Split Testing
Structured A/B test programs that compound. Tests designed from real user behavior, not gut feel. Every test builds on the last, so your knowledge compounds and your lift accelerates.
Free 30-minute call · No obligation · No credit card required
$1B+
in client revenue managed
2,500+
brands
10+
years on Shopify
What is Split Testing?
Split testing at Build Grow Scale means every test is grounded in behavioral analytics, heatmaps, session recordings, and funnel data, not a random idea someone wants to try. One of the things that sets us apart from a lot of other agencies is the way we test, and how frequently we test.
We design test programs the way scientists design experiments: a clear hypothesis, a documented expected outcome, and a defined way to know we were wrong. That is how you actually learn instead of just shipping change after change.
The result is a testing engine where winners stack on top of each other and your conversion rate moves up and to the right, not in random spikes.
urious what a 1% lift is actually worth on your store? Try our conversion rate calculator before we ever talk.
“The first thing that we do when we get to a website isn't test the button color. There's other things that are so much better, but people tend to always gravitate to something simple.”
Matthew Stafford
The deliverables.
Behavioral Research First
Heatmaps, scroll maps, session recordings, and on-site surveys. We find the real problems before we propose solutions.
Hypothesis-Driven Test Design
Every test starts with “we believe X because Y, and we'll know we're right if Z.” No “let's just try this.”
Statistical Rigor
Tests run to proper significance, with sample-size pre-calcs. No calling winners on day three.
Documented Learning Library
Every test result captured: winners, losers, learnings. Your team owns it forever.
Winner Deployment + QA
When something wins, we ship it cleanly across devices and browsers.
Iteration on Winners
A win is the start of a question, not the end. We push winning patterns until they stop winning.
The process.
01
Audit & Hypothesis Generation
We pull behavioral data and identify the highest-leverage friction points. Each becomes a testable hypothesis. Many come from post-purchase survey questions and customer service conversations, since that is where customers describe problems in their own words.
02
Prioritization
Hypotheses get RICE-scored. We start with the ones most likely to move the needle for the least build effort.
03
Build, QA, Launch
Tests built to spec, QA'd across the major browsers and devices, launched to a clean traffic split.
04
Analyze & Decide
Read the data honestly. Ship the winner, document the loser, design the next test from what we learned. We never stop testing, because it's the only thing that keeps a site from going stale.
A recent test result.
A functional opt-in banner we tested for Yankum Ropes, in place of a pop-up, lifted email opt-ins for a giveaway 45% in a 14-day window: a clear enough signal that the pattern got rolled into later tests on the same account. Early best-practice passes alone typically produce 5–20% lifts, depending on how much was broken going in.
Is this right for you?
Strong Fit
- Have enough traffic to run statistically valid tests (~10k+ monthly sessions on the pages you're testing)
- Want a structured program, not random optimization
- Care about why something worked, not just that it did
- Have a goal: paid media efficiency, AOV lift, checkout completion, etc.
Probably Not If
- Pre-product-market-fit: testing accelerates a working product, it can't fix a broken one
- Low-traffic stores where tests will never reach significance
- Want a “tip sheet” of best practices applied without testing
- Not willing to lose tests (testing means losing sometimes. That's the point)
Common Questions
How is this different from just running a plugin's built-in A/B testing?
Every test starts with a documented hypothesis (what we believe, why, and how we’ll know we were wrong) built on heatmaps, session recordings, and funnel data, not a guess about what to try next. That discipline is what makes results stack instead of resetting with every new test.
How much traffic do I need before testing makes sense?
What happens after a test wins?
Where do test ideas actually come from?
Can split testing fix a product that isn't selling?
Related services.
Split testing runs on top of analytics and data consulting to size the opportunity first, feeds into custom reporting on test results, and pairs naturally with ecommerce SEO to grow the traffic you’re testing. See the results in our case studies, or read why BGS tests this way.
$1B+ in client revenue
managed. Let's
talk about yours.
A free 30-minute call to look at your store's data and conversion funnel together.
No obligation. No pressure.