What is A/B testing

Everything you'd need to know, gathered in one place

Introduction

We live in times of data-driven marketing. Lost are the times where marketing is exclusively throwing money at things and hope for a favorable outcome. The modern day marketer has a scientific approach and relies on data. And the best way to remove uncertainty and guts to all marketing or design decisions for websites, ads—anything online actually, is A/B Testing..

Embrace the experimentation mindset.

By basing your strategy on data, tests, you’ll be agile but most importantly you’ll have hard cold feedback on what works and what doesn’t. You’ll be better at making sound business decisions and invest time and money in what your visitors actually want.

A/B Testing as a weapon of mass(ive) marketing success

75% of websites with more than 1 million monthly visitors already do A/B Testing. Maybe it’s time for you too to remove risks from your marketing decisions.

But A/B testing needs work to get into, and more work to practice. You’ll have to create a process, put a framework in place, learn a bit about statistics, set up and learn a new tool, make sure you’re actually getting accurate results, … But with great potential, come great (time) investment—or so Uncle Ben didn’t say..

But fear not, you can get everything you’d need here, with the best content on the topic, from the best blogs and experts.

What is A/B Testing?

Definition: A/B testing is an online experiment conducted on a website, mobile application or ads (among other things), to test potential improvements in comparison to a control (or original) version. Put simply, it it allows you to see which version works better for your audience based on a statistical analysis.



A/B Testing is also known as split testing, which can be either exactly the same thing as A/B testing OR mean split URL testing. For a classic A/B Test, the 2 variations are on the same URL, whereas split URL testing where your changed variation is on a different URL (your visitor doesn’t see the difference of course).


What about multivariate (MVT)?

Sometimes, you want to test several changes on a page, let’s say the banner, header, description and video. To test all of these at the same time, you would do what is called multivariate testing (or MVT).

You would have variations generated to test all the different combinations of these changes to determine the best one.

The big downside of Multivariate testing is that it requires gigantic amount of traffic. If you want more details check this Hubspot’s article on the differences between MVT and A/B Tests.

Example with a banner and a picture
(4 variations, control version not pictured)


Bandit testing or multi-armed bandit testing

Multi-armed bandit testing is when your algorithm automatically and gradually redirect your audience toward the winning variation..

To learn more about multi-armed bandit testing, the best article by far is by Alex Birkett from ConversionXL.


A/B/n testing is when testing more 2 variations of an element or page. You could test 6 versions of a page and do an A/B/C/D/E/F test.




What are the benefits of A/B Testing?

Why should YOU do A/B Testing? Or a better question is: Are you satisfied with the way you’re exploiting your hard-earned traffic? Once you have traffic, increasing your conversions is much less expensive with great potential ROI. And with A/B Testing, it’s even greater. But that's not all, here are a couple of other benefits:

And to give you more operational examples of questions you’ll be able to answer with A/B Testing:


How does A/B Testing work

You compare the current version (control) of a page/element against a (or more) variation of it with the changes you want to test (website page, element in a page, a CTA, picture, …).


You divide your traffic in equal portions, then they are randomly exposed to one or the other variation during a given period of time. Then, their performances (conversions, sales, …) are compared and analyzed to determine if the change(s) are worth implementing.


Getting deeper in the inner workings of A/B testing

Conversion Rate Optimization is key for your success with A/B Testing

What’s Conversion Rate Optimization (or CRO)?

It’s the process of improving your website to increase the rate at which your visitors perform the action(s) you want them to accomplish.


It’s usually either buy something or “convert” and give you their contact info. In other words, get your visitors through the buyer’s journey.

Having lots of traffic is cool—admittedly, but you need to do something with it. That’s where CRO is so valuable.


Best Conversion Rate Optimization Guides out there

How to do A/B Testing:frameworks and methodology

The success of great A/B testing is the process. It’s a science experiment, so your process has to be rigorous, with strong prioritization to focus on the most valuable tests.

Each company has its unique process but it usually resembles something like this:

  1. 1

    Measure, study, analyze you website data. Identify the problems and opportunities.

  2. 2

    Formulate hypothesis (great way to formulate an hypothesis by Craig Sullivan)

  3. 3

    Prioritize your tests ideas: one of the most used prioritization framework is PIE first coined by Widerfunnel.

    With this framework, you rank your tests ideas on 3 criteria to determine which ones you should run first:
    Potential ./10: How much room for improvement is there on this(these) page(s)?
    Impact ./10: How valuable is the traffic on this(these) page(s)?
    Ease (of implementation) ./10: How easy will this test be to implement on your site?

    You then average the 3 and you’ll know which tests to do first. There are of course several other frameworks, try them out and make them your own.

  4. 4

    Test your highest priority hypothesis.

  5. 5

    Analyze your test results and learn from them

  6. 6

    Communicate

  7. 7

    Repeat


Great A/B testing processes and frameworks

What to A/B test: ideas by the dozen

You can basically test everything on your website:


But sometimes, you need inspiration. So here are dozens of A/B testing ideas for you.

