A/B Testing Made Easy with Web Analytics Platforms
Introduction
A/B testing, also known as split testing, is a method of comparing two versions of a web page, email, or other digital asset to determine which one performs better. The goal is to optimize conversions and other key metrics like engagement and retention. With A/B testing, businesses can make data-driven decisions about what content and experiences resonate best with their audiences.
In recent years, web analytics platforms have integrated robust A/B testing tools right into their offerings. This makes it easy for analysts and marketers to set up, run, and analyze split tests without needing IT help. Leading platforms like Google Analytics, Adobe Analytics, and Mixpanel all provide visual workflow editors to create A/B tests as well as integrate with their analytics reporting.
The key benefits of using built-in A/B testing include easier implementation, visual editors, advanced targeting and scheduling options, full integration with analytics, automation features, and support for different test types like multivariate testing. These platforms enable you to experiment continuously and use the results to optimize engagement across websites, mobile apps, email campaigns, and beyond.
In this post, we’ll explore how the top web analytics platforms make A/B testing simple and efficient. You’ll learn:
- How to choose the right analytics platform based on your business needs and goals
- Best practices for designing and running statistically significant split tests
- An overview of built-in A/B testing tools and capabilities of platforms like Google Optimize, Adobe Target, and Mixpanel Experiments
- Tips for analyzing results and running successful optimization tests on an ongoing basis
Whether you’re new to split testing or want to use your analytics provider more effectively, this guide will help you leverage A/B testing to boost conversions and create better customer experiences. Let's dive in!
Choosing the Right Web Analytics Platform
With A/B testing playing an increasingly crucial role in digital experience optimization, it's important to choose an analytics platform that provides robust experimentation capabilities right out of the box. Here's an overview of key platforms and factors to consider when selecting one for your needs:
| Platform | Key Features | Use Cases | Limitations |
|---|---|---|---|
| Google Analytics | Free, easy setup, integrates with Google ads | Basic website analytics and optimization | Limited advanced capabilities |
| Adobe Analytics | Advanced segmentation, attribution, predictive analytics | Enterprise marketing analytics | More complex, expensive |
| Mixpanel | Retention analysis, mobile focus, easy testing | Product analytics and optimization | More limited website analytics |
| Amplitude | User behavior analysis, minimal performance impact | Product usage analytics | No built-in testing features |
| Heap | Retroactive data capture, flexible analysis | Improving UX and products | No optimization or personalization |
Other factors to evaluate include pricing, ease of implementation, data accuracy, advanced capabilities like attribution modeling and machine learning, A/B testing and personalization features, customer support, and whether the platform meets your unique business needs.
Determine whether you need a free solution like Google Analytics, an enterprise platform like Adobe Analytics, or a specialized product analytics tool like Mixpanel. Align your choice with your goals, resources, and technical expertise.
Designing Effective A/B Tests
Creating well-designed split tests is crucial to running successful experiments and achieving statistically significant results. Here are some best practices to follow:
Determine appropriate success metrics aligned to business goals
Clarify your target metrics before creating a test - are you optimizing for conversions, engagement, retention, or something else? Pick metrics that reflect your overall goals.
Avoid testing too many variables at once or changing too much
Limit A/B tests to one isolated change between variants, such as a different headline, button text, image, or page layout. Don't drastically redesign the whole experience.
Understand sample size needed to achieve statistical significance
Use power analysis to determine the minimum sample size needed for each variant. For example, if your baseline conversion rate is 2%, to detect a 5% increase at 95% confidence would require almost 10,000 samples per variation. Test duration should drive sufficient traffic to meet sample size.
Set up proper tracking to measure key events or conversions
Configure tracking to capture all the user actions, funnels, and conversion data you'll need to analyze the impact of your test. Use event tracking for clicks, form submissions, purchases etc.
Create useful segments to analyze results for subsets of users
Leverage segmentation to see how test variants performed for different customer personas, traffic sources, markets, etc. Audience targeting is key for optimization.
