Data driven decision-making is only effective if, in the end, you do make a decision. As we have reviewed, even when you have a wealth of data it can be challenging to make a difficult decision when you have a number of different options to choose between.
But, what if you didn’t have to choose? What if you could choose all your options?
A/B Testing is a method of testing different options at the same time in the real world so that you can choose the one that performs best. It’s name comes from the idea of segmenting your customers into two groups, Group A and Group B, and testing a change to one group (the experimental group) and not another (the control group) and measuring the difference. No more guessing, you can see which of your options performs the best in reality, with actual customers in real situations. A/B Testing is used in a variety of industries and applications ranging from product design to marketing.
For example, if you have two different pricing plans and you are curious which one will generate the most revenue, you can use A/B Testing to test both plans with real customers and see which group generates the most revenue by the end of the test. Similarly, if you are curious how different marketing messages might convert inbound leads, you can A/B Test many messages to see which has the highest conversion rate with actual leads.
If it sounds too good to be true, that’s because there is a lot of complexity hiding in the details. A/B Testing has many traps and challenges you will need to overcome to use it effectively, all of which we will cover this week!
If you do use A/B Testing as part of your product or service development, you should likely use existing tools that automate much of the complexity. However, by understanding how it works and the mistakes you can make along the way, I hope you’ll be an expert in using those tools!
Quote of the Day: “I find it more comforting to believe that this isn’t simply a test.” – Dr. House (TV), Three Stories