What is A/B Testing?
A/B testing (sometimes called split testing) is comparing two versions of a web page to see which one performs better. You compare two web pages by showing the two variants (let’s call them A and B) to similar visitors at the same time. The one that gives a better conversion rate, wins!
All websites on the web have a goal – a reason for them to exist
- eCommerce websites want visitors buying products
- SaaS web apps want visitors signing up for a trial and converting to paid visitors
- News and media websites want readers to click on ads or sign up for paid subscriptions
Every business website wants visitors converting from just visitors to something else. The rate at which a website is able to do this is its “conversion rate”. Measuring the performance of a variation (A or B) means measuring the rate at which it converts visitors to goal achievers.
Why Should You A/B Test?
A/B testing allows you to make more out of your existing traffic. While the cost of acquiring paid traffic can be huge, the cost of increasing your conversions is minimal. To compare, a Small Business Plan of Visual Website Optimizer starts at $49. That’s the cost of 5 to 10 Google Adwords clicks. The Return On Investment of A/B testing can be massive, as even small changes on a landing page or website can result in significant increases in leads generated, sales and revenue.
What Can You Test?
Almost anything on your website that affects visitor behavior can be A/B tested.
- Sub headlines
- Paragraph Text
- Call to Action text
- Call to Action Button
- Content near the fold
- Social proof
- Media mentions
- Awards and badges
Advanced tests can include pricing structures, sales promotions, free trial lengths, navigation and UX experiences, free or paid delivery, and more.
A/B Testing and SEO
Google cleared the air on the SEO implications of A/B testing in their blog post titled “ Website Testing And Google Search “. The important bits from that post are:
Cloaking – showing one set of content to humans, and a different set to Googlebot – is against our Webmaster Guidelines, whether you’re running a test or not. Make sure that you’re not deciding whether to serve the test, or which content variant to serve, based on user-agent. An example of this would be always serving the original content when you see the user-agent “Googlebot.” Remember that infringing our Guidelines can get your site demoted or removed from Google search results – probably not the desired outcome of your test.
Use 302s, not 301s.
Only run the experiment as long as necessary
The amount of time required for a reliable test will vary depending on factors like your conversion rates, and how much traffic your website gets; a good testing tool should tell you when you’ve gathered enough data to draw a reliable conclusion. Once you’ve concluded the test, you should update your site with the desired content variation(s) and remove all elements of the test as soon as possible, such as alternate URLs or testing scripts and markup.
A/B Testing Process
The correct way to run an A/B testing experiment is to follow a scientific process. It includes the following steps:
- Study your Website Data: Use a website analytics tool such as Google Analytics, and find the problem areas in your conversion funnel. For example, you can identify the pages with the highest bounce rate. Let’s say, your homepage has an unusually high bounce rate.
- Observe User Behavior: Utilize visitor behavior analysis tools such as Heatmaps, Visitor Recordings, Form Analysis and On-page Surveys, and find what is stopping the visitors from converting. For example, “The CTA button is not prominent on the home page.”
- Construct a Hypothesis: Per the insights from visitor behavior analysis tools, build a hypothesis aimed at increasing conversions. For example, “Increasing the size of the CTA button will make it more prominent and will increase conversions.”
- Test your Hypothesis: Create a variation per your hypothesis, and A/B test it against the original page. For example, “A/B test your original home page against a version that has a larger CTA button.” Calculate the test duration with respect to the number of your monthly visitors, current conversion rate, and the expected change in the conversion rate. (Use our Bayesian Calculator here.)
- Analyze Test Data and Draw Conclusions: Analyze the A/B test results, and see which variation delivered the highest conversions. If there is a clear winner among the variations, go ahead with its implementation. If the test remains inconclusive, go back to step number three and rework your hypothesis.
- Report results to all concerned: Let others in Marketing, IT, and UI/UX know of the test results and the insights generated.
Your First A/B Test
Starting conversion optimization with Visual Website Optimizer is incredibly easy. Essentially, it is just four simple steps.
- Include the Visual Website Optimizer code snippet in your website
- Create variations using the WYSIWYG Visual Editor
Load your website in the Visual Editor and create any changes using the simple point-and-click interface. Advanced users can even make CSS and JS code changes.
- Choose your goals
All A/B tests have goals whose conversion rate you want to increase. These goals can be straight forward (clicks on links, visits page) or could use advanced custom conversion code.
- Start and track your test
And that’s it, your test is ready to go live. Reporting is real-time so you can start seeing reports as soon as visitors arrive on a live test.