A/B testing, also known as split testing, is when you isolate one element, change it, and then test how that single change affects various metrics. It’s a simple yet powerful tool for gauging whether you’re on the right track or if subtle changes are needed. In this article, we’ll explain how to A/B test your Amazon listings to maximize your traffic and conversions.
Why A/B Testing on Amazon?
The main reason for A/B testing on Amazon is to increase traffic, conversions, and profits. This is one of the ways to improve your Amazon sales performance because you don’t have to guess at all the variables that drive sales.
In order to attract more visitors, you need to make sure your keywords are effective and your images are attractive to your customer base. An increase in CTR (click-through rate) and sessions (number of browsing users) can help sellers increase sales, not only because more visits mean more conversions, but also because more visitors mean a greater likelihood of ranking higher in searches.
One of the more important benefits is an increase in conversion rate, which has a positive impact on sales rank, which in turn increases sales because your products will be easier to find on Amazon. Smart product pricing is a key factor in successful A/B testing, as it allows you to optimize prices so you can earn more profit from higher sales.
However, for A/B testing to be truly successful, there are usually criteria that need to be met, and these criteria include:
1. There is enough traffic to constitute a sufficiently reliable sample size.
2. If you don’t have traffic, you need to find a way to get it. One of the easiest ways to do this is through sponsored product ads. While this may mean some cost, the return on investment is often worth it.
3. You are financially stable and can wait a few days or weeks for the results to come out, as your traffic and the type of split test will determine the time.
4. Finally, there is a chance that the elements you change in your A/B test will have an adverse effect on traffic and conversions, and you need to be prepared to deal with this.
Which factors in a listing can be A/B tested?
Here are some of the most common and important tests:
Pricing
The price of a product is one of the factors that consumers pay most attention to when purchasing. When it comes to finding a suitable price, move it up or down by 5-10%; a higher price can give your product an aura of exclusivity, but at the same time may reduce consumers' purchasing interest, while a lower price is generally more likely to promote sales. The nature of Amazon is that the more sales a seller has, the easier it is to get sales. Therefore, appropriate pricing is crucial.
Featured Image
The featured image is the first product image that shoppers see on your listing, and you have less than a second to make a lasting impression. You can do a quick search for products similar to what you’re selling and take note of what their featured images look like, then run a split test against that, changing something small but also noticeably different.
For example, if you’re selling waterproof phone cases, the search page will often show images of phones submerged in water. A small but noticeable change you can make is to add a more realistic element to the mix, like showing the phone in actual water like a lake or bathtub.
title
Another important element of your product listing is the title of your product. Usually the format for writing titles is: "brand name + color/flavor etc. + size/quantity + keywords". There are many ways you can conduct A/B testing, from keywords to how you describe your product.
Product Description Bullet
Bullet points in your product descriptions aren’t necessary, but without them you’ll have a hard time engaging with your users. When A/B testing your bullet points, you can do the following: summarize and bold the sentence before stating the feature, change the order of the bullet points, the type of bullet points used, and more.
Note that the testing process should be done one decision at a time, and then improvements should be made after observing the results. Don't introduce multiple changes at once, or you won't know what factors are accessing and converting the changes.
