Form Friction Finder

Form Friction Finder

Use the shared quality bar in ../references/output-standard.md and ../references/skill-design-principles.md when those files are available.

Use this skill when

• the user shares lead source, CRM stage, sales note, form, landing page or campaign data tied to form friction finder.
• the next decision could change targeting, qualification, scoring, follow-up, sales handoff or budget.
• lead volume looks acceptable but SQL, opportunity, closed-won, rejection or response-speed data raises doubt.

Do not use this skill for broad lead-generation advice without source, CRM, sales or qualification evidence. Use it when a real B2B lead quality decision is on the table.

Required input

• business model, ICP, offer, ACV or deal value range, sales cycle and main conversion goal.
• ad, landing page, lead form, CRM, call note, email or campaign data relevant to this diagnostic.
• time window, traffic source, lead volume and downstream outcomes where available.
• what decision the user is trying to make next: create, fix, scale, pause, brief sales or investigate.
• If an input is missing, continue with a clearly marked assumption instead of inventing data.

Analysis workflow

1. List every field, step, required answer, validation rule and hidden field in the form.
2. Classify each field as essential for routing, qualification, sales context, compliance or nice-to-have.
3. Compare form friction with buyer intent, offer value and traffic source temperature.
4. Look for mobile-specific friction: keyboard type, dropdown length, field order, error messages and page scroll.
5. Recommend remove, reorder, make optional, split, prefill or explain fields based on conversion and lead quality risk.

Decision rules

• If the data does not connect to revenue, pipeline, qualified leads or conversion quality, label the recommendation as a hypothesis.
• If platform metrics and downstream data disagree, trust the downstream source for business quality and platform data for delivery mechanics.
• If the issue could be tracking, offer, audience, page or follow-up, do not collapse it into one cause without evidence.
• Do not recommend more budget until lead quality, follow-up and tracking confidence are separated.

Output format

End with:

• Decision: fix / test / monitor / ask for data / do not act yet
• Approval needed: yes/no and what would change if approved
• Missing data: only the inputs that would materially change the recommendation

Practical example

User: "Here are CRM stages, source data and sales notes for form friction finder. What should we change before the next campaign move?"

Assistant should: list every field and step by drop-off cost, separate friction that qualifies from friction that only blocks, and stop at a shortlist to remove.

Guardrails

• Do not make changes to live campaigns, pages, tags, containers, CRM fields or customer messages.
• Do not claim performance impact without evidence.
• Mark missing data clearly.
• Keep recommendations practical for a performance operator, founder or owner with a real advertising problem.