Go from one test a quarter to an always-on experimentation program
Omniconvert Explore deploys A/B tests without a developer and reads results by segment.
Placeholder values below. Replace them with your own testing numbers.
See how Explore removes the developer wait ↓
Assumes your win rate stays the same as volume grows and a test's calendar shrinks by the share you enter. Simple arithmetic on the numbers you enter. It is a scenario, not a benchmark or a forecast.
Built on data from 2,500+ eCommerce brands
In shortGo from one test a quarter to an always-on program. Omniconvert Explore deploys A/B tests without a developer and reads results by segment, so more tests mean more wins, not more noise.
What is holding your testing program back?
Tick what is true today. Be honest, nobody is watching.
When every test waits on a ticket, the queue sets your pace
When every test waits on an engineering ticket, the queue sets how many tests launch. Take the wait away and the limit becomes your supply of ideas and your traffic.
Illustrative example, not a benchmark. Without the wait, the limit becomes ideas and traffic.
How Explore scales your testing program
Three shifts take a team from occasional tests to a program that runs continuously.
Explore deploys tests without waiting on a developer
- Variants are built and launched through a visual editor and a single snippet
- A new test does not sit behind an engineering ticket
- Throughput depends on ideas and traffic, not on developer time you can borrow this sprint
Your team still: brings the ideas and decides what goes live.
Explore reads every result by segment, so a win for one audience is not lost
- A variant that looks flat overall can win strongly for mobile, paid or returning visitors
- Explore breaks every result down by segment
- You keep the version that wins where it matters instead of discarding it on a blended average
Your team still: starts from a clear hypothesis and chooses which segments matter.
Run an always-on roadmap of prioritized experiments with Explore
- A standing backlog of prioritized hypotheses, so a next test is in line
- A steady cadence of live experiments instead of testing when someone finds the time
- A program that compounds learnings quarter over quarter
Your team still: prioritizes the backlog and feeds it new hypotheses.
Your testing program, with and without Explore
| Task | Without | With Explore |
|---|---|---|
| Deploying a test | An engineering ticket | A visual editor and a single snippet |
| What limits throughput | Often developer time you can borrow this sprint | Your supply of ideas and traffic |
| Flat results | Often judged on a blended average | Broken down by segment first |
| Cadence | Often whenever someone finds the time | A standing backlog and steady live experiments |
| Learnings | Often a handful of disconnected tests | Compound quarter over quarter |
Leroy Merlin ran an always-on program for 14 months
Over 14 months, Leroy Merlin ran a structured, always-on testing program with Omniconvert Explore rather than occasional one-off tests.
Omniconvert, Leroy Merlin case study
Two things worth knowing upfront
Don't we need a dev team to run more tests?
No, and removing that dependency is the whole point. Explore deploys variants through a visual editor and a single snippet, so how many tests you can run is limited by your supply of ideas and traffic, not by your engineering backlog.
Won't more tests just mean more noise?
Not when the program is structured. Leroy Merlin ran 46 experiments at a 39.13% win rate over 14 months (see the case study above): volume paired with clear hypotheses and segment-level reading is what turns test count into wins rather than noise.
Common questions
How many A/B tests should an eCommerce brand run per month?
There is no single correct number, because the real constraint is how much traffic each variant needs to reach a reliable result. A high-traffic store can keep several tests live at once and aim for a continuous pipeline; a lower-traffic store runs fewer, larger tests for longer. The useful target is a steady, always-on cadence rather than a fixed monthly quota.
What does it take to run more A/B tests?
Three things: a way to deploy tests without a developer in the loop, a prioritized backlog of hypotheses so there is always a next test ready, and enough traffic to reach a result in a reasonable window. Explore removes the developer dependency; the backlog and traffic are what you build around it.
How do I keep test quality high as I run more experiments?
Scale volume without dropping discipline: start each test from a written hypothesis, let it run to a planned sample size instead of stopping at the first promising day, and read results by segment so a win for one audience is not hidden by a flat blended average.
What is a good win rate for an A/B testing program?
Win rates vary widely by site, traffic and how bold the changes are, so there is no universal benchmark. A structured program measures itself against its own baseline over time. For context, Leroy Merlin sustained a 39.13% win rate across 46 experiments in 14 months with Omniconvert Explore.
Do I need a lot of traffic to scale experimentation?
More traffic lets you run more tests in parallel and detect smaller effects, but lower-traffic stores can still run a serious program by testing bigger, higher-impact changes and letting each test run longer. The approach changes with traffic; the discipline does not.
How does Omniconvert Explore help scale experiments?
Omniconvert Explore lets teams deploy A/B tests through a visual editor with no developer dependency, read every result by segment so winning variants are not discarded on a blended average, and manage a continuous roadmap of prioritized experiments instead of one-off campaigns.
See how fast your testing program could move
30 minutes. We'll map your current testing cadence and where the bottlenecks are, not a demo store.