Marketing Glossary

A/B Testing

A technique for evaluating two versions of a mobile app or website page or experience to see which one performs best.

What is A/B Testing?

A/B testing (alternatively called bucket testing or split testing) is a technique for comparing two different versions of a mobile app or website page to discover which works better. A/B testing is a process in which two or more versions of a page are randomly shown to users and statistical analysis is utilized to identify which variation works better for a certain conversion objective.

An A/B test that compares a variant to the existing experiences allows you to ask relevant questions about changes to your mobile app or website page and then gather statistics on the impact of those changes. Testing removes the uncertainty from website optimization and allows for data-driven judgments that alter company discussions from “we think” to “we know.” You can verify that every change has a positive impact on your metrics by monitoring the impact of changes on your metrics.

How Does It Work?

An A/B test involves modifying a app screen or website page to create a second variation of the same page. This modification could be as simple as a new button or headline, or may even entail a complete page overhaul. The original version of the page (known as the control) is then shown to half of your traffic, while the updated version is presented to the other half (the variation).

Visitors’ interaction with each encounter is collected and measured in a dashboard, then evaluated using a statistical engine as they are fed either the variation or control. Then you can see if modifying the experience had a neutral, good, or negative on visitor behavior.

Why Should You Do an A/B Test?

Corporations, individuals, and teams and can utilize A/B testing to make small adjustments to their user experiences while collecting data on the results. This enables them to form theories and discover why particular aspects of their encounters have an effect on user behavior. In another manner, they can be proven wrong—an A/B test can show that their assumptions about the optimum experience for a specific goal are incorrect.

A/B testing can also be used to continuously improve a given encounter or a specific goal like conversion rate over time, rather than just addressing a one-time query or settling a debate. A B2B technology company could wish to improve the quality and quantity of sales leads generated via campaign landing pages.

The team would try A/B testing adjustments to the overall layout, call to actions, form fields, visual images, and headline to achieve that aim. They can determine which modifications had an impact on visitor behavior as well as which ones did not by testing one element at a time. They can illustrate the meaningful change of a new experience over an old one by combining the influence of several successful adjustments from studies over time.

This strategy of delivering modifications to a user experience enables the interaction to be optimized for a preferred result and can improve the effectiveness of critical phases in a marketing campaign. Marketers may understand which variations of ad copy generate the most clicks by putting them to the test.

They can figure out which style transforms people to buyers the best by evaluating the next landing page. If the components of each phase function as efficiently as possible to recruit new clients, the entire cost of a marketing campaign can be reduced.

Product developers and designers can utilize A/B testing to illustrate the effect of technology additions or adjustments to a user experience. A/B testing can be used to improve in-product experiences, modals, user engagement, and product product onboarding, as long as goals are clearly specified and a hypothesis is established.

The A/B Testing Process:

You can get started with A/B testing by using the framework below:

  • Collect information: Your analytics will typically highlight places where you can begin optimizing. To get data faster, start with high-traffic areas of your mobile app or website pages. Look for pages with low conversion rates or high drop-off rates that can be improved.
  • Determine objectives: Your conversion targets are the measurements you’ll use to see if the variation is more effective than their predecessors. Goals can range from clicking a link or button to signing up for an email list or making a purchase.
  • Construct hypotheses: Once you’ve settled on an objective, you can start brainstorming A/B testing concepts and theories about why you think they’ll outperform the present version. After you’ve compiled a list of ideas, rank them by predicted impact and implementation difficulties.
  • Generate variations: Make the needed adjustments to a component of your mobile app or website experience using your A/B testing software.  This might be anything from altering the color of a button to rearranging the order of things on the page to burying navigation elements. A visual editor is available in many popular A/B testing software, making these modifications simple. Make sure to test your experiment to ensure that it functions as intended.
  • Experimentation: Begin your experiments and watch for visitors to join in! Visitors to your application or mobile will be randomly assigned to either the “variation” or “control” experience at this point. To determine how each experience succeeds, their interaction with it is recorded, scored, and compared.
  • Analyze the results: After your experiment is finished, it’s time to look at the data. Your A/B testing software will display the results of the trial and show you the difference in performance between the two versions of your page, as well as whether the difference is huge.

Bravo, if your modification is a winner! Examine whether you can use what you learned from the trial to other pages on your site, and keep iterating to enhance your results. Don’t be concerned if your experiment yields a negative or no outcome. Use the experiment as a learning tool and come up with fresh hypotheses to test.

A/B Testing and Search Engine Optimization (SEO)

A/B testing is encouraged and permitted by Google, which has claimed that doing an multivariate or A/B test poses no danger to your website’s ranking in the search engine result pages (SERPs). However, utilizing an A/B testing tool for objectives such as cloaking can harm your rankings. Google has outlined several suggested practices to avoid this from happening:

  • Avoid cloaking at all costs: Cloaking is the technique of displaying different material to search engines than what visitors would see. Cloaking might cause your website to be downgraded or even wiped from the search engine result pages (SERPs). To avoid cloaking, don’t deploy visitor segmentation to show the Googlebot different content depending on the IP address or user-agent.
  • Use the rel=”canonical” tag: If you’re running a split test with several URLs, the rel=”canonical” property should be used to point the variations back to the original version of the page. As a result, Googlebot will be less confused by many versions of the same page.
  • Shun 301s redirects and use 302: If you’re running a test that redirects the original URL to a variation URL, use a 302 (temporary) redirect rather than a 301 (permanent) redirect. This informs search engines like Google that the redirection is transitory and that the main URL should be indexed instead of the testing URL.
  • Run experiments for a specific time period: Running testing for longer than necessary, especially if you’re displaying one version of your page to a big number of people, can be interpreted as an attempt to trick search engines. Google suggests that you update your site and remove any test variants as soon as a test is completed, and that you avoid performing tests for an extended period of time.

Use-Cases of A/B Testing

A technology company may wish to target a specific buyer type, boost the quantity of high-quality leads for their sales staff, or raise the number of free trial users. They could put the following metrics to the test:

  • Components of their lead form
  • Flow for signing up for a free trial
  • Calls to action and messaging on the homepage

An e-commerce company may aim to increase holiday conversions, the number of completed checkouts, or the average order value. In order to accomplish these targets, they may A/B test:

  • Elements of navigation
  • Promotions on the home page
  • Components of the checkout funnel

A travel business may aim to raise the number of successful bookings or boost revenue from ancillary purchases on their mobile app or website. To enhance these metrics, they may experiment with versions of:

  • Search results page
  • Search modals on the homepage
  • Presentation of ancillary products

A media organization can seek to promote their content through social media, boost readership, or increase the amount of time people spend on their site. To attain these objectives, they may experiment with variants on:

  • Buttons for posting on social media
  • Modals for signing up for email
  • Content that is suggested