Complex Data Made Easy
We'll test your site to figure out all the factors that can turn those visits into sales. And we'll help you sift through a goldmine of data to find the treasure that will move the needle for your business.
We're not just number crunchers. We're analysis experts. We'll help you turn data into a strategic plan that will yield tangible results.
The way we test your site is geared towards one thing and one thing only: turning site visitors into your customers. We put our finger on the pulse of how your visitors interact with your website, and help you turn areas of weakness into strengths.
Develop A Hypothesis
Determine what action you want users to take on your website or app, such as filling out a form, making a purchase, or downloading an app.
Develop multiple design variations that address the problem and test your hypothesis.
Conduct A/B Testing
Show each variation to a randomly selected subset of your target audience and collect data on their behaviour and preferences.
Use data and analytics tools to compare the performance of each variation and determine which is most effective.
Based on the results of the test, implement the winning variation to improve the conversion rate of your website or app.
UX conversion optimisation is an ongoing process, and it’s important to regularly test and iterate on your designs to keep improving the conversion rate.
A/B Testing FAQs
A/B Testing, or split testing, compares two versions of a web page or app to identify which is more effective in achieving a specific goal. This method stands at the forefront of optimising digital experiences.
Every time businesses change their digital assets, it's essential to validate the impact of those changes on user behaviour. A/B testing addresses this need. By serving two versions to separate user groups, it allows for a direct comparison of performance metrics.
The strength of A/B testing lies in its data-driven approach. Instead of relying on assumptions or industry best practices alone, businesses get actionable insights from real user interactions. This results in making informed decisions about design, content, or functionality changes.
During the testing phase, key performance indicators (KPIs) such as click-through rates, conversion rates, and bounce rates become central. By monitoring these metrics, businesses can discern which version encourages users to take the desired actions more effectively.
After an A/B test, with concrete data collected, businesses can confidently implement the superior version, ensuring it aligns with user preferences and drives desired outcomes more efficiently.
Selecting elements for A/B testing is a strategic process. Potential elements are identified based on their impact on user experience and conversion. We rely heavily on data, analysing underperforming pages, user engagement metrics, and direct feedback. As we delve into the digital journey, we identify bottlenecks or stages where users may drop off or feel friction. While traditional A/B testing assesses two versions, multivariate testing allows us to study several elements simultaneously, gauging the performance of different combinations and configurations.
Several factors, including the volume of incoming traffic and the desired confidence level in results, determine an A/B test's duration. It's not merely about letting the test run for a set period; it's about capturing sufficient data to draw reliable conclusions. Ensuring statistical significance is pivotal, as this minimises the possibility that observed differences arose by random chance. Specialised tools and calculators are available that help determine the optimal test duration and validate the statistical significance of results.
The scope of A/B Testing extends far beyond websites. Whether fine-tuning an app's user interface, optimising email marketing campaigns, or honing digital ad creatives, A/B Testing offers insights that drive performance improvements. Within the broader Conversion Rate Optimisation (CRO) framework, A/B Testing is a cornerstone technique. CRO is all about maximising the effectiveness of digital assets, and A/B Testing provides a systematic way to achieve this by aligning assets with user preferences and behaviours.
While A/B Testing aims to optimise user experiences, ensuring these tests don't inadvertently harm your SEO efforts is imperative. Implementing proper canonical tags helps search engines understand that variations are part of a test, not duplicate content. Additionally, avoiding techniques like cloaking, where search engine bots and users see different content, maintains SEO integrity. When testing mobile experiences, it's crucial to account for mobile-specific dynamics, such as screen size, touch interface, and varying bandwidths. However, the core principle — enhancing the user journey based on empirical data — remains consistent across desktop and mobile platforms.