Testing Methodology

Every Testing Method,
Precisely Applied

From simple A/B splits to complex multivariate experiments — Desig supports the full spectrum of design testing methods. Choose the right approach and maximize optimization velocity.

Method 01

A/B Split Testing

The gold standard of conversion rate optimization. Compare two versions of a design element — your control (A) against a challenger (B) — to identify which performs better with statistical confidence.

A/B testing isolates a single variable change, making it easy to attribute performance differences directly to your design modification.

Single variable isolation for clear causation
50/50 traffic split with adjustable ratios
Automatic winner detection at 95% confidence
Segment-level analysis (device, geo, source)
Minimum detectable effect calculator built-in

Method 02

Multivariate Testing

Test multiple page elements simultaneously to understand how they interact with each other. MVT reveals which combination of headline, image, CTA, and layout produces the highest conversion rate.

Ideal when you want to optimize multiple elements at once and understand their interaction effects without running sequential tests.

Test up to 12 variables simultaneously
Full factorial and fractional designs supported
Interaction effect visualization matrix
Automated traffic allocation optimization
Requires higher traffic volumes (50K+ monthly)

Method 03

Split URL Testing

Test completely different page designs by splitting traffic between two distinct URLs. Unlike A/B testing which modifies a single page, split URL testing allows comparing fundamentally different layouts and user flows.

Best suited for redesigns, new page structures, or when testing radically different design approaches.

Compare entirely different page architectures
Test new URL structures and navigation flows
Server-side implementation for performance
Works with SPAs and dynamic pages
CDN-level traffic splitting available

Method 04

Multi-Armed Bandit

An adaptive testing method that dynamically reallocates traffic toward better-performing variants during the test. Unlike traditional A/B testing, bandit algorithms maximize conversions while gathering statistical evidence.

Use when minimizing regret matters more than statistical purity — ideal for promotional pages and seasonal campaigns.

Epsilon-Greedy, Thompson Sampling, UCB algorithms
Real-time traffic reallocation to winners
Lower opportunity cost vs. fixed splits
Contextual bandits for personalization
Configurable exploration/exploitation ratio

Method 05

Redirect Tests

Redirect a portion of users to a completely different page hosted at a separate URL. Especially useful when testing completely rebuilt pages or when comparing different technology stacks.

Redirect tests use JavaScript or server-side redirects and are fully transparent to analytics platforms with session continuity maintained.

Test completely rebuilt pages without risk
JS or server-side redirect options available
Full session continuity maintained
Analytics platform transparency
Compatible with all CMSs and page builders

Method Comparison

Choose the Right Testing Method

Use this matrix to select the optimal approach for your situation and traffic volume.

Method	Traffic Required	Variables	Time to Results	Use Case	Complexity
A/B Testing	2,000+/month	1	7–21 days	Single element tests	Low
Multivariate	50,000+/month	2–12	21–60 days	Interaction effects	High
Split URL	5,000+/month	Full page	14–30 days	Redesigns	Medium
Multi-Armed Bandit	10,000+/month	2–8	5–15 days	Revenue optimization	Medium
Redirect Tests	5,000+/month	Full page	14–30 days	Tech migration	Medium

Decision Framework

When to Use Each Method

Just Starting Out

Begin with A/B tests on high-traffic pages. Test CTAs, headlines, and button colors. Build confidence before scaling to complex experiments.

Scaling Fast

Move to multivariate testing when you have sufficient traffic and want to test multiple hypotheses simultaneously. Reduces total test duration by 60%.

Time-Critical

Use bandit algorithms for promotions, seasonal events, or launches where maximizing conversions during the test window matters most.

Common Questions

Testing Method FAQs

The required sample size depends on your baseline conversion rate, desired minimum detectable effect, and statistical power. As a rule of thumb, you typically need 1,000–5,000 visitors per variant. Our sample size calculator handles this automatically.

Yes, but be careful about interaction effects. Desig's mutual exclusion engine ensures users are placed into only one test at a time by default, maintaining statistical integrity across concurrent experiments.

Stopping tests early (peeking problem) inflates false positives. Our platform uses sequential testing methods and Bayesian stopping rules so you can monitor results without statistical issues. Follow minimum duration guidelines even when significance is reached early.

Frequentist testing controls Type I error rates and requires predetermined sample sizes. Bayesian testing provides probability-of-being-best metrics and allows more flexible stopping rules. Desig supports both approaches.

Every Testing Method,Precisely Applied