Design lead routing and assignment rules
Clear lead and account routing rules that get every lead to the right rep fast, with fallbacks, SLAs, and edge cases covered.
The prompt
Help me design lead routing and assignment rules.
Lead sources: [e.g. demo requests, content downloads, events, partners, product sign-ups]
Teams who receive leads: [e.g. SDRs, SMB AEs, enterprise AEs, partner managers, with headcount]
How we segment: [e.g. employee count, region, industry, named accounts]
Current problems: [e.g. slow response, leads sitting unassigned, reps cherry-picking, conflicts with existing account owners]
Tools: [CRM, routing tool, marketing automation]
Response time goal: [e.g. under 5 minutes for demo requests]
Design:
1. A decision tree in order of precedence: existing customer or open opportunity, named account owner, partner-sourced, segment and territory, then round-robin.
2. Lead-to-account matching rules (domain matching, subsidiaries, personal emails).
3. Distribution method within a pool (round-robin, weighted by capacity, skip reps who are out or at capacity).
4. Response SLAs by lead type, and what happens when an SLA is missed (reassignment, alert, escalation).
5. Edge cases: duplicates, students and competitors, missing data, international leads, re-engaged old leads.
Return:
- The routing logic as a numbered decision tree.
- A rules table: Condition | Assigned to | SLA | Fallback.
- Fields and data required for routing to work.
- Reports to monitor routing health (speed to lead, unassigned count, distribution fairness).
- A testing checklist before going live.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Projects & Operations prompts
What to fill in
- e.g. employee count, region, industry, named accounts
- CRM, routing tool, marketing automation
- e.g. under 5 minutes for demo requests
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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