How to Track Your Facebook Advertising Conversions in Google Analytics
If you’re on the lookout for new ways of enhancing your Facebook advertising strategy, lifting lead acquisition, and ultimately driving ROI through the roof, you’ve come to the right place.
Throughout this post, we’ll focus on using Google Analytics to improve the performance of your Facebook Paid Traffic. More specifically, we’ll show you how to do so with the help of advanced segmentation.
Here’s a quick peek of what you’re about to discover:
- Why you should never count on Facebook Reporting alone
- How to approach advanced segmentation like a top 1% marketer
- How to mine for Facebook optimization opportunities in Google Analytics
Over the last few years, we’ve invested tremendous resources to build our own data analysis methodologies and develop best practices for extracting actionable insights. Our goal was to simplify finding behavior patterns throughout our research by creating a template of questions we could use time and time again.
After working with multiple clients and repeatedly running into the same problems, we realized one of the most important questions to ask is: Am I tracking the correct online data?
After being 100% sure of your data accuracy, you can start leveraging it to boost campaign results. But, don’t worry if it isn’t, because this guide will also give you some pointers on how to track it better.
Enough chit-chat, and let’s get to it!
What’s the Deal With Facebook Reporting?
Here’s the thing with Paid Facebook campaigns. If you don’t put in the effort needed to optimize them, you’ll see a higher bounce rate compared to what you’d see in other marketing channels.
Sure, your traffic will go up. But that’s because Facebook is a master of spending your money on unqualified leads.
Chances are, you’ve seen it first hand. You may already be testing and optimizing your Facebook campaigns. Yet, if you’re only relying on Facebook’s performance reports, you’re missing out on what’s happening on your website.
Facebook Reporting Is not Enough!
Credit is given where credit is due. Sure, Facebook does a decent job of informing you about your campaigns’ performance. It even gives you a few details about your target audience. However, you can never rely solely on this tool because of its incapacity to provide in-depth information about what people do once they land on your website. This is crucial information for helping you understand what to optimize and get more bang for your buck.
Now that you get the complete picture of why you can’t rely only on Facebook Reports, let’s see how you can find a winning and scalable strategy by calling in Google Analytics to help.
How to Approach Segmentation Like a Top 1% Marketer
The image you see above is a sneak peek of how most advertisers use Google Analytics.
Don’t get this wrong – it’s better than nothing! Plus, it’s even more helpful if you’re not looking at all the website data lumped together, but only at Facebook Campaigns or Facebook Campaigns compared to other sources.
However, that’s just the tip of the iceberg. If you don’t pay close attention, you may lose your ship. So let’s look below the surface and make sure we don’t cause a catastrophe, shall we, Captain?
When looking at all our website traffic, Google Analytics informs us it’s coming from multiple channels. Paid Traffic is one of them.
This channel, too, is split into Paid Search, Paid Social, and Display. They might sound alike, but they’re very different advertising channels with unique needs of their own.
Let’s analyze Paid Search.
In a nutshell, this type of traffic comes from people who used Google Search Bar to find something, came across one of your ads and clicked it. This person knows what they want, and she intentionally searched for it (as opposed to someone who saw a promoted ad without asking for it).
Someone searching for something specific on a search engine is in an advanced awareness stage. This person knows her pain and is actively trying to solve it. This means she’s more likely to convert than those who only see promoted ads on Facebook, Twitter, and other such platforms.
As a result, this segment of people behaves differently on your website than those from Facebook (Paid Social).
Let’s see how you can separate this channel in Analytics to form a clear understanding of the results you can expect from it.
The correct approach to segmentation
You might have heard that if you want to track which of your links brings what leads, you need to add UTMs to your Facebook campaigns.
We strongly recommend creating a custom channel for your Paid Social traffic and analyzing it separately from your Paid Search traffic.
A good UTM structure allows you to pinpoint which of your campaigns brings the best leads. This makes it much easier to understand what’s happening in Google Analytics. Here’s what we experience has taught us the best UTM structure looks like:
Let’s see why by looking at how Google defines the Paid Search channel.
This means if you tag your Paid Social traffic with medium PPC (Facebook, Linkedin, Instagram, etc.), it will, for example, get lumped up in the same channel as your Paid Search traffic from Bing or Google Adwords. Choosing this UTM structure might render your optimizing efforts useless.
Setting the UTM medium to paidsocial lets you go ahead and build your custom segment.
If you’ve already created a custom channel from Facebook Paid Traffic, well done! If not, remember two things:
- Create a Custom Segment to get data from the past
- A Custom Channel lets you better organize your data and create reports much easier
Segments are magical tools for helping you understand your customer’s wants and needs and spotting behavioral patterns on your website.
