Three phases. Diagnostics before decisions. Engineering-grade blueprints before development. Statistical certainty before declaring a winner.
Before a single pixel changes on your store, we perform a structured heuristic and behavioural audit of your entire funnel. Not a checklist walkthrough — a clinical diagnosis.
We examine cognitive load at each decision point. We evaluate scientific claim validity against how skeptical health buyers actually read product pages. We run a full GA4 and GTM data integrity audit to verify that every decision downstream is built on accurate signal, not noise.
The output is a 100-point audit board — every friction point categorised, scored by estimated revenue impact, and assigned a test hypothesis. Nothing vague. Nothing generic. Every finding is specific to your funnel, your buyer, and your data.
Most agencies hand developers a PDF of annotated screenshots and call it a brief. We don't. Every experiment variant is produced as a high-fidelity wireframe with exact technical specifications.
Pixel dimensions. Copy hierarchy. Interaction states. Data layer requirements. These aren't "pretty pictures" — they are zero-ambiguity implementation briefs that a developer can ship without a single clarification call.
The design phase also forces rigour on the hypothesis itself. If a variant can't be wireframed precisely, the hypothesis isn't specific enough. That discipline prevents vague experiments and ensures every test is measuring exactly one variable.
The most common failure mode in CRO isn't bad experiments — it's calling winners too early on insufficient data. A standard frequentist A/B test with a p-value of 0.05 still has a 1-in-20 chance of being a false positive. Run enough tests and you'll "win" your way to worse performance.
We use a Bayesian statistical framework — backed by an Executive PG in Data Science from IIIT Bangalore — that accounts for prior knowledge, quantifies the probability of being wrong, and provides a continuous read of confidence rather than a binary pass/fail threshold.
A winner is only called when the math unambiguously says it's a win. That's not a preference. That's a clinical standard applied to conversion.