What is A/B Testing and why you should do this?
Article Source: Optimizely
What Is A/B Testing?
A/B testing (also known as split testing or bucket testing) is a method of comparing two versions of a webpage , app or advertisement against each other to determine which one performs better. AB testing is essentially an experiment where two or more variants of a page are shown to users at random, and statistical analysis is used to determine which variation performs better for a given conversion goal.
Running an AB test lets you ask focused questions about changes to your website or app, and then collect data about the impact of that change.By measuring the impact that changes have on your metrics, you can ensure that every change produces positive results.
How A/B Testing Works
In an A/B test, you take a webpage or app screen and modify it to create a second version of the same page. This change can be as simple as a single headline or button, or be a complete redesign of the page. Then, half of your traffic is shown the original version of the page (known as the control) and half are shown the modified version of the page (the variation).
As visitors are served either the control or variation, their engagement with each experience is measured and collected in an analytics dashboard and analyzed through a statistical engine. You can then determine whether changing the experience had a positive, negative, or no effect on visitor behavior.
Why You Should A/B Test
A/B testing allows individuals, teams, and companies to make careful changes to their user experiences while collecting data on the results. This allows them to construct hypotheses, and to learn better why certain elements of their experiences impact user behavior.
More than just answering a one-off question or settling a disagreement, AB testing can be used consistently to continually improve a given experience, improving a single goal like conversion rate over time.
Testing one change at a time helps them pinpoint which changes had an effect on their visitors’ behavior, and which ones did not. Over time, they can combine the effect of multiple winning changes from experiments to demonstrate the measurable improvement of the new experience over the old one.
This method of introducing changes to a user experience also allows the experience to be optimized for a desired outcome, and can make crucial steps in a marketing campaign more effective.
By testing ad copy, marketers can learn which version attracts more clicks. By testing the subsequent landing page, they can learn which layout converts visitors to customers best. The overall spend on a marketing campaign can actually be decreased if the elements of each step work as efficiently as possible to acquire new customers.
A/B Testing Process
The following is an A/B testing framework you can use to start running tests:
- Collect Data: Your analytics will often provide insight into where you can begin optimizing. It helps to begin with high traffic areas of your site or app, as that will allow you to gather data faster. Look for pages with low conversion rates or high drop-off rates that can be improved.
- Identify Goals: Your conversion goals are the metrics that you are using to determine whether or not the variation is more successful than the original version. Goals can be anything from clicking a button or link to product purchases and e-mail signups.
- Generate Hypothesis: Once you’ve identified a goal you can begin generating A/B testing ideas and hypotheses for why you think they will be better than the current version. Once you have a list of ideas, prioritize them in terms of expected impact and difficulty of implementation.
- Create Variations: Make the desired changes to an element of your website or mobile app experience. This might be changing the color of a button, swapping the order of elements on the page, hiding navigation elements, or something entirely custom.
- Run Experiment: Kick off your experiment and wait for visitors to participate! At this point, visitors to your site or app will be randomly assigned to either the control or variation of your experience. Their interaction with each experience is measured, counted, and compared to determine how each performs.
- Analyze Results: Once your experiment is complete, it’s time to analyze the results. It should show the two versions of your page performed, and whether there is a statistically significant difference.
If your variation is a winner, congratulations! See if you can apply learnings from the experiment on other pages of your site and continue iterating on the experiment to improve your results. If your experiment generates a negative result or no result, don’t fret. Use the experiment as a learning experience and generate new hypothesis that you can test.
