Chances are, you’ve heard of Google Optimize by now. It’s Google’s solution for A/B testing and personalization. Over the years, it has become a popular solution for optimizers around the world who wanted a freemium tool to do A/B testing.
In this post, you will learn what you can really expect from this tool. How do you configure it properly? How do you run your first experiment? Let’s go into details:
Table of contents
- What is Google Optimize?
- Why should you use Google Optimize over other testing tools?
- Is Google Optimize free?
- Google Optimize vs. Google Optimize 360 (free vs. paid)
- Setting up Google Optimize
- Setting up an experience
What is Google Optimize?
Google Optimize is a freemium tool for website experimentation and A/B testing. It allows you to test different versions of a page and analyze which one is the most efficient, depending on the objectives that you set.
Why should you use Google Optimize over other testing tools?
Marketers love tools, and tools love marketers. What results from this romance is tool overload. You have a tool for keyword rankings, a tool for broken links, a tool for social media mention monitoring, a tool for social media analytics, a tool for…you get the idea.
Google Analytics has been trying to diminish tool overload and bring marketers out from their silos for years. It addresses all channels, all conversions. It’s a central heart instead of multiple arms.
Enter Google Optimize, an A/B testing and personalization tool that uses Google Analytics data to power your CRO efforts. Obviously, A/B testing is nothing new, neither is serving personalized content based on customer behavior.
The true progress here is how Google Optimize pairs with Google Analytics, and how easily we can tie our experiments to KPIs in Google Analytics.
McQuaide believes the deep integration allows for:
- Easier setup;
- More advanced targeting;
- More advanced reporting;
- Applying learnings faster.
It’s easy to see how having Google Optimize data in Google Analytics and Google Analytics data in Google Optimize is a big competitive advantage.
One of the things that makes Optimize so powerful is it’s deep integration with Google Analytics. You can use your Google Analytics data to identify key segments of users to target users as audiences shared Optimize. Examples:
- Loyal customers: Been to your site X times and purchased Y instances/value
- Status groups: Premium frequent fliers, Economy standard fliers
- Geo-location: Special offer for San Antonio residents
Once you’ve identified these key audiences, create a unique offer for each target group, and then use Optimize to target that offer to your intended audience.
If you’re reading this, you’re probably already using a testing tool like Optimizely or VWO. So, why give Google Optimize a try?
- It’s a familiar UI.
- Your Google Optimize data will be available in Google Analytics, and your Google Analytics data will be available in Google Optimize, allowing for more advanced targeting, more advanced reporting, more advanced conversion tracking, etc.
- It’s free, so what have you got to lose?
Is Google Optimize free?
I know I just said it’s free, but, of course, there’s a paid version: Google Optimize 360. If you’re a small- to medium-sized business or just getting started with a testing program, the free version will work for you.
If you’re a big enterprise or have a very sophisticated testing program, you’ll probably need the paid version.
Google Optimize vs. Google Optimize 360 (free vs. paid)
Here’s the official breakdown of differences between the two versions:
So, to summarize, the limitations of the free version are:
- No Google Analytics audience targeting;
- Limited multivariate testing (16 variations);
- Only pre-configured experiment objectives (Google Optimize 360 allows you to go back and change objectives to see how it would’ve impacted other Google Analytics goals);
- Limited concurrent testing (5 tests at a time).
Setting up Google Optimize
Now, to get started, head to the Google Optimize site and click one of the “Start for free” buttons:
Now, you’re ready to create your account and container.
1. Creating an account and container
As you move through the onboarding sequence, Google recommends opting into improving Google products, benchmarking, and in-depth analysis. I recommend it as well; the more info you can gather about it and how best to use it, the better.
Once you accept all terms and conditions, you’ll end up in the Experiments view with a default container (“My Container”):
If you click “My Container” in the top left, you’ll be able to see the ID for your account and your container:
2. Linking Google Analytics
Google Optimize will encourage you to start an experiment, but I recommend linking Google Analytics first.
Click “Settings” in the top right:
Then, link your Google Optimize container to Google Analytics:
Once you select a Property from the drop-down menu, you’ll also be asked to select the View you’d like to link. Click “Link” and you’re all set.
3. Installing the Google Optimize snippet
Next, you need to install the Google Optimize snippet on your site. The snippet is just below the section for linking your Google Analytics account, in the Settings menu:
Now, you have two options for getting this Google Analytics tracking code updated: manually updating each page with the Google Optimize snippet or using Google Tag Manager.
How to install Google Optimize with Google Tag Manager
I recommend using Google Tag Manager. So does McQuaide:
Why use GTM? Event tracking, that’s why. Google Optimize uses GA goals as experiment objectives and pulls data from GA to calculate experiment results. So if you want to test objectives that involve user interaction, you’ll need to set up an event-based goal first. The easiest way to do that is by using GTM.
So, head over to GTM and create a new tag. You’ll notice that Google Optimize is right there as a tag type:
Now enter your Google Optimize and Google Analytics IDs:
Now, you can choose your triggering options. I’m going to experiment on my entire site, so I’m going with “All Pages”, but you can choose whatever you’d like.
