Using Custom Channel Groupings in Google Analytics

Published May 11, 2017
Do you ever find yourself confused by some of the default names for metrics and dimensions in Google Analytics?
Do you ever wish you could take a closer look at the specifics of how users behave on your site based on how they got there?
No worries, you’re in good company on both counts.
Google Analytics contains an array of hidden secrets that allow you to slice and dice data in unique ways to meet your needs. In this article, we’ll touch on the names of metrics and dimensions and show you a method to customize channels in a way that is tailored to different types of traffic and marketing efforts.
Before diving in further, let’s start by defining some key Google Analytics terms.

Source indicates the origin of a visit, such as a domain ( or search engine (Google).

Medium indicates the broad type of traffic, such as organic (from non-paid search), cpc (from paid search), or email.

Channel indicates the higher level category of traffic defined by the combination of source and medium.

For more details on how Google Analytics categorizes traffic into channels, see our previous post about Understanding Google Analytics Channels.
In this article, we’ll delve further into customizing channel groupings to more accurately evaluate your data.


Google Analytics Custom Channel Grouping


Why Custom Channel Groupings

Many times, the default channel groupings just don’t work for advanced tracking purposes. For instance, say that you want to segment “newsletter” traffic into a different channel bucket from standard “email” traffic, since those users may behave differently. You have two options for channel groupings:

  • Create a standard new “Channel Grouping” that defines how traffic falls into set buckets moving forward. In this case, previous traffic tagged with a source of “newsletter” and medium of “email” would stay the same, but future traffic tagged that way would fall into a “newsletter” channel.
  • Create a “Custom Channel Grouping” that will apply channel definitions to all existing data, placing historical and future data into the right “bucket.” In this case, all traffic tagged “newsletter/email” would fall into the correct “newsletter” channel, including retroactive data.

If you’re not overly familiar with the interface, we don’t recommend making major modifications to the Default Channel Grouping. If you’re not careful, you can miscategorize data permanently stored in your account.

The best approach is to create a Custom Channel Grouping, which will avoid damaging the raw data coming into the account and give you the ability to see retroactive numbers. You can potentially create up to 100 Custom Channel Groupings under an Analytics view (vs. 50 for standard groupings). However, keep in mind that custom groupings are accessible only to the user who sets them up.

To start, access the Admin section of Analytics and go to Custom Channel Groupings under your desired view. Select the “New Channel Grouping” button.


New Google Analytics Channel Grouping


Next, begin defining the parameters you’d like in your channels. You’ll have to manually build any channels that should appear in the interface; any traffic not included will fall into an (Other) category.

For this example, we’ll build a Newsletter channel that includes any relevant traffic. Our rule states that Source/Medium must match regex “newsletter” (essentially matching any traffic containing the word “newsletter” in URL tags). For instance, a URL for a spring newsletter may be:$utm_campaign=spring

To learn more about creating URL parameters for trackable links, see our article on Consistent Tagging for Better Campaign Tracking.


Newsletter Custom Channel in Google Analytics


Next, we’ll build an “Other Email” channel that includes all email traffic except the newsletter. The first rule will include any traffic normally included in the default “Email” category. The second rule will include only traffic that doesn’t contain a “newsletter” tag.


Custom Google Analytics Channel for Email


After saving these channels, we now have a basic Custom Channel Grouping to segment newsletter traffic from other email traffic. Going into the main Analytics reporting interface, we’ll navigate to Acquisition > All Traffic > Channels. From the dropdown to the lower left of the graph (right after the “Primary Dimension” text), we’ll select our new “Email Newsletter” grouping.


Selecting a Channel Grouping in Google Analytics


Now, we can see traffic broken down by Email vs. Newsletter categories, with all non-email visits bucketed under (Other). We can now compare aggregated stats for how newsletter visits perform against other email visits. Interestingly, newsletter visitors actually spend less time on the site and look at fewer pages, this may be attributable to the fact that they’re often driven to select individual article links. Next, newsletter visitors are less apt to convert; however, this data is likely due to the fact that newsletter signups are in themselves a large portion of overall conversions.

You can also use Custom Channel Groupings in the Multi-Channel Funnels reports, which allow you to see how multiple sources contributed to conversions.