Choosing What to Test
Some ideas for experiment variables:
- Page layouts, calls-to-action, headlines, images
- Signup flows or checkout processes
- Prominence, placement or copy of key elements
- Email newsletters and notifications
- Search or recommendation algorithms
- Pricing and packaging options
- User onboarding and tutorials
- Mobile app user flows and in-app messages
Setting Up the Test
Tips for proper test implementation:
- Keep the variable isolated - only test one change at a time
- Use proper redirects and URL structure for clean data
- Split traffic evenly between variants to start
- Run test long enough to achieve sufficient sample size
- Avoid testing too many variants - 2 or 3 at most
- Use power analysis to determine minimum duration
Analyzing the Results
Metrics to review when assessing test results:
- Calculate lift for the winning variant vs the control
- Review results across meaningful user segments
- Check for statistical significance with a t-test
- Monitor effects on downstream conversions and goals
- Share results across teams and implement winning variant
Leveraging Built-In A/B Testing Tools
Leading analytics platforms provide powerful A/B testing capabilities without needing extra tools or coding:
Google Optimize
- Easy drag and drop editor to create page variations
- Limited to simple A/B and split URL testing
- Fully integrated with Google Analytics
- Free for basic optimization needs
- Works on websites as well as mobile apps
Adobe Target
- Robust visual and form-based editors for web and mobile
- Supports multivariate, A/B, and experience (XT) testing
- Integrates with Adobe Analytics for reporting
- Options for AI-powered personalization
- Enterprise level capabilities and pricing
Mixpanel Experiments
- Intuitive workflow for creating mobile and web tests
- Reporting focuses on conversion and retention metrics
- Easy integration with Mixpanel for analysis
- Can target user segments and cohorts
- Automation features to continuously run tests
VWO
- Intuitive visual editor for web and mobile testing
- Integrates with Google Analytics, Adobe Analytics, more
- Options for multivariate and split URL testing
- Can target logged-in users
- More limited analytics compared to enterprise platforms
Other platforms like Optimizely, Qubit, and HubSpot also provide built-in testing and personalization.
Tips for Running Successful Tests
Follow these tips to achieve statistically significant results from your A/B tests:
- Start with clear hypotheses and define success metrics upfront
- Use power analysis to determine sufficient sample size
- Limit one variable per test, keep rest of experience identical
- Let tests run long enough to achieve statistical significance
- Analyze results across segments and measure multiple conversions
- Avoid over-optimizing or changing too much at once
- Understand natural variability - don't overreact to every fluctuation
- Watch out for selection bias, seasonal impacts, testing fatigue
- Balance speed of iteration with rigor of analysis
- Document insights and share winning variants across teams
For example, testing 4 headline variants may show a lift for one option, but it could be statistically insignificant noise. Run tests long enough and don't over-optimize.
Optimizing Mobile App Experiences
A/B testing principles also apply to optimizing mobile apps. Platforms like Mixpanel, Apptimize, and Firebase provide mobile-focused capabilities:
- Test in-app messages, onboarding flows, notifications
- Target tests to user segments based on behavior
- Analyze effects on retention and engagement KPIs
- Run tests without needing app releases using runtime SDKs
- Measure impact on in-app conversions like purchases
- Pair testing with push and in-app messaging for optimization
Testing mobile experiences requires sufficient volume of engaged users. Target tests to your most active segments initially.
Start Split Testing Today
A/B testing presents a huge opportunity to optimize digital experiences using data-driven experimentation. With the built-in capabilities of analytics platforms, anyone can leverage split testing to boost conversions across websites, mobile apps, emails and more.
To get started, sign up for DevHunt - the platform for discovering and showcasing the latest developer tools. Access a library of analytics solutions to help you begin experimenting and optimizing your product experiences right away using the guidelines provided in this guide. Implement the winning ideas from your tests to engage users and achieve your key business goals.