Combining Google Analytics with Facebook Insights for Better Marketing

Published February 18, 2015
You may already know that Facebook Insights can provide you with demographic data about the people who like and engage with your page. But did you know you can use Google Analytics to compare that data with insights about the people who actually click through to your website?
Did you just get really excited? Us, too.
Informing Google Analytics with Facebook Insights allows you to refine messaging and ad targeting to more strategically reach the people who are most likely to engage with your brand. So, what data can you see about your audience in Facebook Insights, and how can you connect this data to the people who come to your website? We’ll walk you through it below.

Blog Image Combine Facebook Insights with Google Analytics


Facebook Insights Reports

To get started, let’s look at the main People reports available in Insights to see how they differ and what they offer you. You’ll want to click the Insights option above the cover photo for a page you manage to view this information.

Your Fans

The “Your Fans” report in Insights shows demographic data on the people who like your page on Facebook. You can see age, gender, location and language categories. In this example, we see men aged 25-34 represent the largest age/gender category, with women in the same age bracket close behind. Albany, NY represents the location with the most fans, with New York City next. This is helpful in generating a baseline understanding of who makes up our Facebook audience.


Facebook Insights Your Fans


People Reached

The “People Reached” report shows the people who saw your posts, whether they like your page or not. Reach can actually include your fans, friends of anyone who’s liked or commented on a post, or people targeted with promoted posts – anyone who may have encountered your content on Facebook. Because of this, demographics of the audience actually reached will often vary a bit from the people who like your page, especially if you’re running paid promotions. Note in this example that women aged 25-34 slightly outnumber men in the same age bracket, whereas the reverse was true for fans.


Facebook Insights People Reached


People Engaged

The “People Engaged” report shows demographics for people who liked, commented on, shared posts or engaged in any other direct interaction with your page. The numbers here will be the smallest of any report, but will best reflect the people taking an interest in your brand. Note that in the example below, men aged 45-54 actually make up the top category, while this category was less represented in fans or reach.


Facebook Insights People Engaged


Comparing to Google Analytics

The information covered above is helpful in identifying the demographics you’re reaching and engaging on Facebook. But now you want to compare that data to the data you’re receiving from people who click through from Facebook to visit your website. Let’s take a look at demographics in Google Analytics, segmenting to specifically focus on people visiting from Facebook.

First, you’ll want to create a Facebook Segment in Google Analytics. For help doing this, revisit the Who Are Your Facebook Users section in this article.

Once you’ve created a segment to include Facebook sessions, apply it to your view and navigate to Audience > Demographics > Age. We’ll now ensure we see data broken down in the same subsets as Facebook Insights, by both age and gender.


Google Analytics Adding a Secondary Dimension


Select the “Secondary Dimension” and search for “Gender” and choose this dimension. Now, you can see a complete breakdown of Facebook visitors by age and gender.


Google Analytics Reporting on Age and Gender


From this data, we can see the most website Sessions are coming from females aged 25-34, followed by females 35-44 and males 25-34. So even though males 25-34 slightly outnumber females 25-34 as fans, significantly more females in that age bracket are actually coming to the site from Facebook.

Interestingly, this data reflects the same highest bracket as the People Reached report. Also, while the People Engaged report shows men aged 45-54 as the highest category, these people rank near the bottom in people actually coming to the site, showing that while men 45-54 are more likely to interact with the Facebook page, women 25-34 are more likely to click through to the website.

One takeaway from this analysis relates to Facebook advertising. If your goal is to use Facebook ads to drive traffic to your website, this analysis suggests you’d be most successful targeting women 25-34 rather than men aged 45-54.

Determining Times for Post Engagement

Facebook also offers a section in Insights to show when your fans are online. This data can help inform when to post content, as to when they are most likely to click through to your site. To view this information, go to Posts > When Your Fans Are Online.


Facebook Insights When Your Fans are Online


In this example, we can see that day-to-day usage does not vary much, but time-of-day usage dips from evening to the next morning. Page fans are online at a consistent level for a large portion of the day.

We can also note slightly increased activity taking place at 4 p.m. and 8 p.m., with the most volume of fans online. This may be the result of people checking Facebook right at the end of a workday and later in the evening after getting home. This report can help to provide general guidance as to when to schedule important posts such as those containing links to blog articles on your site. It indicates that posting at around 9 a.m. is best to maximize the chance of reaching your fans over the course of the day, while subsequent posts around 4 p.m. and 9 p.m. may also prove effective.

Once again, we can go into Google Analytics to cross-reference data about people who are actually reaching your site. We’ll apply our Facebook segment again to specifically filter sessions from Facebook.

Navigating to the Behavior > Site Content > Landing Pages report, we can break down the performance of specific pages from Facebook visitors and further segment by hour. This way, we can see not only what content was most popular but at what time people were most likely to click through to view that content.

Within this report, select the “Secondary Dimension” dropdown and search for the “Hour” dimension to add. Note that hours will display based on military time or a 24-hour clock.


Google Analytics Using Hour as Secondary Dimension


Now, you can see a breakdown of landing pages from Facebook, with sessions broken down by hour. The top landing page was an article on 5 Tips to Reduce Inflammation viewed at 3 p.m. (shown as “15” when based on a 24 hour clock).


Google Analytics Tracking Posts by Hour


This data meshes with mid to late afternoon being a good time for posting links back to the site. You can also see midnight (00) as well as morning times (08 and 09) reflected. Based on this data, you can begin test posting more content at these times to see the impact on engagement. In addition, you can see what types of content perform best at what times. For example, you may see that a post targeted to moms may be more popular in the morning, while a post targeted to web developers may be more popular later in the evening.


Facebook Insights offers a large amount of reach and engagement data segmented by demographics to help marketers understand who their audience is on the social platform. However, if driving Facebook users to your site is one of your primarily social media goals (and it should be!), Google Analytics can offer an additional level of insight to optimize your marketing. When targeting by demographic, consider not just the segments Facebook indicates as being most engaged, but drill down into Google Analytics to understand who is most likely to click through and continue that engagement. Look not only at when Facebook says your audience is online, but when Google Analytics shows people are more likely to visit your website. By considering both Facebook Insights and Google Analytics data, you put yourself in the best position to optimize your Facebook marketing for better results.


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.