What is A/B testing?
A/B testing (split testing) is a controlled online experiment in marketing, during which different versions of one page are compared. To conduct tests, the web page traffic is evenly distributed between two (sometimes more) versions, where user activity is compared and recorded, primarily the number of conversions. Based on the results of the experiment, an informed decision is made about the full implementation (or not) of the test version.
A/B testing is used to check:
Text – CTA (Call to Action – short asia mobile number list to-the-point text that is placed on buttons or next to them), product descriptions, headlines, advertising texts; content of emails for newsletters.
Elements of e-commerce – pricing, limited offers, discounts, cross-selling.
Design – shape, size and color of the CTA button, product images, placement of infographics, overall look of the site, text font and background.
Changes in structure and navigation – creating new categories or changing the path to checkout.
Registration and feedback forms – simplifying or increasing the number of fields.
Placement of elements on the page – banner, CTA, basket, etc.
Types of testing
A/B tests are most often use o confirm or refute a hypothesis, but there are other types:
Multivariate testing (MVT) is a hypothesis testing method that tests multiple variables. The goal is to determine which combination of variations works best. For example, from three types of headlines and two image variations, six combinations can be creatd and tested simultaneously. This type is usd for complex user interface testing.
Multi-page testing is testing changes to individual elements on multiple pages. The method is usd to determine the impact of repeating elements on different stages of the sales funnel.
Split URL testing is the placement of test variants on web pages with separate URLs. The site traffic is distribute between the main address and the one creatd specifically for the experiment. In classic A/B testing, the variants are placed on one URL.
Read also : Widgets for e-commerce: a selection of effective assistants that will save your conversion rate
Benefits of A/B Testing for Website Optimization
Conversion rate optimization is often associatd with testing. Split tests are, although important, only a
Part of optimization. Tests are used to:
Test a hypothesis before rolling salesforcen integrointi hubspotin kanssa it out at scale. This is how marketers and other digital professionals statistically confirm whether a particular change to a website will improve conversions.
Identify site pain points – controlld experiments provide insight into which elements are not living up to expectations.
Reduce bounce rate – the percentage of visitors who leave a website without taking a targetd action, relative to the total number of users.
Make low-risk changes after testing several variations without the risk of losing some conversions if users don’t like the changes.
Increase the ROI of your advertising and website optimization spend by pre-testing your options with real users.
It is better to study the target audience – the results will demonstrate the real preferences of users.
A/B testing and its types
Read also : The “buy in one click” button is an effective solution for increasing conversions in e-commerce
How to conduct A/B testing?
The process occurs according to a sequential scheme, within which one or more hypotheses can be
Tested.
Step 1: Data collection and goal definition
The goal of testing is to alb directory increase conversion by a certain percentage by changing a specific element. If the experiment shows an insignificant increase in conversion (significantly lower than planne. Athen implementing the experimental hypothesis will not make sense.
Using various tools, you can identify pages and areas of the site with the least efficiency:
Google Analytics is a free tool from Google that gives you insight into the traffic to your website and its individual web pages, and shows the number of conversions.
Heat maps – visualize user behavior on a website
