A/B testing compares the efficacy of two distinct versions of a design: the original (also referred to as “control”) and the “B” version (also known as “variant”). After driving equal traffic to both, you can determine which is more effective at achieving your objective.
Personalization is a data-driven strategy that entails delivering the appropriate content to the appropriate user at the appropriate time based on their past behavior and stage of the consumer journey. Marketers are constantly analyzing data to determine the effectiveness of their campaigns. Rather than calculating an average, personalization attempts to serve the most relevant page for each type of traffic. It does not rely on chance, as A/B testing does, but rather segments traffic based on demographics, behavior, referrer, and other criteria, and then presents a page tailored to those characteristics.
Personalization and A/B Testing both aim to improve a page’s performance. Both, however, have their highs and lows.
For instance, you can optimize as much as you want with A/B testing, but you will never achieve truly optimal performance by building a progressively better average page. Simultaneously, you can personalize based on critical identifying information, but not everything is covered by data.
Every marketer wishes for increased traffic and conversion rates. A/B testing and personalization are just two of the techniques available to accomplish this goal.
Typical Customer Experience Obstacles
- Assists in Obtaining Clear Evidence: It’s easy to see why users prefer site A over site B when it comes to completing transactions. The evidence is grounded in real-world behavior and concrete facts that money men adore (and can be presented in a simple-looking, complex hitting chart).
- Allows for the Evaluation of Novel Ideas: If you have a novel idea for an existing website, A/B testing will determine whether or not the idea will work. However, before you can test it in this manner, you must first implement the complex concept in complex code.
- Contributes to Optimization One Step At A Time: If you have a large site or multiple sites, A/B testing is an excellent way to “patch” test by beginning in a small corner and working your way up to the main pages. Can smaller sites with lower traffic, on the other hand, afford to gamble with real users by providing a suboptimal site experience to half of them?
- Responds to Specific Design Issues: Are green buttons more appropriate for the design of your website than red buttons? A/B testing can address this and numerous other questions by allowing the designer to experiment with different colors, button placement, page layouts, and graphics, all of which are excellent areas to gradually improve a website.
- It can consume a significant amount of time and resources: A/B testing takes significantly longer to set up than other types of testing. While setting up the A/B system can be time consuming and resource intensive, third-party services can help. Depending on the size of the company, there may be numerous discussions about which variables to include in the testing. Once a set of variables has been selected, designers and coders will need to successfully work with double the amount of data. Additionally, for low-traffic sites, tests can take weeks or months to complete.
- It is only effective when a single objective is being pursued: This type of testing is appropriate when a single objective is being pursued, such as determining which product page produces the best results. On the other hand, pure A/B testing will not produce results if your objectives are more difficult to quantify.
- It will not improve a dud: If your site had usability issues to begin with, and the variants are simply iterations of that, it will almost certainly retain the same fundamental flaws as the previous site. A/B Testing will not reveal these flaws or indicate user dissatisfaction, and it will not reveal the root causes of the site’s problems. It is entirely irrelevant to B that A resulted in increased sales. Identifying and resolving the underlying usability issue could be significantly faster and result in significantly improved results.
- Could Result In Constant Testing: Once the test is complete, that is all that is required. The data is completely useless for any other purpose. Additional A/B tests will have to be conducted from scratch. When the rejected version may have provided equally relevant information, different types of testing will almost certainly be applied exclusively to the more successful site. Consider the following hypothetical A/B test conducted for a sweater company in order to determine the most effective sweater color for a hero image. Conversion rates significantly increase for users who see the blue version. Green sweaters are another option. Red is the least effective color. In traditional A/B testing, these results may lead you to display the winning blue sweater image to all users, as it has the highest conversion probability. Assume 60% of your consumers prefer blue sweaters, 35% prefer green, and 5% prefer red sweaters. Thus, even if you optimize for the majority, 40% of visitors will not be immediately drawn in by your hero image and will leave your site. Personalization addresses the 40% “minority” problem by serving the most relevant page to each traffic segment (red to those who prefer red sweaters, green to those who prefer green sweaters, and so forth) rather than the average.