account-based-marketing-agent

Agent Functions

```yaml

abm_agent_capabilities:

account_intelligence:

- Company research automation

- Technographic data gathering

- Intent signal detection

- Buying committee mapping

- Competitive intelligence

personalization:

- Dynamic content generation

- Account-specific messaging

- Multi-stakeholder personalization

- Journey orchestration

campaign_automation:

- Multi-channel coordination

- Timing optimization

- A/B test management

- Budget allocation

analytics:

- Engagement scoring

- Account health tracking

- Pipeline attribution

- ROI calculation

```

---

ICP Scoring Model

```yaml

ideal_customer_profile:

firmographic_criteria:

company_size:

tier_1: "1000+ employees"

tier_2: "200-999 employees"

tier_3: "50-199 employees"

weight: 25

industry:

primary: ["SaaS", "FinTech", "Healthcare IT"]

secondary: ["E-commerce", "Manufacturing"]

weight: 20

revenue:

tier_1: "$100M+"

tier_2: "$20M-$100M"

tier_3: "$5M-$20M"

weight: 20

technographic_criteria:

tech_stack_fit:

must_have: ["Salesforce", "HubSpot"]

nice_to_have: ["Segment", "Snowflake"]

weight: 15

current_solutions:

competitor_user: "+10 points"

legacy_system: "+5 points"

weight: 10

behavioral_signals:

intent_data:

high_intent_topics: "+15 points"

competitor_research: "+10 points"

weight: 10

```

Account Tiering

```yaml

account_tiers:

tier_1_strategic:

count: "10-25 accounts"

characteristics:

- Perfect ICP fit

- High revenue potential ($500K+ ACV)

- Known buying intent

- Executive relationships possible

engagement_model:

- Dedicated account team

- Custom content creation

- Executive-to-executive outreach

- In-person events/dinners

- Annual budget: "$10-50K per account"

tier_2_target:

count: "50-100 accounts"

characteristics:

- Strong ICP fit

- Medium revenue potential ($100-500K ACV)

- Some intent signals

engagement_model:

- Shared account resources

- Semi-custom content

- Multi-channel campaigns

- Virtual events

- Annual budget: "$2-10K per account"

tier_3_scale:

count: "200-500 accounts"

characteristics:

- Good ICP fit

- Lower revenue potential ($25-100K ACV)

engagement_model:

- Automated campaigns

- Industry-personalized content

- Programmatic advertising

- Annual budget: "$500-2K per account"

```

---

Stakeholder Identification

```yaml

buying_committee:

champion:

role: "Day-to-day user who benefits most"

typical_titles:

- "Manager"

- "Director"

- "Team Lead"

messaging_focus:

- Productivity gains

- Pain point solutions

- Ease of implementation

decision_maker:

role: "Has budget authority"

typical_titles:

- "VP"

- "C-level"

- "Head of"

messaging_focus:

- ROI and business impact

- Strategic alignment

- Risk mitigation

technical_evaluator:

role: "Assesses technical fit"

typical_titles:

- "IT Director"

- "Solutions Architect"

- "Security Lead"

messaging_focus:

- Integration capabilities

- Security and compliance

- Technical specifications

influencer:

role: "Shapes opinion but doesn't decide"

typical_titles:

- "Consultant"

- "Board member"

- "Industry analyst"

messaging_focus:

- Industry trends

- Competitive positioning

- Thought leadership

blocker:

role: "May oppose the purchase"

typical_titles:

- "Procurement"

- "Legal"

- "Finance"

messaging_focus:

- Risk mitigation

- Compliance

- Vendor stability

```

Contact Discovery Automation

```python

# Example: LinkedIn + Intent data enrichment

def discover_buying_committee(account_domain: str) -> dict:

"""

Automated buying committee discovery

"""

contacts = []

# Step 1: LinkedIn Sales Navigator search

linkedin_results = linkedin_api.search_people(

company_domain=account_domain,

titles=[

"VP Marketing", "CMO", "Head of Marketing",

"VP Sales", "CRO", "Head of Revenue",

"VP IT", "CTO", "Head of Technology"

],

seniority=["Director", "VP", "C-Level"]

)

# Step 2: Enrich with intent data

for contact in linkedin_results:

intent_score = intent_provider.get_contact_intent(

email=contact.get("email"),

topics=["marketing automation", "ABM", "sales engagement"]

)

contact["intent_score"] = intent_score

contact["role_classification"] = classify_buyer_role(contact["title"])

# Step 3: Prioritize by intent + seniority

contacts = sorted(

linkedin_results,

key=lambda x: (x["intent_score"], x["seniority_rank"]),

reverse=True

)

return {

"account": account_domain,

"buying_committee": contacts[:10],

"champion_candidates": [c for c in contacts if c["role_classification"] == "champion"],

"decision_makers": [c for c in contacts if c["role_classification"] == "decision_maker"]

}

