kenny589/gtm-flywheel/account-qualification

Account Qualification

When to Use

• Evaluating a new lead list before launching campaigns
• Prioritizing which accounts to target first from a large TAM
• Building qualification workflows for SDRs or AI enrichment pipelines
• Deciding whether to continue pursuing an account after no initial response

Framework

The Qualification Stack

Account qualification happens at three levels. Each level adds confidence but also adds cost (time, data credits, manual research). Match the depth to the deal value.

Level 1: Automated Screening (seconds per account)
    Filters: firmographic + technographic data
    Purpose: Remove obvious disqualifiers
    Output: "Pass" or "Fail" — binary
 
Level 2: Enriched Scoring (minutes per account)
    Adds: intent signals, hiring data, funding status
    Purpose: Rank and tier qualified accounts
    Output: Score (0-100) + Tier assignment
 
Level 3: Deep Qualification (30-60 min per account)
    Adds: manual research, contact mapping, trigger analysis
    Purpose: Build account plan for Tier 1 targets
    Output: Full account brief + recommended approach

When to use each level:

• Level 1: Every lead, always. This is your spam prevention layer.
• Level 2: Leads that pass Level 1 (typically 40-60% of your list).
• Level 3: Only Tier 1 and high-value Tier 2 accounts (top 5-15% of your list).

Level 1: Automated Screening

Run every lead through these binary filters before any outreach:

Hard Disqualifiers (Instant Remove)

Soft Disqualifiers (Flag for Review)

Level 2: Enriched Scoring

For accounts that pass Level 1, build a composite score across four dimensions:

The FITS Framework

Total: 100 points

Firmographic Fit Scoring (25 points)

Intent Signal Scoring (35 points)

Technographic Match Scoring (20 points)

Structural Readiness Scoring (20 points)

Tier Assignment

Level 3: Deep Qualification (Tier 1 Only)

For your highest-value targets, build a full account brief:

Account Brief Template

ACCOUNT BRIEF: {{companyName}}
Qualification Score: {{score}}/100 (Tier {{tier}})
Date: {{date}}
Researcher: {{name}}
 
--- COMPANY OVERVIEW ---
Company: {{companyName}}
Website: {{url}}
Industry: {{industry}}
Size: {{employees}} employees
Revenue: {{revenue}} (estimated)
Stage: {{fundingStage}}
Founded: {{year}}
HQ: {{location}}
 
--- WHY THIS ACCOUNT ---
Primary signal: {{strongestSignal}}
Secondary signals: {{additionalSignals}}
Timing rationale: {{whyNow}}
 
--- CONTACT MAP ---
| Name | Title | Role in Deal | LinkedIn | Email |
|------|-------|-------------|----------|-------|
| ___ | ___ | Economic Buyer | ___ | ___ |
| ___ | ___ | Champion | ___ | ___ |
| ___ | ___ | Influencer | ___ | ___ |
 
--- RECOMMENDED APPROACH ---
Lead with persona: {{primaryContact}}
Opening angle: {{angle}}
Personalization hook: {{specificHook}}
Expected objection: {{likelyObjection}}
Proof point to use: {{bestCaseStudy}}
 
--- COMPETITIVE CONTEXT ---
Current solution: {{currentTool}}
Likely alternatives they'll evaluate: {{competitors}}
Our positioning: {{differentiator}}

Re-Qualification: When to Stop Pursuing

Not every qualified account will respond. Here's when to move on:

Batch Qualification Workflow

For processing large lists (1,000+ leads) efficiently:

Step 1: Import raw list
    ↓
Step 2: Run Level 1 automated screening
    → Remove disqualified (typically 20-40% of list)
    ↓
Step 3: Enrich remaining leads
    → Add firmographic, technographic, intent data
    ↓
Step 4: Run Level 2 FITS scoring
    → Assign tiers (Tier 1-4 or DQ)
    ↓
Step 5: Review Tier 1 accounts
    → Build account briefs (Level 3)
    → Validate contact data
    ↓
Step 6: Route to campaigns
    → Tier 1 → Multi-channel sequence
    → Tier 2 → Signal-based email sequence
    → Tier 3 → Bucket personalization email sequence
    → Tier 4 → Test batch (validate before scaling)

Expected conversion through the funnel:

• Raw list: 5,000 leads
• After Level 1 screening: 3,000-4,000 (60-80% pass)
• Tier 1: 150-500 (5-10%)
• Tier 2: 600-1,200 (20-30%)
• Tier 3: 1,200-2,000 (40-50%)
• Tier 4 or DQ: remainder

Templates

Quick Qualification Scorecard

Account: {{companyName}}
Date: {{date}}
 
Level 1 Screening: [ ] PASS  [ ] FAIL
  Reason if fail: ___
 
FITS Score:
  F (Firmographic):  ___/25
  I (Intent):        ___/35
  T (Technographic): ___/20
  S (Structural):    ___/20
  TOTAL:             ___/100
 
Tier Assignment: ___
Recommended Action: ___
Priority Signal: ___

Tips

• Qualification is an investment, not a cost. Every minute spent qualifying saves 10 minutes of wasted outreach on bad-fit accounts.
• The most common qualification mistake: over-weighting firmographics and under-weighting intent. A small company that's actively hiring for your use case is a better prospect than a large company with no buying signals.
• Build your qualification scoring model from closed-won data, not assumptions. Which attributes did your actual customers have when they bought? Those are your highest-weight factors.
• Intent signals decay fast. A job posting from 3 months ago is stale. A funding round from 6 months ago is old news. Recency matters — weight recent signals 2x over older ones.
• When in doubt, qualify OUT. It's better to email 1,000 highly qualified leads than 5,000 mediocre ones. Your reply rate, deliverability, and team efficiency all improve with a tighter list.
• Qualification criteria should be different for different campaign types. An ABM campaign (5-50 accounts) needs Level 3 qualification. A scaled outbound campaign (5,000 accounts) only needs Level 1-2.

Progressive disclosure: load industry-specific qualification benchmarks and data provider integrations only when qualifying accounts for a specific campaign.