67 lines
2.3 KiB
Markdown
67 lines
2.3 KiB
Markdown
---
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name: qualify-lead
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description: Evaluate prospect fit and intent to determine next-best action for sales teams.
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usage: /sales-prospecting:qualify-lead --company "Acme" --persona "VP Revenue" --signals intent.json
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---
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# Qualify Lead Command
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Score leads against ICP, intent signals, and buying triggers to decide whether to route, recycle, or nurture.
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## Command Syntax
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```bash
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/sales-prospecting:qualify-lead \
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--company "<name>" \
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--persona "<title>" \
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--firmographics firmo.json \
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--technographics tech.json \
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--signals intent.json \
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--threshold 75
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```
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### Parameters
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- `--company`: Account name or domain.
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- `--persona`: Primary buyer role under evaluation.
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- `--firmographics`: JSON/CSV input of company attributes.
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- `--technographics`: Technology stack details.
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- `--signals`: Intent data (product usage, web visits, 3rd-party intent).
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- `--threshold`: Minimum composite score to pass to sales (default 70).
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- `--stage`: Lead stage (MQL, PQL, recycled) to influence scoring weights.
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## Workflow
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1. **Data Normalization** – clean and map firmographic, technographic, and behavior fields.
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2. **Fit Scoring** – apply weighted ICP model (industry, size, geography, use case, tech stack compatibility).
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3. **Intent Scoring** – incorporate behavioral data (content engagement, trials, intent providers, product telemetry).
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4. **Timing Assessment** – evaluate trigger events (funding, hires, tech churn) plus buying cycle alignment.
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5. **Recommendation Engine** – produce route/recycle/nurture guidance with rationale, next steps, and owner.
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## Output Schema
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```json
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{
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"company": "Acme Corp",
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"fit_score": 82,
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"intent_score": 76,
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"composite_score": 79,
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"decision": "route-to-ae",
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"urgency": "high",
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"rationale": [
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"ICP match: SaaS, 200 employees, US",
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"High technographic overlap",
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"Recent product trial and pricing page visits"
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],
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"next_steps": [
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"Assign to AE Sarah Lee",
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"Send enterprise case study",
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"Schedule discovery call within 48h"
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]
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}
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```
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## Best Practices
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- Keep scoring models transparent so SDRs/RevOps can adjust weights.
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- Blend qualitative notes (call transcripts) with quantitative data.
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- Auto-sync outcomes to CRM and track model drift monthly.
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- Use different thresholds for inbound vs outbound vs product-led leads.
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---
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