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Google Analytics A/B Testing in 15 Easy Steps

For landing pages optimization, an A/B  test is essential. One of the easiest and most cost effective ways is split testing  with Google Analytics A/B testing features. The allows you to use the existing set of data and users behavior to create landing page experiments.  It may be important to conduct experiments on your images, languages, call to action, content or a color swap.  You can perform Google Analytics A/B testing with ease. Follow these easy to follow steps.

The set up requires steps :

  • Definition(11 Steps)
  • Implementation( 3 Steps)
  • Measurement(1 Step)

Part I: Defining your Test.

Objective: What is the purpose of your test? For any test or scientific experiment, you must clearly define what you are testing. For this example, our objective is to improve the conversion rate of a particular page. Typically, it’s important to have a control and hypothesis.  In this experiment , we will test two versions of a single web page with different button colors. The objective is to increase page views.

My hypothesis: Green buttons will improve click through rates and page views. 

  1. Open Google Analytics.
  2. Click Behavior icon.
  3. Click Experiments.
  4. Click Create an Experiment.
  5. Define the metric of the experiment by altering the options.
  6. You have to define the metric. ( page views, bounce, duration or event)
  7. Define the amount the traffic you will send to the page.
  8. See Advanced Options below.
  9. Click Next Step.

Google Analytics Experiment









This is the content experiment which will allow you to set the experiment.

Once you've opened Google analytics, its important to open the content experiments in Behavior section of Google Analytics

Important Note: This a high profit page that you are trying to improve. Consider limiting the amount of traffic. Altering the traffic could have a significant effects on revenue.

Advanced Options

  • Duration– You can set the time you want the experiment to run.
  • Distribution– If you are running more than one variation, it will be important to distribute the traffic evenly across all variations.
  • Confidence– You can improve the confidence of the experiment by increasing the threshold to 99%. However, this will increase the duration of the experiment.

Configuring the Experiment

  1. Add the original content URL and the variation URL(s).
  2. Click Next Step.

For most AB test, its important to change one variable of the same page which is a true AB test.

Part II: Implementation

  1. Collect the code snippet.
  2. Install this code on the original and variation pages.
  3. Launch Experiment.

To start the Google Analytics AB testing, you need to insert code on both the original and test page.

Part 3: Measurement

  1. Check the experiment for the results.

Google Analytics AB Testing


A/B Testing at Udacity