By: Alon Cohen Jan 14, 2018
A/B testing is the main tool used by marketing people trying to optimize conversions in the digital marketing world. It’s a method to find the better converting version of a webpage or an ad.
The way A/B testing works is that you randomly present page A or page B to your website visitors and check which version of the page converted more visitors. There are tools (like https://www.optimizely.com) that make that process relatively simple however it takes time to collect sufficient evidence to make a clear decision about which page performed better.
So why even bother?
The problem with webpage design is that it is hard to get it right the first time. A designer might think that call to action button is in the way and move it to where it becomes no effective. Color schemes, market trends all affect how people perceive, understand, and operate a page.
To validate your design assumptions and improve on them an A/B test will provide the answer.
What can go wrong?
If for instance, you did not assign a 50/50 impression between the A & B versions you might think that one page performs better.
In many cases, unless one page is really bad, the other one will perform pretty close to the first one and the statistics can change week after week. You need to wait sufficiently long time sometimes few weeks to get a decisive answer.
Picking the wrong page will reduce your conversions.
Since you normally use multiple channels to target customers, changes in one channel might affect the A/B test results. So in-spite the fact that A/B testing could be effective when where your two pages are almost similar in performance it is very hard to get conclusive results and ongoing testing is required.
The main problem:
Say you have used the best tools for A/B testing. You waited, and you get some slightly statistical confirmation that one page is better. Why? Because one page was good for some people and the other page was also good but for other people.
The audience is not homogenous. When the results are close it means half of the audience like version A and the second half liked version B. The sad result is that in-spite all your optimization efforts and patience your bottom line did not change at all.
The solution:
Ideally, what you need to have is a different version for each individual or at least for a sufficient number of market segments. Only then you could see improvement in your total performance.
Unfortunately, I have not seen good marketing tools that can tell you (the site) in real time which version of the page to render to which user. The hope is, and this what we are working towards at hoo-r-u is to enable true personalization of the web pages and so improve the A/B drastically. It is not about knowing the name of the site visitor, it is about knowing what the viewer social or behavioral profile is and optimizing the page for each one of those profiles.
Only then we can improve conversions and move from a "local" maximum on the optimization graph to a more "global" optimization. To achieve this goal the mathematical tools we use require data and data analysis.
If you want to help us create this next generation of marketing tools you can start by filling up our questionnaire that will help us segment the market according to whatever segments will emerge from the data.
Thoughts?
A/B testing is the main tool used by marketing people trying to optimize conversions in the digital marketing world. It’s a method to find the better converting version of a webpage or an ad.
The way A/B testing works is that you randomly present page A or page B to your website visitors and check which version of the page converted more visitors. There are tools (like https://www.optimizely.com) that make that process relatively simple however it takes time to collect sufficient evidence to make a clear decision about which page performed better.
So why even bother?
The problem with webpage design is that it is hard to get it right the first time. A designer might think that call to action button is in the way and move it to where it becomes no effective. Color schemes, market trends all affect how people perceive, understand, and operate a page.
To validate your design assumptions and improve on them an A/B test will provide the answer.
What can go wrong?
If for instance, you did not assign a 50/50 impression between the A & B versions you might think that one page performs better.
In many cases, unless one page is really bad, the other one will perform pretty close to the first one and the statistics can change week after week. You need to wait sufficiently long time sometimes few weeks to get a decisive answer.
Picking the wrong page will reduce your conversions.
Since you normally use multiple channels to target customers, changes in one channel might affect the A/B test results. So in-spite the fact that A/B testing could be effective when where your two pages are almost similar in performance it is very hard to get conclusive results and ongoing testing is required.
The main problem:
Say you have used the best tools for A/B testing. You waited, and you get some slightly statistical confirmation that one page is better. Why? Because one page was good for some people and the other page was also good but for other people.
The audience is not homogenous. When the results are close it means half of the audience like version A and the second half liked version B. The sad result is that in-spite all your optimization efforts and patience your bottom line did not change at all.
The solution:
Ideally, what you need to have is a different version for each individual or at least for a sufficient number of market segments. Only then you could see improvement in your total performance.
Unfortunately, I have not seen good marketing tools that can tell you (the site) in real time which version of the page to render to which user. The hope is, and this what we are working towards at hoo-r-u is to enable true personalization of the web pages and so improve the A/B drastically. It is not about knowing the name of the site visitor, it is about knowing what the viewer social or behavioral profile is and optimizing the page for each one of those profiles.
Only then we can improve conversions and move from a "local" maximum on the optimization graph to a more "global" optimization. To achieve this goal the mathematical tools we use require data and data analysis.
If you want to help us create this next generation of marketing tools you can start by filling up our questionnaire that will help us segment the market according to whatever segments will emerge from the data.
Thoughts?
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