Traditionally, in roundtable discussions, many ideas are shared, and the two best ideas are compared and analyzed. People then voice out their opinion about a particular idea, and the company finally settles with the most-liked idea. Even then, so many ideas miserably fail in the market. The reason: those were the opinion of the decision maker and not the customer. Fast-forward a few years and we have technology that can implement both of the top two ideas in the real world and show you which works better. That is exactly what A B testing is all about. A B testing is extensively used in digital marketing to test everything from website layout, ad copy, to sales emails. A well-planned A B testing process can make a huge difference in determining the effectiveness of a company’s marketing campaigns. Although option A may prove to be 5% better than option B, when it comes to real-world implications, that 5% can amount to thousands of dollars or hundreds of customer conversions.
Develop a hypothesis
It is crucial to have an idea of the desired outcome before setting out for A B testing. A hypothesis should be laid out before setting out to test. Otherwise, A B testing will merely be A/B guessing. Without a hypothesis, it may be impossible to measure the actual impact of the design or copy changes and may provide difficulty in further future testing. Before setting out for A B testing, it is thereby important that the marketer has at least a rough idea of what will happen. The goal of the A B testing may be to build brand awareness, or improve conversions, or just increase the click-through rate. For instance, before A B testing two ad copies, the marketer should have a goal in mind saying this ad copy should better entice the customer to click.
Test for the right duration with adequate sample
To adhere to the best practices of A B testing, one should start out testing at an earlier stage. Although the first test results may not provide real insights; over time, it can give the marketers a real understanding of what design or copy choices have a measurable impact on the conversions. One key thing to note is one shouldn’t end the testing too soon or too late. Completing the test too soon may provide results that are not statistically significant. Running it for too long means the test could be affected by multiple variables. Also, A B testing on the website with very low data may not yield a valid result, as the outcome may have occurred due to pure coincidence. You can quickly check the statistical significance of you’re A B test result by entering variables and outcomes such as number of test subjects, conversions or clicks achieved through each result, type of hypothesis, and level of confidence.
Be patient with multivariable test results
Apart from testing just A or B, multivariate tests could be used to measure how the combination of different aspect affects the outcome. For instance, A B testing can only measure if copy A performs better than copy B. However, multivariate testing can measure if copy A in design A is better or copy B in design B, or copy A in design B, and all such possible combinations. As discussed, for a test to show significant results, there should be enough data, but since multivariate testing uses multiple combinations, data for each combination may be limited. It requires a lot of time to gather such a large amount of data, even for websites that have millions of unique monthly visitors. So it is advisable to be patient when carrying out a multivariate A/B testing.
Always keep on testing
Once you start seeing patterns and know what works to increase the conversion, you might think that further A B testing may not be required. Marketers may think that by repeating what worked in the past will work in the future. However, that is far from the truth, as trends are ever-changing. Customer expectations are constantly evolving, and what worked yesterday may not work today. As a result, it is essential to always test ad copy, headlines, designs, and all other elements.
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