Is your website design effective? Does it provide a good user experience, and thus, increase leads and conversions for your business?

If you’re not sure, it’s time to do some testing. Such testing is usually done in an A/B format. But now, there is a better way to swiftly test far more options with the cost of designing them all.

Hence we look into Randomization Test! (based on random forest algorithm)

Why Test Randomization in Your Website?

Studies have shown testing your website design can result in vast improvements in conversion rates. According to SteelHouse, using correct targeting and testing methods can increase conversion rates up to 300 percent.

However, about 61 percent of companies carry out fewer than five tests each month. Furthermore, companies spend up to $92 per person to bring customers to their site, but only $1 to convert them. Why work so hard to bring in traffic if those people are just going to bounce?

why to test your website

Approaches to Improving a Website

Typically, website improvements are made using one (or both) of two approaches: CRO and AB.

A/B testing strategy, also called split testing, is comparing two different versions of a website (or anything) to see which one performs better. For best results, you change one aspect of the site at a time. You might change the color of the call-to-action button from blue to green. Each page is served randomly to visitors, and you compare the two to see which is performing better by your chosen metric, such as filling out the contact form. Once a large enough number of tests have occurred, the final design for the website is taken from one of the A/B testing tools.

For example, one client discovered that by changing the buttons to hyperlinks, they experienced an increase in customer purchases. This was purely by mistake after a developer made a hyperlink instead of a button by accident.

While you will often only see a slight difference — up or down — a series of such changes that each increases conversion will make your site stronger overall. On an interesting note, it is often the version that is less visually pleasing. (Note: You may also hear about A/A testing, which is a test to make sure your A/B testing tools are operating smoothly.)

CRO is Conversion Rate Optimization Testing. The term is actually an umbrella to cover many things marketers might do to optimize campaigns. This process uses analytics and user feedback to improve the performance of your website. If you don’t have enough traffic to conduct A/B testing, you might still use several tools to optimize your website. You might make small changes in the wording of your call-to-action or placement of those buttons. You can make such changes without testing them against another version. You might use surveys, focus groups, or other such tests to improve CRO.

Some make the argument that CRO testing tools are better than A/B testing, but there is another way.

CRO vs AB testing

Randomization Testing

Even with dozens of A/B tests per month, there may be a version of the website that would be much more effective than the ones tested in the AB test. After all, there are hundreds or even thousands of different potential versions of any website. Everything from the colors, the arrangement of elements, text, photos, button placement, and more can change.

One can add more versions of the test for a potentially better result, but this comes with problems, too. The cost of making multiple designs is expensive. Also, one must have an even larger amount of targeted web traffic to make the tests viable.

However, with randomization (random forest algorithm), we can build just a few potential pages. Then, we run calculations on the potential for a page based on the results of actual A/B tests. Using programming, we can establish a pattern during thousands or millions of visits. We now have the most effective parts of each design and can then determine if making X change would help or hurt the goal.

This approach is used in many fields, including medicine and banking. In e-commerce, randomization can predict the likelihood of someone liking a recommend products based on what similar customers have liked. For businesses seeking to improve their websites, this randomization testing statistics approach requires far less time and money than long-term CRO and covers more ground than A/B tests.

Talk to us to start seeing your higher conversion rates.


Pete Peranzo

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