Google Analytics is a widely-used analytics tool that allows businesses to track website traffic, user behavior, and other important metrics. However, despite its popularity, user tracking accuracy in Google Analytics is becoming less accurate in 2023. In this article, we will explore the reasons for this decline.
How Google Analytics 4 is Less Accurate:
1.) Increased use of ad blockers, cookie blockers, and other privacy-enhancing technologies
Ad blockers are browser extensions or applications that prevent ads and tracking scripts from functioning properly. They also prevent the triggering of activities such as website to website referrals, which can limit the ability of Google Analytics to track user behavior accurately. In many cases webmasters are seeing unexplained increases in direct visits and declines in referrals from previously high converting referrers. According to a 2021 report, ad blocker usage increased to 38.8%, reaching a total of over 1 billion active users worldwide and the adoption rate continues to grow each year.
When a user clicks on a referral link and visits a website, a cookie is typically set to track that referral source in Google Analytics. However, if a user has a cookie blocker enabled that blocks or restricts cookies, it can affect the accuracy and completeness of referral tracking in Google Analytics.
By blocking or limiting cookies, users may prevent Google Analytics from properly attributing referral sources, leading to incomplete or inaccurate referral reports. It's important for website owners and marketers to be aware of the potential impact of cookie blockers on referral tracking and to consider alternative tracking methods, such as implementing first-party cookies or utilizing other tracking technologies, to ensure accurate referral data in Google Analytics.
In addition to ad blockers, VPNs and secure browsing technologies such as HTTPS also limit the ability of Google Analytics to track users. VPNs can mask a user's IP address, making it difficult to track their location or identify them as a unique visitor. HTTPS encrypts data sent between the user's browser and the website, making it harder to intercept and analyze.
2.) Changes in consumer behavior and data collection regulations
Consumers are becoming increasingly aware of privacy concerns and are more cautious about sharing their personal information online. This has led to the introduction of data protection regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). These regulations require businesses to obtain explicit consent from users before collecting their data, which can make it harder to collect accurate tracking data.
3.) Limitations of cookie-based tracking
The end of cookies is expected to have an impact on referral tracking. Cookies are commonly used to track and attribute referrals by storing information on a user's device when they click on a referral link. With the increasing privacy concerns and changes in web browser policies, the use of third-party cookies is currently being phased out.
As a result, alternative methods are being developed to track referrals effectively. One approach is to rely on first-party data, where websites directly track referrals through unique identifiers or codes embedded in the URL. This method allows websites to maintain control over their own data and track referrals within their own ecosystem.
The phasing out of third-party cookies will indeed have an impact on referral reports in Google Analytics. Currently, Google Analytics relies on cookies to track and attribute referrals from external websites. When a user clicks on a referral link and visits a website, a cookie is set to track that referral source.
With the changes in web browser policies and the decreasing support for third-party cookies, the accuracy and completeness of referral reports in Google Analytics may be affected. As more browsers block or limit the use of third-party cookies, some referral sources may not be properly attributed, leading to incomplete or inaccurate data in the reports.
4.) Issues with data quality
Inaccurate or outdated data can significantly impact decision-making, and it is becoming increasingly challenging to maintain data quality in the face of changing user behavior and data collection regulations. Data cleansing and validation processes are necessary to ensure that the data collected is accurate and up-to-date.
5.) Increasing use of mobile devices and the impact on user tracking accuracy
Mobile devices have become the primary means of accessing the internet, with over half of all website visits coming from mobile devices in 2022. However, tracking users across different mobile devices can be challenging due to the fragmentation of mobile platforms and operating systems. Google Analytics' mobile tracking capabilities are also limited, which can result in inaccurate data.
6.) The challenges of tracking users across different platforms and devices
With the rise of cross-platform usage, where users switch between their desktops, laptops, mobile devices, and other platforms such as smart TVs and voice assistants, tracking users across different platforms and devices is becoming increasingly challenging. Google Analytics' cross-platform tracking capabilities are limited, which can lead to inaccurate data.
7.) The impact of data privacy scandals on user trust and willingness to share data
Data privacy scandals such as the Cambridge Analytica scandal have eroded user trust in the way businesses collect and use their data. This can lead to lower willingness to share data, making it harder to collect accurate tracking data.
8.) The limitations of Google Analytics' attribution modeling and its impact on accuracy
Google Analytics' attribution modeling assigns credit to various touchpoints in a customer journey, such as ads, social media, and email campaigns. However, accurate attribution can be challenging in multi-touchpoint customer journeys, which can result in inaccurate data.
9.) The role of AI in user tracking and its impact on accuracy
Artificial intelligence (AI) is becoming increasingly important in user tracking, as it can help to identify patterns and trends in user behavior that may not be visible through manual analysis. However, AI-based tracking also has its limitations, such as the risk of overfitting and the need for high-quality training data. The accuracy of AI-based tracking also depends on the quality of the data it is trained on.
What can businesses do to adapt to the changing landscape of user tracking?
Use a variety of tracking methods
Businesses should use a variety of tracking methods to ensure that they are collecting accurate data. This can include a combination of cookies, fingerprinting, and other tracking methods. It is also important to track user behavior across different devices and platforms to get a more comprehensive view of user behavior.
Focus on data quality and validation
Data quality and validation should be a top priority for businesses. This can include implementing data cleansing and validation processes, as well as regularly auditing data to ensure that it is accurate and up-to-date.
Consider alternative analytics tools
While Google Analytics is a popular tool, businesses should consider alternative analytics tools that may be better suited to their specific needs. This can include tools that focus on specific types of data, such as social media analytics or mobile analytics.
Utilize Server-Side Analytics
Server-side analytics refers to the process of collecting, analyzing, and interpreting data about website or application usage on the server rather than relying solely on client-side tracking. By implementing server-side analytics, organizations can gain deeper insights into user behavior, performance metrics, and other valuable data points. This approach offers several advantages, including enhanced privacy and security, as user data is processed and stored on the server side rather than being exposed to potential vulnerabilities on the client side. Server-side analytics also allows for more accurate data collection, as it captures information directly from server logs and eliminates the impact of ad blockers and other client-side limitations. Furthermore, server-side analytics provides flexibility in data processing and integration, enabling organizations to combine and analyze data from multiple sources to generate comprehensive and actionable insights.
Stay up-to-date with data privacy regulations
Businesses should stay up-to-date with data privacy regulations and ensure that they are obtaining explicit consent from users before collecting their data. This can help to build trust with users and ensure that businesses are collecting data in a responsible and ethical manner.
In conclusion, the accuracy of user tracking in Google Analytics is becoming less accurate in 2023 due to a variety of factors, including the increased use of privacy-enhancing technologies, changes in consumer behavior and data collection regulations, and limitations of cookie-based tracking. Businesses should adapt to the changing landscape of user tracking by using a variety of tracking methods, focusing on data quality and validation, considering alternative analytics tools, and staying up-to-date with data privacy regulations. By doing so, they can continue to collect accurate data and make informed decisions about their online presence.