什么是 AB 测试
A/B testing is a strategy where marketers compare two different versions of an asset and measure the outcome. The winner between the two tested assets is then used to generate better KPIs for the end goal.
You can perform A/B testing on almost any part of an asset in your advertisement or campaign.
In A/B testing, two versions are tested within a controlled environment. This typically means showing each asset an equal division of the target audience. The 'winning' version is determined in regards to the goals set by the marketer.
Once the winner is determined, marketers usually go on to test other versions with the goal to convert more and more leads to paying customers.
What Is An Example Of A/B Testing?
在优化广告系列的点击和转化数时，首先需要识别受众的当前行为。这通常需要借助 热力图 or other analytics tools.
You would use these tools to answer questions like:
- What are my users clicking
- 受众没有点击什么？
- 受众没有点击什么？
The next step is utilizing this information about click behavior to generate various hypotheses that could lead to more and more converting users.
If your end goal of A/B Testing is to get more people to sign up to your product or offering, then you can use the places where users are currently clicking the most to lead to the sign up page.
Alternatively, you can conclude that if users are not clicking on a particular image or CTA due to weak imagery or unattractive choice of words and replace them.
An A/B test should be run between a treatment and a control group where the treatment group experiences the change and the control group remains as it was.
Alternatively, if you are running a new campaign altogether and don't have any past data on clicks and other user behavior, you would be testing out two new hypotheses at the same time.
After a set amount of time, you would compare the conversion rates for the asset (or the treatment group) to the control group.
Did the new design get you more clicks, and therefore more conversions? If your answer is yes, then you would stick to the new design and move on to the next A/B test.