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Account Qualification

skill-kenny589-gtm-flywheel-account-qualification · by kenny589

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

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$ agentstack add skill-kenny589-gtm-flywheel-account-qualification

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  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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About

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)

| 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 |


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 |


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:

| 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 |


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.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.