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Use Amazon A/B Testing to Drive Success

Marc Aufzug
Marc Aufzug
3 min read
26 July 2022

Creating not only quality content, but also the right content on Amazon plays a crucial role in turning clicks into customers.

Businesses aiming for Amazon success often tend to ask the following questions:

  • How can I test my content ideas in an in-market environment and determine which ideas are worth investing in?
  • What would be the impact of content optimization on my product?
  • How can I improve my overall content using a data-driven approach?

One of the best solutions would be to try A/B testing.

What is A/B testing?

A/B testing (also known as bucket testing or split-run testing) is a frequently used experimentation method in product development and marketing.

In the Amazon cosmos, consumers are shown two different versions of content in a given time period. One version serves as a “control” while the other one tests a new element.

At the end of the monitoring period, Amazon metrics on user behaviour (e.g. conversion rate or units sold) indicate which version has a better impact on your business.

Live A/B Testing can be implemented with the following elements: Main Images, Product Titles, and A+ Premium Content.

These tests can be used as a basis for deciding which type of main image/product title or A+ Premium Content should be used for a specific product line or target group, in a specific category or in a specific country.


The process of testing different versions online always starts with a brainstorming session to raise ideas, which are then translated into a viable hypothesis to test. From there on, we support you with the test upload, monitoring and evaluation of results.


  • Optimization of your content on an ongoing basis
  • Quantitative monitoring of the impact of best practice content on your business
  • Data-driven decision-making on content opportunities

Case study: A/B Testing for Electrolux in the US

Main Challenges:

  • Understanding the target group and performing optimizations based on customer behaviour on site
  • Achieving maximum effectiveness through isolated changes
  • A convenient and effective opportunity to collect tangible and valuable data

Our Solution:

  • Identify two different main images that can be tested and could be more effective than the current setup to ensure relevant testing
  • Qualify the test in terms of hypothesis and expected outcome to ensure that the results are recorded in high quality
  • Evaluate the findings of the A/B Test to determine the most effective and appealing product image for customers, the impact that the main image has and gather insights on the target group

The Outcome:

  • 98% probability Version A is better
  • +148% sales of Version A compared to Version B
  • +162% conversion of Version A compared to Version B


View all insights



Marc Aufzug