Three Reasons Why A/B Testing Is A Winning Strategy For Businesses

Jan 26, 2021
Matthias Mueller

Would you rather make an important business decision based on your intuition or based on data? For a long time, leadership intuition and expertise was the leading driver for companies when executive decisions were made. However, businesses are increasingly relying on the data to make better, informed decisions. One method they are using is A/B testing. Here are three reasons why A/B testing is a winning strategy for businesses.

1. Data outperforms intuition

Research from leading consulting firms like Deloitte and McKinsey show that data-driven companies vastly outperform those that rely on intuition to make decisions. Thus, companies are increasingly shifting to look at analytics rather than basing their judgment on subjective opinion. This is where A/B testing comes in: A/B testing, sometimes referred to as split testing, is the process of running a controlled experiment to compare two groups ("A" and "B"). With the advent of the digital age, A/B testing is now inherently woven into the fabric of virtually any business vertical: In pharmaceutical and health, A/B tests might be used to find the efficacy of a certain drug by giving one group of subjects an experimental drug, while the other group would be given a placebo. In marketing, two different website variants could be tested against one another to see which one of them performs the best in terms of generating new business leads.

2. Minor changes may lead to major gains

Top-tier companies like tech giant Google have figured out that forcing data-driven decision-making is a winning recipe in the long run. As the New York Times reports, Google once tested 41 different shades of blue to find the most user-friendly solution to their users. At first glance, this sounds frivolous, but not so much when we learn that this small experiment is linked to $200m in additional revenue. The opposite may also be true: a seemingly small change implemented by a given company may have disastrous consequences that could have been avoided through A/B testing.

3. It's easy to do!

A/B testing can be understood and implemented by anyone. While it is true that A/B testing relies on statistical concepts, there is no need to have majored in statistics or applied mathematics to run an A/B test, and the fundamentals of the math behind most A/B testing can be taught relatively easily. 

So what are you waiting for? What business decisions are you making that you can apply A/B testing to?

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Topics: Learning, Analytics + Data Science, Technology Insights