How to use A/B testing for a recommendation system
Recommendation systems, a common feature of e-commerce websites selling multiple products, use AI algorithms to suggest products to consumers. Suggestions can be based on purchase or search history, demographic information, or other parameters. In this manner, consumers discover products they might like or otherwise have missed. A simple example would be various e-commerce sites that recommend products based on user search history. However, recommendation systems can be complex to implement or maintain in the long run. They have many components, and a machine learning model is one of them. To assess the feasibility of a recommendation system, we use A/B testing.
In A/B testing, two groups of consumers use different versions of a recommendation system, and the recommendation system that engages the customers most and brings in more sales is kept. An A/B test measures if there is a relationship between the outcome and the different versions of the recommendation system the two groups are introduced to. This means we are testing whether changing one feature of a recommendation system for the test group from that exposed to the control group impacts the experiment’s outcome. A website needs to generate high traffic for A/B testing. For that, SEO or paid advertising might be required.
How to carry out A/B testing
An A/B test involves measuring two versions of a recommendation system. For explanation purposes, we’ll analyze how to test the recommendation system of an e-commerce website.
The following steps are carried out for the A/B testing:
Customers of the website are randomly divided into two groups, A and B.
Group A uses one version of the recommendation system, and group B uses another. One is the control group, and the other is the test group. The test group will use a recommendation system that differs by one feature from that given to the control group. Everything else—the time when the customers will use the system, the product catalog, and the website layout—is kept the same, except for the placement of the recommendation system. The following points show how the two recommendation systems could have different features:
Title of the recommendation system.
The action to be taken after being recommended various products, for example, Add to Cart or Know More.
Placement of the recommendation system.
Type of product recommended, for example, a single product or a bundle.
Interactivity, for example, by the use of pop-ups.
Different SEO keywords.
During the testing phase, both groups interact with their recommendation systems. To decide which recommendation system had a higher performance, we collect data that tells us which group bought more products, spent more time interacting with the website, and gave higher ratings such that the desired performance was achieved—called conversion. The recommendation system with the highest conversion is selected.
The diagram given below shows how an A/B test is conducted. Groups A and B were directed to use two different versions. In the end, recommendation system 1 gave a greater conversion rate of 23%.
Metrics used to test recommendation systems
Several metrics are used to evaluate the performance of any recommendation system. Some can be revenue generated per customer, weekly or monthly active users, free trial to paid conversion rate, long-term value, and weekly or monthly churn rates (the rate at which customers stop using your service for a period).
How to select the performance metrics?
The selection of performance metrics depends upon a few factors. They are listed as follows.
Business goals: Business goals greatly drive what performance metrics to select. If our business goal is to increase revenue, we would opt for metrics like revenue generated per customer or customer turnover rate.
Revenue model: If the revenue model of our business is the sales revenue model as is commonly used in e-commerce platforms, then revenue generated per customer or conversion rates would be important.
Nature of recommendations: It can depend upon the type of recommendation — content, product, or service. For content, we can consider click-through rate.
Balancing Metrics: Having multiple metrics means optimizing one metric may affect the value of another metric. Hence, it’s essential to strike a balance.
Types of A/B testing
There are three types of A/B testing, as given below:
Split testing: In this type of A/ testing, we test the performance of a new version of an already present web page.
Multivariate testing: In this complex A/B testing, a combination of features, taken from multiple pages, is tested to decide which combination increases performance.
Multi-page testing: We test changes to specific features on multiple web pages.
Pros and cons of A/B testing
A/B testing has its own set of advantages and disadvantages; they are given as follows.
Pros | Cons |
Provides objective data on which recommendation system performs best | A time-consuming process |
Can experiment and test which features work well for a recommendation system | Requires a large website traffic |
Works well for testing recommendation systems of large websites | Small technical errors while carrying out the test can have a major negative impact on its results |
It is flexible and can be carried out alongside other tests like prototype testing | May overlook the long-term effects of changes made |
Easy to carry out | Results in false positives, which can be misleading |
Results are easy to interpret | Doesn’t always lead to improved results |
Summary
A/B testing is crucial to check how effective the recommendation system is. It reduces the risks by analyzing the conversion rates for small incremental changes already made to present recommendation systems. Through this method, a business can improve the consumer experience on its website for greater conversion, click-through rates, and customer loyalty.
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