What is A/B Testing?How it is Useful for your business?

A/B testing is a method of comparing two variation of a web page against each other to know which one performs better. In A/B testing we show two different version of web pages to different users randomly, and statistical analysis is used to determine which variation of web page is doing better in terms of visits and application conversion.

digital marketing training

How A/B testing works?

We analysis our website traffic on regular basis, sometimes it happens that some part of our web page is not working well means user are not clicking for a particular features. In this case what we should do, we should create a second version of the same web page. Then, half of your traffic is shown original page and for rest of the users shown variation of the same page.

A/B Testing Training

Why should you do A/B Test?

A/B testing helps us to make more conversion with the existing traffic. We are getting multiple type of traffic for our website and conversion totally depend on our web-page. In case of A/B testing, no more investment required, in a minimal cost we can increase our Goal application.

What we can test in A/B Testing?

We can test almost all object of the web page through A/B test. There are some example, which we can test in A/B testing.

  • Headlines
  • Sub Headlines
  • Content Paragraph
  • Testimonials
  • Call to Action
  • Links
  • Images
  • Social Proof
  • Award and badges

In case advanced A/B testing we can test product price, sales promotions etc.

A/B Testing and SEO:-

When we are doing A/B testing then we should tack care about the SEO, We are creating a new web page that is why there are some below important points, which should be tack care.

  • No clocking
  • Use correct redirection for variation that is 302
  • Run the experiment till the time you required

A/B Testing Processes:-

There are many steps, which we should follow at the time of A/B testing, some of them I am sharing here.

  • Enough time to study your website data
  • Try to understand user behavior
  • Analysis test data and then draw conclusion
  • Test your Hypothesis