A Practical Guide to A/B, Multivariate, Bayesian, and Other Experimentation Methods—When to Use What (and When Not To)

Companies today face constant pressure to iterate quickly. But knowing what to test, how to test it, and which method to use is what separates meaningful progress from wasted effort.

This guide outlines best practices for running digital experiments, drawing from real-world use cases. Whether you lead experimentation at an enterprise or run product at a fast-moving startup, these frameworks will help you choose the right method for your testing goals and constraints.

A/B Testing: The Standard for Validating Individual Changes

Use it when you want to test a single change or feature against a control to determine whether it improves a specific metric such as conversion rate, product engagement, or sign-ups.

Best practices:

• Limit the test to a single change if diagnostic clarity is important.
• Run the test for at least two to four weeks, depending on traffic.
• Monitor both primary outcomes (e.g., enroll start rate) and secondary behaviors (e.g., bounce rate, time on page).
• Use frequentist statistical methods if you have high enough traffic.

Ideal for: Testing whether a redesigned plan chart increases enrollments, or whether asking for a ZIP code early in the journey adds or reduces friction.

Multivariate Testing: Understanding Combinations of Page Elements

Use it when you want to test multiple elements on a page simultaneously—such as the headline, imagery, CTA placement, or subheaders—and want to understand not just which individual element performs best, but which combinations drive the greatest lift.

Best practices:

• Limit to two or three variables, with no more than two or three versions of each.
• Keep the total number of variations under 10 unless you have very high traffic (e.g., over 100,000 visitors per month).
• Prioritize primary conversion metrics over subjective or cosmetic outcomes.

Ideal for: Homepage optimization, where layout, media, and copy variations need to be tested as a group.

Avoid if: You’re working with limited traffic or if implementation costs are high.

Sequential Testing: Isolating Which Change Drives Results

Use it when you’re introducing multiple changes but want to isolate which element actually caused the impact.

Rather than testing everything at once, break the experiment into phases.

Best practices:

• Test one change at a time.
• Use the winner of each test as the new control in the next round.
• Plan tests in sequence to reduce confounding variables.

Ideal for: Evaluating a full journey experience by first testing customer questions, then testing a personalized product recommendation feature afterward.

Bayesian Testing: Faster Results for Startups and Low-Traffic Pages

Use it when traffic is too low to support long or large-sample frequentist tests, or when quick directional decisions are needed.

Bayesian testing doesn’t ask “Is this different?” but instead calculates the probability that one variant is better than another, given observed data and prior knowledge.

Best practices:

• Use with small-to-moderate traffic or fast product cycles.
• Choose tools that handle Bayesian inference automatically (e.g., VWO SmartStats).
• If prior experiments suggest a likely effect (e.g., personalization increases engagement), set a high-confidence prior to accelerate convergence.

Ideal for: Startups testing MVP changes, or growth teams validating features quickly.

Synthetic Control Modeling: Estimating Impact When A/B Testing Isn’t Feasible

Use it when you can’t run a randomized controlled test, such as when launching a change in only one region or channel.

Synthetic control modeling creates a “synthetic twin” of your treatment group using a weighted combination of other similar units (e.g., markets, user cohorts) that didn’t receive the change. You then compare real results to the modeled baseline to estimate impact.

Best practices:

• Use for geo-based launches, policy changes, or non-random rollouts.
• Requires strong historical data and sound econometric modeling.
• Tools like Google’s CausalImpact and Microsoft’s DoWhy can help automate the process.

Ideal for: Testing regional pricing or localized UX changes where A/B splits are not possible.

Ghost Variants and Backtests: Testing Without Launching

Use it when you want to evaluate or simulate the potential impact of a feature without actually launching it.

Ghost variants track what users would have seen had the feature existed. For example, placing a heatmap or click tracker where a future CTA might go can give insight into expected engagement. Back testing uses historical data to simulate performance as if the test had already occurred.

Best practices:

• Use to prioritize high-value changes before deploying live tests.
• Combine with behavioral data, scroll depth, or predictive models.

Ideal for: Understanding if adding a “Recommended Plan” banner would change behavior before investing development time.

Summary: Choosing the Right Test Method

There is no one-size-fits-all approach. The method you choose depends on your goal, your traffic, and how confident you need to be in the result.

Final thought:

Testing isn’t just about running experiments. It’s about asking the right questions, designing them well, and choosing the method that gives you the most insight—without wasting time or traffic. If you’re not doing that, you’re not really learning.