UI Improvement

UI Improvement Skill

Purpose

Turn existing UI problems into reusable, testable improvement rules, and produce complete outputs from hypothesis to experiment plan.

When to Use

• Requests such as "increase CVR on this screen" or "reduce drop-off"
• Optimization work for existing pricing, paywall, onboarding, checkout, or PDP UI
• Interpreting A/B outcomes, planning follow-up tests, and converting losses into reusable learning

Improvement Framework (Required)

1. Assess the current state
   • Break down target behavior, current flow, and blockers (anxiety, friction, ambiguity).
2. Classify the problem type
   • Information gap / information overload
   • Flow friction (input, steps, authentication)
   • Weak value communication
   • Trust deficit (guarantees, transparency, social proof)
   • Meaning mismatch (copy/icon interpretation)
3. Create comparative hypotheses
   • Always produce baseline-like + improved + balanced variants.
4. Define guardrails
   • Reject any variant that harms transparency or user experience quality, even if conversion rises.
5. Attach an evaluation plan
   • Include primary metrics, secondary metrics, and explicit learning goals for failed tests.

Improvement Rules

1) Information Design

• Limit simplification to noise reduction; preserve decision-critical information.
• Use progressive disclosure while keeping access to comparison details.
• Prioritize explainability and predictability in pricing and terms.

2) Friction Reduction

• Minimize required upfront input and move heavy steps later where feasible.
• Compare single-page vs multi-step forms, and include progress visibility.
• In checkout, avoid unnecessary pre-purchase auth; consider account setup post-purchase.

3) Persuasion and Trust

• Always compare feature-led and benefit-led messaging.
• Prioritize clear trust elements: reviews, guarantees, return/cancellation transparency.
• Emotional messaging is valid, but pair it with concrete proof (numbers, specific terms).

4) Pricing and Plan Optimization

• Evaluate conversion winner and profit winner separately.
• Even with default-plan emphasis, keep alternative paths clearly visible.
• Treat trial eligibility, trial duration, and billing-condition presentation as separate levers.

5) Micro UI

• Favor icons and CTAs with immediate, unambiguous meaning.
• When testing emotional expression, always include a neutral control variant.

6) Learning Operations

• Do not treat losing tests as dead ends; document boundary conditions (where it works / fails).
• Promote winning patterns into reusable rules for future baseline proposals.

Output Format

Always return improvement proposals in this structure.

## Current Problem Hypothesis
- [Structured summary of blockers]

## Improvement Hypotheses
- H1:
- H2:

## Variants (Comparative)
- Variant A (baseline-friendly):
- Variant B (improved):
- Variant C (balanced):

## Elements to Change
- Information design:
- Copy / CTA:
- Trust signals:
- Flow / input:

## Measurement Plan
- Primary metric:
- Secondary metrics:
- Guardrails:
- Decision criteria:

## Learning Output
- Reusable rule to keep for both win and loss outcomes:

Prioritization

• Start with low-cost, high-impact changes (copy, ordering, visibility of existing elements).
• Then move to medium-cost changes (layout and flow redesign).
• Finally evaluate high-cost changes (new features, data/infrastructure changes).