Stay a step ahead of digital data disasters and avoid common Google Analytics mistakes with our expert insights. In the fast-paced world of digital marketing, accurate data plays a pivotal role in driving informed decisions.
It’s easy to fall into the trap of common errors that can derail your analytics efforts. From incorrect implementation to misguided tracking setups, this article delves into the critical Google Analytics mistakes that you need to steer clear of.
Whether you’re a seasoned marketer or just starting out, understanding these mistakes can save you time, money, and most importantly, ensure that your data is reliable and trustworthy. With our actionable tips and best practices, you’ll have the knowledge you need to navigate the complex landscape of digital data analysis successfully.
Don’t let common Google Analytics mistakes hold you back from making data-driven decisions. Read on to discover how to sidestep these pitfalls and take control of your analytics game.
Importance of accurate data in Google Analytics
Google Analytics provides valuable insights into your website traffic, user behavior, conversion rates, and more. You can use this data to identify your strengths and weaknesses, test different strategies, and improve your online presence.
However, if your data is not accurate, you might end up wasting time and money on ineffective or harmful actions.
For example, you might overestimate or underestimate your traffic sources, miss out on important user segments, or fail to track your key performance indicators (KPIs). Therefore, it is essential to ensure that your Google Analytics data is reliable and trustworthy.
Common Google Analytics mistakes to avoid
There are many factors that can affect the quality and accuracy of your Google Analytics data. Some of them are technical, while others are related to your configuration and analysis.
Here are some of the most common Google Analytics mistakes that you should avoid and how to fix them.
- Incorrect installation and tracking setup
- Not setting up goals and conversion tracking
- Ignoring bot and spam traffic
- Overlooking data sampling issues
- Failing to implement filters and segments
- Neglecting to track events and custom dimensions
Mistake #1: Incorrect installation and tracking setup
One of the first steps to use Google Analytics is to install the tracking code on your website.
This code allows Google Analytics to collect and send data from your website to your account. However, if you install the code incorrectly or incompletely, you might miss out on some or all of the data that you need.
For example, you might forget to add the code to some pages, use an outdated or incompatible version of the code, or have multiple instances of the code on the same page. These errors can cause duplicate or missing data, skewed metrics, or broken reports.
How to fix this mistake?
Check your website source code and make sure that you have only one instance of the latest version of the Google Analytics tracking code on every page. You can use tools like Google Tag Assistant or Google Analytics Debugger to verify your installation and troubleshoot any issues.
Mistake #2: Not setting up goals and conversion tracking
One of the main purposes of using Google Analytics is to measure how well your website is achieving your objectives.
These objectives can be anything from generating leads, sales, subscriptions, downloads, etc. To track these actions, you need to set up goals in Google Analytics.
Goals are specific and measurable outcomes that you want your users to complete on your website. By setting up goals, you can track your conversion rates, revenue, return on investment (ROI), and other important metrics.
How to fix this mistake?
Define your website goals and set them up in Google Analytics. Use different types of goals depending on what you want to track, such as destination, duration, pages per session, or events.
You can also assign a monetary value to each goal to estimate how much each conversion is worth to your business.
Mistake #3: Ignoring bot and spam traffic
Not all traffic that comes to your website is human. Some of it is generated by bots and spammers that crawl or visit your website for various reasons.
According to the latest Bad Bot Report from Imperva, bots are an increasingly significant part of the web, accounting for over 42 percent of traffic overall in 2021.
More concerning still is that bad bots accounted for a record-setting 27.7 percent of all global website traffic in 2021, up from 25.6 percent in 2020.
For example, some bots are used by search engines to index your website content, while others are used by malicious actors to scrape your data, inject malware, or inflate their own traffic numbers.
Bot and spam traffic can distort your Google Analytics data by inflating your metrics, such as sessions, bounce rate, referrals, etc. This can make it harder for you to analyse your real user behaviour and performance.
How to fix this mistake?
Filter out bot and spam traffic from your Google Analytics reports. You can do this by enabling the “Exclude all hits from known bots and spiders” option in the view settings of your account.
Create custom filters or segments to exclude specific sources or domains that are sending bot or spam traffic to your website.
Mistake #4: Overlooking data sampling issues
Google Analytics uses a technique called data sampling to reduce the processing time and complexity of generating reports for large datasets.
Data sampling means that Google Analytics uses a subset of your data instead of the entire data to calculate your metrics and statistics.
While this can speed up the report generation process, it can also introduce some inaccuracies and discrepancies in your data. For example, if you use a small sample size or a low sampling rate, you might get different results than if you use a larger sample size or a higher sampling rate.
How to fix this mistake?
Be aware of when and how data sampling occurs in Google Analytics and how it affects your reports. You can check if your report is based on sampled data by looking at the shield icon at the top right corner of the report.
Hover over the icon to see the sample size and sampling rate used for the report. If you want to reduce the impact of data sampling, you can try the following methods:
- A shorter date range or a smaller segment to reduce the amount of data in your report.
- Standard reports instead of custom reports, as they are less likely to be sampled.
- Use Google Analytics 360, the paid version of Google Analytics, which offers higher sampling limits and unsampled reports.
Mistake #5: Failing to implement filters and segments
Google Analytics collects and displays data from all your website visitors by default. However, not all of this data is relevant or useful for your analysis.
For example, you might want to exclude your own or your team’s visits from your reports, or you might want to focus on a specific group of users based on their characteristics or behavior. To do this, you need to use filters and segments in Google Analytics.
Filters and segments are two ways of modifying or refining your data in Google Analytics. Filters are rules that you apply to your views to permanently exclude or include certain data, while segments are temporary conditions that you apply to your reports to isolate and compare certain data.
How to fix this mistake?
Use filters and segments to customize and optimize your Google Analytics data.
For example, you can use filters to remove internal traffic, IP addresses, query parameters, etc. from your views. You can also use segments to analyze different user groups, such as new vs returning, organic vs paid, mobile vs desktop, etc.
Mistake #6: Neglecting to track events and custom dimensions
Google Analytics tracks and reports on many metrics and dimensions by default, such as sessions, users, pageviews, bounce rate, source, medium, device, etc.
However, these default metrics and dimensions might not capture all the information that you need about your website and users. For example, you might want to track how many users click on a button, watch a video, fill out a form, download a file, etc.
You might also want to collect additional information about your users or pages that are not available in the default dimensions, such as user ID, product category, content type, etc. To track these actions and information, you need to use events and custom dimensions in Google Analytics.
Events are user interactions that you can track and measure on your website, such as clicks, plays, downloads, etc. Custom dimensions are additional attributes that you can assign to your users or pages to enrich your data.
How to fix this mistake?
Use events and custom dimensions to track and collect more specific and relevant data about your website and users.
For example, you can use events to track how many users complete a certain action on your website, such as signing up for a newsletter or adding a product to the cart. You can also use custom dimensions to track additional information about your users or pages that are not available in the default dimensions, such as user ID, product category, content type, etc.
Conclusion: Taking steps to avoid Google Analytics mistakes
Google Analytics is a powerful tool that can help you measure and optimize your website performance. However, if you don’t use it correctly, you might end up with inaccurate or misleading data that can harm your decision-making process.
To avoid this situation, you should take steps to avoid some of the common Google Analytics mistakes that we discussed in this blog post. By doing so, you can ensure that your Google Analytics data is reliable and trustworthy and that you can use it to improve your online presence.
