Why A/B?
Although there appears to be an overwhelming amount of different methods that can be used to track customer engagement throughout content, A/B testing allows for a more focused process of testing marketing alternatives. Digital marketers can use A/B testing to gain even more insight into what type of content consumers react with the most, and how to make changes in order to attract more traffic. A/B testing can be used to help having to make marketing changes more trackable and allows for more extensive insight to be used when determining user interface and user experience decisions.
A/B testing allows marketers to compare changes to a web interface side by side with an original version. Typically, 50% of users will be given the controlled design and 50% will be given the experimental design. This allows for researchers to set hypotheses and manipulate individual variables over time such as headlines, visual content, colors, call to actions, language, etc. Through this testing, marketers can pin point certain changes, and use data analytics to measure the success or failure of the predetermined hypotheses. A/B testing enables changes to be made and tested with little to no risk involved. The user will ordinarily not be aware of the changes being measured, as they will interact with the website and flow through prompts as they see fit.
To use A/B testing effectively and ensure accurate results, there are a few steps to follow that marketers can use as guidelines when designing A/B experiments. First, data must be created in order to identify a potential problem within a site. Data analytics are used to determine where customers are leaving websites, failing to complete a call to action, or not following a desired click path. This information can be used to create goals, such as generating more clicks or consumers reaching a purchase confirmation page. From here, hypotheses can be created based on goals, and researchers can begin the process of formulating an experiment, which leads us to creating variations. Changes within variations can be on a large scale or a small scale, however the smaller the change, the easier it becomes to track the results. From here, researchers can begin to run the experiment, which is when the changes are actually made to a site, and users start to engage. Finally, goals can begin to be tracked, and analytics systems can start collecting data.
One brand that I am exposed to almost on a daily basis, if not a weekly basis, is Misguided. Misguided is a UK based online clothing store, which uses web personalization to set themselves apart from their competition. Web personalization allows for firms to segment their audience based on interests, which uses the IP address to organize data analytics and build useful information about the interests and preferences about each individual user. Misguided has directly benefitted from A/B testing, as they have experienced a 300% click through uplift, 177% conversion uplift, and a 33& direct increase in revenue.
A/B testing allows firms to use user interface and user experience research to make concrete marketing decisions based on real time results and interactions. A/B testing creates a space for direct comparisons and highly controlled experiments to enhance user engagement and increase Search Engine Optimization.