Reference documentation

The scoring model

Everything needed to run RUCF without us. The same anchors apply whether you run it by hand or through the scorecard.

Four surfaces, twelve signals

SurfaceWhat it capturesSignals
TaskCan people do the thingTask completion, error recovery, navigation clarity
CommitmentDo they keep doing itActivation, depth of use, return intent
AccessCan everyone do itContrast and legibility, input and assistive support, content clarity
BeliefDo they trust itVisual hierarchy, brand coherence, trust signals

Twelve rather than any other number because each surface maps to a distinct evidence source, and three signals is the smallest set that covers a surface without one signal carrying two ideas.

Signal definitions

Task completion. The share of users who begin the primary task and finish it in the same session. Evidence: funnel telemetry.

Error recovery. When something goes wrong, whether the user gets back on track without leaving or contacting support. Evidence: post-error session continuation, support contacts per error event.

Navigation clarity. Whether users know where they are and where to go next. Evidence: search use, back-and-forth loops, help visits before the primary task.

Activation. Whether new users reach first meaningful value. Evidence: cohort progression to a defined activation event.

Depth of use. Whether users go beyond the single entry feature. Evidence: feature adoption breadth per active user.

Return intent. Whether users come back. Evidence: month-over-month active retention.

Contrast and legibility. Whether text and interface elements are perceivable. Evidence: automated WCAG 2.2 AA scan of the main flows.

Input and assistive support. Whether flows work by keyboard, screen reader, touch alone, and on constrained devices. Evidence: manual keyboard traversal, assistive technology testing.

Content clarity. Whether interface language is understood without external explanation. Evidence: share of support contacts answering questions the interface should have answered.

Visual hierarchy. Whether the design directs attention to what matters. Evidence: first-interaction analysis, first-click testing.

Brand coherence. Whether the product uses one vocabulary and one visual language across interface, documentation, marketing and support. Evidence: terminology audit, whether users adopt your vocabulary back.

Trust signals. Whether users believe the product is safe, legitimate and honestly priced. Evidence: drop-off at payment, permission and personal data collection points.

Scoring criteria

Each signal scores 0 to 5 against published anchors, every anchor stating a countable range. The anchored answer options in the scorecard are the criteria — the tool and the manual method use identical anchors, so a hand-run score and a tool score are comparable.

Each signal is asked twice: once from behaviour (what the data shows) and once from perception (what users say), and every answer carries an evidence declaration — measured, estimated or guessed.

Composite

Behaviour drives the score. Perception drives the diagnosis. The composite is computed from behaviour only.

surface score  = mean of its three behaviour signals
composite      = Σ(surface score × weight) ÷ 100 × 5
Product typeTaskCommitmentAccessBelief
SaaS0.300.300.200.20
Ecommerce0.350.150.200.30
Content or media0.200.350.200.25
Internal tool0.400.150.300.15

Adjust the weights if your product does not fit. Publish the weights you used alongside any score you report.

Score bands

BandRangeReading
Failing0.0 – 1.9Users are completing tasks despite the product
Costly2.0 – 2.9Working, at a price you are probably not counting
Adequate3.0 – 3.7Nothing urgent, several things worth money
Strong3.8 – 4.4Optimisation territory, protect what works
Exceptional4.5 – 5.0Rare, and usually means a measurement error, so check confidence first

Read the band with the confidence attached. A 4.6 at 0.30 confidence is a claim, not a result.

Reliability

Two people scoring the same product independently, both using observed evidence, should land within 0.4 on the composite. If your team is further apart than that, the disagreement is about the evidence rather than the criteria, and that disagreement is worth having before you continue.

Run it against your product