A/B testing is how you know whether a change actually works, rather than relying on opinion or instinct.
A/B testing is how you know whether a change actually works, rather than relying on opinion or instinct. System Strats sets up rigorous experiments for the changes that matter: prioritizing what to test, defining success metrics, running tests long enough to trust the results, and documenting what the data actually says.
Experiment prioritization tied to business impact
Statistically valid test design and sample size planning
Clear reporting on what worked, what did not, and why
A growing library of validated wins that compound over time
We identify the highest-impact UX issues using a combination of heuristic review and real user data, then prioritize by expected business impact.
For each high-impact issue, we form a hypothesis about what to change and why, and we design the intervention. This is where opinions become testable ideas.
We implement the changes and run A/B tests where traffic supports it. For lower-traffic areas, we use before/after analysis with proper controls.
We measure against the metrics that matter, document what worked, and feed the wins into the next round of optimization. UX is an ongoing practice, not a one-time project.
We treat UX optimization as a measurable practice, not an opinion exercise. Changes are tied to hypotheses, and results are tied to metrics that matter to the business. We are also honest about when a flow needs a redesign versus when small changes will do, so you do not end up paying for scope you do not need.
Common questions about UX Optimization A/B Testing & Experimentation, and how System Strats can help.
We work with Google Optimize alternatives, VWO, Optimizely, Convert, and platform-native tools like Shopify's built-in experiments. We pick based on the site, traffic volume, and budget.
It depends on the effect size, but generally you need enough traffic to reach statistical significance within a reasonable timeframe. For low-traffic sites, we use other validation methods like before/after analysis instead.
Long enough to reach statistical significance and to include at least one full weekly cycle, typically 2 to 4 weeks. Tests that are stopped early based on early results are unreliable.
The changes with the biggest potential impact and the strongest evidence behind them. We prioritize tests using a framework that balances expected impact, confidence, and effort.
Tell us about your project and we'll get back to you within 24 hours.