What is A/B testing?
We live in an era of data-driven marketing, far away from the times when marketers simply made decisions based on guesswork and intuition and hoped for favorable outcomes. The modern day marketer has a scientific approach and relies on data. And A/B testing is the best way to remove uncertainty and gut feel when making marketing or design decisions for websites, ads or other digital campaigns.
Embrace the experimentation mindset.
By basing your strategy on data and A/B tests, you’ll be agile but most importantly you’ll have guaranteed feedback on what works and what doesn’t. You’ll be better placed to make sound business decisions and to invest time and money in what your visitors actually want.
A/B testing to drive marketing success
75% of websites with more than 1 million monthly visitors already run A/B testing programs.
But successful A/B testing requires preparation and education, and time and effort to put into practice. You’ll have to create a process, put a framework in place, learn about statistics, set up and learn a new tool and make sure you’re actually getting accurate results. But the effort and time is worth it, given the potential to achieve your marketing goals.
To help we’ve brought together in-depth content on A/B testing, from the best blogs and experts.
What is A/B testing (or Experimentation/Split testing)?
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 two variations are on the same URL. Alternatively with split URL testing your changed variation is on a different URL (although this is hidden from your visitors).
What about multivariate testing (MVT)?
Sometimes, you want to test several changes on a page, for example the banner, header, description and video. To test all of these elements at the same time, you use multivariate testing (or MVT).
In this case you have multiple variants 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 an enormous amount of traffic to be statistically accurate. Before starting a multivariate testing project, you need to check that your audience is high enough to provide representative results.
Dynamic traffic allocation or multi-armed bandit testing
Multi-armed bandit testing (or dynamic traffic allocation) is when your algorithm automatically and gradually redirects your audience toward the winning variation.
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.
Introductory resources on A/B testing to help you get started
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 it’s not all, here is a couple of other benefits:
- Learn deeply about your audience with every test: what they like, how they react, their needs and habits.
- Remove gut decisions from your marketing strategy by adopting an experimentation culture and testing everything.
- Focus your time and money on what your visitors respond to best, thanks to the learnings of your A/B Tests.
And to give you more operational examples of questions you’ll be able to answer with A/B Testing:
- Which elements drive sales, conversions or impact user behavior
- Should you have long or short forms
- Should you implement this new feature
- Which title for your article generates more shares
- Which steps of your conversion funnels are underperforming
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
Having lots of traffic is cool—admittedly, but you need to do something with it. That’s where CRO is so valuable.
How to optimize your conversion rate with A/B testing?
- Why experimentation is at the heart of success in digital marketing
- The CRO Best practices
- The Beginner’s Guide to Conversion Rate Optimization (ConversionXL)
- Building an optimization culture for conversion ROI
- The Definitive How-To Guide For Conversion Rate Optimization
- The Advanced Guide to Form Conversion Optimization
- Widerfunnel’s case studies
How to do A/B Testing: frameworks and methodology
Measure, study, analyze you website data. Identify the problems and opportunities.
Formulate hypothesis (great way to formulate an hypothesis by Craig Sullivan)
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.
Test your highest priority hypothesis.
Analyze your test results and learn from them
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.
Best practices to help you win
- Highlighting the link between digital experimentation and growth
- Is Web Personalization an extension of A/B testing?
- Why testing and personalization are key to meeting changing consumer needs during COVID
- How to create an optimization culture
- Multivariate testing: save time and refine your analysis
- Dynamic traffic allocation: optimize your A/B tests
- How Intelligent Tracking Prevention (ITP) impacts A/B testing - and how marketers can overcome its restrictions
Note: We have a monthly newsletter with in-depth content A/B testing and CRO, you’re welcome to subscribe here.
A/B testing mistakes
(false data will make you lose money, careful)
- [INFOGRAPHIC] 19 Ways A/B Testing Is Ruining Your Site (And How To Fix It)
- Should You Run an A/A test?
- Why Your Brain Is Your Worst Enemy When A/B Testing
- Are You Misinterpreting Your A/B Tests Results?
- Warning! Is the world sabotaging your A/B Tests?
- Are You Stopping Your A/B Tests Too Early?
- 7 Mistakes Most Beginners Make When A/B Testing
- What is the flicker effect and how can you get rid of it in your A/B testing?
- 11 ways to stop FOOC’ing up your A/B tests
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
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.
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 making the right decision. 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.
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 skills & 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.
Sharpen your skills for better A/B tests
What A/B Testing blogs should you read?
86 A/B Testing experts to follow
Lance Jones > @userhue
Jason Kincaid > @jasonkincaid
Noah Kagan > @noahkagan
Hiten Shah > @hnshah
Dave McClure > @davemcclure
Avinash Kaushik > @avinash
Daniel Gonzalez > @HiDanielG
David Kirkpatrick > @davidkonline
Shanelle Mullin > @shanelle_mullin
Steve Blank > @sgblank
Matt McGee > @mattmcgee
Rand Fishkin > @randfish
Bart Schutz > @BartS
Rick Perreault > @rickperreault
Sean Ellis > @SeanEllis
Campaign Monitor > @CampaignMonitor
Moz > @Moz
Bryan Eisenberg > @TheGrok
Shopify > @Shopify
Scott Brinker > @chiefmartec
Chris Goward > @chrisgoward
Brian Massey > @bmassey
Jeffrey Eisenberg > @JeffreyGroks
Sherice Jacob > @sherice
Carlos del Rio > @inflatemouse
Pam Moore > @PamMktgNut
Angie Schottmuller > @aschottmuller
Ryan Deiss > @ryandeiss
Ian Lurie > @portentint
ashukairy > @ayat
Khalid Saleh > @khalidh
Anne Holland > @AnneHolland55
Lincoln Murphy > @lincolnmurphy
Amy Africa > @amyafrica
Unbounce > @unbounce
Raven Tools > @RavenTools
Roger Dooley > @rogerdooley
Neil Patel > @neilpatel
Nichole Elizabeth > @NikkiElizDemere
Craig Sullivan > @OptimiseOrDie
Peep Laja > @peeplaja
Jon Henshaw > @RavenJon
Marketing Nutz > @MktgNutz
Dan Siroker > @dsiroker
Tommy Walker > @tommyismyname
Joanna Wiebe > @copyhackers
Rich Page > @richpage
Tiffany Da Silva > @bellastone
Jason Quey > @jdquey
Ton Wesseling > @tonw
Michael Aagaard > @ContentVerve
Matt Gershoff > @mgershoff
Andy Johns > @ibringtraffic
Brian Balfour > @bbalfour
Oli Gardner > @oligardner
Tim Ash > @tim_ash
Paul Rouke > @paulrouke
Linda Bustos > @edgacentlinda
Theo van der Zee > @theovdzee
MAA1 > @MAA1
Talia Wolf > @TaliaGw
Justin Rondeau > @Jtrondeau
Tyson Quick > @TysonQuick
KlientBoost > @KlientBoost
Andre Morys > @morys
Conversion.com > @conversion_com
Anna Talerico > @annatalerico
Kelly Cutler > @kfcutler
Brooks Bell > @brooksbell
Andrew Youderian > @youderian
Alhan Keser > @AlhanKeser
Conversion Sciences > @ConversionSci
Alex Birkett > @iamalexbirkett
Kaitlyn Nelson > @kaitlynelson
Kevin Hillstrom > @minethatdata
Dan Wang > @danwwang
Malachi Leopold > @livethetreplife
Aaron Orendorff > @iconiContent
Chief Conversionista > @Conversionista
Joel Harvey > @JoelJHarvey
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.