The following is a guest post by Niraj Rajput, cofounder and Head of Engineering at Chisel Labs
A/B testing is a great way to test new ideas and measure the effect they have on your website’s conversion rates.
But how do you know which idea will be best?
Qualitative data can help you make more informed decisions about what works best for your customers.
This blog post will show you how qualitative data can optimize A/B tests results so that no time or money is wasted on ineffective changes.
What is A/B testing?
The steps to take to begin with A/B testing
What is qualitative data?
The steps for using qualitative data to optimize A/B testing
Conclusion
What is A/B testing?
A/B or split testing is a great way to test new ideas and measure the effect they have on your website’s conversion rates.
You can compare two versions of a page; for example, version A vs B – with results showing which variation performs better over time.
A/B testing is used in all kinds of areas: from email marketing and search engine optimization to personalization, content management, and website design.
This type of testing plays a huge role in the optimization of your website.
It can be used to improve conversions, increase engagement and help guide focus on content and layout changes that will make a difference in terms of user behavior – all with quantitative data collected from tests run against two or more variations of any given page/element/blog post title, and so on.
A/B testing is basically a marketing technique that helps you test the effectiveness of different versions of your website, blog posts, and other content.
It can help you improve conversions, increase engagement and make a difference in terms of user behavior.
It works by showing different versions of your website to users at random and allows you to collect data from tests run against those variations.
You can use this type of testing for any given page, element on the blog post title, and so on.
The steps to take to begin with A/B testing
- Define your goals clearly.
- Set up experiments correctly – identify pages/elements where changes will have the biggest impact & how long it takes to determine results accurately (you may want to set them longer than you expect).
- Ensure statistical relevance by setting confidence level and sample size accordingly before starting an experiment(if not done so already). Also, ensure sufficient traffic volume – enough visitors in order to put the results into context.
- Determine the right set of metrics.
- Calculate statistical significance through an estimation tool or by using a calculator.
- Monitor the experiment closely in order to determine if one variant is superior, and when it’s time to end the test before reaching ‘statistical noise.’
You can also use qualitative data in conjunction with A/B testing.
This way you’ll be able to understand why some changes are effective and why others aren’t doing so well.
But before that let us learn what exactly is qualitative data.
Read More: How to Perform Effective A/B Testing For Your Website
What is qualitative data?
Qualitative data provides feedback about user behavior which will give you more insights on what works well & what doesn’t work at all! It helps you identify anomalies that would otherwise go unnoticed without such information (ex: drop-off rates). This type of data is not numerical and cannot be compared to other metrics.
For example, if you were to test two designs for a mobile app, qualitative data would provide additional information about each design that might not be immediately clear from the A/B testing results.
You can collect qualitative data a few different ways, including user testing or user surveys.
Without qualitative data:
If you don’t collect any qualitative data, then you can’t know if your users are struggling with certain areas of your website or app. Additionally, it will be difficult to determine whether changes made to user experience have been successful or unsuccessful.
Read More: Qualitative Interviews: Pitfalls to Avoid
The steps for using qualitative data to optimize A/B testing
The first step to using qualitative data for optimization of your A/B tests is having the right tools in place.
The next steps are figuring out what kind of feedback you expect from users and then actually collecting that feedback.
There are several different ways to collect this information, including phone calls, emails, or online surveys. It all depends on the accessibility and ability of the participants.
Once you have collected this information it’s time to analyze the results. Then they can be used to optimize your A/B test.
The final step is using the quantitative data that comes from your experiments as a supplement.
Another way to implement qualitative data is to provide a set of alternative design options that participants can choose from after they have experience using the current website.
This information is very beneficial when planning A/B tests. You gain more insight into what exactly helps your target audience to use your product or service without any issues.
There are four major factors that will affect how people react and interact with different elements on each version of the web page:
- Visual appeal (color schemes, font size, etc.)
- Emotional impact (how much does this make me feel?)
- Ease-of-use (is this easy enough?)
- Usefulness (does anyone even want/need this?)
These factors should all play a role in optimizing your experiments.
The first thing you need to do when designing your experiments is to create a hypothesis. This should be based on what you already know about the target audience and their behavior (i.e. if they like blue, green, or red; using sans-serif fonts or serif fonts; favor minimalist designs over embellished ones, etc.).
Look at which elements of each version are most important for reaching your objectives. Make sure that these items play an equal role in both versions. This will prevent one from overshadowing the other.
Afterwards, set up your experiment so that there’s only room for minor changes like font size or color scheme. Everything else should remain identical between both versions – including text content where applicable.
By doing this, you can ensure that the differences in results are due to your changes and not other factors.
Conclusion
In a nutshell, you can say that qualitative data plays a huge role in optimizing A/B test results. Hope this article has given you a better insight into how to use qualitative data for your A/B tests.
Niraj is co-founder and Head of Engineering at Chisel Labs, a premiere agile product management software company that brings together roadmapping, team alignment, and customer connection. Niraj is passionate about building scalable infrastructure and systems and he also happens to be a huge fan of Cricket!
