account-qualification

name	account-qualification
description	Systematically evaluate whether a target account is worth pursuing. Scoring frameworks, qualification criteria, and prioritization models that prevent wasted outreach on bad-fit prospects.

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).

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Level 1: Automated Screening

Run every lead through these binary filters before any outreach:

Hard Disqualifiers (Instant Remove)

Filter	Disqualify If	Rationale
Company size	Outside your serviceable range	Can't serve them / can't afford you
Industry	In an excluded vertical	Regulatory, ethical, or capability reasons
Geography	In a restricted market	Can't sell there (legal, timezone, language)
Existing customer	Already in your CRM as active	Don't cold email your own customers
Competitor	They are a direct competitor	Creates awkwardness, unlikely to buy
Email validity	Invalid/catch-all email	Hurts deliverability if you send
Do-not-contact list	Previously opted out or requested removal	Legal compliance (CAN-SPAM, GDPR)
Duplicate	Already in an active campaign	Don't double-email prospects

Soft Disqualifiers (Flag for Review)

Filter	Flag If	Action
Company age < 1 year	Very early stage, may not have budget	Move to nurture, not outbound
No website	Can't verify legitimacy	Research manually before including
Generic email only	No personal email found	Lower priority, but don't auto-remove
Title mismatch	Title doesn't match target persona	Check manually — titles vary widely

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Level 2: Enriched Scoring

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

The FITS Framework

Dimension	What It Measures	Weight	Scoring Range
F — Firmographic Fit	Does the company match your ICP?	25%	0-25 points
I — Intent Signals	Is the company in-market now?	35%	0-35 points
T — Technographic Match	Does their stack indicate fit?	20%	0-20 points
S — Structural Readiness	Can they actually buy and implement?	20%	0-20 points

Total: 100 points

Firmographic Fit Scoring (25 points)

Attribute	Tier 1 Points	Tier 2 Points	Tier 3 Points
Company size in sweet spot	8	5	2
Revenue in target range	5	3	1
Industry is primary vertical	5	3	1
Growth stage matches	5	3	1
Geography is primary market	2	1	0

Intent Signal Scoring (35 points)

Signal	Points	Detection Method
Hiring for role your product serves	10	Job board monitoring
Recent funding (< 6 months)	8	Crunchbase, news alerts
Evaluating competitors (G2, review sites)	10	Intent data providers
Leadership change in target dept	5	LinkedIn alerts
Website visits (if available)	7	Website tracking
Content engagement (webinar, guide download)	5	Marketing automation

Technographic Match Scoring (20 points)

Signal	Points	Detection Method
Uses your integration partners	6	BuiltWith, tech detection
Uses a competitor (displacement opportunity)	8	Tech detection, G2 reviews
Recently adopted adjacent tech	4	Job descriptions, tech detection
Tech sophistication matches your buyer	2	Overall stack analysis

Structural Readiness Scoring (20 points)

Signal	Points	How to Assess
Has the right budget authority title	6	LinkedIn search
Team size indicates need	4	Company data, job postings
Not in a buying freeze (no layoffs)	4	News, LinkedIn
Short sales cycle indicators	3	Company stage, deal size
Decision committee is small (< 5 people)	3	Company size/stage proxy

Tier Assignment

Score Range	Tier	Action
80-100	Tier 1: Bullseye	Multi-channel, hyper-personalized
60-79	Tier 2: Strong Fit	Signal-based personalization
40-59	Tier 3: Good Fit	Bucket personalization
20-39	Tier 4: Stretch	Small batch test only
0-19	Disqualified	Remove from list

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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}}

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Re-Qualification: When to Stop Pursuing

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

Scenario	Action	When to Re-engage
No reply after full sequence (4 steps)	Pause. Move to nurture.	Re-engage only with a NEW signal
Replied "not interested"	Remove from active campaigns	Never re-engage on same angle. Wait 6+ months with new signal only
Replied "not now"	Add to time-based nurture	Re-engage in 30-60 days with new value
Replied "talk to someone else" (referral)	Contact the referral immediately	This is a win, not a rejection
Bounced email	Find alternate contact or remove	Only re-engage if you find a valid contact
Company went through major change (layoffs, merger)	Re-score the account	May upgrade or disqualify based on change

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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.

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