Conversion optimization rebuilt around evidence.
Most CRO programs are a graveyard of button-color tests with no power to detect anything. We rebuild the research layer first — session replay, qualitative interviews, on-site surveys, funnel analysis — then run experiments that are powered to deliver real, defendable lifts. The win isn't a positive test. The win is a learning that compounds.
Why this discipline earns its budget.
Conversion rate is the lever that touches every other marketing investment. Lift CVR by 20% and every paid dollar gets 20% more efficient. Every SEO visitor gets 20% more valuable. Every email click drives 20% more revenue. CRO is the only discipline whose output multiplies every other discipline's output, which is why mature marketing teams treat it as core infrastructure and not as a side project.
The practice, however, is broken in most companies. The industry default is button-color tests at sample sizes too small to detect anything statistically real, declared 'winners' that fail to replicate, and a graveyard of 'tests we ran' in a notion doc nobody opens. Roughly 60% of published A/B test results would fail a proper power calculation. The discipline has a credibility problem because most teams have been doing it wrong for a decade.
Done right, CRO is a research discipline first and a testing discipline second. The research surfaces problems worth solving. The testing validates whether the proposed solution actually solves them. Skip the research and you're guessing at hypotheses. Skip the testing and you're shipping guesses to production. Most teams skip one or both, then wonder why CRO 'doesn't work' for their business.
The work, spelled out.
Four phases. No surprises.
Diagnose
Funnel analysis, session replay tagging, and customer interviews. Most CRO programs skip this step. It's the step that makes everything else work.
Hypothesize
We score every potential test on impact, confidence, and ease (ICE), then prioritize a 90-day experiment roadmap.
Experiment
Tests are powered, instrumented, and pre-registered. Sample size calculated up front. No peeking. No false positives shipped because someone got impatient.
Ship
Winners ship to production. Losers go in the learning library. Every quarter we update the program thesis based on what we learned.
Our signature, named.
Our CRO program runs on what we call the Research-Hypothesis-Experiment-Ship loop, executed in 6-week cycles. Each cycle starts with a research week: session replay tagging, on-site survey deployment, customer interviews, funnel analysis in GA4 and Mixpanel or Amplitude. Out of that research come 8–15 hypothesis cards, ICE-scored and prioritized by predicted lift, confidence, and ease of execution. The top 3–5 become tests in the next four weeks. The remaining go in a hypothesis library, not the trash.
Tests are powered correctly — sample size pre-calculated, duration locked, no peeking — and run on Statsig, Eppo, VWO, or GrowthBook depending on the client stack. Results are reviewed in a structured readout that includes effect size, confidence interval, segment-level breakdown, and a written recommendation: ship, kill, or iterate. Winners ship to production. Losers go in the learning library with a written post-mortem on why the hypothesis was wrong. Quarterly we update the program thesis based on what we've learned about the customer, the funnel, and the business.
Common mistakes, and the truth instead.
- Testing without enough traffic to detect a meaningful lift.
- For a 10% relative lift on a 3% baseline conversion rate, you need roughly 1,200 conversions per variant — and most tests run on a fraction of that, declare a 'winner' at 90% confidence, and ship a result that's noise. We pre-calculate sample size and refuse to call tests early. Discipline beats optimism in CRO every time.
- A/B testing without qualitative research first.
- If you don't know why users drop off, you're testing random hypotheses against a fog. Session replay, on-site surveys, customer interviews, and post-purchase surveys are where the actual hypotheses come from. Quantitative testing without qualitative inputs is expensive guessing.
- Stopping tests when they 'look like they're winning.'
- Peeking — checking results before the planned sample size — inflates false-positive rates to 30–60%. We pre-register every test, set a planned duration, and refuse to look at results before sample size is hit. The discipline costs nothing and prevents the most common cause of 'wins' that don't replicate.
- Treating every variant as deserving a test.
- Some ideas are good enough to ship without testing — a fixed broken nav, a removed dead element, an actual bug fix. Other ideas aren't worth testing because the predicted lift is below the noise floor of your traffic. We use ICE scoring to triage; about 40% of proposed tests die at intake, which saves the program's calendar for the work that actually matters.
- Stopping CRO at the checkout flow.
- Most CRO programs focus on the bottom funnel and ignore the much larger lifts available in landing pages, category navigation, search-to-product page transitions, and lifecycle email click-through. The biggest experiments we've shipped lifted top-of-funnel discovery, not checkout. The funnel is wider than the checkout cart.
What people actually ask us about cro.
For statistically powered A/B testing on transactions, you need roughly 1,000 conversions per variant per month. Below that, we work on research-led UX redesigns and small-sample qualitative work, not testing theater.
VWO, Optimizely, GrowthBook, Convert, AB Tasty, Statsig, and Eppo. If you're on Google Optimize legacy, we'll help you migrate.
Yes. Design, copy, and engineering done in-house. We don't hand you a strategy doc and a Jira ticket.
Successful programs deliver a 15–35% lift in primary conversion rate over a 12-month engagement. Anyone quoting much higher in a sales call is cherry-picking.
Most engagements run $10K to $35K per month, depending on traffic volume, test cadence, and whether design and development of variants are in scope. Below $8K we don't take on dedicated CRO retainers — the research overhead has a floor.
First powered tests typically ship in week 4–6, results readouts in week 9–11. By month 6, a well-run program is shipping 2–4 winners per quarter that compound. The ROI math typically goes positive at the 4–7 month mark; anyone quoting faster is selling you peeking-based 'wins.'
Yes — differently. With under 1,000 conversions per variant per month, we shift the program toward research-led UX redesigns, qualitative customer interviews, and Bayesian small-sample testing where appropriate. The discipline becomes 'be right because we did the research,' not 'be right because the p-value said so.'
UX design proposes what the experience should be. CRO validates whether the proposed experience actually performs against an alternative. They're complementary disciplines and the best programs run them as a single loop — UX writes the hypothesis, CRO runs the test, and the result feeds the next round of UX. Treating them as separate departments is how testing becomes theater.
The channels that amplify this one.
↳ CRO brief
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