SKILL.md
- name:
- account-selection-framework
- description:
- Use this skill when building, scoring, or managing a target account list for ABM — reverse-engineering how many accounts you need from revenue targets, ICP fit scoring, tiering, list staging, and ongoing list hygiene.
Account Selection - Framework
How to build, score, stage, and manage target account lists for ABM campaigns.
The Account Selection Principle
ABM starts with accounts, not leads. You're choosing who to pursue before anything else. Every dollar of ad spend and every BDR hour gets concentrated on accounts that actually fit your ICP.
The math matters: To close $1M in ARR from ABM with a $50K ACV, 25% close rate, and 75% qualification rate, you need ~3,250 target accounts in your campaigns (working backwards through stage conversion benchmarks).
How Many Accounts Do You Need?
Reverse-Engineering from Revenue Target
Revenue Target ÷ ACV = Deals Needed
Deals ÷ Close Rate ÷ Qualification Rate ÷ Considering Rate ÷ Interested Rate ÷ Aware Rate
= Total Target Accounts
Example:
$1,000,000 ÷ $50,000 = 20 deals
20 ÷ 0.25 ÷ 0.75 ÷ 0.20 ÷ 0.30 ÷ 0.55 = ~3,250 accounts
Stage Conversion Benchmarks (ABX Benchmarks)
| Stage | Definition | Conversion to Next |
|---|---|---|
| Identified | All accounts targeted in campaign | 55% become Aware |
| Aware | 50+ ad impressions | 30% become Interested |
| Interested/Engaged | 5+ ad clicks OR 10+ engagements | 20% become Considering |
| Considering | Booked a demo / signed up for trial | Close rate applies |
| Selecting | Open deal in pipeline | Win rate applies |
The headline number: across these stages, roughly 2.5% of targeted accounts convert to pipeline (an open, qualified opportunity) and about 0.6% close. So a ~3,250-account program produces ~80 opportunities and ~20 deals at a $50K deal size.
Account Selection Criteria
Layer 1: Firmographic Fit
| Criteria | Example | Source |
|---|---|---|
| Company Size | SMB (50-500) or Mid-Market (500-2000) | Clay, Apollo, LinkedIn |
| Revenue | $5M+ annual revenue or comparable funding | Clay, Crunchbase |
| Industry | Digital-first (SaaS, eCommerce, EdTech, FinTech, HealthTech) | Clay, LinkedIn |
| Location | USA, Canada, Australia, NZ, Ireland, Israel, Western/Northern Europe | Clay, Apollo |
| Business Model | Product-led growth, B2B SaaS | Manual + Clay enrichment |
Layer 2: Technographic Indicators
| Criteria | What It Signals | Source |
|---|---|---|
| Currently using a competitor | Active buyer in category | BuiltWith, HG Insights, Clay |
| Using competitor lacking your key feature | Upgrade opportunity | BuiltWith + manual analysis |
| Using redundant tool combo | Consolidation opportunity | BuiltWith |
| Recently changed tech stack | Active buying window | BuiltWith, 6sense |
Layer 3: CRM Intelligence
| Criteria | What It Signals | Source |
|---|---|---|
| Closed-Lost (past 6-12 months) | Had the problem, bad timing/price | CRM export |
| Lost to competitor (missing feature you now have) | Re-engagement opportunity | CRM closed-lost reason field |
| Previously engaged outbound (didn't convert) | Aware of you, recycle into ABM | CRM + outbound logs |
| Churned customers | Circumstances may have changed | CRM churn data |
Layer 4: Lookalike Modeling
Build lookalikes from your best customers:
- Export top enterprise/growth customers from CRM
- Identify shared attributes (industry, size, tech stack, funding stage)
- Use Clay to find similar companies that match the pattern
- Cross-reference with BuiltWith for technographic match
List Building Process
Step-by-Step
1. Define ICP criteria (firmographic + technographic)
→ Use win-loss analysis from CRM to identify patterns
2. Build initial account list
→ Clay + BuiltWith API for technographic targeting
→ Apollo for firmographic + contact discovery
→ CRM export for recycled/closed-lost accounts
3. Enrich accounts
→ Clay enrichment (revenue, tech stack, funding, headcount)
→ BuiltWith for technology detection
→ ICP scoring (0-100)
4. Score and tier
→ A-tier (80-100): Perfect fit + strong signals
→ B-tier (60-79): Good fit
→ C-tier (40-59): Okay fit - maybe for 1:many only
→ D-tier (<40): Exclude
5. Import to CRM (HubSpot)
→ Create company records
→ Set ABM Campaign Name property
→ Set ABM Stage = "Identified"
→ Add to ABM campaign active list
6. Sync to LinkedIn Campaign Manager
→ Push company lists from HubSpot to LinkedIn
→ Filter by persona using LinkedIn's native targeting
→ Wait ~48 hours for audience to be ready
→ Minimum 300 LinkedIn members required to start a campaign
Tools for List Building
| Tool | Role | Notes |
|---|---|---|
| Clay | Primary enrichment + list building | Firmographics, technographics, ICP scoring |
| BuiltWith | Technographic detection | API integration with Clay for tech stack data |
| Apollo | Contact discovery + firmographics | Used for initial contact lists for matched audiences |
| HubSpot | CRM + audience management | Lists, workflows, audience sync to LinkedIn |
| LinkedIn Campaign Manager | Ad targeting | Native persona filters on company lists |
| Crunchbase | Funding data | For funding-based targeting |
Account Scoring Model
Keep It Simple
Critical lesson from real ABM programs: Teams that overcomplicate scoring by adding website visits, page-level intent signals, and weighted scores across multiple data sources struggle to execute because:
- Website visitor de-anonymization is unreliable (in one test, a de-anonymization tool identified only 1 company out of 300 visitors - itself)
- Complex scoring models break in practice
What actually works: Use quantitative ad engagement data from LinkedIn pushed to CRM, plus qualitative campaign engagement data for personalizing outreach.
