Rapid Prototyping & Micro-tests

Test Fast, Learn Faster

Instead of building big features and hoping they work, we design small, focused tests that validate assumptions in days—not months. Each micro-test answers a specific question with measurable data.

What Makes a Good Micro-test

A micro-test is small enough to build quickly, specific enough to measure clearly, and meaningful enough to inform real decisions. We avoid "let's see what happens" experiments—every test has a hypothesis, a success metric, and a clear decision point.

The Process

We start with a clear hypothesis: "If we change X, we expect Y to improve by Z." Then we build the smallest version that can test this assumption, deploy it quickly, and measure the outcome against our success criteria.

Building Effective Micro-tests

The key to effective micro-testing is focus. Each test should answer one question clearly. If you're testing multiple things at once, you won't know which change caused the result.

Hypothesis Formation

Every micro-test starts with a clear hypothesis. Not "let's try this" but "we believe X will cause Y because Z." This clarity makes it obvious what to measure and when to call the test successful or not.

Minimal Viable Test

We build the smallest version that can validate the hypothesis. If you're testing whether a new CTA color improves clicks, you don't need to redesign the entire page—just change the button color and measure the result.

Clear Success Criteria

Before launching a test, we define what success looks like. Is it a 10% increase in clicks? A 5% reduction in bounce rate? Clear criteria make it obvious when to scale a successful test and when to abandon a failing one.

Early Validation

By testing assumptions early, you avoid investing time and resources in solutions that don't work. You also discover what does work faster, allowing you to scale successful patterns while abandoning dead ends.

Minimal Risk, Maximum Learning

Because micro-tests are small and focused, they're easy to roll back if they don't work. This means you can take calculated risks without betting the farm. Each test teaches you something, whether it succeeds or fails.

Fast Rollback

If a micro-test doesn't work, we can roll it back immediately. There's no need to keep a failing change live while you figure out how to fix it. This safety net encourages experimentation.

Learning from Failure

Failed tests aren't wasted effort—they're learning. When a test fails, we analyze why. Was the hypothesis wrong? Was the implementation flawed? Did external factors interfere? This analysis informs the next test.

From Test to Scale

When a micro-test proves successful, we scale it. When it doesn't, we learn why and adjust. This iterative approach compounds learning over time, building a knowledge base of what works in your specific context.

Scaling Success

A successful micro-test becomes a pattern to apply more broadly. If changing a button color improved clicks on one page, we might test it on other pages. If a new form layout reduced abandonment, we might apply it to other forms.

Iterative Refinement

Even successful tests can be improved. We use what we learned to design the next iteration, making small improvements that compound over time. This is how good becomes great.