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E-commerce Link: Commit to True Online Testing

How to use A/B testing to improve online conversion

March 2007 By Jeffrey Eisenberg


Having squabbles in the conference room about which checkout button is best? Or which product image to feature? Or is the debate about two different offers? Maybe it’s the lifestyle image, or possibly the header copy on your landing page?

Instead of wasting time and resources guessing about things that used to seem subjective, just commit your campaigns to true online testing and optimization. When properly conducted, A/B testing is a proven method for increasing conversion.

In my experience with our clients, I hear the same two excuses that keep them from testing. The first is cost; the client just doesn’t have the money. The second is education; many just don’t know where to start.

If you’ve been holding off on testing due to cost, last October Google launched its Website Optimizer, a Web-based service similar to Optimost and Offermatica, that assists marketers
in conducting efficient A/B and multi-
variate testing. Since this tool is free, the cost excuse no longer holds water.

Knowing how to start testing is a legitimate concern. We have identified more than 1,100 factors that contribute to the successful completion of a single conversion funnel. Multiply this by the number of campaigns, offers, customer motivators and visitor types, and the volume of factors easily climbs into the tens of thousands. And when you consider that each factor is a potential variant to test, just getting started can seem a bit daunting.

The good news is that not all factors are equal in their impact on conversion, and with a little insight and planning you can maximize your testing.



How to Test



Now, let’s talk about some practical tips you can use to see results sooner rather than later.

First, it helps to know a little about the science behind testing. Let’s say you want to determine whether Nolan Ryan is a better baseball player than Homer Simpson. How should you proceed? First, you might set a metric for what you mean by a “better” baseball player. You can measure evidence in concrete ways, noting the two subjects’ different batting averages or RBIs. You’re searching for a formula that will lead you to a correct decision. Such a formula is called a “fitness function.”

In virtually all such measures, Ryan is the better candidate. If you were choosing a player for your team, you’d certainly pick Ryan.
 

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