Fast Mold Testing

How we assess

We work top-down, from why people buy to whether the machine actually converts them:

1. Ground in the qualitative. Who the target customer is, the job they hire you for, their triggers, fears, and the words they use. Everything downstream serves this.
2. Read the design of the business. The product, the offer, differentiation, positioning, and brand — how well the business is built to meet that customer's need, and where the match can be deepened.
3. Check the mechanics & the data reality. The tool stack, the acquisition channels, the conversion platforms — what's actually wired, and what each one can tell us.
4. Score performance at each stage. Are we visible — getting in front of eyeballs (impressions)? Are we activating that attention — earning the click onto a surface we control? Once they're there, are they converting — booking, buying? And do they return? We use the data to find the strengths and the weaknesses.

This maps directly onto the three layers below (Context → Funnels → Surfaces) and the funnel stages on the Funnel tab.

How we diagnose

Finding where the funnel leaks is only half the job; the value is explaining why. At each leak we ask what's adding fear, uncertainty, doubt, or friction on the way through — why CTR isn't higher on an organic result, why qualified traffic lands and bounces instead of converting, why a ready buyer stalls at the form. The answer is always one of four levers we can pull:

Every recommendation in the roadmap is one of these four moves, aimed at a specific leak. The LIFT lens below is how we make that diagnosis concrete on each surface.

The three layers

Every funnel is assessed top-down through three layers. A weak layer caps the ones below it: a brilliant landing page can't save a business with no offer, and a great offer can't save a funnel with no way to capture the lead. We use plain names in this report — the engine's internal terms are noted in parentheses.

The scorecard

Every area — a Context domain, a funnel piece, a surface — is scored the same way: against a defined checklist of what great looks like, each criterion drawn from a named framework. Every criterion gets one of three verdicts:

The score is how many criteria are done well over how many apply (e.g. Customer 4/7); criteria that don't apply are excluded, never counted against you. As a rough read, 70%+ is healthy, 40–69% is mixed, under 40% needs work. We show the passes as well as the misses, so you see what's working and exactly what to fix to raise the score — a count you can act on, not a vague grade.

The LIFT lens

LIFT is a lens we read each surface through — not the leading frame. We first score a surface against the concrete elements it should have (a hero that names the outcome, proof at the decision, a one-tap mobile call), then use LIFT to explain why a surface converts or doesn't. A page can pass every LIFT factor and still be a poor surface if the elements are disconnected, so the surface criteria lead and the lens follows. Value proposition is the central lever; relevance, clarity, and urgency lift it; anxiety and distraction drag it down; attention sits upstream of all of them.

On every surface we read those factors across three dimensions — because a leak can come from any of them:

Scoring & sequencing

Beyond the pass-rate scores, every recommended finding is rated on three 1–10 axes, then sequenced. We never publish an invented number — the metric named on each card is the real one we expect to move, and magnitude is left to the measured result rather than a fabricated forecast.

Each finding is then routed by certainty and reversibility: high-certainty, easily-reversible changes are a Ship; uncertain-but-reversible ideas are a Test; uncertain-and-costly questions are Research first.

Causal chains

Where a symptom, its cause, and the fix line up, we say so explicitly — so the recommendation reads as a chain, not a guess.

Evidence & confidence

Every claim in this report is backed, and we show the backing. A finding earns confidence from up to five kinds of evidence — the more that stack, the higher the confidence we report on it.

When a number is measured, we show it. When it's an estimate or an inference — because the tracking isn't live yet — we flag it estimate and give it as a range, never a guarantee. We never assert a competitor's conversion rate or invent a "lift to X%" — magnitude is left to the measured result.

Observations & solutions

The audit produces two linked things. An observation is a problem or opportunity we found, with its evidence — it stands on its own, even where we don't yet prescribe a fix (we say why). A solution is an action that addresses one or more observations. The Full Audit leads with the observation; the Roadmap leads with the solution and links back to the problem it solves. One master list feeds both, so nothing is double-counted and every recommendation traces to something we observed.