Recommended Scoring Approach
| Data Type | How It's Used | Source |
|---|---|---|
| Quantitative | Impressions, engagements, clicks → stage progression | LinkedIn → ZenABM/Fibbler → HubSpot |
| Qualitative | Which campaigns they engaged with → intent detection | LinkedIn → ZenABM → HubSpot company properties |
Stage Thresholds
| Stage | Threshold | Content Shown |
|---|---|---|
| Identified | Added to campaign list | - |
| Aware | 50+ ad impressions | Awareness content ads |
| Interested | 5+ ad clicks OR 10+ engagements | Solution-oriented ads |
| Considering | Booked demo / signed up for trial | Product-oriented ads + BDR outreach |
| Selecting | Open deal in CRM | Personalized sales engagement |
HubSpot Configuration for ABM
Company Properties to Create
| Property | Type | Purpose |
|---|---|---|
ABM Campaign Name |
Dropdown (custom) | Which ABM campaign the account belongs to |
ABM Stage |
Dropdown (custom) | Identified / Aware / Interested / Considering / Selecting |
LinkedIn Ad Engagements - 7d |
Number (custom) | Rolling 7-day engagement count (from ZenABM/Fibbler) |
LinkedIn Ad Engagements - 30d |
Number (custom) | Rolling 30-day engagement count |
LinkedIn Ad Engagements - 90d |
Number (custom) | Rolling 90-day engagement count |
LinkedIn Ad Clicks - 7d |
Number (custom) | Rolling 7-day click count |
LinkedIn Ad Clicks - 30d |
Number (custom) | Rolling 30-day click count |
LinkedIn Ad Clicks - 90d |
Number (custom) | Rolling 90-day click count |
ABM Intent |
Multi-checkbox (custom) | Which intents detected (from campaign engagement) |
ICP Score |
Number (custom) | 0-100 firmographic/technographic fit |
ICP Tier |
Dropdown (custom) | A/B/C/D |
Active Lists Setup
Create separate active lists for each stage of each campaign:
[Campaign Name] - Identified(ICP score ≥ threshold, ABM Campaign = X)[Campaign Name] - Aware(cumulative impressions ≥ 50)[Campaign Name] - Interested(cumulative clicks ≥ 5 OR engagements ≥ 10)[Campaign Name] - Considering(demo booked OR trial signup)[Campaign Name] - Selecting(deal stage = open)
Workflow: Stage Progression
Trigger: Company property "LinkedIn Ad Clicks - 30d" changes
IF Clicks ≥ 5 AND ABM Stage = "Aware":
→ Update ABM Stage = "Interested"
→ Remove from Aware LinkedIn audience
→ Add to Interested LinkedIn audience
→ Trigger BDR notification workflow
IF Impressions ≥ 50 AND ABM Stage = "Identified":
→ Update ABM Stage = "Aware"
→ Content changes automatically via audience list updates
Account Engagement Tracking Tools
| Tool | What It Does | Cost | Key Feature |
|---|---|---|---|
| ZenABM | Pushes LinkedIn engagement data + intent to CRM, account scoring, ABM stage management, analytics dashboards | ~$59/mo+ | Bi-directional CRM sync, qualitative + quantitative data, auto-updates ABM stages |
| Fibbler | Pushes LinkedIn ad engagement data to CRM | Lower cost | Quantitative engagement data (impressions, clicks, engagements) per account |
| Factors.ai | Account identification + engagement tracking | $$ | Impression capping per account, cross-channel attribution |
| HubSpot (native) | CRM + workflow automation | Included | Lists, workflows, audience sync - but no native LinkedIn engagement push (as of early 2025) |
Note: As of early 2025, HubSpot cannot natively pull company-level engagement data from LinkedIn Campaign Manager. You need a connector tool (ZenABM, Fibbler, or custom API).
Campaign Duration and Pacing
| Parameter | Recommendation |
|---|---|
| Campaign duration | 12 weeks (3 months) per campaign |
| Monitoring cadence | Weekly account stage progression vs. benchmarks |
| When to assess | Pipeline per $ spent becomes meaningful after week 6-8 |
| Pipeline expectation | $10+ in pipeline per $1 ad spend = healthy |
| When to adjust | If stage progression falls below 50% of benchmark after week 4 |
Illustrative Program Economics
The figures below are illustrative of a well-run program. They show the shape of ABM economics over time rather than a guaranteed result.
First Campaign (roughly 90 days)
| Metric | Illustrative Result |
|---|---|
| Accounts touched | ~1,400 |
| Total cost | ~$52K (ads + tools) |
| Pipeline generated | ~$655K |
| Pipeline per $ spent | ~$12 |
| Team | ~4.5 FTE |
Mature Program (cumulative, 12-18 months)
| Metric | Illustrative Result |
|---|---|
| Accounts touched | ~26,000 |
| Total LinkedIn ad spend | ~$490K |
| Pipeline generated | ~$5.3M |
| Pipeline per $ spent | ~$11 |
| ROAS (Closed Won) | ~2x |
| Team | ~4.5 FTE |
Versus cold outbound: a mature ABM program typically reaches the same pipeline faster and at a lower cost than cold outbound alone.
By Ivan Falco - Frontal