What is A/B testing?
A/B testing, also known as split testing, compares two versions of an app component, web page, email, or other elements of marketing material to determine which one performs better. The goal of A/B testing is to improve conversion rates, engagement, or different desired outcomes.
During an A/B test, the original version (version A) is compared to a modified version (version B). Version B might have different headlines, images, calls-to-action, or other elements. The two versions are shown to different segments of website visitors or email recipients. Their interactions and conversions are measured and compared to determine if one version outperforms the other.
According to IndustryARC, by 2025, the market for A/B Testing Software is likely to reach $1,151 million. Marketers rely on A/B testing to fine-tune their marketing campaigns. A/B testing is a data-driven way for marketers to refine and optimize marketing assets. It removes guesswork and assumptions by directly measuring how changes impact metrics. A/B testing is used by large and small companies to maximize the effectiveness of websites, mobile apps, emails, ads, and more.
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What is the general usage of A/B testing?
A/B testing is used to optimize all aspects of the customer journey and funnel. Typical uses of A/B testing include:
A/B testing can optimize landing pages to increase conversion rates. For example, version B may change the headline, layout, images, or call-to-action button so that they are entirely different from similar elements in version A. Testing determines which version convinces more visitors to convert.
Email marketers use A/B testing to refine subject lines, preview text, images, calls-to-action, and content in the body of emails. Testing improves open rates, click-through rates, and conversions.
Entire websites can be A/B tested by showing version A to some visitors and version B to others. During the A/B test, site owners can test changes like navigation, layouts, content, etc.
App developers use A/B testing to optimize everything from user onboarding flows to in-app purchase prompts. Testing can occur within the live app or with a remote testing platform.
Pay-per-click ads can be A/B tested to optimize elements like ad copy, keywords, landing pages, and more to get more clicks and conversions within a target cost-per-click.
Marketers can refine social posts through A/B testing of elements like images, captions, calls-to-action, and video. The goal is to boost engagement and clicks. A/B testing is often used to test:
- Headlines and titles
- Page layouts and navigation
- Images and videos
- Content and messaging
- Email/ad copy and subject lines
- Pricing and offers
- Checkout flows
- Forms and data capture
Essentially any element that impacts users can be A/B tested to see if a variation improves performance.
Best practices for effective A/B testing include:
- Having a clear goal in mind (e.g., increase conversion rate by 15%)
- Only testing one variation at a time
- Using large enough sample sizes for statistical significance
- Letting the test run long enough to collect sufficient data
- Using relevant metrics to identify the winning variation
- Analyzing results to understand why one variation performed better
- Making evidence-based decisions, not decisions based on assumptions
Proper analysis is critical to extracting insights from A/B tests. Statistical significance testing should determine if results are due to chance or if one variation actually outperforms the other. Marketers should track valid metrics like conversion rate, clickthrough rate, time on page, and bounce rate. Multivariate testing combines multiple page elements into a single test for more insights.
How Singular leverages A/B testing?
As a mobile marketing analytics company, Singular helps mobile marketers use data to optimize their marketing strategies and spend. A core part of this process is leveraging A/B testing to refine campaigns and assets based on performance data.
Singular’s platform works with A/B testing tools to track the results of tests. Doing so allows Singular customers to connect test data with other campaign analytics to make data-backed decisions about the best marketing variations.
Specifically, Singular can:
- Help you track A/B test results across multiple marketing channels in a unified dashboard. Doing so avoids the siloed data that comes from running tests in individual tools.
- Analyze how A/B test variations impact downstream user actions in the mobile app. This reveals how landing page or ad copy changes influence in-app behavior.
- Attribute test results to marketing channels and campaigns, uncovering which are driving the most valuable users. Doing so helps optimize budget allocation.
- Visualize A/B test data alongside other analytics like ROI and LTV. This connects test results to revenue impact.
- Run multivariate tests combining changes to multiple campaign elements.
- Continuously launch new tests to optimize campaigns daily, not just set periodic tests.
- Test faster by launching experiments across audience subsets to collect data before rolling out globally.
- Automate testing and optimization of multiple campaign elements simultaneously.
Singular gives mobile marketers a holistic view of A/B testing results instead of siloed data. Doing so empowers smarter optimization of creatives, targeting, budgets, and more based on actual user data and revenue impact. A/B testing through Singular ensures tests are continuously run across channels to maximize ROI.