abm-specialist

Эксперт ABM. Используй для account-based marketing, target account selection и personalized campaigns.

181 stars

Best use case

abm-specialist is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Эксперт ABM. Используй для account-based marketing, target account selection и personalized campaigns.

Teams using abm-specialist should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/abm-specialist/SKILL.md --create-dirs "https://raw.githubusercontent.com/majiayu000/claude-skill-registry/main/skills/data/abm-specialist/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/abm-specialist/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How abm-specialist Compares

Feature / Agentabm-specialistStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Эксперт ABM. Используй для account-based marketing, target account selection и personalized campaigns.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Account-Based Marketing Specialist

Strategic expertise in account-based marketing for enterprise growth.

## Core Competencies

### ABM Strategy
- Account selection
- Tier definition
- Persona mapping
- Play development
- Sales alignment

### Campaign Orchestration
- Multi-channel coordination
- Personalization at scale
- Timing and sequencing
- Content mapping
- Touchpoint optimization

### Measurement
- Account engagement scoring
- Pipeline attribution
- ABM ROI
- Coverage metrics
- Influence tracking

## ABM Tier Framework

### Tier 1: Strategic (1:1)
- **Accounts:** 10-50
- **Investment:** High
- **Personalization:** Fully custom
- **Content:** Bespoke for each account
- **Plays:** Executive engagement, custom events

### Tier 2: Scale (1:Few)
- **Accounts:** 50-500
- **Investment:** Medium
- **Personalization:** Industry/segment
- **Content:** Templated with personalization
- **Plays:** Industry campaigns, webinars

### Tier 3: Programmatic (1:Many)
- **Accounts:** 500+
- **Investment:** Lower per account
- **Personalization:** Automated
- **Content:** Dynamic fields
- **Plays:** Targeted advertising, sequences

## ABM Plays

### Executive Engagement
- Executive briefings
- Advisory boards
- VIP events
- Executive sponsorship

### Digital Engagement
- Personalized ads
- Custom landing pages
- Targeted content
- Retargeting

### Direct Engagement
- Direct mail
- Personalized gifts
- Custom experiences
- Field events

## Account Selection Framework

### ICP (Ideal Customer Profile)
```
Firmographic Criteria:
- Industry: SaaS, FinTech, Healthcare
- Company size: 500-5000 employees
- Revenue: $50M-$500M
- Geography: North America, Europe

Technographic Criteria:
- Current tech stack alignment
- Integration compatibility
- Digital maturity level

Intent Signals:
- Researching solution category
- Competitor engagement
- Content consumption patterns
```

### Account Scoring Model
```python
def calculate_account_score(account):
    score = 0

    # Firmographic fit (40%)
    score += firmographic_score(account) * 0.4

    # Technographic fit (20%)
    score += technographic_score(account) * 0.2

    # Intent signals (25%)
    score += intent_score(account) * 0.25

    # Engagement history (15%)
    score += engagement_score(account) * 0.15

    return score

def assign_tier(score):
    if score >= 80:
        return "Tier 1"
    elif score >= 60:
        return "Tier 2"
    else:
        return "Tier 3"
```

## Account Engagement Scoring

| Activity | Points |
|----------|--------|
| Website visit | 1 |
| Content download | 5 |
| Event registration | 10 |
| Demo request | 25 |
| Meeting scheduled | 50 |
| Opportunity created | 100 |

## Multi-Threading Strategy

### Persona Map
```
C-Suite:
- CEO: Business outcomes, ROI
- CFO: Cost reduction, efficiency
- CTO: Technical capabilities, security

Directors:
- VP Sales: Revenue impact
- VP Marketing: Pipeline contribution
- VP Operations: Process improvement

Users:
- Managers: Day-to-day workflow
- End users: Ease of use, adoption
```

### Engagement Sequence
```
Week 1: Research & mapping
- Identify all stakeholders
- Map reporting structure
- Find common connections

Week 2-4: Initial outreach
- LinkedIn engagement
- Personalized emails
- Content sharing

Week 5-8: Value delivery
- Custom content
- Industry insights
- Peer introductions

Week 9-12: Meeting conversion
- Multi-threading emails
- Executive referrals
- Event invitations
```

## ABM Tech Stack

- **Orchestration:** 6sense, Demandbase, Terminus
- **Intent Data:** Bombora, G2
- **Advertising:** LinkedIn, Display
- **Personalization:** Mutiny, PathFactory
- **Gifting:** Sendoso, Postal
- **CRM:** Salesforce, HubSpot
- **Analytics:** Tableau, Looker

## Measurement Framework

### Leading Indicators
- Account coverage (% of personas engaged)
- Account engagement score
- Content consumption
- Meeting conversion rate

### Lagging Indicators
- Pipeline generated
- Pipeline velocity
- Win rate by tier
- Average deal size
- Customer acquisition cost

### ROI Calculation
```
ABM ROI = (Revenue from ABM accounts - ABM investment) / ABM investment

ABM Investment includes:
- Technology costs
- Content creation
- Advertising spend
- Events & gifts
- Headcount allocation
```

## Best Practices

1. **Start small** - Pilot with 10-20 accounts before scaling
2. **Align with sales** - Weekly syncs on target accounts
3. **Personalize genuinely** - Generic personalization backfires
4. **Multi-thread early** - Don't rely on single champion
5. **Measure incrementally** - Compare ABM vs non-ABM cohorts
6. **Iterate plays** - Test and optimize continuously

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