If you are selling on Amazon, you may find yourself scratching your head wondering why your competitors are outranking and outselling you. There are lots of variables that affect success on Amazon, including how you market your products through Sponsored Ads, A+ Content, Basic Content, etc. That said, crafting a product listing to persuade shoppers to choose your product over your competitors requires an in-depth understanding of testing methods and analyzing results. Since this is not a one-time task, it is critical to run A/B testing on various elements of your listing and identify which version performs best.
What is A/B testing?
In a digital environment where everything can be tracked and measured, testing your strategies is a no brainer. Content strategy plays a key role in driving organic traffic and converting sales, so by leveraging and optimizing your content, you can increase the chances of shoppers purchasing your product over your competitors. A/B testing is one of a handful of the best practices you can use to enhance content on the platform.
Also known as split testing, A/B testing on Amazon is a method of determining the best-performing product listing variation. By comparing two versions of the same content, you can identify the exact element that is driving purchase-ready shoppers to act, taking into account metrics such as conversion rate, sessions, and total sales. That being said, you should have a strong understanding of your current metrics, performance and challenges before building an A/B testing plan. This will allow you to have a solid base foundation before making any changes.
Start by running an A/B test experiment on Amazon.
Amazon launched its own A/B testing tool in 2019 called Manage Your Experiments, which allows US brand owners to test two variations of one product listing element. As a brand owner wanting to test different A+ content elements, you must have eligible ASINs, otherwise they will not be displayed in the A/B test experience. To be eligible, ASINs must belong to your brand, and they must have high enough traffic in their respective categories to determine content winners with confidence.
Keep best practices for A/B testing in mind.
By following a few tips, you can begin your test experiment with confidence. Here are some recommendations:
- Strategically create a hypothesis to learn something from the experiment regardless of the outcomes.
- To get a larger sample size, experiment on high-traffic ASINs.
- Stick to making one change at a time to avoid confusion on which variables influenced the experiment outcomes.
- Don’t stop early – you can run experiments for four, six, eight or ten weeks to ensure accurate results.
Get to know the elements of A/B testing.
Nearly any element of your A+ content can be A/B tested. However, you should use your own judgment to determine what to test based on your current performance. Consider working with an Amazon Ads Partner to strategically outline the testing method. Some variation ideas for your A/B experiment:
- Utilize a comparison chart.
- Rearrange and update your modules and images.
- Present the same images and text using a different module layout.
- Include lifestyle images.
- Highlight one set of product features versus a different set.
- Add your brand name to the product title.
- Use different headlines to engage and motivate shoppers to learn more about your product.
Keep in mind that your experimental content should differ from the existing content, otherwise it is less likely to affect consumer behavior and you may not be able to confidently determine a winning variation. The key to a unified branding strategy is consistency, which applies to your color scheme, icons, layout, and even text fonts.
Wait for optimal data.
Patience is key here as most tests need several weeks to gather enough data to determine a winning variation. While Amazon recommends testing for eight to ten weeks, you can adjust the schedule or even turn off the test while it is running. You will start to see data within one or two weeks, although this preliminary data is not representative of the true impact of your experiment. Be sure to let your experiment run its full course before interpreting the results and making a decision.
Gauge the effectiveness of your experiment.
While launching new marketing initiatives on Amazon can lead to an increase in traffic, you should know how to gauge the effectiveness of each initiative to dial in on what works best.
Once your A/B test ends, you will get the following from Amazon:
- Recommendations on which content variation is more effective
- A confidence level of the recommendations
- A confidence interval of likely outcomes from that content
- Estimated 12-month impact on sales
These insights will highlight the most effective content for your product detail pages to boost total sales and conversions for your Amazon products. By learning from these experiments, you can optimize and improve your other product listings. You might find it beneficial to run experiments during different seasonal periods as this will provide you with a better understanding of your consumers and their expectations.
When analyzing your results, be sure to keep your audience in mind to make the most informed decision. Although it takes time and patience to run A/B tests, the optimization is worth it if you want to dominate your market. Happy testing and selling!
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