A/B Testing

What is A/B Testing?

A/B testing is a type of randomized experiment in which you change a single element on a web page (or in an email, paid ad, etc), and then split your traffic into two groups – showing one group Version A and the other Version B to see which of the two performs better.

TWILIO ENGAGE

A growth automation platform

Scale your growth strategy with a blend of automation, communications APIs, and real-time data.

How A/B testing works

An A/B test works by changing a single variable on a landing page, in an email, a paid ad, etc., and then splitting your audience so that one group sees the first version, and another group sees the second version. The goal is to understand if this single element has an impact on user behavior (e.g. conversion rates, click-through rate, and so on). 

Here’s a general framework for how to build an A/B test:

1. Define your hypothesis (i.e. what are you testing? Who is your target audience?) 

2. Determine the sample size you’ll need to reach statistical significance. Optimizely has a sample size calculator you can use to help figure this out. 

3. Choose the right metrics to measure the impact of your A/B test.

4. Prioritize data integrity. Ensure that the data you’re working with is clean, consolidated, and updated in real-time (CDPs can help automate this). 

5. Iterate on your findings!

4 benefits of A/B testing

Let’s break down a few of the benefits tied to A/B testing. 

1. Quickly test and iterate

With the right tools, an A/B test can be relatively quick to design to execute. This means teams can launch a test, analyze its findings, and iterate on their campaigns at a faster rate. In short, it helps businesses become more agile: adapting to consumers’ shifting expectations and preferences with the help of data-driven insights. 

Recommendations on how long an A/B test run range from a minimum of one week to at least 3–4 weeks, depending on your sample size. We dive deeper into statistical significance in A/B testing here. 

2. Base your decisions on solid data

Data-driven decision making is an absolute must by today’s standards. Organizations that leverage data to inform their strategies consistently outperform those that do not. 

One study found that data-driven businesses were 6% more profitable than their competitors (and 5% more productive). With the speed of digital acceleration, businesses need to prioritize data-driven strategies to avoid going off course, having a mismatch with consumers’ wants, and becoming irrelevant. 

Customer data platforms can be essential in your A/B testing strategy for this reason: they help protect the integrity of the data you collect.

3. Continuously improve campaigns and engagement

A/B testing helps to continuously improve campaign performance and customer engagement rates by showing what’s working and what isn’t. 

In other words, you can’t fix a problem, when you’re not sure what the problem is. 

With A/B testing, teams can dive deeper into what motivates their customers, and have concrete data to back up decision-making before making any permanent changes to their overarching strategy. 

Take WallMonkeys, which is a retailer known for its wall decals. Their team wanted to improve the customer experience, and decided to run a series of A/B tests. One test replaced a content slider on their homepage to make their search bar more prominent on their homepage. The result of this relatively minor change? A 550% increase in conversions. 

3. Boost ROI

A/B testing often helps businesses boost their ROI. 

A great example of this is with Norrøna, a leading retailer for outdoor clothing in Scandinavia. Norrøna’s product manager believed that algorithmic recommendations on their website would outperform manual ones (not to mention drastically decrease their workload). 

Using Twilio Segment and BigQuery, Norrøna built a recommendation engine to put this idea to the test: showing users similar products, rather than the manually suggested complementary products. And at the end of a 16-week A/B test, they found that the algorithmic version had 50% more conversions than the original.