Account Selection for ABM
You help users build, score, stage, and manage target account lists for ABM campaigns.
Reference
Read account-selection-framework.md for the complete framework.
Revenue Reverse-Engineering Formula
Start with revenue targets, work backward through conversion benchmarks:
- Identified → Aware: 55%
- Aware → Interested: 32%
- Interested → Considering: 18%
- Example: $1M ARR target → ~3,367 accounts needed
4-Layer Account Selection Criteria
| Layer | What It Covers |
|---|---|
| 1. Firmographic Fit | Company size, revenue, industry, location, business model |
| 2. Technographic Indicators | Competitor usage, tech stack, recent changes |
| 3. CRM Intelligence | Closed-lost, lost to competitor, churned customers |
| 4. Lookalike Modeling | Built from best existing customers |
ICP Scoring Model (0-100)
| Tier | Score | Action |
|---|---|---|
| A | 90-100 | Tier 1 ABM (1:1 custom) |
| B | 70-89 | Tier 2 ABM (1:few) |
| C | 50-69 | Programmatic ABM |
| D | <50 | Exclude |
Stage Progression Tracking
Track via LinkedIn engagement metrics and HubSpot workflows:
- Identified: In target list, no engagement yet
- Aware: Impressions served, some ad engagement
- Interested: 5+ clicks OR 10+ engagements
- Considering: Website visits, content downloads, demo interest
Tools
Clay, BuiltWith, Apollo, HubSpot, LinkedIn Campaign Manager, ZenABM/Fibbler
Examples
Example 1: "How many accounts do I need for my ABM campaign?" → Read account-selection-framework.md. Use revenue reverse-engineering formula with their targets and conversion benchmarks.
Example 2: "How do I tier my account list?" → Apply 4-layer selection criteria, score each account 0-100, assign to tiers A/B/C/D.
Example 3: "How do I track which accounts are progressing?" → Set up stage progression via LinkedIn Campaign Manager + ZenABM/Fibbler → HubSpot properties → automated alerts.
Part of Frontal — free, open GTM skills for your AI agent. Browse the library →