Use Amazon A/B Testing to Drive Success

Marc Aufzug
Partner

Length

3 min read

Date

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.

Methodology

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.

Implications:

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: