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Paras Chopra is founder of Visual Website Optimizer, a simple A/B split and multivariate testing tool. Used by 5000+ companies worldwide, it allows marketers and designers to create A/B tests and make them live on website in less than 10 minutes.

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Multivariate Testing 101: A Scientific Method Of Optimizing Design

In a previous article on Smashing Magazine, I described A/B testing and various resources related to it. I have also covered the basics of multivariate testing in the past, yet in this post I’ll go deeper in the technical details of multivariate testing which is similar to A/B testing but with crucial differences.

In a multivariate test, a Web page is treated as a combination of elements (including headlines, images, buttons and text) that affect the conversion rate. Essentially, you decompose a Web page into distinct units and create variations of those units. For example, if your page is composed of a headline, an image and accompanying text, then you would create variations for each of them. To illustrate the example, let’s assume you make the following variations.

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Multivariate Testing in Action: Five Simple Steps to Increase Conversion Rates

The attention span on the Web has been decreasing ever since Google had arrived and changed the rules of the game. Now with millions of results available on any topic imaginable, the window to grab a visitor's attention has decreased significantly (in 2002, the BBC reported it is about 9 seconds). Picture yourself browsing the Web: do you go out of your way to read the text, look at all the graphics, and try to thoroughly understand what the page is about? The answer is most likely to be a straight "no." With bombardment of information from all around, we have become spoiled kids, not paying enough attention to what a Web page wants to tell us.

A/B testing example

We make snap decisions on whether to engage with a website based on whatever we can make out in the first few (milli)seconds. The responsibility for making a good first impression lies with designers and website owners. Given that the window of opportunity to persuade a visitor is really small, most designs (probably including yours) do a sub-optimal job because the designer in you thinks in terms of aesthetics. However, most websites do not exist just to impress visitors. Most websites exist to make a sale. Whether it is to get visitors to subscribe to the blog feed, or to download a trial, every website ultimately exists to make a sale of some kind.

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In Defense Of A/B Testing

Recently, A/B testing has come under (unjust) criticism from different circles on the Internet. Even though this criticism contains some relevant points, the basic argument against A/B testing is flawed. It seems to confuse the A/B testing methodology with a specific implementation of it (e.g. testing red vs. green buttons and other trivial tests). Let’s look at different criticisms that have surfaced on the Web recently and see why they are unfounded.

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Jason Cohen, in his post titled Out of the Cesspool and Into the Sewer: A/B Testing Trap, argues that A/B testing produces the local minimum, while the goal should be to get to the global minimum. For those who don’t understand the difference between the local and global minimum (or maxima), think of the conversion rate as a function of different elements on your page. It’s like a region in space where every point represents a variation of your page; the lower a point is in space, the better it is.

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The Ultimate Guide To A/B Testing

A/B testing isn’t a buzz term. A lot of savvy marketers and designs are using it right now to gain insight into visitor behavior and to increase conversion rate. And yet A/B testing is still not as common as such Internet marketing subjects as SEO, Web analytics and usability. People just aren’t as aware of it. They don’t completely understand what it is or how it could benefit them or how they should use it. This article is meant to be the best guide you will ever need for A/B testing.

A/B testing example

At its core, A/B testing is exactly what it sounds like: you have two versions of an element (A and B) and a metric that defines success. To determine which version is better, you subject both versions to experimentation simultaneously. In the end, you measure which version was more successful and select that version for real-world use.

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