Run Growth Experiments
That Produce Real Signal, Not Noise
A rigorous framework for designing, running, and interpreting growth experiments — so your team ships the changes that compound, not the ones that sound good in theory.
Start ExperimentingWhy growth experiments breaks down for most teams
Running A/B tests without statistical validity — declaring winners from noise
Testing tactics before validating the hypothesis and expected mechanism of action
No experiment backlog system, so the team tests whatever someone thought of last week
Shipping winning tests that don't move the needle because they weren't connected to a growth lever
Growth Experiments built for products with real constraints
We build the hypothesis framework that forces teams to define mechanism before testing output
We set statistical validity requirements so tests produce signal, not stories
We design the experiment backlog by ICE score — impact, confidence, ease — ranked and ready
We connect every experiment to a growth lever so wins compound instead of standing alone
What strong growth experiments produces
Higher Test Win Rate
Hypothesis-first testing produces more winning experiments than intuition-first testing.
Faster Learning Cycles
A prioritized backlog keeps the team running the highest-leverage experiments continuously.
Compounding Results
Experiments connected to growth levers stack — each win makes the next test more valuable.
Cross-Team Alignment
A shared experiment framework aligns product, marketing, and growth on what to test and why.
The growth experiments process
Write the hypothesis
Define: 'If we change X for users doing Y, we expect Z because of mechanism M.'
Score and prioritize
ICE-score the backlog — impact on the metric, confidence in the hypothesis, ease of implementation.
Set validity conditions
Define sample size, confidence interval, and minimum detectable effect before running the test.
Document and share learnings
Publish every result — winners and losers — so the whole team builds from the same knowledge base.
Growth Experiments insights for your category
Growth Experiments plays out differently across product categories. Find the insights matched to your constraints.
SaaS
scalable MRR with strong NRR and low CAC
Fintech
trusted activation with high user lifetime value
EdTech
high course completion rates and strong learner retention
E-commerce
high repeat purchase rate and strong customer LTV
Healthcare
sustained engagement and measurable health outcomes
B2B Software
shorter time-to-revenue with higher ACV
Marketplace
liquid two-sided marketplace with strong retention on both sides
Developer Tools
high developer adoption with deep workflow integration
Media & Content
loyal, direct audience with strong engagement rates
Agencies
productized service with recurring revenue and reduced delivery costs
Real Estate
efficient lead nurturing with higher conversion from interest to transaction
Logistics
strong retention through deep integration and measurable reliability
HR Tech
high adoption across all stakeholder layers with strong renewal rates
PropTech
trusted platform with cross-market expansion capability
LegalTech
high-trust adoption with measurable efficiency gains for legal teams
Growth Experiments reading list
Apply growth experiments
to your actual product.
Turn frameworks into live systems — Greta builds the products and infrastructure that make growth strategies real.