A/B Testing in Optimizely: What It Is and Why It Matters

A/B testing (also known as split testing or bucket testing) is a methodology for comparing two versions of a webpage or app against each other to determine which one performs better. It works by showing two variants of a page to users at random and using statistical analysis to determine which variation achieves better results for your conversion goals.

In practice, this is how A/B testing works:

• Creating two versions of a page - the original (control or A) and a modified version (variation or B)
• Randomly splitting your traffic between these versions
• Measuring user engagement through a dashboard
• Analyzing results to determine if the changes had positive, negative, or neutral effects

The changes you test can range from simple adjustments (like a headline or button) to complete page redesigns. By measuring the impact of each change, A/B testing turns website optimization from guesswork into data-informed decisions, shifting conversations from "we think" to "we know."

Why you should A/B test

A/B testing allows individuals, teams, and companies to make careful changes to their user experiences while collecting data on the impact it makes. This allows them to construct hypotheses and to learn what elements and optimizations of their experiences impact user behavior the most. In another way, they can be proven wrong—their opinion about the best experience for a given goal can be proven wrong through an A/B test.

More than just answering a one-off question or settling a disagreement, A/B testing can be used to continually improve a given experience or improve a single goal like conversion rate optimization (CRO) over time.

Examples of A/B testing applications:

• B2B lead generation: If you're a technology company, you can improve your landing pages by testing changes to headlines, form fields, and CTAs. By testing one element at a time, you can identify which changes increase lead quality and conversion rates.
• Campaign performance: If you're a marketer running a product marketing campaign, you can optimize ad spend by testing both ad copy and landing pages. For example, testing different layouts helped identify which version converted visitors to customers most efficiently, reducing overall customer acquisition costs.
• Product experience: The product teams in your company can use A/B testing to validate assumptions, prioritize features that matter, and deliver products without risks. From onboarding flows to in-product notifications, testing helps optimize the user experience while maintaining clear goals and hypotheses.

How to do A/B testing

The following is an A/B testing framework you can use to start running tests:

1. Collect data

• Use analytics tools like Google Analytics to identify opportunities
• Focus on high-traffic areas through heatmaps
• Look for pages with high drop-off rates

2. Set clear goals

• Define specific metrics to improve
• Establish measurement criteria
• Set target improvements

3. Create test hypothesis

• Form clear predictions
• Base ideas on existing data
• Prioritize by potential impact

4. Design variations

• Make specific, measurable changes
• Ensure proper tracking
• Test technical implementation

5. Run the experiment

• Split traffic randomly
• Monitor for issues
• Collect data systematically

6. Analyze results

• Check statistical significance
• Review all metrics
• Document learnings