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A/B testing is an applied method that allows you to influence various site metrics. Therefore, the choice of test object depends on the goal and objectives set by the marketer.
For example, if the bounce rate of a landing page is 99% and most visitors leave the page within 2-3 seconds after "landing", it is worth thinking about changing the visual components of the page. With an A/B test, the marketer can find the best option for the page layout, choose an attractive color scheme and images, use a readable font.
And if the marketer is tasked with increasing the number of subscriptions, he can try to change the appropriate conversion form. The split test will help the specialist choose the optimal button color, the best text option, the number of fields in the subscription form or its location.
The method is often used in web design, typical applications are to study the effect of color scheme, layout and size of interface elements on website conversion rate. In web design, two very similar web pages (page A and page B) that differ in only one element or a few elements are often tested (then the method is called A/B/n-testing).
Pages A and B are shown to different users in equal proportions, with visitors generally unaware of this. After a certain period of time, or when a sufficiently large number of impressions is achieved, the numerical target values are compared and the most appropriate version of the page is determined. The advantage of the method is the use of objective data in the design.
For A/B-testing web design tools are often used from web statistics services; in this case it is also often important to use a mechanism to divide the users who will be shown a particular type of design (the same user should be shown the same design option), for example, based on IP-address and then setting HTTP cookie
Utilize this agenda to assist you with picking the best A/B testing device for your business. Investing a little energy examining these inquiries with your promoting group can set aside you time and cash later.
1. What is the expertise level of my group?
Does your group have progressed improvement abilities? Do you have the programming information to compose complex tests?
Few out of every odd business has these assets, not to mention each promoting group. All things considered, you'll need to pick a brilliant A/B testing apparatus that permits you to alter your pages without coding. Various straightforward testing stages have a WYSIWYG (what you see is the thing that you get) page altering apparatus. With one of these alternatives, any individual from your showcasing group can alter and test pages.
Does your group have insight with factual investigation?
Once more, this is in no way, shape or form guaranteed. Truth be told, even experienced analysts can be confused by knotty issues brought about by A/B testing. On the off chance that you don't want to swim through crude information and obsessing about the meaning of your outcomes, you need an instrument that will do it for you. Generally A/B testing devices accompany worked in measurable investigation, however they regularly adopt totally different strategies to insights. Think about this: picking a device with a less modern factual adding machine can broaden the length of your test by a few days.
2. What technical resources will be required?
Now that you've evaluated your team, let's look at the technical requirements for the tool you're considering. Is it easy to install, or do you need your IT department to manage the setup? "More features" often translates to "harder to use," and the extra options often come at the expense of the user experience.
Your main goal is to increase your conversion rate, not spend hours of frustration mastering a new tool. Any A/B testing tool that helps you do this will do the job.
Be careful when choosing software so that you don't increase the resources you are required to invest. Reducing the learning curve from the beginning of your optimization program is one of the keys to success.
3. What skills will be needed to use the software?
Technical resources are one thing, but your team's analytical skills (both quantitative and qualitative) may also be needed during the optimization program.
For most marketing teams, creating and completing an A/B test is challenging, and many mistakes are possible throughout the process. CROs often talk about the need for a good "conversion manager" to test the right variables and ensure reliable results. To help you decide if your team is up to the task, here's a summary of the steps involved in split testing or A/B testing:
Research: Before you begin testing, it's important to examine the site's analytics. In addition, customer journey analytics will help you build a picture of how visitors are using your site. You may want to supplement this with tools such as heat maps or exit surveys. Is your marketing team capable of interpreting site analytics?
Hypothesis: How do you decide what to test? Analytics will tell you where the problem is, but they won't tell you how to solve it. Does your team understand consumer behavior well?
Test development: This can be as simple as dragging and dropping an interface, or it may require advanced coding skills. You should be comfortable choosing between different types of tests, and you should decide on a statistical significance threshold.
Experiment: Before you start a test, you must decide on the percentage of traffic that will be allocated to each option. By default, 50% of the traffic is sent to the original page (the control version) and 50% to the variation. You can leave the distribution of traffic for the duration of the test 50/50, but then you risk sending half of the traffic to the less effective page. Another strategy is to try to maximize your earnings during the test by allocating more traffic to the better version. But what percentage do you allocate and when?
Analysis: To determine the winning option, you must first consider which version of the page has the highest conversion rate. However, this could easily be the result of chance. So how do you know if you can trust the conclusions your results suggest? Statistical knowledge is essential to making such judgments. Is your team equipped to interpret the "statistical significance" of the test, understand the "p value," decide when the test has collected enough results?
Your choice of A/B testing tool should reflect the answers to these questions. Lack of statistical expertise or CRO know-how is not a barrier to conducting effective tests, but it does mean that you will need a tool that is easy to use.
4. What level of support is available?
Perhaps you're worried that buying an A/B testing tool is a gamble for your business? It may not produce reliable results and, worse, your team may struggle to use it. In this case, it's important to know what support is provided by the tool you choose.
After a little research, it's clear that the level of support provided by A/B testing vendors varies. You should consider what style of customer support best suits your team:
Real-time support - with a phone number you can call.
Automated - with pre-recorded responses
Live chat - with embedded messaging
Email support - with an address to contact
Documentation - available online
You should also consider whether your team needs access to CRO experts to help guide the process. Beware of the phrase "targeted support," for example. When A/B testing providers use this phrase, it usually means that the customer service specialist (who has no experience in website testing or CRO) is on the other end of the line. The consultant simply searches their documentation and copies-paste the appropriate section.
It's rare to find customer support from real experts. However, if you can discuss your project with CROs, you will likely be able to run more effective tests and get more reliable results. Giving this opportunity to your marketing team can turn a tool like this from a gamble to a safe bet.
5. How much volume is needed for the test?
In answering this question, you have to think about whether your ambitions match the traffic attracted to your site. Again, it's all about statistics.
In a previous article, we suggested that software packages that allow 5,000 visitors a month are useless! The reason we say this is simple: achieving statistically significant results requires a noticeable effect and a large sample size.
An A/B test with reliable results requires a minimum number of visitors. If your site has fewer than 10,000 visitors per month, you'll have a hard time doing A/B testing or split testing, no matter which tool you choose.
