What is A/B testing?
A/B testing, commonly referred to as split testing or bucket testing, compares two iterations of a website or app to see which one performs better.
In a nutshell, A/B testing is an experiment in which consumers are randomly presented two or more variations of a website, and statistical analysis is utilized to ascertain which variation performs better for a specific conversion objective.
You can gather information about the effects of changes to your website or app by doing an A/B test that directly contrasts a variant against the current experience. This allows you to ask targeted questions regarding the changes on your website, if it is working or not.
By evaluating the impact that changes have on your metrics, you can ensure that every change provides positive results, taking the guesswork out of website optimization and enabling data-informed decisions that move business dialogues from “we think” to “we know.”
The process of A/B testing
A/B testing involves altering a webpage or app screen to produce a second version of the same page. A single headline, button, or a complete page redesign can all be included in this modification. Then, half of your traffic sees the page’s updated version also known as variant and the other half sees the original version, also known as the control.
Visitors are shown with either the control or variant, and their interaction with each is tracked, gathered at a single place, and then analyzed using a statistical methods. Then you may assess whether altering the experience had a favorable, unfavorable, or neutral impact on visitor behavior.
Reasons to conduct A/B tests
A/B testing enables people, groups, and businesses to modify their user experiences while gathering data on the outcomes. They can utilize this to develop hypotheses and understand how specific aspects of their experiences on websites or apps affect user behaviors’. Also A/B test is also used to disprove their opinion that a certain experience is the optimal one for achieving a particular goal. Basically, your opinions doesn’t matter.
A/B testing can be used to progressively improve a particular experience or a single goal, like conversion rate rather than merely addressing a one-time query or resolving a dispute.
A B2B technology firm could wish to increase the quantity and quality of sales leads coming from campaign landing pages. The team would try A/B testing adjustments to the headline, visual graphics, form fields, call to action, and overall structure of the page in order to accomplish that aim.
They can identify which modifications had an impact on visitor behavior and which ones did not by testing one change at a time. They can eventually aggregate the effects of several winning experiments to show how a new experience is measurably superior to the old one.
This approach to making adjustments to a user experience enables the experience to be optimized for a desired outcomes and can increase the effectiveness of key performance indicators in a marketing campaign.
Marketers can discover which versions of ads generate more clicks by tweaking the copy. They can discover which design is most effective at converting prospects into customers by doing A/B test on the landing page. If every component of each phase contributes as effectively as feasible to the acquisition of new clients, the entire cost of a marketing campaign may even be reduced. Also, CAC goes down.
Product designers and developers can utilize A/B testing to show how new features or modifications to a user experience will affect sales. A/B testing can be used to improve product onboarding, user engagement, modals, and in-product experiences as long as the goals and the hypothesis are clearly established.
A/B testing method
An A/B testing framework that you may use to begin conducting tests is as follows:
Research & gathering data: Your analytics will frequently reveal areas where you may start optimizing. To enable you to collect data more quickly, it can be helpful to start with high traffic regions of your website or app. Look for pages that can be enhanced that have low conversion rates or high drop-off rates.
Establish goals: The metrics you’ll use to gauge whether the variant is more effective than the original version are known as your conversion goals. Goals can range from simply clicking a button or link to making a purchase or signing up for emails.
Create hypothesis: Hypotheses for A/B testing is something why you believe that new variant will be superior to the present control version once you have decided on a goal. When you have a list of suggestions, order them according to predicted impact and implementation difficulties.
Creating test variants: Make desired adjustments to a component of your website or mobile app experience using your A/B testing tools, such as Google Optimize, VWO or Optimizely. This could be done by altering the color of a button, rearranging the page’s elements, hiding the navigation, or doing something else entirely. A visual editor is available in many popular A/B testing tools, which will make these modifications simple. You ensure that your experiment performs as anticipated, make sure to QA it.
Running experiments: Start the experiment, then watch for website visitors to join in! At this point, users of your website or app will be randomized to either the control or to a variant. To ascertain how each performs, their engagement with each encounter is quantified, counted, and compared against each other.
Analysis of outcomes: Once your experiment is over, it’s time to assess the findings. Your A/B testing tool will present the experiment’s data, show you how the two versions of your page performed differently, and determine whether there is a statistically significant difference between them.
Winner deployments: Congratulations if your variant is chosen as the winner! And deploy it in production. Also, check to see if you can use the experiment’s lessons on other pages of your website and keep refining the experiment to get better results. Don’t worry if your experiment yields no or a negative result. Create new hypotheses to test and use the experiment as a learning opportunity.
Whatever the results of your experiment, make use of what you learned to guide future research and keep refining the user experience of your app or website.
SEO and A/B testing
A/B testing is permitted and encouraged by Google, which also claims that there is no inherent harm to your website’s search engine ranking when you do A/B or multivariate tests. However, utilizing an A/B testing tool improperly for things like cloaking could put your search rank in danger. In order to prevent this, Google has provided some best practices:
- Avoid URL cloaking: The practice of giving search engines content that is different from what a typical visitor would see is known as cloaking. Cloaking may lead to the demotion or removal of your website from the search results. Avoid abusing visitor segmentation to show Googlebot alternative content based on user-agent or IP address to avoid cloaking.
- Use the rel=”canonical” property to link variations back to the original version of the page if you’re conducting a split test with several URLs. By doing this, you can reduce the chance that Googlebot will become baffled by many iterations of the same page.
- Use a 302 (temporary) redirect rather than a 301 (permanent) redirect when running a test that changes the original URL to a variation URL. Search engines like Google are informed by this that the redirect is temporary and that they should continue indexing the primary URL rather than the test URL.
- Run tests only as long as necessary: Running tests for an extended period of time can be interpreted as an attempt to trick search engines, especially if you are serving one version of your page to a significant portion of people. Google advises against performing tests for an excessively lengthy period of time and upgrading your website as soon as a test is finished.
A/B testing ideas across industries
A media company might wish to attract more readers, lengthen readers’ visits to the site, and encourage social sharing of its content. They might experiment with different versions of:
- Sign-up forms for emails
- Recommended resources
- Buttons for social sharing
A travel agency can wish to boost the quantity of successful bookings made through their website or mobile app, or they might want to boost ancillary sales revenue. They might experiment with different versions of:
- modals for homepage searches
- results page for a search
- Additional product display
An ecommerce retailer can aim to boost holiday sales, the number of completed checkouts, or the average order value. They may do an A/B test to achieve this:
- website promotions
- navigational aids
- the parts of the checkout funnel
A technology company may wish to raise the quantity of high-quality leads for their sales team, boost the amount of people who sign up for a free trial, or draw in a certain kind of customer. They might examine:
- Lead-based materials
- flow of free trial signups
- Calls to action and messaging on the homepage
