Introduction:
A/B testing is one of the most valuable tools in a marketer's toolkit. By comparing two variants of a web page, ad creative, email subject line, or other asset, A/B testing enables you to determine which performs better. However, many businesses fail to utilize A/B testing to its full potential due to common mistakes. Avoiding these pitfalls is key to running effective split tests and extracting meaningful insights.
In this article, we will explore the top 10 A/B testing mistakes that marketers make along with how to avoid them. By sidestepping these errors, you will be able to set up properly-designed experiments, draw accurate conclusions from your tests, and implement impactful changes based on the data. Read on to learn how you can improve your A/B testing approach and take your optimization strategy to the next level.
1. Not Having Enough Traffic
One of the biggest mistakes in A/B testing is running an experiment without enough traffic to produce statistically significant results. For your test data to be meaningful, your test needs sufficient users in each variation.
As a general rule of thumb, you need at least 1,000 unique visits per variation to achieve significance. So a test with the original A version and a new B version would need at least 2,000 total visits. The more visits, the more statistical power your test will have.
Be sure to use a statistical significance calculator to determine the minimum sample size and traffic volume you need for your test. Input factors like your current conversion rate and minimum effect you want to detect. This will tell you how much traffic is required.
Getting this wrong means your test essentially provides no usable data, since any results would be meaningless. Make sure you have the visitor volumes needed before launching any test.
2. Changing Multiple Variables
When structuring your A/B test, stick to testing just one element of your page at a time. For example, test just the headline, a button color, image, or body text. Don't test multiple variations simultaneously.
Testing more than one change at once makes it impossible to determine which variable impacted the results you see. If you test a new headline, button, and image together and see a difference, you won't know which one changed user behavior or if it was a combination.
Isolating each element in its own test provides clear data on how that individual change performs. You'll know exactly what worked and what didn't. Testing multiple elements together provides unclear, unreliable data.
3. Ending the Test Too Early
One of the most common A/B testing mistakes is stopping a test prematurely before collecting enough data over an adequate time period. Tests should run for a minimum of 1-2 weeks in most cases.
Don't make the mistake of ending a test early just because one variation leaps ahead initially. Early trends and results can be misleading as more data comes in over the full test duration. A version that looks better at first may end up underperforming given more time.
For example, a new version B might get a spike in conversions early on from people excited to try something fresh and different. But that novelty may wear off over time. The original version A could pull ahead in the end. Ending the test at say 3 days because B appeared better would lead to the wrong conclusion.
Let your experiment run its full course to account for variance over time. Early test results especially can fluctuate wildly with limited data. A proper, full-length test provides accurate, statistically significant findings.
4. Not Having a Hypothesis
Before launching any A/B test, you should start by forming a hypothesis around what you intend to test. Your hypothesis states why you believe a new variation B might outperform the original version A.
Having a clear hypothesis serves two purposes:
1) It focuses your experiment around a testable idea of why the new version will cause better outcomes.
2) It provides a framework to analyze your results and understand why you saw the outcomes you did.
A hypothesis could be as simple as "Version B will increase conversions by 15% because the brighter call-to-action button grabs more user attention."
Starting your test with a sound hypothesis results in higher quality experiments and learnings. Don't skip this important step.
5. Testing Too Many Variants
When setting up your A/B test, resist the temptation to test more than two variants at once. The standard approach is to test the original version (A) against one new variation (B). Comparing just A and B produces clear, direct data on how that one change you introduce performs.
Adding multiple variants like A vs B vs C vs D muddies the data and makes analysis much less clear. With multiple versions, you can't isolate the impact of each change. Any difference you see could be attributed to any one of the changes.
Keep it simple and focus on just two variants: the original and your new proposed change. This singular change could be a new headline, image, body text, etc. Limiting to A vs B makes it easy to analyze what worked and what didn't.
The only time more than two variants makes sense is in multivariate testing (MVT). MVT tests combinations of changes simultaneously to identify the optimal mixture. But this requires huge amounts of traffic and is complex to analyze. Most tests should stick to a simple A vs B approach.
6. Not Tracking the Right Metrics
A critical mistake is not defining upfront what metrics you will use to determine whether your A/B test succeeded or failed. You need to carefully choose what metrics to track that will clearly indicate if your new version B meets your goals.
For example, if your test goal is to boost conversions, ensure you are tracking overall conversion rate as your key metric. If the goal is reducing bounce rate, measure that. Define exactly what a "win" looks like before ever launching your test.
Additionally, look at secondary metrics like click-through-rate on buttons and time on page. These can provide insights into why you see the results you do on your primary conversion metric. Tracking the right metrics is key to both identifying the winning version and understanding the reason behind it.
7. Running Tests Without Enough Conversions
If your website overall gets low traffic and visitor volumes, your A/B test may fail to generate enough conversions to produce statistically relevant data. The lower your conversion rates, the more visitors you need.
For example, say your site gets 1,000 visits per week but only converts 1% of those. That's just 10 conversions per week. Testing with so few conversions will make it extremely difficult to see any statistically significant differences between A and B variants.
Before running a test, consider the number of conversions you realistically expect to occur during the test. Use a significance calculator to determine the minimum conversions needed for a valid test.
If the conversions will be too low, you'll need to increase traffic first before you can effectively test. Building more robust traffic sources takes time but is necessary if on-site conversions are low.
8. Not Checking for Sample Ratio Mismatch
A common A/B testing mistake is failing to check that visitors are evenly split between the A and B variants. Uneven test groups, known as sample ratio mismatch, can heavily skew your results.
Always monitor the traffic split during your test. It should follow the 50/50 or other intended ratio you set closely. Sample ratio mismatch happens when one variation receives excessive traffic through a technical glitch.
If the traffic split diverges significantly, it invalidates your test. The version receiving higher traffic has an unfair advantage in conversions. You must resolve the technical issue and re-run the test to get clean data free from sample ratio mismatch errors.
9. Ignoring Negative Results
One of the biggest mistakes in A/B testing is failing to analyze and learn from negative results where neither variant emerges as the clear winner. Just because no variant achieves your goal metric better doesn't mean the test was a failure.
Analyze why both the original and your new proposed version failed. Look at key metrics like click-through rates and scroll depth to understand how visitors engaged with each version.
These insights into user behavior are invaluable even if your hypothesis was wrong. Negative results prevent you from going down the wrong path and reveal flaws in your assumptions. Don't ignore them. Dig into the data and uncover learnings for future tests.
10. Not Creating a Testing Culture
The final common mistake is failing to obtain company-wide buy-in and build a culture that truly values experimentation and testing. A/B testing can't be treated as a one-off project.
To achieve long-term success, rigorous testing needs to be woven into company culture. Educate colleagues on what A/B testing is, its benefits, and the insights it can provide. Show them early small wins.
Promote an experimentation mindset across teams. Foster curiosity and the exchange of new ideas. Obtain leadership support to dedicate resources. Develop competency in analyzing data.
Building a thriving culture of testing takes work but pays off exponentially. It leads to better customer experiences, higher conversions, and data-driven decision making. Make it a priority.
Conclusion:
A/B testing provides invaluable insights, but only if done correctly. Steer clear of these 10 common mistakes and you will be well on your way to testing excellence. Focus on having clear goals, proper sample sizes, isolated variables, and the right tools. Analyze both winning and losing results. Foster a culture of experimentation in your team.
While the specifics of each company's testing program will differ, the fundamental best practices remain the same. Follow these guidelines to build a rigorous A/B testing process tailored to your business needs. Continually iterate and optimize based on learnings. With consistent, high-quality experimentation, you will be able to create standout customer experiences that maximize conversions.
