What is A/B Testing?
A/B testing, also referred to as split testing or bucket testing, is a method used to compare the performance of two versions of a digital asset, such as a web page, mobile app screen, email, or advertisement. A/B testing enables you to ask a focused question about a singular change to your website, app, or other digital presence, make that change, and then collect data on the impact of that specific change.
How does A/B testing work?
The core premise of A/B testing is that you take an existing asset, such as a web page, and modify it slightly to create a second variation. You then present each version to website visitors or app users at random and use statistical analysis to see which one achieved your desired goal better, whether that’s a higher click-through rate, increased time on the page, more conversions, or other metrics.
By measuring performance on key metrics, A/B testing takes the guesswork out of optimisation and provides hard data to guide business decisions confidently. Rather than relying on assumptions or opinions about the “best” user experience, A/B testing allows you to try out changes directly with your audience and let their behaviour provide insights.
What are the benefits of A/B testing?
A/B testing is a powerful tool that can help you make data-driven decisions, continually improve your performance, maximise your return on investment, reduce risk, and prevent debate within your team.
Make data-driven decisions:
Rather than relying on assumptions or opinions, A/B testing gives you empirical data directly from your customers about the impact of changes. This enables confident decision-making optimised for your audience.
Continually improve performance:
A/B testing makes optimisation an iterative process. You can keep testing new ideas to see which evolve your experience and metrics.
Maximise return on investment:
By tweaking low-effort changes that produce big results, you can get more value from your existing web traffic, ppc/ search advertisement, and email marketing.
A/B testing allows you to trial ideas safely without having to fully commit to any change upfront before seeing performance data.
How to run an A/B test
While every organisation will have its own process nuances, these steps capture the core elements of a structured approach to A/B testing:
- Identify Opportunities
Use data and analysis to find areas for optimisation. This might involve reviewing analytics to identify poor-performing pages or leveraging tools like heatmaps to see where customers struggle. Prioritise testing opportunities based on potential impact and ease of implementation. Focus on quick wins first.
- Form a Hypothesis
Develop an informed hypothesis about how making a specific change could improve the experience. For example: “Making the submit button red will increase form conversions by 15%.”
- Design Testing Variations
Use your testing tool to create versions A and B – the original and modified variant. Ensure the only change is the one thing you are testing. Keep everything else the same.
- Run the Test
Configure your test and kick it off. Visitors will be randomly shown either version. Let the test run until it reaches statistical significance. Tools will indicate when enough data has been collected.
- Analyse Results
Once complete, the testing tool will analyse the data and highlight the difference in performance between version A and B. Assess whether the change had a positive, negative or neutral effect.
- Implement Findings
If the new version performs better, put it live on your site. If not, revert to the original. Use the insights to inform future tests.
- Retest and Iterate
Continue the optimisation process by creating new variants and challenging the new “champion” against potential improvements.
What are A/B testing metrics to measure success
In order to assess the efficacy of your A/B tests, you need to identify key metrics that indicate performance for your goals. Some top metrics include:
- Conversion Rate
Did more people convert on the new version, e.g. purchases, ‘added to bag’, mailing list sign-ups?
- Click-through rate
Was there increased engagement with buttons or links?
- Bounce rate
Did fewer people leave immediately from that page?
- Engagement time
Did visitors spend more time interacting with the new version?
Did the new version directly generate higher sales or revenue?
Was the impact of the test significant enough to justify the effort?
The goals for your business will determine which metrics to focus on. Consistently track them to quantify A/B testing success.
When to segment A/B testing
Some platforms support the ability to segment visitors by attributes such as location, browser, device, traffic source, and user profiles. This allows you to analyse performance differences between segments.
For example, you may find that the new version performed much better for mobile visitors compared to desktop. You can then further optimise specifically for top-performing segments.
However, be wary of getting too granular. Very small segments may mean insufficient sample sizes, reducing statistical significance. Only use segmentation where you have enough volume for robust data.
A/B Testing Examples
A/B testing can be applied across a wide range of digital assets and experiences:
- Website Design:
- Headline: Testing different headlines to see which one catches users’ attention and leads to more clicks.
- Call-to-Action (CTA) Button: Testing different CTA button placements, text, and colours to see which one converts more visitors into customers.
- Product Images: Testing different product images to see which ones generate more interest and sales. For eCommerce sites, this could be AB testing products within the Product Listing Page.
- Form Design: Testing different form lengths, layouts, and fields to see which ones result in more form submissions.
- PPC Marketing:
- Ad design: Testing a variation of design elements, ensures your social media ads or search display ads are performing best.
- Ad content: Testing different ad content with a variation of key benefits can lead to better performing ads and insight.
- Call-to-action: Find out which call-to-action drives the highest clicks/ engagement such as contact us, learn more, book a demo, or sign up for a free trial.
- Email Marketing:
- Subject Line: Testing different subject lines to see which ones have higher open rates.
- Sender Name: Testing different sender names to see which ones resonate more with recipients and lead to more opens and clicks.
- Email Content: Testing different email layouts, images, and calls to action to see which ones generate more clicks and conversions.
- Mobile App Design:
- Navigation Menu: Testing different navigation menu layouts and icon placements to see which ones are easier to use and lead to more app engagement.
- Onboarding Process: Testing different onboarding processes to see which ones help new users get started quickly and increase retention rates.
- In-App Notifications: Testing different notification frequencies and messaging to see which ones lead to more app engagement and conversions.
Essentially, any customer touchpoint that can be modified and measured digitally is a candidate for A/B testing.
Why choose Taggstar?
In the world of eCommerce, staying ahead of the curve is essential. As the leading expert in enterprise social proof messaging, Taggstar helps retailers and brands deliver more dynamic and engaging shopping experiences, build customer trust and increase online conversions and sales.
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