Convertmind knowledge base
Filters allow you to modify what data gets tracked by Google Analytics, and how it gets tracked. By creating a filter, you can make Google Analytics process data in a way that’s clearer to analyze, for example by combining or excluding certain data, like certain URLs.
This allows you to make your data more accurate, easier to process, or more unified. For example, you can exclude various subdomains from being tracked. We, for example, could use a filter to exclude traffic from blog.convertmind.com so we could focus on data from convertmind.com. We could even create a second view with an opposite filter where we could look at just blog.convertmind.com. Another common use is filtering out IP addresses, which would allow you to exclude yourself and your team from the results and thus make the results more accurate.
There are four predefined filters you can use to to easily create commonly used filters. In addition to this, you are able to create your own custom filters to filter whatever you desire.
The four predefined filters are:
All four of these filters can be set to either ‘exclude’ or ‘include only’. ‘Exclude’ will of course create a filter what will exclude your input from the gathered data. ‘Include only’ will make it so that data that includes what you set as a filter is the only data that’ll be recorded and shown.
In addition to these predefined filters, you can create your own custom filters. You can use these to exclude or only include data based on basically any parameter you want, from screen resolutions to browsers to campaign names to Google Ads campaigns to even the version of Adobe Flash being used if you really want to.
There are five main types of custom filters:
Filters are used to change the captured data to include, exclude or otherwise change it. You can use this for a wide variety of purposes. Here, we’ll just run through some of the most common ones.
Many sites include subdomains. For example, blog.convertmind.com is a subdomain of convertmind.com. It’s not unthinkable you may want to track a subdomain separately from the rest of the site, or vice versa. You can use filters to make this possible.
You can do this for specific pages on your site too. Only want to track convertmind.com/beta? Or want to exclude that from the results? With the predefined subdirectory filter, you can do that.
If you run the site, naturally you’ll be on it often. You may want to keep this out of your Analytics data to make it more accurate. After all, you’re not on there as a customer. You can do this using filters, by filtering out your IP address from the results using the predefined filter.
If you have multiple people working on a campaign, they may use different capitalization within their work. This could cause ‘Great Campaign’, ‘Great campaign’ and ‘great campaign’ to appear separately in your results. Using a filter, you can simply force these all down to lowercase to make sure they’re all captured together.
Filters are incredibly useful tools when used right. However, they shouldn’t be taken lightly. When used carelessly, they can be hugely destructive and bring you a lot of trouble. On top of this, there are simply some restrictions that you need to keep in mind when you use filters. Here, we’ll run through the most prominent ones.
First of all, filters are destructive. A filter doesn’t just change how data is shown: it changes the data itself. If you use a filter to exclude certain data, that data won’t just be hidden, it will be completely erased. There is no way to recover this potentially lost data! Because of this, it’s highly recommended to always keep an extra view without any filters set, so you will always have a backup with all data included.
When creating filters, also keep in mind that they don’t just exist on a view level. They exist on the account level. You can view all filters under an account by going to ‘All Filters’ under your Account settings. Here, you can also select what views the filters should be applied to. Make sure you always check this after editing filters to make sure they’re doing what you want them to do.
When applying a filter, also keep in mind it takes up to 24 hours before it goes into effect. The advantage of this is that you have some time to catch potential mistakes before they take effect. But the downside is that you won’t immediately see results. Bottom line: after applying a filter, wait a day to check on what it’s doing.
You should now have an idea of what type of filter you want to use. If you don’t, just go through the explanation above again, compare the different filter types and determine which one works best for the goal you want to accomplish.
If you know what type of filter you want to use, follow the steps below:
Congratulations! Your filter is now applied and ready to go!
Note: when deciding on a string, keep in mind that Google Analytics won’t always take your string literally. There are certain characters which Google Analytics could interpret differently, which are known as regex. These could really mess up your data if you’re not aware of them, but can be incredibly powerful if you know how to use them. So make sure you read up on them.
Filters applied within Google Analytics do not apply retroactively. In other words, if you apply a filter in Google Analytics, all data gathered prior to the implementation of said filter will not change to match the filter. Similarly, removing the filter will not change data gathered while the filter was active.
This means that if you enable or disable a filter, this could cause differences to occur between data gathered before and after doing this. Keep this in mind when analyzing the data afterward.
You can apply multiple filters to a single view. When it comes to how these filters are handled, Google Analytics is rather simple. It simply processes the filters in the order you apply them.
There may be some cases you want a specific filter to be applied first. For example, when a certain filter is applied second it may miss some data already pushed out by the first filter (this can also happen accidentally, so be wary of this). In these cases, you may want to change the order of filters. You can do that like this:
And voila! Your filter order has been changed!
In Google Analytics, a filter pattern is essentially the thing you are filtering for. Everything that you set up after the filter type is essentially the filter pattern. For example, take a look at the image below. Everything included within the blue border is the filter pattern.
If you manage a website, it’s very likely you will frequently visit it. If you work for a large company, other people from said company may often visit the site too. This data is usually not very interesting in Google Analytics. After all, none of these people will actually convert on your site. Because of this, you may want to filter them out from your Google Analytics results. Thankfully, this is possible. However, the way you do this depends on what you want to filter.
Before you start, it’s important that you determine what you want to filter. Do you want to filter a single IP adres, a whole subnet of IP addresses or an IPv6 address? Determine this and choose the correct plan based on this. If you are unsure which technique you need, or you are unfamiliar with working with IP addresses in the first place, it’s recommended you consult an expert for help.
To filter out a single IP address, follow these steps:
And done! Your filter is now set.
You may want to filter out a subnet of multiple IP addresses, such as 192.168.0.*. If you want to do this, follow these steps:
Filtering out IPv6 addresses works the same way as filtering out regular IPv4 addresses. If you want to filter out an IPv6 address, follow these steps:
And done! Your filter is now set!
Tip: using regex, you can create more complex filters where you could filter on multiple things at once.
Technically it can. In order for Google Analytics to function, it needs to collect the IP addresses of users in order to collect their data. However, by default Google Analytics will anonymize this data and make it unavailable for you to view. This makes sense, as IP addresses are personal information that, when easily accessible, could raise a lot of privacy concerns.
There are some ways to bypass this anonymization and gain insight into the IP addresses that trigger Google Analytics. However, these require the use of extra code from external sources. Doing this also raises a lot of privacy concerns, so make sure you consult a legal expert before you try to do such things.
By default, Google Analytics does track your own visits. If you visit your site, Google Analytics will treat you the same as any other visitor, and thus add your data to its collection.There are ways to prevent this, however. Using filtering, it’s possible to exclude yourself from your Google Analytics results. Specifically, you can do this using IP filtering.
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