A/B testing consists of presenting two different versions of a Web Page or Email to specific customer groups. Then, analyzing metrics like Click-Through Rates, Bounce Rates, Lead Conversions, etc. Then, in order to determine the best possible permutation of said page. That's why it is also known as Split Testing.
A/B testing can be done to analyze multiple elements of your web page. However, it's performed chiefly to one element at a time.
In the digital landscape of eCommerce & online marketing, competition is ever-growing. That means numerous sites with similar products competing for the same group of consumers. Thus, ensuring maximum visitor experience & engagement is vital for the survival of your business.
That is where A/B testing is essential, as it helps you implement changes that maximize user engagement. In addition to that, there are several benefits of A/B testing:
Increase Visitor Engagement - Every element of your Page/Email can be tested individually and replaced with their best permutations. This, in turn, optimizes your design as a whole.
Effective Content – Content is half the game. That is to say, your content needs to be captivating & exciting to gain the users' attention. Copies generally go through a vast number of changes & suggestions, and A/B testing allows you to test out these changes & implement the best of them.
Bounce Rate Reduction – The entire purpose of A/B testing is to make your webpage as immersive and engaging as possible. By implementing change on different elements of the webpage to maximize the user experience.
This, in turn, translates into an extensive drop in the Bounce Rate.
Improvements in Conversion Rates – A/B testing is one of the most efficient & effective methods of increasing the number of visitors that convert into buyers.
Easy to Analyse - A/B testing offers straightforward results. In addition, there are many optimization services/software that will automatically implement changes according to the A/B testing results & guidelines/objectives set by the tester.
Risk Reduction – One of the most basic utilities of A/B testing is, removing unnecessary & ineffective elements of your web page and replacing them with a better alternative. Instead of experience & conjecture, A/B testing provides solutions based on cold hard facts & metrics.
Improved Sales – In conclusion, A/B testing provides accurate and compelling data that allows users to implement changes on their web page that maximizes customer interaction & lead conversion. As a result, boosting your sales.
There are a total of 5 steps that you need to take to conduct an A/B test using Google Optimize:
The first step in conducting an A/B test is to come up with a null hypothesis & alternative hypothesis. A hypothesis is the first order of business in any scientific experiment/test. Let's discuss the difference between both types of hypotheses.
Null Hypothesis –This states that there is no calculable difference between the results obtained by the original page & the variant.
Alternative Hypothesis – This states that there will be a significant enough difference/improvement between the evaluation metrics of the original & variant page.
Also, you should remember to keep all but one element of the page similar. This ensures the accuracy of the test.
After the initial step, we have to decide the basis on which we'll split the control group & the test group. This includes deciding:
Secondly, you need to set the test's metrics to evaluate the variant's performance. These metrics can be Click-Through-Rates (CTR), Conversion Rates, Bounce Rates, etc.
Next, we decide how to sample the visitors for the A/B testing. The most important thing about creating a sample for A/B testing is that it should be completely randomized. This is important for two different reasons,
That is to say, randomizing the sample increases the chances of diversifying the sample group. Thus, ensuring that every type of visitor from the population is considered during the evaluation.
After that, the next step is to set the minimum sample size of your evaluation. This is important because: As the sample size drops, the chances of the sample being biased or not equally representing different groups within the sample population increases.
Once you set all the necessary criteria, you have to decide the length of the test. This can be done using any A/B testing calculator. Several factors determine the appropriate length of the A/B testing to ensure you get accurate results.
Lastly, you need to put in the Alpha value & P-Value. In order to determine whether or not the evaluation results are statistically significant or not. Then, start the A/B test.
Once the test has run for sufficient time, you can close it or simply view the evaluation data up to that point. Then, simply head over to the Reporting tab. There, the first thing you'll see is the summary of the test, with:
However, you cannot make your decision based on just this simple data representation. You will need to dive a little deeper into the results. Thus, move on to the Improvement Overview card.
In this card, you will have a tabular representation of the evaluation of the original & the variant across all metrics.
For instance, you can see the performance of all the pages in regards to:
Although it can be pretty confusing to decide which variant is the best due to differences in performance across all these different metrics. However, you need to look at your objective for conducting the A/B Test and use that to interpret the evaluation data's efficacy.
For instance, your objective/hypothesis was that the variant would have increased sales. In that case, even if the variant has a decreased Session Duration or Sign Up, the Purchases have gone up. So, the variant is better at accomplishing your objective & should be implemented.
In order to do that, you'll need to see the Objective Detail Report.
It's a card that showcases how the variant performed according to a specific pre-identified objective. To see the performance, just click on an object to get a graphical representation of the variant's performance.
The colored area will represent the whole range of the performance, and the line going through the middle of the colored region will be the median value.
However, over time the colored interval will narrow. This is due to the fact that the number of test subjects is inversely proportional to the effect they have on the overall data. Thus, with time, the evaluations get more & more accurate.
Above, we learned the significance & the basics of running accurate A/B testing to optimize your web page using Google Optimize. In addition to that, we learned how to interpret the data of the said result & select which permutation of your web page will yield the highest performance & sales.
Disclaimer:
TheWebLeads is a digital marketing service provider. We create, promote, and provide services to our clients for their benefit. Our sole motive is to get quick and efficient results.
