Amazon AB testing requires attention to some details

Amazon A/B testing, which has been around for a long time, is a common measure to make Amazon advertising more cost-effective and increase clicks and transaction conversion rates.

What is Amazon A/B Testing?

To put it in more basic professional terms , the purpose of segmentation testing is to increase click-through rate, that is, the proportion of consumers who click on the product after finding the product in the search keyword, and its transaction conversion rate, that is, the proportion of consumers who purchase the product after searching the listing.

Higher click-through rates will increase your page views. Improved conversion rates will increase your profits and make better use of your traffic.

There is another added advantage to doing this: on Amazon, for every additional unit you sell, your competitors will sell one less unit, so your keyword ranking will be higher than your competitors.

Amazon’s optimization algorithm will reward listings with higher transaction conversion rates and better positions in search keywords.

Amazon sellers often encounter various problems when conducting A/B testing. Here are a few points we need to pay attention to:

Without investigation, there is no right to speak. Testing is not carried out casually. Directly conducting tests based on a few test items introduced on the Internet is an extremely inefficient and time-wasting behavior.

Before we do any testing, we first need to do some research.

Know the characteristics of your product category and which ones are the key points and which ones are secondary points.

Another common mistake that operators make when conducting A/B testing is testing without a purpose.

Know that you need to do testing, but you are also doing testing work.

But you ask him, what is the purpose of doing this test? For high conversion, for high clicks.

Seems right? But it's too rough and too general.

For example, if we modify an image, what we need to test is what impact the key factors of the image have on conversions and clicks.

The style, clarity, color and other aspects of the picture are more concrete.

One taboo in A/B testing is making changes that are too small.

Operations staff are worried about performance, fearing that they are inexperienced and too soft. To change the idea, simply change a word.

To change the image, just change one color.

Small changes often do not produce obvious differences, resulting in the A/B test seeming to be meaningless and ineffective.

Sometimes we need to make bigger changes, for example, if your product is virtual and non-physical, you need to change it to a real physical product.

First of all, we need to understand a practical fact. When the amount of your data is not large enough, there will be uncertain fluctuations between each day or even each week.

If we only conduct a few dozen hours of testing, the data we obtain may not be accurate or even wrong.

After putting aside the impact of holidays, we must be patient and allow our test content enough time to ferment and conduct sufficient testing. The larger the sample, the more accurate the data.

New and old customers have different levels of understanding of your website and products, so they often receive completely different feedback on the same modification points.

Especially for some old customers who frequently visit our website, they may ignore even some defects of the website out of habit.

When you make a change that is clearly beneficial, the feedback you receive from regular customers may not be positive.

Because people don’t like change. And new users may make up for some deficiencies, making your data positive.

The biggest difference between humans and animals is that humans can create and use tools. The birth of tools will greatly improve work efficiency. As more people understand and use A/B testing, there are now many professional testing tools online.

Some are paid tools, and some are free. In order to avoid suspicion, I will not go into details here. Students who are interested can search on Baidu.

You just need to understand that when using a testing tool, you must first ensure the accuracy of the data in the tool. Generally speaking, only data that is accurate enough can help you make the right decisions.

Once the data is wrong, it may mislead you to make wrong decisions and have a negative impact on the test results. Therefore, before you start using it, you must have a good understanding of the tool.

Find the right tool for you. Once you have learned some A/B testing knowledge, don’t pursue perfection too much. Start small-scale testing as soon as possible.

Only when you start running can you run faster and faster. Grow and improve through mistakes.