strategy while accounting for them.
01Reason 1 - Data processing lag as big as 48 hours
GA4 was launched with a promise of giving real-time information about customer data. However, it did not deliver information as expected. In some cases, the lag between data capturing and processing went beyond 24 hours, reaching up to 48 hours. As a result, the reports lost their credibility and made it impossible for the analysts to come up with a workable strategy in case of retargeting requirements.
For instance, the accuracy of customer data becomes crucial when businesses aim to retarget prospective customers who made an online enquiry or added the product to the cart, but abandoned the session without completing the process. Due to a processing lag of about 48 hours, the businesses failed to have a sound retargeting strategy in place. It led to improper allocation of budget and a big hit to the ROAS figure eventually.
Thus, GA4 processing the data as late as 48 hours after capturing it is one of the reasons that is reflected in the quality of reports.
What is the solution:
To ensure that daily dashboard information or media bidding optimizations are based on correct results, one has to find a solution for this processing lag of 48 hours. One of the best technical fixes available is BigQuery Export. All you need to do is bypass the GA4 interface and export the event directly into BigQuery Export. It helps get the data of events as fresh as a few seconds back.
02Reason 2 - No work done towards Historical Data Imports
GA4 reports stop making sense to analysts because of an important limitation attached to historical data imports. The tool is conspicuously ambiguous due to the absence of a historical data import feature in it. This absence leaves the analysts with no previous data to compare and analyze. As a result, the trend analysis loses its teeth and becomes a point of pain for the analysts who are required to deliver work-worthy information to their business clients.
Very important information like seasonal trends, year-on-year growth or decline in customers’ interest in a product, or changes in buying habits are missed. Thus, reports don’t deliver the data as expected and make the process of analysis altogether unreliable.
What is the solution:
A little proactivity in the approach of using GA4 can help overcome the analysis struggles caused by the absence of a historical data import feature. Experts suggest exporting Universal Analytics data as CSV or JSON files before its deletion by Google. This simple practice can allow comparison of previous data. Additionally, tools like Looker Studio, designed to cache historical reports, can help bridge the knowledge gap caused by no historical data import feature.
03Reason 3 - Cookie information collection restrictions
General Data Protection Regulation governs the collection and use of consumer data. To meet its compliance requirements, the GA4 tool requires a cookie consent banner. It leaves the tool at the mercy of the users, who may or may not give their consent to collecting their information. This simply means a technical disconnect or data collection snag that denies access to the whole customer behavior picture.
GA4 is required to seek permission from the customer mandatorily to ensure compliance. This is because the moment a user visits a business’s website, GA4 places a text file as a unique client identifier on the user’s device. Thus, the process of capturing the data requires straightforward consent from the visitor. This is where the data becomes incomplete because users may not give consent at every instance. Consequently, the reports turn out to be a nightmare for analysts requiring complete data.
GA4’s mode of studying consumer behavior in the case of lack of consent is where the problem lies. The tool switches to estimation to analyze customer behavior, making reports based on algorithmic approximations instead of facts. It causes errors in attribution at times. Since the unconsented visitor is excluded due to lack of identifier, the reports show a difference between actual visitor count and total visitor count.
What is the solution:
There are tools like the ones used by Analytic Route that work on a privacy-first model. These tools use a cookieless tracking mechanism without deviating from customer privacy compliance norms.
Using Google Consent Mode v2, after integrating it with the Content Management System, is another helpful way to address cookie consent-driven restrictions. Advanced Consent Mode use is another expert-recommended option that helps address cookie consent requirements.
04Reason 4 - Ambiguous URL Query Parameters treatment
The users of GA4 have reported that they get the same page again and again when the query parameters are attached to the page links. For instance, a blog viewer may land on the post from a social media network, say Facebook, or from an emailer, or via a Google Search. GA4 treats every page link as a unique entity and thus exaggerates the total number of visits. This problem is more prominent on WordPress sites.
GA4 users find it hard to analyze traffic or identify uniquely assigned traffic due to this page link treatment.
What is the solution:
Talk to your GA4 analyst about the methods they will use to exclude page counts based on common query parameters. By using this approach, a cleaner report will be generated. Third-party tools are available that allow analysts to use the “Exclude Query Parameters” option in their settings. It helps avoid undue data fragmentation and deliver reliable results.
05Reason 5 - Internal traffic filtering not done
A business website owner and its analytics team may need to visit various pages of the digital property to check components, readability, and other factors. While using GA4, it is important to turn on the internal traffic filter; otherwise, GA4 will present the traffic size in an inflated manner. Thus, the oversight of an important function by the internal team can become a reason for GA4 reports giving wrong data.
It becomes more crucial for those sites where the traffic volume is yet to catch up.
What is the solution:
A simple solution lies in setting up the internal traffic filter. It can be done easily through the following steps:
- Reach GA4 admin
- Click Define Internal Traffic under Data Collection and Modification
- Mention workplace or home IP as a traffic type
- Reach Data Filters and click ‘Activate the Internal Traffic filter
Though this solution works, it is most effective for static IPs only. It may require changes at the code level if you use multiple devices and locations to visit the website pages. GTM users can send a traffic_type custom parameter to establish differentiation of internal traffic.
06Reason 6 - Double tracking inflates GA4 pageviews by 100%
It is a common problem encountered with WordPress sites. Various WordPress site themes have the gtag.js hardcoded into them. So, both GA4 and built-in GTM send the signal pointing to the same measurement ID. Another duplication case happens when the two GTM containers are installed on the same site. It occurs when the handover between the developer and the analytics team is out of sync.
What is the solution:
Check the WordPress site for double tracking with the help of Google Tag Assistant. Access the tag log and look for cases where the same measurement IDs are fired twice for a target page. It can be fixed by removing the duplicate connection. The built-in GTM container can be deactivated when you plan to use the GA4 WordPress plugin to avoid double tracking. If the two GTM tags are installed at different points in time, merging them into one also helps avoid the condition of double tracking.
Conclusion
GA4, though supposed to be an advancement over Google Analytics, can turn out to be a pain point if its shortcomings are not studied in advance before finalizing its installation plan and deployment. Also, all human errors, like creating duplicate GTM tags, messy handover from developer to analyst, and so on, need to be kept in check to ensure the receipt of correct data in GA4 reports. In case of cookie consent banner requirements, the analysts can employ tools that bypass the requirement and still conform to user privacy norms.
If you come across any of the situations mentioned in the blog, take help from the proven conversion tracking experts who are well-versed with GA4 limitations and have the suitable tricks up their sleeves to work around those. These experts can also audit and pinpoint the errors related to GA4 use and suggest suitable fixes.