How to Do Effective A/B Testing of Landing Pages

There are a lot of “what ifs” when it comes to A/B testing of landing pages…

  • What if you make changes and your conversions plummet?
  • What if results don’t improve and you waste time and money?
  • What if you lose valuable traffic or ruin something that’s already working well?

Maybe you’ve heard some A/B testing horror stories – tests that weren’t set up properly and bad decisions made using dodgy data.

Nobody needs that kind of headache.

Or maybe you’ve tried A/B testing your landing pages, but it’s overwhelming, especially on the tech side – setting up tracking, ensuring the variations are showing correctly, and analyzing the results.

Wherever you’re at with A/B testing, the good news is – you can always get better.

All it takes is a tried and true strategy for setting it up and monitoring. Read on to find out how it’s done.

1. Set Clear Landing Page Testing Goals and Hypotheses

It’s not a good idea to dive headfirst into A/B landing page testing. 

First, you need to identify the risks, then come up with ways to mitigate them. This involves setting out your goals and creating clear hypotheses.

Identify the “why”: 

Before you jump in and change anything on your landing page, you should first ask “Why? Why am I changing this element? What am I trying to test?”. Here are some examples of why you may want to test certain elements:

  • Call-to-action not getting enough clicks – copy or colors not grabbing attention
  • Visitors scrolling past hero section too quickly – not engaging enough
  • People leaving page very quickly – high bounce rate
  • Visitors dropping off at certain points on the page – conversion drop-off
  • Struggles navigating your page – confusing layout

Set clear goals:

Clarity is key. Avoid generic goals like “improve user experience” or “increase sales.” Be specific. For example:

  • Increase conversions by 5%
  • Reduce bounce rates by 10%
  • Improve click-through-rate on CTA by 5%

Build your hypotheses:

If you’re wondering what a hypothesis is, it’s an educated guess about what’s likely to happen if you change something on your page. For instance, you may hypothesize that:

  • “Changing our CTA button from blue to green will result in a 5% increase in click throughs”
  • “Optimizing our headline and hero section will reduce bounce rate by 5%”

To sum up, the first step before actually testing anything on your landing page is to know why you’re doing it, set goals for the results you want to get, and then come up with educated guesses to guide your changes.

2. Test One Thing at a Time 

When you first start A/B testing, you may be tempted to throw the kitchen sink at it and start tweaking lots of elements.

Don’t do this!

You’ll end up with too many moving parts which are impossible to track.  Follow these guidelines instead:

Test in isolation  

Let’s say you change the main headline, CTA button copy and color, and the page layout all in one go. When you see changes in conversion rates, you may assume that it’s down to the CTA changes, but it may have been caused by the layout of the page, making it easier for people to follow. That’s why you should only change one piece of the puzzle at a time. That way you get an understanding of its precise impact.

Single out certain elements 

So, the next step is to decide which elements to test one-by-one. Here are the most commonly tested elements:

  • Headlines: Does your current headline hook readers instantly? A slight tweak here could be just the magic you’re looking for.
  • CTA button: Sometimes, it’s not just the words that count but the visual appeal. A change in size, text, or color can yield surprising results.
  • Form Length: Who likes long forms? But how short is too short? Testing different lengths can offer deeper insights into user preferences.

Make micro changes  

Small changes can lead to big results. For example, switching from passive to active voice in your CTA can make a world of difference: instead of “More Info” you could try “Send Me Info!” or even “Get More Info!” or “Discover More.”

3. Find the Right Balance – Sample Size and Duration for A/B Landing Page Testing

When it comes to successfully A/B testing your landing page – it’s all about the numbers.

You need to decide the sample size (or, how many visitors will view each version) in order to get statistically significant results. You also want to decide how long you’ll run the test for to give the clearest possible picture. Here’s how to do it right:

Balance your sample size 

Imagine sending out a survey. If you only get a few responses, the feedback could be misleading and not reflect the majority. Similarly, you need to test a good number of landing page visitors to be able to accurately judge the effect of an A/B test. 

The actual figure depends on your landing page traffic. In the digital marketing world, the consensus is that you need at least 1,000 visitors for each variant you’re testing. But it depends on:

  • Current conversion rate: If you have a higher conversion rate, you may need fewer visitors to see meaningful results. Conversely, if your conversion rate is low, it might require more visitors to detect a significant change.
  • Minimum detectable effect: This is the smallest change in performance that would be considered important in your test. For example, if you want to detect a 5% change in your conversion rate, you’d need a different sample size compared to detecting a 20% change.

