What's the difference between A/B testing, multivariate testing, and AI?
Last updated
Last updated
Depending on your goals and digital maturity, you may need to use a different testing method to get the results you’re looking for. Understanding the difference between A/B testing, multivariate testing, and AI is a great first step to advancing your digital strategy.
A/B testing is when one variant is tested against a second variant or a control group. The number of “participants” in each group is equally distributed.
In this case, there are two equal groups that will be exposed to another variation of the experiment. For example, Group A might see “Eco-friendly”, while Group B sees “Sustainable Material”.
Advantages:
Best used when testing simple design modifications or for sending emails or newsletters
Can be used to test the effectiveness of a campaign
Low technological barrier
Low cost
No need for a data analyst to understand the outcomes
Disadvantages:
“Winner takes all” scenario: Instead of always showing the best variation to a specific visitor, you will always show a variation that is best for the majority of visitors. This fails to take into consideration the nuances between individuals.
It doesn’t take into account seasonality or localization, so real-time results are more difficult.
You can only modify one element at a time.
A/B testing has its place in the digital environment, especially for page optimization. In many cases, you might just want to learn whether a message is effective at driving behavior or not. At these times, there’s no need to go for a more complex option.
You can A/B test your badges with Crobox’s Custom Badging. This is especially useful for short-term campaigns or always true messages you want to promote to your shoppers. For example, you can test a campaign message of your choice (e.g., “Black Friday Deal”) against a control group where no message is shown to measure the performance of the message. The distribution of traffic can be easily adjusted in the campaign settings.
When you test multiple variants against each other at the same time, this is multivariate testing. It uses a similar core concept as A/B testing, only compares more variables whilst revealing more information about the relationships between those variables. The purpose of multivariate testing is to measure the effectiveness of each combination of variables on the end goal.
For example, using the same example as above, you can test the combination of message and badge color to understand what drives the most impact. Multivariate testing can be used on as many variables as you want as long as the data (e.g., traffic volume) is large enough.
Advantages:
You can test combinations of changes (design, text, color) simultaneously.
Insights are likely more detailed than A/B tests.
Because you’re able to test more variables, optimizations can be made faster than with A/B testing.
Relatively low cost and resource-heavy.
Disadvantages
The more variants you have, the more data you will need for the testing to work. Thus, you need a large amount of data to complete the test.
This is a complex process with outcomes that may need input from a data analyst.
AI is used to describe machines that are taught a set of rules to autonomously predict patterns. The intelligence AI generates is thus considered “artificial” because it is based on predetermined rules and input from a human. AI is made up of knowledge and machine learning. Knowledge is the information being “fed” to the system and machine learning being the algorithms used to register patterns within the knowledge.
For eCommerce testing, AI uses machine learning algorithms that learn from previous webshop visitors to make its predictions. For example, given the example above. AI selects the product to display a “Sustainable” or “Eco-friendly” badge based on the shopper it’s being shown to, as it will predict which products a shopper would be interested in. It will also select the message variation and color based on the knowledge it has gained with previous shoppers.
Advantages
Automated process
Less risk of “human error”
Streamlines decisions from millions of data-points
Information is processed much faster
Personalizes messages to the visitor, increasing the potential for relevancy
Disadvantages
Lacks human angle in terms of thinking outside the box and empathy
Higher costs
The need for hiring a data scientist or external party/vender
Bias in predictions if knowledge is not well-rounded
Our technology leverages AI for our Campaign experiences. This ensures we can serve the optimal message at the right time on the right product.