Google Analytics Assisted Conversions


In the example above, we’ve applied our Email Newsletter grouping to the Assisted Conversions report, which counts sources that drove visits before a final visit from another source resulted in a conversion. For more insight on assisted conversions, see our extended post on how they work.

We can see that the newsletter has played a role in assisted conversions vs. direct/last click conversions (where the conversion occurs immediately after the click from the source).

In the next example, we’re looking at the Top Conversion Paths report, which shows the top combinations of channels in cases where 2 or more visits were involved before conversion. We’ve applied a grouping that includes branded and generic (non-branded) paid search terms. In this way, we’ve segmented out searches in which someone likely had a pre-existing knowledge of a company vs. those in which someone was searching for a service in general and saw an ad for the company.


Google Analytics Brand vs Generic Search Terms


Based on this data, we can make several observations about how brand and generic searches differ. First of all, the top multiple-channel combos leading to conversion includes two generic searches. This information indicates that people are likely to search for a service more than once, maybe to compare companies or refine a search to become more specific, before finally submitting their information.

Next, brand searches are more likely than generic to result in a later direct visit to the website that leads to conversion. Of course, people already familiar with a brand may be more likely to type in a URL directly once it’s saved in their browsers.

Next, note some instances in which users searched a generic keyword first and then returned via brand terms to convert (or vice versa). This data indicates that the research process includes people who are visiting for the second or third (or greater) time. In turn, ad messaging and landing pages should account for returning visitors, using features like remarketing for search to tailor messaging more specifically to these people.


Think about ways that Custom Channel Groupings can make your Analytics data more specific and allow you to analyze better segmented categories. For instance, you may want to break out paid social media traffic from standard social traffic (a default channel doesn’t exist for paid social) or divide organic traffic based on homepage visits (more likely to be brand) and inner page visits (more likely to be long-tail non-branded keywords). Remember, with Custom Channel Groupings, you won’t “break” any existing data, so have at it. Ask yourself questions that can be answered by more detailed data, then create your own groupings and experiment with how you can segment the data to produce new insights.


When the client first came to you, you talked up the value of Google Analytics. You emphasized the importance of seeing where your traffic was coming from. You went on and on about how Google Analytics can show traffic sources to pinpoint whether people came from search, social media or a specific site referral, and how valuable this data was. You sold them on it, so much so that your client looked forward to receiving that first report, the magical day when they would finally understand where visitors were coming from.
But then the report came, and it looked like this:



It showed that 10% of your client’s traffic came from “(direct)/(none)”. What does this label mean? How do you explain Direct traffic to your client? Better yet, how do you explain “none”?
Let’s take a closer look at understanding Direct traffic in Google Analytics and how we can address it with clients.
Digital marketers spend a lot of time focused on PPC and SEO campaigns in order to drive desirable traffic to a website. The phrases we’re ranking for and bidding on get meticulous attention, so much so that we often forget about some of the other ways that visitors find us.

We put a tremendous amount of the effort we put into reviewing organic search data and PPC campaign performance in analytics. But how closely do we monitor referral reports?

If that’s not a channel you review regularly, you may be missing out on seeing traffic that is coming directly from links you’ve obtained around the web, local business listings, news mentions, and more. Many times, links are only considered as a means to an end, a metric that Google uses in determining how to rank sites in the SERPs (search engine results pages). But the fact is, many of a site’s links may be directly contributing to its traffic.

In this article, we’ll review how to look at referral reports in Google Analytics, and some of the many ways to use that data to better inform your web marketing decisions.


Remember how your mom told you not to stand too close to the television because it might hurt your eyes?

The same rules can apply to data. If you’re too close, you may miss the patterns and trends that are crucial to understanding your website’s performance. You can’t judge a site’s performance looking at data in the bubble of a single day, you must consider any day’s traffic compared to the days before and after.

Google Analytics makes it fairly easy to analyze trends over long periods of time. But it also allows you to stand right in front of that TV, to look at more granular levels of time, right down to the hour.
There’s a better way to get that close to the data, without burning your retinas. We’ll cover how to analyze traffic effectively in today’s post.