Save it, preview it, debug it. And you’re done!
How to install Google Optimize without Google Tag Manager
Without Google Tag Manager, simply follow the instructions from Google. You’ll add a snippet of code to the top of the <head> tag on every page on which you want Google Optimize to run.
How to avoid the “flicker effect” with Google Optimize
Are you familiar with the flicker effect? The flicker effect is when the visitor is shown the control quickly before seeing the correct variant. Of course, this has a number of negative impacts on both user experience and the validity of your test results.
Google created the page-hiding snippet to prevent the flicker effect. You can read more details about how to avoid the flicker effect with your installation here.
Setting up an experience
On to the fun stuff! Let’s create an experience. You can click “Let’s go” on the main Google Optimize screen.
You’ll be asked to enter the name of the experiment, the URL of the page you’d like to test, and the type of experiment you’d like to run:
Perhaps you’re familiar with all of these experiment types. If so, just skip ahead to the “Configuration” section. If not, here’s a little about each.
This is the most familiar experiment type. You compare two versions of the same page to see which one performs better: A vs. B, control vs. variant.
Visually, it looks something like this:
If you want to brush up on your A/B testing know-how, I recommend reading this massive, incredibly useful A/B testing guide.
A multivariate test allows you to test multiple variants of multiple elements at the same time to see which combination produces the best results.
Technically, redirect tests are a type of A/B test. Instead of testing two versions of the same page, you test two pages against each other.
Personalization allows you to test the impact of showing elements to users based on their past browsing behavior, location, and other factors.
For example, you could personalize your site by “promoting seasonal clothing based on your visitor’s geography or offering free shipping to your best customers.”
For the sake of simplicity, let’s continue forward with an A/B test. You’ll start by adding a variant to test against the control:
You can also change the variant weights and preview the variants here.
Google Optimize visual editor
Google offers a WYSIWYG visual editor, which should feel very familiar and intuitive to anyone who has ever used one before. (And you probably have—I’m using one right now to write this post.)
As I said, the experience is fairly straightforward and familiar. Here’s what you really need to know:
- The app bar at the top. Here you can change the experiment name and status, show changes, switch between variants, etc.
- The palette. This floats along as you scroll and contains all of the editable elements of your current selection.
- Current selection. The portion of the page you’re editing.
If you’re confused about anything as you get started, Google has a “what’s what” guide you can use.
When you scroll below the variant section, you’ll end up in the configuration section. Here, you can manage your objectives and targeting.
You can choose from among basic objectives like pageviews, session duration, and bounces. But what makes Google Optimize awesome is that you can also choose from any of the Google Analytics Goals in your linked account.
In the free version, you can choose one primary objective and two secondary objectives. Remember that you can’t retroactively change these objectives in the free version, so be sure to choose all of the relevant objectives upfront.
You’ll also notice room to add a test hypothesis.
You can choose your variant weights in the top section:
So, in this case, I’m showing each of my two variations 50% of the time.
Now on to the conditions that determine the subset of visitors who will be part of the test:
Instead of explaining all of these targeting options in detail, as Google does in each of the pages linked to below, here’s a high-level summary:
- URL targeting: specific URLs;
- Behavior targeting: new vs. returning, specific referral sources;
- Google Ads targeting: accounts, campaigns, ad groups, or keywords;
- Geo targeting: specific country, state, city, etc;
- Technology targeting: specific device, browser, OS;
- First-party cookie: users that have a cookie from your site;
- Query parameter: specific pages or sets of pages;
- Data layer variable: key values stored in the data layer;
- UTM parameter targeting: specific utm_campaign.
If you had Optimize 360, you could also do audience targeting.
Reporting is another area where Google Optimize really shines.
Seiden explains how that native integration with Google Analytics comes into play again:
Your test stats are available in the Reporting tab within the Optimize UI. They are also available in Google Analytics in a number of ways: Every hit from Optimize is sent to GA with an Experiment Name, Experiment ID, and Variant number automatically attached.
This means that you can get much more creative with how you analyze your test data outside of the Optimize UI.
- Segment and add secondary dimensions to a report with Variant #, Exp ID, and Exp name
- Create audiences and segments based on previous test behavior, and even target to future test experiments based on being a part of a prior test.
(Both of Krista’s quotes in this article were taken from an article on her blog, which you should read if you’re looking to go beyond the basics after this beginner’s guide.)
If you’re keeping it simple and sticking to the Google Optimize reporting UI, here’s what you’re working with:
- Summary header. You’ll see the experiment status and a summary of the results (so far). The leader, improvement, probability to be best, etc.
- Objective card. The performance of each of your variants against whichever objective you’ve selected from the drop-down list. Note that at the beginning of your experiment, the graph will show more uncertainty, but that uncertainty will narrow over time as more data is collected.
Google Optimize is going up against giants like Optimizely and VWO, but the value of the native integration is hard to ignore. Especially with a $0 price tag.
At the very least, create an account and run an experiment. Hopefully this guide makes that process even easier for you. Then, see for yourself how it compares with your current A/B testing and personalization tool.