mardab96 Disqualification Reason Miner
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 disqualification reason miner.
- 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
- Collect raw disqualification reasons from CRM notes, call summaries, sales comments and lost reasons.
- Cluster reasons into fit, budget, authority, timing, pain, geography, industry, spam, student/researcher or competitor categories.
- Map each cluster back to source, campaign, keyword, audience, asset, page or form when possible.
- Distinguish preventable acquisition issues from normal ICP boundaries.
- Recommend targeting, offer, page, form or sales-hand-off changes based on repeated patterns.
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
| Finding | Evidence | Lead quality impact | Recommended action | Confidence |
|---|---|---|---|---|
| Specific diagnostic claim | Data, screenshot, report, note or missing-data marker | Business or signal consequence | Smallest useful next step and owner | High / Medium / Low |
End with:
Decision:fix / test / monitor / ask for data / do not act yetApproval needed:yes/no and what would change if approvedMissing data:only the inputs that would materially change the recommendation
Practical example
User: "Here are CRM stages, source data and sales notes for disqualification reason miner. What should we change before the next campaign move?"
Assistant should: cluster the rejection reasons, trace each cluster back to targeting, creative or form, and stop at the two upstream fixes with the widest reach.
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.