How to Start With Google Optimize?
Google Optimize what is that? It’s Google’s solution for A/B testing and personalization. Google Optimize is launched in last year, which left optimizers around the world waiting in line to try it out. Now that it’s out of beta, you can give it a try without the wait.
But what can you expect? How do you configure it properly? How do you run your first experiment? Marketers love tools and tools love marketers. What results from this romance is tool overload. You have a tool for keyword ranking, 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.
What is Google Optimize?
Google Optimize 360 is Google’s A/B testing and personalization platform. Like most A/B testing platforms, it allows marketers to test variations of a site in order to improve conversions. Unlike most A/B testing platforms, it natively integrates with Google Analytics.
Google Optimize (free) vs Google Optimize 360
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.
Here’s the official Table of the difference of that two product.
|Made for…||Small to medium-sized businesses getting started with experimentation.||Larger enterprises and businesses with more sophisticated testing needs.|
|Native Google Analytics integration||√||√|
|Basic URL targeting||√||√|
|User behavior and technology targeting||√||√|
|Google Analytics Audience targeting||–||√|
|Web app support||√||√|
|Multivariate testing (MVT)||Up to 16 combinations.||Up to 36 combinations.|
|Experiment objectives||Up to 3 preconfigured.||Up to 10 preconfigured,
additional available once started.
|Simultaneous experiments||Up to 5.||More than 100*.|
|Administration||Basic administration with unlimited users.||Analytics 360 Suite administration.|
|Support and services||Self-service help center and
support, and SLAs
|Payment options||Free.||Invoiced monthly.|
So, to summarize, the limitations of the free version are.
- No Google Analytics audience targeting.
- Limited multivariate testing (16 variations).
- Pre-selected experiment objectives. Google Optimize 360 allows you to go back and change the experiment objective to see how the experiment would’ve impacted other Google Analytics goals.
- Limited concurrent testing (3 tests at a time).
How is Google Optimize Implemented?
As Google Optimize matures, we’ve seen shifts in recommendations for how to implement it on your site.
Many options exist, but the recommended way to add Optimize to your site is by adding a line of code to the Universal Analytics snippet installed on your website. The general process for setting up Google Optimize looks like this:
- Create an account and container
- Link the container to Google Analytics
- Install Google Optimize on the site
We cover this and other elements of installing Google Optimize in this blog post:
Creating Your First Experiment
Creating your first experiment is very simple.
1. From the Optimize Container page, click the blue “Create Experiment” button.
2. Enter your experiment name, editor page, and the type of experiment you would like to run. The editor page is the page you will make modifications to using the visual editor. For example, if you’re running and experiment on blog pages, enter one blog entry URL. Later you will use experiment targeting to apply your changes to some or all of your blog posts.
3. Select the type of experiment you would like to run. You have three basic options here:
- A/B Test. Tests two or more variants of a page, also called an A/B/N test. This is the most common of the experiments.
- Multivariate Test. Tests variants with two or more different sections on the same page (or page template). This is great for when you want to try multiple combinations of elements on the same page (or page template).
- Redirect Test. Test separate web pages identified by different URLs or paths. If you’re making large changes to page code it can slow down the page. If you find yourself in that situation, it’s better to run a redirect test. Don’t forget to add a no index tag to the test page.
For the rest of this blog post we will focus on an A/B test. Let’s dive into the experiment interface.
There are two main tabs, “Details” and “Reporting”. Details is where you’ll be able to find and modify experiment information, Reporting is where experiment data is reported (it’s also reported in GA).
There are two main sections here: variants and configuration.
Variants is where you’re able to see:
- How many variants are in your experiment
- What percentage of traffic each variant will receive (an even split is recommended)
- Options for previewing how the experiment will look on desktop and mobile. It is also where you can generate a preview link for your team.
- Number of changes made to the variation.
- Additional options which include edit variant name and delete variant.
The configuration section is where you are able to provide a description of the experiment, select experiment goals (objectives), and select targeting parameters.
Selecting Objectives is Important. Unlike Optimize 360 (the premium version) you can not retroactively change objectives to see how your experiment affected other goals. So make sure you have all of your objectives selected before you start your experiment.
Hypothesis Best Practices. If you’re just getting started with testing, you may be tempted to simply write a description of test and skip the hypothesis. This is not recommended. Writing a clear hypothesis will keep you honest when the results roll in. Follow this basic formula when generating a hypothesis: If [I do this], then [this will happen].
