Save Dozens of Hours You’d Spend Comparing A/B Testing Tools.

Businesses use A/B testing tools to implement A/B tests, split tests, multivariate tests, and full-funnel tests. The aim is to incrementally improve the performance of online assets by eliminating issues that keep traffic from converting (or taking the desired action).

Over the years A/B testing has gone from a nice-to-have that large enterprises invest in once they have exhausted all other marketing and growth channels, to a mainstay of small, agile teams that mitigate risks associated with unvalidated web design, copy, and even operational changes through intelligent testing.

Currently A/B testing tools are a way to eliminate the dreaded HiPPO (Highest Paid Person’s Opinion).

Done right, their results speak from a place of scientific rigour and hard facts. It is difficult to argue against a large sample size of ideal customers and their projected behavior simply because of intangibles like gut instinct and “experience”.

Most A/B testing tools are client-side. They require the user to place a snippet of JavaScript code on the online assets they want to experiment on. The calculations are done back-end and a winner is declared based on chosen significance and power settings.

Some A/B testing tools like – Convert Experiences – also give users the option to further “validate” these winners as a personalization by attempting to replicate the lifts seen during the experiment without hard coding the changes right away.

Yes, free A/B testing tools do exist.

While enterprise solutions can charge more than 100k USD annually, there are solutions like Google Optimize, Nelio, and Vanity that allow experimenters to find their feet and flex their testing muscles for free.

But there is no such thing as free lunch. If you wish to scale your testing program, and eliminate common issues that plague most A/B testing drives – like the dreaded Flicker of Original Content (FOOC) and slow, unresponsive support, you will need to opt for a paid tool.

We’ve written extensively about free A/B testing tools.

An A/B testing open source software solution is one that allows you – the optimizer – to be in complete control of the code that serves variations to traffic, collects results, and creates the reports.

You have the privilege of owning the experiments you hypothesize and the customer data you collect, across all the channels you wish to improve conversions for.

Most A/B testing software open source solutions lead with the promise of better security, more customizations, ease of deployment, and a robust API that delights developers looking to dig deeper into a platform and amp up its capabilities.

The most talked about open source A/B testing tools include – ABBA, SIXPACK, PROCTOR and WASABI.

You can also read our piece on open source A/B testing tools and what to do when you outgrow them.

So how do you perform an A/B test?

An A/B test costs you. Let’s be upfront about it.

1. A/B tests involve considerable resources. You need to pay for the A/B testing tool, you need to factor in payments made to agencies, or the time spent by your team members managing the program.

2. In an A/B test if the variant loses then over the test cycle you are actually converting site visitors at a poorer rate than if you had not conducted the experiment at all.

3. Sometimes A/B testing tools flicker. This flickering skews data because visitors get an inkling that they are being exposed to a test (or they are just inconvenienced by the flickering) and don’t behave as they would if they saw the variant without any sort of external interference. Moreover the flicker effect can also have an adverse impact on your Web Vitals and ultimately push your page down on the SERPs.

But there are ways to ensure that these issues do not derail your A/B testing program. And they have to be accounted for during test ideation and set-up.

• Understand the maturity of your testing needs. A/B testing makes sense when you want to keep learning from experiments and let the research and the data snowball into large conversion lift opportunities. A free A/B testing tool with capped features or visitor volume constraints may be tempting but it is not the way to kick-off a program that has aspirations to scale. There are many A/B testing solutions that are affordable and don’t lock you into exorbitant annual plans. Go for them.

• Gather data for a robust hypothesis. Data gathering can be qualitative (from heat maps and survey tools), or quantitative (from analytics solutions).
  Both have their place. Quantitative data points in the direction of metrics and KPIs that indicate a potential problem. Qualitative data then supplies the “WHY” to the “WHAT”. It is through qualitative data that you can begin to build a case for changes that can be validated through A/B testing. A hypothesis and its prioritization will make or break your experiment. We’ve created a hypothesis generator that guides you in the right direction.

• QA like your ROI depends on it. Cause it likely does. An A/B test should never be rolled out unless it has the best possible chance of encountering a sample of visitors who can interact with the Control and the Variant in the way it is intended, and without glitches. This may include checking variants for:
  
  
  • Display responsiveness
  • Flickering (FOOC)
  • Targeting accuracy
  • Goal firing accuracy

We have created a checklist that lets you QA your experiment for 30+ critical factors. Get a free copy.

YES. The answer is an emphatic yes.

If you are committed to an experimentation mindset.

According to Convert’s research, only 1 in 5 tests is conclusive. The rest don’t even reach a conclusion. Out of that 20%, some win and some lose.

So the proportion of winning tests is probably 1 in 10.

But each test teaches you something about what not to do. It can not only protect you from misguided decisions that are based on organizational power dynamics and peer pressure, it can also, over time, build your judgment and train you to identify big lift opportunities with greater alacrity. Mind you, we are not talking about opinions. We are talking about the maturity to interpret data better.

Most importantly, even tiny lifts accumulate over time to deliver large returns.

The ROI of a single A/B test may not be persuasive, but the ROI of an A/B testing program undertaken with diligence is exponential.

A/B testing is the flavor of the season.

Almost all aspects of Digital Marketing, from landing pages to ads to emails can benefit from it and solution providers have managed to incorporate basic A/B testing in their tools.

Email marketing vendors allow users to test out different subject lines, body copy, and links for metrics like open rate and click through rate. Most even stop sending the “losing” variant once it’s been identified.

Landing page apps like Unbounce also come with A/B testing that is undertaken for a key conversion action – like a form submission.

Even third party advertising platforms like Facebook and LinkedIn encourage testing.

While it’s good to keep in mind that the version you are launching with is likely not your best work, it also pays to remember that one against the other type scenarios are not robust A/B testing.

Sample sizes aren’t calculated for the desired Minimum Detectable Effect (MDE), significance and power settings are not specified, and there are no rules to prevent premature peeking.

Want to learn more about the statistics behind A/B testing? Try our free statistical significance calculator.