Use online tools for sample size

If you want to remove some of the guesswork, there are plenty of free A/B testing calculators available online. Simply input your current metrics, such as baseline conversion rate and minimum detectable effect, and you’ll get a recommended sample size. Here’s a good one to use: A/B Test Sample Size Calculator | Statistical Significance Calculator.

Choose the duration

Good things come to those who wait.

Don’t be tempted to rush your A/B testing. Visitor behavior may fluctuate from day to day, so the best advice is to run your landing page A/B test over the course of one full week. 

If your business has fluctuating weekly cycles, such as e-commerce sites that are busier on weekends or at the end of the month (after pay day), then it may be worth running your test over multiple weeks. 

4. Randomize and Split Traffic 

Remember those science experiments you did in school?

A/B testing is kind of similar, but instead of testing chemical reactions, you’re testing your landing page. But remember – one of the most common techniques of scientific experimentation is to make sure you randomize your variables.

Why is this so crucial?

Well, imagine this – you decide to test two versions of your landing page at different times of the day. The first version runs in the morning when people are sipping their coffee. The second version goes live in the evening when most people are relaxing on their sofa. If you find significant differences in, let’s say, the conversion rates, can you confidently attribute it to the changes you made? Or could it be the time of day that’s influencing user behavior?

That’s why randomizing your visitors is so important. It ensures that external factors, like time of day or week, don’t cloud your test results. Here are some key guidelines for how to randomize your landing page A/B test:

Split traffic evenly and randomly:

If you want reliable results, first make sure that your visitors are randomly assigned to either the:

  • Control group: your current, unaltered landing page
  • Variant group: the landing page where you’ve made changes 

This will give you a much clearer picture of what’s going on, as it gives both versions a fair shot at proving itself.

Use the right tools for the job:

You can use automation tools for most of the A/B testing process, so don’t worry about having to do it all manually. These tools ensure that traffic is split evenly between versions, preventing any bias. Platforms like Optimizely, VWO, or Google Optimize are popular choices, and they can make the randomization process smooth and efficient.

The advantage of these tools? They’re not just about splitting traffic. They also provide analytics, insights, and help you to track KPIs related to your landing page testing and overall SEO strategy.

5. Analyze and Interpret Landing Page Test Results 

Data is everything in the digital world. But collecting it is only half the battle. The real magic comes when you roll up your sleeves and dig into the data.

When A/B testing your landing page, you need to ensure that you’re drawing an accurate picture from the results.

Here are a few useful tips to help you make sense of all the data:

Dive deep Into key metrics

Your landing page performance isn’t gauged on just one parameter. You need to look at multiple metrics to get a complete picture, for instance:

  • Conversion Rate: Tells you how effective your page is at persuading visitors to take the desired action.
  • Click-Through Rate (CTR): Do your CTAs resonate with the audience? This metric gives you insights.
  • Bounce Rate: If users land on your page, only to instantly leave, you’ll want to know. A high bounce rate might indicate problems in the immediate impression your page creates on visitors.
  • Average Time on Page: If visitors spend a lot of quality time on your page, it’s a good sign they find your content engaging and relevant. If not, then you need to up your content game.

Statistically speaking

Numbers don’t lie, but they can be misleading if you don’t analyze them correctly. When comparing the control and variation versions of your landing page, you need to ensure that the observed differences didn’t just happen by chance.

To achieve this, you can lean on statistical tests like the chi-square or t-tests to work out whether the variations had a genuine impact or if it’s just statistical noise.

Context is crucial

Numbers don’t exist in isolation. When analyzing your results, take into account external elements that might sway your data, such as:

  • Seasonality: Does your business see a spike during the holidays or a slump during certain months? This can dramatically affect user behavior.
  • Traffic sources: Users coming from social media might behave differently than those coming from an email campaign.
  • Major events: Maybe a global event, industry trend, or even a viral meme related to your niche, has temporarily influenced user behavior – so make sure to consider the context. 

The A-Z of A/B Testing for Landing Pages

When you’re starting out with A/B testing, things may be a bit daunting. There’s a lot to consider – setting goals, making methodical changes, balancing samples, ensuring fair and random tests, and monitoring the results. Make sure to review the guidelines above next time you A/B test your landing pages, so you are setting yourself up for success. 

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