Using Google Analytics Audiences for Display Ad Targeting

Published October 13, 2016
Everyone from marketing professionals to public speakers will tell you to “know your audience.” But that means a little something different to everyone. When it comes to putting that knowledge to work, there’s a lot more to understand than just who sees your ads.
While demographics should help define whom you target with online advertising, you should also use the data that’s available about your website visitors. If a site has a Google Analytics account, you can learn a ton of information about visitors and apply it to ad targeting.
Google Analytics tracks user demographics based on information stored in third-party cookies and mobile advertising IDs which is compiled using behavior hints from web browsing activity. Be aware that this data is not 100% perfect but reflects the best work of Google’s algorithms to gauge user ages, genders, and interests. The key to targeting is realizing that even if these categories aren’t perfect, the users who show up in a particular “bucket” when visiting your site are the people interested in your brand.
The age subsets and interest categories you see in Google Analytics correlate to targeting options available within Google AdWords. You can choose from these categories when setting up display ad campaigns in order to serve ads to the most relevant people.
First, let’s review how to look at demographic information in Google Analytics and then move on to applying what we learn in AdWords bidding.


Display Advertising


Applying Google Analytics Age Information for AdWords Targeting

Viewing Google Analytics Demographic Reports

To get this data you’ll have to enable demographic reports to see demographic data in Google Analytics. Once you’ve done this and allowed some time for data to accrue, start with the Audience > Demographics > Overview in the main Google Analytics reporting view. Here, you’ll see graphs showing user breakdowns in age and gender categories. The ages fall into brackets of 18-24, 25-34, 35-44, 45-54, and 55-64.


Google Analytics Audience Overview


This example, from a retirement community’s website, shows 65+ as the highest age bracket, with 55-64 close behind. In addition, users are predominantly female.

Now, this information shows a high level breakdown of who’s visiting the site overall, but it doesn’t tell the whole story of which visitors are most valuable. We need to dig a bit deeper to see which people from this group we should be targeting.

Pinpointing Converting Categories

To find out which age brackets are most valuable, we’ll switch to the Audience > Demographics > Age report. Here, we can see more specific data breaking down engagement and conversions from each age group.


Google Analytics Age Report


Looking in the Goal Conversion Rate column, we can see that people aged 35-44 are most likely to convert (1.90%), followed by those aged 55-64 (1.73%). Interestingly, we previously saw that the 35-44 bracket was one of the lowest for Session volume. This data likely indicates people aged 35-44 are researching a retirement home for aging parents.

Applying Bids by Age in Google AdWords

From this research, we can then move to apply what we learned to bids in AdWords. Navigating to our desired AdWords display campaign and ad group, we’ll select the Demographics tab. Within that section, choose Demographics to see options for setting bids by age and gender.


AdWords Age Bids


Under the Age tab, we’ll set bid adjustments higher for the age categories that have shown the best conversion rates, making ads more likely to show to these people. We can apply a similar methodology on the Gender tab if either female or male users show higher interest.

Besides demographics, you can also correlate user interests from Google Analytics to AdWords.

Applying User Interest Data from Google Analytics to AdWords

Seeing Interests in Google Analytics

To view behavior by user interests, go to the Audience > Interests dropdown. In addition to the Overview report, you can see three subcategories of interests:

  • Affinity Categories include subgroups of people who tend to research topics correlated to hobbies and lifestyles, such as Travel Buffs
  • In-Market Services indicate interest in researching products and services to purchase, such as searching for a hotel
  • Other Categories include other general interests from web browsing, like viewing weather predictions

For the purposes of this article, we’ll analyze In-Market Services for an HVAC company’s website. While some categories, like Home Improvement, may seem like obvious targets, we also want to look at other categories that may not directly relate to the company’s service but may correlate with people more likely to convert.


Google Analytics In-Market Services


In the example above, we’ve sorted by the categories that drove the most conversions. While Travel/Hotels & Accommodations doesn’t necessarily relate directly to interest in HVAC services, that interest group showed the highest conversion volume and rate. An excessive interest in finding hotels while traveling could be one indicator of a higher income homeowner who’s looking to install a new air conditioning unit. The same goes for the three Real Estate categories all showing high conversion performance.

Next, see that Employment is the second highest converting category, likely due to job seekers filling out the contact form on the site. Extrapolating the user intent helps designate this as a good category to exclude from interest targeting, as typically you don’t want to pay for job seekers to click ads.

Applying Your Findings to AdWords

Now, we’ll move to AdWords to apply what we’ve learned to an ad group promoting HVAC services. As before, we’ll navigate to the Display Network tab within the ad group where we wish to set up targeting.

Next, we’ll select the Interests & Remarketing section, choosing In-Market Audiences from the dropdown within the box. Now, we can select the specific interest categories we want to target, finding the high-performing ones identified in AdWords.


AdWords Demographic Interests


You can layer these interests together with other types of targeting. For example, you can show ads to people who fit these interest categories and are also viewing select placements. In this instance you can reach people who are looking at a local news site and are also in a Home Improvement category based on their previous behavior.

In this example, we’ve layered interest targeting to reach people who are viewing pages within an HVAC & Climate Control topic.

Also, we want to exclude the Employment category to keep job seekers from clicking on ads. To do that, we’ll scroll further down on the same page until seeing Ad Group Exclusions. Here, we’ll choose the Employment category.


AdWords Ad Group Exclusions



We’ve shown you a couple of examples for using Google Analytics audiences to target potential converters in Google AdWords display campaigns. However, the possibilities for fine-tuning your ad targeting are endless and highly customizable based on your industry.

First, take a deep dive into your data on user demographics and interests. You may be surprised by what you find. From there you can apply that data to bidding and targeting within your AdWords campaigns. The more you can hone in on all of the lifestyle factors that make up a profile of your target persona, the more accurately you can match what you have with what they need and where they are. Being able to deliver content with that kind of precision is the ultimate version of knowing your audience.


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.