A/B testing involves comparing two versions of a web page (Version A and Version B) to determine which performs better. It directly tests changes to a website or app by showing each version to similar visitors and measuring which has a higher conversion rate. Testing removes guesswork from optimization by providing data on how changes impact metrics. The process involves studying data, observing user behavior, constructing a hypothesis, testing the hypothesis through an A/B test, and analyzing results to determine if a variation improved conversions.

1. A/B Testing

2. 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!

3. • Running an AB test that directly compares a variation against a current
experience lets you ask focused questions about changes to your
website or app, and then collect data about the impact of that change.
• Testing takes the guesswork out of website optimization and enables
data-informed decisions that shift business conversations from "we
think" to "we know." By measuring the impact that changes have on
your metrics, you can ensure that every change produces positive
results.

4. HOW A/B TESTING WORKS
• In an A/B test, you take a webpage or app screen and modify it to
create a second version of the same page. This change can be as
simple as a single headline or button, or be a complete redesign of the
page. Then, half of your traffic is shown the original versionof the page
(known as the control) and half are shown the modified version of the
page (the variation)

5. 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.

6. Why Should You perform 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.
• Less cost compared to other tools such as Google Adwords.
• 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.

8. What Can You Test?
Almost anything on your website that affects visitor behavior can be
A/B tested.
• Headlines
• Sub headlines
• Paragraph Text
• Testimonials
• Call to Action text
• Call to Action Button
• Links
• Images
• Content near the fold
• Social proof
• Media mentions
• Awards and badges

9. 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
1. 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.
2. 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."

10. 3. 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.”
4. 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.
5. 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.

11. Example
Control Variant