Why can there be different data in Google Analytics and other analytics systems?

Google Analytics is a powerful tool that provides detailed information about your website traffic. However, sometimes the data you see in Google Analytics may differ from the data collected by other analytics systems such as Piwik, Adobe Analytics, Facebook Analytics, etc.

There are several possible reasons for the discrepancies in the collected data:

1. Data collection methodology: Each analytics system has its own data collection methodology. They may use different algorithms to determine sessions, unique visitors and other metrics. This may lead to slight differences in the result.
2. Traffic Filters: Google Analytics has the ability to apply filters to data to exclude certain types of traffic or IP addresses. If you use such filters, they may result in differences in the data collected compared to other systems where filters are not applied.
3. Blocking Analytics Scripts: Some users may block analytics scripts on their devices or use ad blockers, which may result in their data being missing from Google Analytics. This can also lead to disagreements.
4. Reporting Latency: Google Analytics usually has some delay in displaying data. Therefore, when you compare the data with other analytics systems, it may differ due to the different time period that is taken into account when processing and displaying the data.
5. Metric settings: Each analytics system has its own metric settings that can affect the calculation and display of data. For example, some systems may consider robots (bots) in their reports, while others may ignore them.
6. Different code placements: If your website uses different analytics systems, it’s important to make sure that all the analytics codes are placed in the same way on the pages. Even a slight difference in the location of the codes can lead to deviations in the collected data. Check that the analytics codes are correctly installed on all required pages and make sure they are running correctly.
7. Time zone settings: Discrepancies in collected data can also occur due to different time zone settings in analytics systems. If your website or analytics systems are located in different time zones, this may affect the time it takes to collect data and display reports. Ensure that all analytics systems are operating in the appropriate time zones and have the correct time settings.
8. Mirror sites: Another reason for data discrepancies between Google Analytics and other analytics systems can be mirror sites. Mirror sites are copies of your main website that may have differences in analytics settings or individual URL parameters. This can lead to double counting of visitors and distortion of the overall analytics map. Carefully check for similar issues and resolve them by configuring filters or excluding mirror sites from data accounting.
9. Logged-in users on your website: If your website provides login for registered users, discrepancies in the collected data may occur due to the processing of logged-in sessions. Google Analytics may not capture all traffic and activity of logged-in users due to restrictions related to the use of cookies or other privacy restrictions. In such cases, it is recommended to use additional analytics tools or configure additional analytics code to track logged-in users and get a complete map of their activity.
10. Logged-in and non-logged-in browser users: Logged-in users who have a Google Chrome account typically provide more information about themselves, including gender and age, when registering or in their user profile. Such data may be collected and used for audience analysis.

Non-logged-in users, in turn, do not provide direct information about their gender and age. However, analytics systems, including Google Analytics, may attempt to determine these characteristics based on some indicators, such as behavioral patterns, keywords used, demographic data from previous analytics, and others. However, these assumptions may be inaccurate and may not always accurately reflect the gender and age composition of the audience.

It is important to understand that gender and age data, even if collected, may be inaccurate or incomplete. This data depends on how accurate users were when registering or filling out a profile, as well as the methodology used to determine the data in the analytics system.

Therefore, the display of gender and age-related indicators in Google Analytics and other analytics systems may be limited or unreliable due to the presence of both logged-in and non-logged-in users, as well as the instability of the algorithms for determining these indicators. Such data should be considered indicative.

However, gender and age data, when available, can be useful for understanding your audience and used to refine marketing strategies and personalization. However, it is recommended to supplement this data with other sources, such as surveys or additional research, to get a more accurate and complete picture of your audience.

How to reduce discrepancies between data in Google Analytics and other analytics systems?

To reduce discrepancies between data in Google Analytics and other analytics systems, you should consider the following recommendations:

1. Check Settings: Make sure all settings in Google Analytics are configured correctly, including traffic filters and metrics settings. Check that you have correctly installed the analytics code on your website.
2. Compare methodologies: Explore the difference between data collection methodologies in Google Analytics and other systems. Understanding the differences in defining sessions, visitors, and other metrics will help explain the discrepancies.
3. Use additional tools: To obtain additional data and check for discrepancies, use other analytics systems that can complement data from Google Analytics. For example, you can use server log files or additional analytics tools.
4. Carefully analyze the results: When comparing data from different analytics systems, pay attention to general trends and changes in indicators over time, not absolute numbers. It is better to focus on general trends and changes in indicators over time. Evaluating trends and dynamics will allow you to get a more objective picture of the website and user behavior. For example, pay attention to the increase or decrease in traffic, changes in conversions or the effectiveness of marketing campaigns.

It is also recommended to conduct a regular review and audit of the analytics system to make sure that the settings are correct, the data is correct and to identify possible problems. Note the consistency of traffic and reporting metrics with other independent sources, such as server logs, ad analytics, or a CRM system.

By considering these recommendations and features of analytics systems, you will be able to get a more accurate and complete picture of your site’s web analytics. Thorough data analysis and focusing on general trends will help you draw informed conclusions and make strategic decisions based on the data you collect.

Ultimately, it’s important to understand that no analytics system is completely accurate. Differences in the collected data may be minor and not have a serious impact on the overall analysis and understanding of the behavior of your visitors. However, being aware of these possible discrepancies will help you perform a more objective analysis and make the right decisions based on the collected data.