The targeting section is where you will define what conditions will fire the experiment. Targeting options are evaluated on page load.
Each targeting option links to the Optimize targeting docs which have much more information about how to use each of these options.
Target specific pages and sets of pages. URL targeting allows you to pick the web pages where your experiments run. URL targeting is useful for presenting experiment variants on a specific set of pages, easily defined by their URL. You can target a single page, a narrow subset of pages, or even Hosts and Paths.
Audiences (360 only)
arget Audiences that you create in Google Analytics. Optimize 360 allows target your experiments to Analytics Audiences. This allows you to focus your experiment on a group of users who have exhibited specific behaviors on your site.
Target users arriving to your site from a specific channel or source. Behavior targeting allows you to target first time users and visitors coming from a specific referrer.
Target visitors from a specific city, region, metro or country. Use Geo targeting to target users from a particular geographic area. For example, you might invite users from a specific city to attend an in-person event or to visit your retail location. While typing in the Values field, you’ll see suggestions from the AdWords Geographical Targeting API to help speed rule creation.
Target users visiting from a specific browser, operating system or device. Optimize looks at the browser’s user agent string to identify which browser is being used, what version, and on which operating system. You can use these data as targeting criteria in Optimize.
Target the value of a first-party cookie in the visitor’s browser. Optimize can check to see if a visitor has a first-party cookie from your website and use that information in targeting rules.
Target specific pages and sets of pages. Optimize can check query parameters and use them in targeting rules.
Data Layer Variable
Each targeting option has a variety of different match types.
- Equals/Does Not Equal. Every character, from beginning to end, must be an exact match of the entered value for the condition to evaluate as true. Evaluate as true when the query parameter does not equal any of the entered values.
- Contains/Does Not Contain. The contains match type (also known as a “substring match”) allows you to target any occurrence of a substring with a longer string.
- Starts With/ Does Not Start With. The starts with match type matches identical characters starting from the beginning of the query string up to and including the last character in the string you specify.
- Ends With/Does Not End With. An exact match of the entered value with the end of the URL. You can target shopping cart pages that use /thankyou.html at the end of their URLs.
- REGEX Matches/Does Not REGEX Match. A regular expression uses special characters to enable wildcard and flexible matching. Regex matches are useful when the stem, trailing parameters, or both, can vary in the URLs for the same webpage. If a user could be coming from one of many subdomains, and your URLs use session identifiers, you could use a regular expression to define the constant element of your URL.
Free REGEX Book (PDF). If you’ve never used regular expressions, you’re missing out. They’re endlessly useful. LunaMetrics CEO Robbin Steif wrote a short book about using regular expressions for Google Analytics. It’s a great resource for those just starting out.
Running Your Experiment
In this paragraph once you have made your modifications, click “Save” and navigate back to experiment page. Double check your objectives and targeting options, the you’re ready to start your experiment.
Firstly It is recommended that you let an experiment run for at least two weeks before looking at results.
As your experiment runs, the first card of your reporting tab will populate with the current winner. Once enough data is collected, Google will declare a clear winner.
Secondly card on the reporting tab shows how each variation performed for each objective that you set.
Thirdly and final card in your report will show you more granular data about each objective, as well as a nice performance graph.
- Improvement – For a given objective, the difference in conversion rate, measured as a percentage, between the variant and the baseline.
- Experiment Sessions – An experiment session is the period of time a user is active on your experiment. By default, if a user is inactive for 30 minutes or more, any future activity is attributed to a new session. Users who leave your site and return within 30 minutes are counted as part of the original session.
- Probability to beat baseline – The probability that a given variant will result in a conversion rate better than the original’s conversion rate. Note that with an original and one variant, the variant’s Probability to Beat Baseline starts at 50 percent (which is just chance).
- Probability to be best – The probability that a given variant performs better than all of the other variants. Because there can be only one “best,” the sum of all percentages in this column should equal 100 percent.
Meanwhile You set up a test and you ran it. Now what? Iterate. The success or failure of your experiment has taught you something that you can use to run additional experiments. Think about different forms of testing, or different targeting options.
Remember that who you test is just as, if not more, important than what you test. So note what you learned, note what questions came from your experiment, and begin thinking about what your next test would look like if you changed the offer or changed the people who saw the offer. Repeat.