Think of segments as magnifying glass. The more you zoom in at a more granular level, the better you begin to understand what you’re looking at. That’s where you can start writing down hypotheses and extract actionable and meaningful insights. It might sound tedious, but when done right is all you need to get to the valuable data that powers growth.
This is why it’s essential to isolate your Paid Social traffic. Once separated, simply apply this segment to your reports and start mining for insights.
Advanced segmentation – Finding Facebook Optimization Opportunities in Google Analytics
Now that you know the ground rules of isolating your Facebook campaigns traffic, it’s time to dive into more advanced segments.
To do that, we start by asking specific questions.
Question #1: How are the top Facebook landing pages performing?
The table above contains some data gathered from an e-commerce clothing store. But, first, let’s look at where the Facebook traffic ends up and analyze page performance.
Remember, we need to go as granular as possible.
- It makes sense to analyze data by device, gender, and the number of pages viewed separately.
- Don’t look at macro goals (conversions) alone! Micro goals, such as newsletter subscriptions, can also be helpful. People may not buy from you straight away, but they’ll have a much higher chance of doing so once they’re on your list.
Let’s move on.
Question #2: What pages macro-convert the best/worst?
We’re looking at the same table as the one before. The difference now is we begin asking more specific questions to spot online behavioral patterns.
We spot a couple of things:
// Analytics Insight #1
- Category pages have the highest conversion rate, both on mobile and desktop
- The Deals page bounce rate is significantly above average on both devices – 75%. It also has the worst conversion rate.
- The Category page for Female performs best in both conversion rate and average order value – $164.
- The conversion rate of the Category pages is four times higher for returning visitors compared to new visitors.
Based on these insights, we can draw out the following hypotheses.
// Hypothesis #1
- We can expect a higher ROI by setting up Category pages as landing pages for re-marketing campaigns.
Start off with these types of insights. However, you want to get even more specific with them. This means you can continue the segmentation process by asking questions such as:
Is there a significant difference between new and returning visitors who land on category pages and come from Paid Facebook campaigns?
This is how you’re coming up with hypotheses that have a high chance of becoming winners when you A/B test them.
For example, we know that the Deals page’s bounce rate is 75.99%. However, after looking at the data, we discover that certain age groups perform significantly better than the rest.
// Analytics Insight #2
- After looking at how different age groups convert on the Deals page, we notice two age segments [18-24] and [25-34] have a pretty good conversion rate (1.5% and 2%, respectively).
This leads us to the following hypothesis.
// Hypothesis #2
- We can expect a higher ROI by only targeting visitors between 18 and 34 and sending them on the Deals page.
Question #3: What pages micro-convert the best/worst?
As we were saying earlier, looking at macro-goals alone is not enough. But how do you know which micro-goals to look for? This depends on website complexity and the amount of traffic available. For our example, however, we’re going to focus on just two of them: product search rate (website search bar) and newsletter subscription rate – which might be more relevant regarding new visitors.
// Analytics Insight #3
- The search bar rate is significantly higher for Category pages.
- The newsletter subscription rate is significantly higher on the Homepage, both desktop and mobile.
- The email conversion rate is 6.5%.
// Hypothesis #3
- We can expect an increase in ROI by sending new visitors to the Home page. Once they get there, they’ll receive a first-time purchase incentive related to the Newsletter subscription (e.g. a discount code received via email).
Question #4: What are the main differences between mobile and desktop?
Now we’re keeping an eye out for specific behavioral patterns related to the device visitors use.
// Analytics Insight #4
- The average order value is 50% higher on desktop.
- The average number of products purchased on mobile is 1.1 whereas on desktop it’s 1.75.
- The [18-24] and [25-34] age groups convert better on mobile
- The bounce rate on mobile is very high – 75%.
// Hypothesis #4
- Given that mobile visitors tend to buy 1 product per purchase, we can expect a conversion rate increase by giving up on the cross-selling section on the checkout pages.
- We can expect a higher ROI on mobile by only targeting people from the 18-34 age group.
What about your hypothesis? Have you got any of your own that you thought of based on the insights provided? We’d be super curious to read about them in the comment section below.
When it comes to optimizing your Facebook campaigns, it’s important not to miss the forest because of the trees. You need more than just the data Facebook Reports provides to do this. To make sure you zoom out and get a bird’s eye view of the whole picture, segmentation in Google Analytics is your best friend.
If you’d like us to help guide you through the intricate maze of boosting your Facebook campaigns’ performance through Google Analytics segmentation, book a free strategy call now or drop us an email, and we’ll get back to you ASAP.