Every visitor is a signal. Every click is data. CRO is the discipline of reading those signals and turning more visitors into customers through statistically rigorous experimentation.
Most teams only optimize traffic. Smart teams know that improving conversion rate has the same impact with less spend.
Most "CRO" is just random changes with fancy names. Change the button color. Move the form. Add urgency messaging. Then declare victory when numbers go up (ignoring that they would have gone up anyway).
Real CRO is science. It requires statistical rigor, proper sample sizes, and the discipline to call tests inconclusive when they are. It means testing hypotheses, not hunches.
We run experiments that actually prove causation. When we say a change improved conversion rate, we mean it with 95%+ confidence.
We start with data, not opinions. Funnel analysis, heatmaps, session recordings, and user behavior data to identify where conversions are actually being lost.
Every test starts with a clear hypothesis: "If we change X, we expect Y because Z." No random "let's try this" experiments.
Proper sample size calculations. Bayesian or frequentist analysis depending on the context. Controls for seasonality and external factors.
We wait for statistical significance. We segment results. We look for interaction effects. And we're honest when tests are inconclusive.
Small, proven improvements compound. A 5% lift here, 8% there. Over a year, that's transformative. We build a testing velocity that compounds gains.
Price presentation, discount structures, bundle configurations, urgency mechanics.
Information hierarchy, content ordering, social proof placement, friction reduction.
Form optimization, payment options, shipping presentation, cart abandonment recovery.
Value propositions, headlines, product descriptions, objection handling.
Average results across our CRO engagements after 6 months of testing.
Traditional CRO tests one thing at a time. That's necessary for causal inference, but it's slow. Our ML models help us move faster in two ways:
Hypothesis generation: We analyze behavioral patterns across thousands of sessions to identify where conversion is being lost and why. This surfaces high-impact test ideas faster than manual analysis.
Segment discovery: Our models identify user segments that respond differently to changes. A test that's flat overall might be +20% for mobile users from paid search. We find those signals.
The result: higher testing velocity, better hypotheses, and compound gains that add up to transformative improvement over time.
Let's talk about CRO that's actually rigorous.
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