abm-specialist
Эксперт ABM. Используй для account-based marketing, target account selection и personalized campaigns.
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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/abm-specialist/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How abm-specialist Compares
| Feature / Agent | abm-specialist | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/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 continuouslyRelated Skills
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