How to do A/B testing with Google Analytics
What is A/B testing
With it two or more versions with modified fragments are tested. Based on the result of the website's AB testing, the two conversions of the unchanged original control group A and group B with some indicators changed are compared, for instance, "interface elements" or "call to action" are added.
One of the main advantages of A/B testing is that the website's development strategy is based on objective, not intuitive data. You can test the following items:
- site color scheme;
- page titles;
- contextual advertising;
- the design and color of the conversion button;
- texts on the page;
- online chat rooms;
- element location on the page and much more.
Features of the A/B-testing
- First of all, you need to choose indicators that will be improved with the help of split testing, measure their current values and select target ones;
- when carrying out A/B testing, users are divided into two groups, and each group sees only one version of the website. It is important to make sure that the visitor does not see two versions of the resource. At the same time, technically, the monitoring of displaying versions is carried out by memorizing the IP address of each user and entering this data into cookies;
- dividing into groups is carried out respectively, taking into account the traffic source- organic search, contextual advertising, social networks;
- In order to get the correct results, it is important to test two versions of the website in parallel at the same time, otherwise external factors like holidays, weekends, weather conditions may affect the statistics;
- data on company employees is excluded from the sampling; this can be configured using Google Analytics filters. In this case, the reports do not include actions taken from the IP addresses of the project team;
- you can use the calculator to calculate the minimum sampling;
- for topics that require a long decision in order to make purchase should be tested for at least two periods needed for this decision. For example, if the decision is made on average in a month, then testing is carried out for two months;
- the most significant parameter that shows the impact of changes is the number of completed orders. The increase in conversions at intermediate stages, such as the number of clicks on an ad, sign-ups, additions to the cart, may not affect the growth of orders;
- after obtaining a sufficient number of results, we compare website conversions in both groups and select the one with higher rates.
A/B testing tools
Google Analytics A/B Testing
Then change additional settings: activate the uniform distribution of traffic between all the options, set the minimum time of the experiment 2 weeks and set the confidence threshold from 95% to 95.9% depending on the required accuracy of the results.
Also set the goal of the experiment: choose from the offered ones or create a new one. By default, the proposed goals associated with the website usage are: session duration, pageviews or bounces.
In this case, the actions of employees who have access to the resource should not be taken into account.
This article is a part of Serpstat's Checklist tool
|Try Checklist now|
Speed up your search marketing growth with Serpstat!
Keyword and backlink opportunities, competitors' online strategy, daily rankings and SEO-related issues.
A pack of tools for reducing your time on SEO tasks.
Cases, life hacks, researches, and useful articles
Don’t you have time to follow the news? No worries! Our editor will choose articles that will definitely help you with your work. Join our cozy community :)