A/B Testing & Experimentation Services
A test program that decides things, not a backlog of guesses.
Start a projectMost teams that say they’re “doing A/B testing” are really running a backlog of guesses. Button colors. Headline swaps. Tests launched without a hypothesis, stopped the moment a variant looks good, and called a win on noise. The result is motion without learning — a tool subscription, a pile of inconclusive runs, and no real answers about what moves the number. Our A/B testing and experimentation services exist to fix that: to run a program that actually decides things.
The discipline is the product. Every test starts as a written hypothesis tied to a real friction point in your funnel. We size it before launch — baseline rate, the smallest effect worth detecting, the traffic you have — so we know the runtime the math demands and whether the test can prove anything at all. Our engineers build and QA the variants so a broken experiment doesn’t masquerade as a null result. Winners get called on pre-committed thresholds, not gut feel, and we’ll tell you plainly when a test is underpowered instead of dressing up a coin flip.
Then we do the part most agencies skip: we build it and we run it. When a test wins, we ship it to 100% and confirm the lift holds. When it loses, we write down what it rules out — the cheapest research you’ll buy. The backlog is prioritized on impact, confidence, and effort, and it compounds over quarters because every result, win or loss, sharpens what we believe about your customers. That’s experimentation as a standing practice for mid-market brands, not a one-off report you file away.
Questions, answered.
How much traffic do we need before A/B testing is worth it?
Enough that a test can reach significance in a sane runtime — usually a few weeks, not a quarter. The honest math depends on your baseline conversion rate and the size of the effect you're hoping to detect: a page converting at 1% needs far more traffic than one at 8%. In the first conversation we'll size a representative test against your real numbers and tell you plainly whether experimentation is the right tool yet, or whether you're better served by analytics and judgment-led fixes until volume catches up. We won't sell you a test program you can't statistically support.
What's the difference between this and just running tests in Optimizely or VWO ourselves?
The tool is the easy part. The hard parts are picking tests that can actually move revenue, sizing them so the result means something, building variants that don't break, and reading the data without fooling yourself. Most in-house programs stall because they run underpowered tests, stop early on a hot variant, or test trivia. We bring the experiment design, the statistics, the engineering, and the discipline to call results honestly. We're comfortable working in whatever platform you already own, or recommending one if you don't.
How long does a typical test take?
It's set by math, not preference. We calculate the required runtime up front from your baseline rate, the minimum effect worth detecting, and your traffic. Many tests resolve in two to four weeks; lower-traffic or smaller-effect tests take longer, and we'll tell you that before launch so there are no surprises. The one thing we won't do is stop a test early because a variant looks like it's winning — early peeking is the most common way teams ship results that don't hold up.
What happens when a test loses?
You learn something, and we make sure that learning is captured. A loss rules out a hypothesis and often points at a better one — that's the cheapest research you'll buy. We document what was tested, what we expected, what actually happened, and what it implies for the roadmap. A program where every test 'wins' is usually a program that's only testing safe, trivial changes. Real experimentation produces a mix, and the losses sharpen the next round.
Do you only test the homepage, or the whole funnel?
The whole funnel, wherever the traffic and the leverage are. Homepages get attention, but the bigger wins are often deeper — the add-to-cart step, the checkout, the pricing page, lifecycle email, the signup flow. We map where users actually drop off, then aim experiments at those points. For high-traffic steps we test; for low-traffic steps where significance is out of reach, we fix on best practice and instrumentation rather than waiting on a result that won't arrive.
How does this connect to the rest of your Analytics & CRO work?
Experimentation is the proving stage. Analytics tells us where users struggle and what's worth investigating; CRO research forms the hypotheses; experimentation decides which changes are real and ships the ones that are. Because we build and operate the program rather than handing over a plan, the loop stays closed — data informs the next test, the test informs the roadmap, and the wins get rolled out and confirmed. It's one continuous practice, not a report you file away.