A/B Testing: What makes one click!

What is the best? Red or Blue? Green or Orange? So many variations and so many choices. A standard developer makes a guess, but a good one uses ab testing. What is it?

It a random experiment method which gives us a statistically significant performing result from different population.

In a world of 7.7 billion populations, which thrives in uniqueness, business organizations worldwide want their websites to be on the top to increase marketability. 

How does it work? Simplest way possible! Increased customer engagement is only when the website is user-friendly.

To determine that, two or more slightly varying versions are created (version A and version B) and are given to different sets of the population (beta testers). Their responses are statistically analyzed. 

The reason for this testing is,, we can’t predict what a user may like. For example, version A and version B may differ in color scheme, where the buy option is located, night mode, day mode, etc. We can create as many varying factors as we like to ensure maximum user activity.

There many A/B testing parameters for optimization of conversion rate, the significant methods: 

A/B Testing is divided into two broad categories

Multivariate testing– This technique is done based on a hypothesis upon which several variables are modified. It is usually done to calculate which combination of variations performs better above all. This test is usually done on websites and mobile apps.

In Multivariate testing, a developer determines the best combinations of the variations that are there and have the best potential for higher performance and are randomly distributed to varying populations/statistics.

For example, the ‘option’ button, the background color variations, presence of chat-bots, etc. The review can be obtained via feedback surveys and monitoring user activities like higher click rates.

Full-factorial test, as the name suggests, tests out all possible combinations in every possible order. In real statistical analysis, a developer has more than two parameters to compare.

In such a scenario, a full-factorial test is the best way to go as it compares all parameters in every order by sorting them in different levels.

SplitURLtesting– This method is exactly considered as the copy of AB testing. As the split URL suggests, its separate/split URL functions in each experiment or variation.

Why A/B Testing?

AB testing is done with the aim of comparison and ultimately to tell the user about the best. To construct a hypothesis to know users’ experience on certain elements to check whether they are moving towards their conversion rate goal.

Steps involved in AB testing?

Proper research of website/application of which AB testing is done

Before going for an AB test, you need to go through the website/application properly and analyze it to know its current situation like CRO, traffic rate, DA PA, etc.

After analyzing, know your goals.-

After researching, know your goals for which you are working for your targets for which you have done the AB test.

Generation of hypothesis.

Create your strategies, ideas upon which you want greater/increased results.

Create variations if you want any.

If you are not happy/satisfied with the current working material, you can change something according to your wish.

Go for the experiment.

After analyzing each and everything, start your AB test and work according to your strategies/plans/targets that you have planned for yourself before.

Check results if desired or not.

After the testing is done, analyze the new results go through them, and check whether you have achieved the desired results /increased CRO or not.

The success results will be varying based on the parameters that are considered. It might also depend on supply and demand factors. The most important take-away of performing A/B testing is that it reduces the risk of losing profit due to lousy UI and efficiently is the best analysis method and produces the most accurate results.

Image source: all images are obtained from Google Images.

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