Understanding about conversion optimization and setting up an A / B test
One of the main tasks in order to earn more money online, is understand how to optimise for conversions. A very good method of doing this is to do what is termed split testing or often referred to as A / B testing.
The most important at a glance
- The importance of the conversion rate for the success of the website
- Tips for A / B testing and tool recommendation
What is conversion optimisation?
Conversion Optimisation is defined as the increase in the proportion of those visitors to the site to purchase something, or to increase somehow another action to perform better (for example, signing up for a newsletter, or opting in for a free report).
By tweaking or changing individual elements of your website or the blog, you can measure the impact of these changes on the actions of the visitors.
Ideally, you constantly try something new to see what works. Over time you can then gradually help perfect your site.
What Can Conversion Optimization Bring?
To earn more money on the Internet, a typical strategy is to increase the traffic to your website. Many bloggers and site owners therefore only care about SEO measures, social media releases, advertising and new content.
Although this approach certainly is an important component for the success of a blog or a website, these operators neglect very often the existing visitors and give away a lot of potential earnings.
By understanding and implementing conversion optimisation properly, you can help achieve more sales and more revenue with the same number of visitors.
Many tests show that small changes can have large effects. There is clear evidence that the turnover of online stores has increased by upward changes in the conversion rate.
100 Conversion optimisation examples on kissmetrics.com (https://blog.kissmetrics.com/100-conversion-optimization-case-studies/1/) give a lot of practical insights and show you that this can be used on every site and on every subject.
Is It Worthwhile to focus on conversion optimization on your website / blog?
The first question on the subject of conversion optimisation is usually: “Will that help me?”
Even if you can not give a general answer to this question, it should be clear that it is worth trying some form of A / B testing with your visitors.
If for example you have 100 visitors a month to your website it is difficult to get statistically reliable results. At the end of the day you are working with small numbers. So when it displays 50 who see Button A and 50 who see Button B, it is not a large quantitative measure.
But if you have a few thousand visitors, then you should definitely implement Conversion Optimisation. With free tools available that is relatively easy to implement and can be connected and set-up with little effort.
However, one should be clear that the Conversion optimisation is an ongoing process that will help you get the most value from your hard work.
A / B tests use
An A / B test, also called a split test, works quite simply. You simply create 2 versions of a site-element and test them to see which works better.
This can, for example, be a different style and type of order button or even a sales page with or without video. You could also simply change the test on a button.
It is important that you make only one change. For example, if you changed a number of elements such as the button, the text, image and other elements, you do not know in the end what is responsible for any change be it better or worse.
Only if you change one element can you say that is exactly what made the difference.
In addition, you have to compare the two versions in parallel and not in succession. That is because the results can get distorted by seasonal effects.
Step by step example of Optimizely.com
You can perform such A / B testing without external tools. So you could create 2 sales pages for the product and create a different element on each page. These sites would then alternately show the visitors the two versions of your website page.
These could then be measured with a statistics tool and you could then compare the clicks and conversion rate of the two variants. However, this is costly and can be difficult to do.
For a simpler implementation however there are certain special conversion optimisation tools, such as optimizely.com.
With this online service you can register for free and conduct an experiment at no cost. That’s enough for most at first.
After logging in, click on “new experiment”, give this a name and enter the URL of the optimised page.
Then you create a new variant, where you change an element of the page. The nice thing is at optimizely.com this change can be made directly into the tool. This saves you having to change the website code.
You can also create multiple versions with a change. When starting out leave it with 2 variants.
Now you have to specify one goal. It’s about what the user should do. This can, for example, Be the “Thank you for shopping” page store, the registration confirmation page for a newsletter, or which button type gets more clicks.
Anyone who wants to improve the affiliate revenue, can not specify the store site of the partner programme as a target in a rule, since you can not install the optimisely tracking code there.
Instead, you can “only” count clicks on an affiliate link, but that is a good optimisation goal and sufficient for most needs.
Now you need some patience until enough data is gathered to assess which option worked better. When you see the best variant then you can start using that on your website. Once that has been completed then start a new experiment in which you test something else again.
Warning: It is necessary to incorporate a reference to the Optimizely cookie in the privacy statements of its own Web site and deliver an OptOut link.
It sounds easy to make the decision between 2 variants. In practice, however, this is often not so trivial. Not only that, the results of different variants are often quite close to each other, so they are not always reliable.
Statistics indicate the reliability of such data as “significance” which can be measured on a scale of between 0 and 100%.
The higher the significance fails, the more reliable is the result. 95% significance means that there is a 5% chance that the result is coincidence.
So it makes little sense to opt for a variant, if there is only a 10% significance. You should experiment a long run until it has at least 90-95% significance. Optimizely.com brings this analysis so you can also see the same significance in the results of the individual variants.
A / B tests can be very useful in order to increase revenues, and to help to optimise other goals. However, you should use a professional tool and only change when there are reliable results.
Conversion optimisation is an ongoing task.