Lead To Opportunity Drop Off Review

Lead To Opportunity Drop Off Review

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 lead to opportunity drop off review.
• 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. Map funnel stages from lead captured to MQL, SQL, meeting, opportunity and closed-won.
2. Calculate or estimate drop-off by source, segment, owner, score, offer and time window.
3. Identify the first stage where qualified intent stops progressing.
4. Check whether the cause is lead quality, routing, follow-up, qualification criteria, offer mismatch or CRM hygiene.
5. Recommend the highest-impact stage fix and the metric that should move next.

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 lead to opportunity drop off review. What should we change before the next campaign move?"

Assistant should: locate the stage where qualified leads stall, separate a process cause from a fit cause, and stop at one fix per stage.

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.