Note : Before we let you dive in these listicles, a small disclaimer: what worked for others might not work for you. Don’t blindly apply what you read there, make sure to analyze thoroughly if it’s relevant for you and how (if) you can adapt for your business.


Dozens of A/B tests ideas for you to get inspired

A/B best practices and … mistakes

A/B testing can be hard … and easy to mess up at the same time. So it’s good to be aware of what could go wrong and have safeguards in place. Thankfully, people have gotten into lengths on both these topics.

Make sure you set up for success by studying best practices and possible mistakes. But like with tests ideas, don’t take everything at face value. Put things to the test, see if it applies for your business.


Books to go even deeper

There aren’t many books (or ebooks) on A/B testing, but here are a couple you can quench your thirst for knowledge with.


A/B Testing reporting & results

A/B testing is about making data-driven decisions AND learning. Meaning your reporting and results have the outmost importance. Be it to extract the lessons, communicate with your colleagues or get ideas for your next tests.


How to handle A/B testing results and reporting

Dive deep in A/B Testing statistics (or don’t, it’s scary down there)

A/B testing is based on statistical methods. You don’t need to know all the maths behind, but a little brush up in statistics won’t hurt and certainly improve the chances of your success.

There are 2 main statistical methods behind A/B testing solutions. There aren’t one better than the other, they just have different use. Here is how we handle it with Kameleoon’s statistical engine.

Frequentist approach allows a simple read on the results reliability thanks to a confidence level: with a level of 95% or more, you have a 95% chance of obtaining the same result should you reproduce the experiment in the same conditions. But this method has a downside: it has a « fixed horizon », meaning the confidence level has no value up until the end of the test.

Bayesian approach provides a result probability as soon as the test starts. No need to wait until the end of the test to spot a trend and interpret the data. But this method also has prerequisites: you need to know how to read the confidence interval given to the estimations during the test. With every additional conversion, the trust in the probability of a reliable winning variant improves.


A/B testing statistics decrypted

A/B testing skills and management

To put all odds in your favor, there are a number of skills and management tips you can polish. Web analytics, UX design, communicating results, are some examples.


A/B Testing tools to maximize your chances of success

Be it project management, sample size or duration calculator, or toolkits for your process, there are many tools to help you win.


What A/B Testing blogs should you read?

There are some awesome blogs with stellar A/B testing or Conversion Rate Optimization related content you can follow to learn, get inspired, and do better A/B Testing.


86 A/B Testing experts to follow

You might want to keep yourself in the loop as the CRO world moves quite fast. Best way to do that is to follow the most prominent experts. Here are their handle on twitter, and a list so you can follow all of them (and us of course @kameleoonrocks)


Twitter list:

https://twitter.com/AlarconJB/lists/cro-experts


Individual Twitter handles


Lance Jones

Jason Kincaid

Noah Kagan

Hiten Shah

Dave McClure

Avinash Kaushik

Daniel Gonzalez

David Kirkpatrick

Shanelle Mullin

Steve Blank

Matt McGee

Rand Fishkin

Bart Schutz

Rick Perreault

Sean Ellis

Campaign Monitor

Moz

Bryan Eisenberg

Shopify

Scott Brinker

Chris Goward

Brian Massey

Jeffrey Eisenberg

Sherice Jacob

Carlos del Rio

Pam Moore

Angie Schottmuller

Ryan Deiss

Ian Lurie

ashukairy

Khalid Saleh

Anne Holland

Lincoln Murphy

Amy Africa

Unbounce

Raven Tools

Roger Dooley

Neil Patel

Nichole Elizabeth

Craig Sullivan

Peep Laja

Jon Henshaw

Marketing Nutz

Dan Siroker

Tommy Walker

John Teevan

Joanna Wiebe

Rich Page

Tiffany Da Silva

Jason Quey

Ton Wesseling

Adam Hutchinson

Michael Aagaard

Matt Gershoff

Andy Johns

Brian Balfour

Oli Gardner

Tim Ash

Paul Rouke

Linda Bustos

Theo van der Zee

Get Elastic

Conversion Conference

MAA1

Talia Wolf

Justin Rondeau

Tyson Quick

KlientBoost

Andre Morys

Conversion.com

Anna Talerico

Kelly Cutler

Brooks Bell

Andrew Youderian

Alhan Keser

Conversion Sciences

Alex Birkett

Steven Jacobs

Kaitlyn Nelson

Kevin Hillstrom

Dan Wang

Malachi Leopold

Pete Koomen

Aaron Orendorff

Chief Conversionista

Joel Harvey

Jean-Baptiste Alarcon

Clément René

Outsource your A/B Testing with these agencies

Outsourcing your A/B testing can be a great way to still do it without the necessary resources. Here are some of the best ones out there.

Note: we sell an A/B testing tool but we also can handle your entire testing, check out our customer success page


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