Every e-commerce site has the common goal of turning as many site visitors as possible into paying customers. There is more to turning a profit than enticing visitors with good prices and quality merchandise. The user’s experience is just as important if not more, and there are many elements that contribute to it. From consistency in content voice to simplicity of a menu layout, businesses have many site features to test and change until they reach an optimal solution. A/B testing is useful for determining what to keep and what to change. It also provides insight as to why aspects must be changed or remain the same.
Conversion Rate Optimization: The A/B Testing Foundation
Conversion science looks at every element that turns site visitors into customers. A/B testing is the tool used to determine which factors work in favor of the business and which ones have no effect or a negative effect. In many instances, a test phase involves showing two versions of the same page to users. There may be subtle or noticeable differences. One may have an interactive menu while the other does not. One page may have a larger optimized conversion button while the other has a small button. Testers gather data from the users and compare the results to see which options turn into more conversions. However, the science is not as simple as that. Testers also consider other factors that may affect conversions.
Factors That Affect A/B Test Results
If testers do not read and use the results correctly, they may hurt the business. For example, a company that hosts a semi-annual sale may notice that conversions increase with a new interactive design during a sale month. If the company assumes that the new site is the reason for increased conversions and does not take the large discounts of the sale into consideration, the business may lose money by investing in a new design. There are plenty of other factors to consider such as whether a design is more attractive to new or returning customers while also considering whether new or returning customers spend more money.
Testing Takes Time
As tests are continued over time, small modifications are made to make the site as friendly as possible to users and as profitable as possible to the business. Since tests usually involve a small change, measuring the significance of the change may take months or more than a year. When the results reach a statistical significance, they show a positive connection between the change and profitability despite outside variables. The A/B testing process can be expensive. However, it is an investment that more than pays for itself over time when executed correctly. One of the biggest mistakes made by companies is jumping on a new design too quickly and losing money. Since companies often hire people to design the site’s new changes, the investment is quickly lost along with profits when information is interpreted incorrectly.
Multivariate Testing For Efficiency
When multiple tests are run at once, this is called multivariate testing. Since tests often span several months or years, focusing only on one small test change at a time can be detrimental. However, testing too many variables at once or not allowing for overlaps between data while conducting multiple tests can be problematic. The key is to find the right balance of tests to run simultaneously without the risk of confusing similar factors and skewing the results. One example is website traffic. Overlapping site traffic on different tests without considering other factors can skew the results considerably. Test analysis must allow for identifying and analyzing even the smallest changes or differences on individual tests.
How AI Tools Simplify A/B Testing
Artificial intelligence is taking A/B testing to completely new levels. This disruptive technology is based on evolutionary computation. It uses test variables and an extensive amount of existing data to automatically design page candidates. Marketers can test the pages themselves on their sites to see which ones perform better. Anything from the color of a conversion button to the tone of a sales message may be changed. Designs, text options and formats can be altered. Companies can test mobile or PC versions of pages as well. In the past, marketers had to comb through design templates and develop ideas for changes. With AI tools, they have access to databases of design, text and formatting options. The tools use multiple complex data sets to generate possibilities.
With evolutionary computation, A/B testing programs with AI tools create page samples that are referred to as genomes. Each genome may have several single deviations such as the placement of buttons or the length of a sales text. When a specific genome and its derivatives convert well, the program starts to fine-tune those options. Each change or derivative is a new generation. The AI tools track which generations are most successful to perpetuate them with new improvements and their own derivative generations. Some of the leading tools in the industry use this method. Since leading AI testing programs can test many variables and derivatives at once, they can quickly identify and discard options that are not profitable. Since the tools also take factors such as traffic, sales and others into account, there is little to no worry of overlapping data and skewing the results.
The Ascend Difference
Sentient Ascend is one of the industry’s leading A/B testing tools with advanced AI. One unique feature that is not available with any other program is Ascend’s funnel. It allows many tiny changes to be tested at once across multiple pages and generations or on just one page. Also, it is constantly updating data about successful changes to form the most powerful page candidates. The program is becoming a favorite among many top marketers because of the heightened accuracy of data analysis along with the customization of change suggestions.
With the ability to make so many beneficial changes over a shorter period of time, the program creates a more profitable solution. Ascend is capable of identifying hard-to-find combinations such as a call-to-action button being more profitable in a green color but only if it is transparent and has right-aligned text as opposed to a solid color with center-aligned text. These types of differences could otherwise take years to detect and develop. Since companies should strive for a program that increases ROI on short, medium and long terms, Ascend is a wise AI tool that excels in every aspect of A/B testing.