What is A/B Testing

A/B testing (otherwise called split testing) is a method of showing the same site page with two different modifications to various segments of users simultaneously and looking at which variety drives more traffic to the website. 

Typically, in A/B testing, the modifications that give higher transformations are the successful ones, and that can assist you with upgrading your site for better outcomes. Well-planned Shopify A/B testing has an enormous effect on making your marketing success and can put forth your advertising attempts substantially more productive and fruitful.

What A/B testing can do for your business?

In today's business world, most of the eCommerce stores are struggling with a high cart abandonment rate and unqualified leads per month. These are decided by some common issues like conversion funnel leaks, cart pages, checkout process, and so forth. 

Let’s see why should you do A/B testing to plug off of these site optimization issues:

1. Get Better ROI from Existing Traffic

Most merchants understand the expense of gaining any quality traffic can be enormous. A/B testing lets you make the most out of your current traffic and encourages you to increment change without spending on getting new traffic. 

2. Reduces Bounce Rate

One of the most significant measurements to be followed to pass judgment on your site's exhibition is its bounce rate. There might be numerous explanations behind your site's high bounce rate, for example, such a large number of alternatives, desires confound, etc. As various sites serve various objectives and take into account various crowds, there is no certain shot method of fixing the bounce rate. One best method is to do A/B testing. With this, you can test numerous segments of your Shopify site until you find the most ideal pattern. Thus, you can improve your client experience and reduce the bounce rates.

3.Gain Statistical Analysis

As A/B testing is a data-driven approach with no space for mystery or senses, we can decide a "successful" and a "failure" variations of the same site page statistically in terms of metrics like an average session of the page, CTR, cart abandonment rate, active visitors, etc.

4.Low-risk Modifications

A/B Testing reduces the risk of venturing your current conversion rate by enabling us to make minor changes to your Shopify site rather than redesigning your entire site. 

Consider you are planning to launch a new feature, then you can do A/B testing on your website copy to predict your audience. It can be very profitable if the changes improve the purchase funnel


How does A/B Testing work?

Consider you need to focus on the number of individuals pursuing a free demo on your site. You choose to play out an A/B test with your click button with two different variations. 

Let us say, A as first variation and B as the second variation will be shown to your users in various fragments than the variation which is getting higher traffic after a set measure of time is the "winner". They'll even consequently apply the changes when they pick a success. 

How long should you run your A/B Test?

A/B testing isn't a short-term venture. Based on the measure of traffic you get, you should run tests from a couple of days to a long time. What's more, you'll just need to run each test in turn for the most exact outcomes. 

You won't get a huge amount of users if you prefer running your tests for a short time. Meanwhile, running a test for a long time can likewise give slanted outcomes. Since there are more factors you can't power over a more extended period. Ensure that you remain side by side of whatever may influence your test outcomes with the goal while inspecting your outcomes. In case you're in doubt, it's sensible to retest. 


 ‎Prioritizing A/B Test Ideas 

When you research A/B testing ideas, you will get a lot of ideas, and each plays a vital role in optimization and that’s the place where prioritization of A/B testing begins.

So, let's walk through into some of the prioritizing A/B Testing frameworks you can utilize.

ICE:

 ICE represents Impact, Confidence, and Ease.

Here,

 Impact - Impact of your Shopify site page you are testing.

Confidence - Describes how confident you are in your test.         

Ease - How easy is it to create the test?

For instance, if you could undoubtedly do the self-analysis test without any assistance from professionals which means you're employing your decision. 


PIE: 

PIE stands for Potential, Importance, and Ease. 

Similarly, in PIE

P- Potentiality of your Shopify page you are testing

I - Impact of your Shopify site page you are testing.

E- How easy is it to create the test?

For instance, you have done with running your test and got a maximum of your traffic from your existing audience. 

In both the cases of ICE and PIE prioritization model, users need to set the score values to range from 1 -10 and the resultant is a sum of three variables.


PXL 

PXL prioritization model is the different one set up by conversion which means the users have to choose the answers yes or no and the score is calculated based on the user's answers. The total score is a sum of all the variables.

The effective CRO platform by default sets the prioritization models such as PIE, PXL, or ICE. You can also plan your unique model with your experiments.


A Plan for A/B Testing 

You can test anything in your promoting resources such as features, headlines, call to action buttons, CTA text, pictures, product descriptions, and so on. To know what are the potential patterns to A/B test on your Shopify site take a walk through our CRO based A/B Testing Patterns.

Conclusion

After reading this blog on A/B testing, we hope you are fully provided with a complete plan for your own A/B testing and optimization roadmap. Remember, A/B testing is valuable when it comes to increasing your conversion rates. If you require support on testing or desire to get done with your A/B testing and optimization efficiently in no time! Explore our exceptional A/B Testing Patterns and Conversion rate optimization packages now! You may also reach with your queries to our friendly support team at support@hulkcode.com

Happy Testing :)