Experiments run fast, scaled only when they hold up
Growth marketing built on a structured experimentation process across acquisition, activation, and retention.
Where this usually breaks down
Most growth marketing programmes fail for the same reason: someone reads a case study about a tactic that worked for a completely different business, tries a version of it with no clear hypothesis, and calls whatever happens next a result. When it doesn't move anything, "growth hacking" gets blamed instead of the actual cause - there was never a metric being tested against, so there was no way to tell a real signal from noise.
The second failure mode is scaling before the signal is real - a test shows a mild uplift on a small sample and gets rolled out to the whole funnel before anyone checks whether it holds up, burning a channel or a segment on a result that was never real to begin with.
What this service actually solves
Growth marketing done properly is a repeatable cycle: form a specific hypothesis, run the smallest test that can validate or kill it, and only scale what actually holds up against a real sample size. Acquisition, activation, and retention get tested as connected stages rather than one channel in isolation, because a win in acquisition that quietly damages activation isn't a win.
How we run it
We run growth work off a prioritised experiment backlog rather than a wish list, scoring each idea on the effort it takes versus the impact it could plausibly have, so the queue reflects what's actually worth testing next. Every experiment gets a hypothesis and a defined success metric before it launches, and nothing gets scaled off a result that hasn't cleared a real sample size.
Capabilities & deliverables
Experiment Design & Prioritisation
- Hypothesis-driven experiment briefs
- ICE-style scoring for the experiment backlog
- Clear success metrics defined before launch
Full-Funnel Testing
- Acquisition channel experiments
- Activation flow testing
- Retention and re-engagement experiments
North Star Metric Definition
- A single metric the whole growth programme is judged against
- Supporting input metrics mapped to that North Star
Rapid Test-and-Learn Cycles
- Short experiment cycles with a defined end date
- Kill criteria agreed before a test launches
- Documented learnings whether a test wins or loses
Scaling Discipline
- Statistical significance and sample-size checks before scaling
- Phased rollout rather than switching everything on at once
How an engagement runs
Metric & North Star Definition
We agree the single metric the growth programme is actually accountable to before any test gets designed.
Experiment Backlog Build
Hypotheses are collected and scored by likely effort versus impact, so the queue reflects what is worth testing first.
Test Design
Each experiment gets a stated hypothesis, a success metric, and a kill criterion agreed before launch.
Rapid Testing
Tests run in short cycles across acquisition, activation, and retention, with a defined end date rather than running indefinitely.
Result Review
Results are checked against sample size and significance, not just whether the top-line number moved.
Scale or Kill
What holds up gets rolled out in phases; what doesn't gets documented and retired rather than quietly repeated.
Who needs this
Teams running tactics without a shared metric
If different people would give different answers about what "working" means, that is the sign to fix first.
Businesses past the early-stage guesswork phase
Once there is enough traffic or user volume to get a real read on a test, structured experimentation starts to pay off.
Related work
We're still building out published proof for this specific service — ask us directly and we'll walk through relevant examples.
How to engage us for this
Project-based
A defined outcome with a start and end date - an audit, a migration, a campaign build, a tracking overhaul. Fixed scope, fixed price, agreed upfront.
Ongoing retainer
Continuous management and optimization once the initial build is live - campaigns, SEO, reporting, and iteration run every month under one accountable team.
Advisory
Strategy and oversight without full delivery - we review what's already running, unblock decisions, and point an in-house or existing team in the right direction.
Common questions
Faster than a full rebrand or a content programme, but not overnight - a proper test needs enough volume to reach a real sample size, which can take anywhere from a couple of weeks to a couple of months depending on your traffic. We'd rather report a slower, trustworthy result than a fast one that doesn't hold up.
No - growth rate depends on market conditions, your product, and your existing baseline, none of which a testing process controls outright. What we can guarantee is a disciplined process that finds real wins and avoids scaling false ones, which compounds over time even without a guaranteed number attached.
They get documented, not discarded. A failed hypothesis is still information - it rules something out and often points toward what to test next, so retesting the same dead end gets avoided.
You need enough to reach statistical confidence in a reasonable timeframe. Very low-traffic sites can still run growth marketing, but tests take longer to reach a trustworthy read, so we usually prioritise fewer, higher-impact tests rather than a high volume of small ones.
Running ads is a channel; growth marketing is the discipline that decides which channel, message, and funnel stage is actually worth testing next, and whether a result is real before more budget goes toward it.
The experiment backlog is scored jointly, weighing likely impact against effort, but the North Star metric set at the start is what keeps the priority list honest rather than driven by whichever idea is most exciting that week.
Testing tactics with no way to tell if they worked?
We'll help you define the metric that actually matters and build a testing process that scales only what holds up.
Ready to get started?
We usually reply within 24 hours.
Growth Marketing, in detail
Growth marketing is structured experimentation - testing specific hypotheses against a defined North Star metric across acquisition, activation, and retention, then scaling only what a real sample size actually supports.
Scope, area by area
| Area | What we deliver |
|---|---|
| Experiment Backlog | A prioritised, scored list of hypotheses ready to test |
| North Star Metric | A defined top-line metric with supporting input metrics mapped underneath it |
| Test Reports | A documented result for every experiment, including the ones that didn't work |
| Scaling Plan | A phased rollout plan for anything that clears the bar to scale |
How this compares
| Structured Growth Marketing | "Growth Hacking" Tactics |
|---|---|
| Every test starts with a hypothesis and success metric | Tactics get tried because they worked somewhere else |
| Results are checked against sample size before scaling | A small uplift gets rolled out immediately |
| Learnings are documented whether a test wins or loses | Failed tests are forgotten and get tried again later |
Both approaches involve testing - the difference is whether a result is trusted before it's acted on.
What this changes for the business
- The growth programme has a single metric everyone is actually accountable to
- Wins get identified because they cleared a real bar, not because a small sample looked good
- Failed experiments stop getting quietly repeated because the result is documented the first time