Conversion Rate Optimisation
Review conversion journeys and measurement quality, then design testable improvements around available traffic and decision rules.
Why?
Conversion work is most useful when the team can trust what is measured and understand why a change is worth testing. We connect funnel evidence, customer friction, and experiment design so decisions reflect both outcomes and limitations.
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Evidence-led priorities
Hypotheses connect to observed funnel or user problems rather than a list of cosmetic changes.
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Context for user behaviour
Where permitted and available, behavioural data complements funnel analysis without treating clicks as proof of intent.
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Honest experiment reporting
Reports separate observed effects from uncertainty and show trade-offs such as lead quality, errors, or cancellations.
Our process
Check measurement and funnel data
We review event definitions, data quality, consent constraints, funnel steps, and relevant segments before interpreting drop-off.
Prioritise testable hypotheses
We turn observed friction into hypotheses and rank them by evidence, potential importance, effort, and risk.
Design variants and test rules
We specify variants, the primary metric, guardrails, traffic allocation, sample assumptions, and stopping rules before launch.
Prepare and check the experiment
Within the agreed platform and implementation scope, we check variant behaviour, tracking, eligibility, and data collection before reading results.
Analyse outcomes and uncertainty
We report observed effects, uncertainty, data-quality concerns, and guardrail changes without treating inconclusive results as wins.
Recommend the next decision
We recommend adopting, revising, rejecting, or continuing a test based on the agreed decision rules and document the next learning priority.
Questions?
We choose a primary outcome tied to the journey, such as qualified enquiry or completed purchase, and guardrails such as errors, cancellations, or lead quality. Metric definitions are agreed first.
There is no fixed duration. It depends on traffic, baseline conversion, the effect worth detecting, variability, and the agreed analysis method. We may recommend research or non-experimental improvements when traffic is insufficient.
Where useful and authorised, we review these tools with appropriate consent, access, and data-masking settings. They provide context, not proof that a proposed change will increase conversion.
You receive the agreed hypotheses, designs, experiment specifications, findings, and next-step recommendations. Tool access, licences, implementation ownership, and ongoing monitoring are defined in the proposal.