```

---

Intent Data Sources

```yaml

intent_signals:

first_party:

website_behavior:

- Page visits (especially pricing, demo, comparison)

- Time on site

- Return visits

- Content downloads

- Webinar registrations

email_engagement:

- Open rates

- Click-through rates

- Reply rates

- Forward rates

product_signals:

- Free trial signup

- Feature usage

- Support tickets

- API calls

third_party:

research_intent:

provider: "Bombora, G2, TrustRadius"

signals:

- Topic surge

- Competitor research

- Category research

hiring_signals:

provider: "LinkedIn, job boards"

signals:

- Relevant job postings

- Team expansion

- New leadership

technographic_changes:

provider: "BuiltWith, HG Insights"

signals:

- New tech adoption

- Contract renewals approaching

- Vendor changes

```

Intent Score Calculation

```python

def calculate_account_intent_score(account_id: str) -> dict:

"""

Multi-signal intent scoring

"""

scores = {

"first_party": 0,

"third_party": 0,

"composite": 0

}

# First-party signals (weight: 60%)

website_score = get_website_engagement_score(account_id) # 0-100

email_score = get_email_engagement_score(account_id) # 0-100

product_score = get_product_engagement_score(account_id) # 0-100

scores["first_party"] = (

website_score * 0.4 +

email_score * 0.3 +

product_score * 0.3

)

# Third-party signals (weight: 40%)

topic_surge = get_bombora_topic_surge(account_id) # 0-100

hiring_signals = get_hiring_signal_score(account_id) # 0-100

tech_changes = get_technographic_change_score(account_id) # 0-100

scores["third_party"] = (

topic_surge * 0.5 +

hiring_signals * 0.3 +

tech_changes * 0.2

)

# Composite score

scores["composite"] = (

scores["first_party"] * 0.6 +

scores["third_party"] * 0.4

)

# Classify intent level

if scores["composite"] >= 80:

scores["intent_level"] = "hot"

scores["recommended_action"] = "immediate_sales_outreach"

elif scores["composite"] >= 60:

scores["intent_level"] = "warm"

scores["recommended_action"] = "accelerated_nurture"

elif scores["composite"] >= 40:

scores["intent_level"] = "engaged"

scores["recommended_action"] = "standard_nurture"

else:

scores["intent_level"] = "cold"

scores["recommended_action"] = "awareness_campaign"

return scores

```

---

Multi-Channel Playbook

```yaml

abm_playbook:

name: "Enterprise Account Activation"

trigger: "Account reaches intent score >= 70"

duration: "90 days"

week_1_2:

goal: "Awareness and research facilitation"

channels:

linkedin_ads:

- Sponsored content to buying committee

- Thought leadership pieces

- Budget: "$500/account"

display_retargeting:

- Account-based display ads

- Case study promotion

- Budget: "$300/account"

direct_mail:

- Research report + handwritten note

- To: Champion and Decision Maker

- Cost: "$50/piece"

week_3_4:

goal: "Engagement and education"

channels:

email_sequence:

- 4-email nurture sequence

- Personalized by role

- Content: Industry insights

linkedin_outreach:

- SDR connection requests

- Value-first messaging

- Target: 5 contacts per account

webinar_invitation:

- Industry-specific webinar

- Executive speaker

week_5_6:

goal: "Conversion push"

channels:

personalized_video:

- Custom video for champion

- Demo of relevant features

executive_outreach:

- AE reaches decision maker

- Reference customer intro

gifting:

- High-value gift to decision maker

- Budget: "$100-250"

week_7_12:

goal: "Deal progression support"

channels:

sales_enablement:

- Custom ROI calculator

- Business case template

- Reference calls

expansion_content:

- Additional stakeholder content

- Technical documentation

- Security questionnaire support

```

Campaign Automation Rules

```yaml

automation_rules:

intent_spike_response:

trigger: "Intent score increases >20 points in 7 days"

actions:

- notify_account_owner

- add_to_accelerated_sequence

- increase_ad_spend_2x

- create_sales_task_urgent

champion_engagement:

trigger: "Champion visits pricing page 2+ times"

actions:

- send_personalized_pricing_email

- assign_sdr_call_task

- add_decision_maker_to_parallel_sequence

multi_stakeholder_activity:

trigger: "3+ contacts from account active in 7 days"

actions:

- create_opportunity_if_none

- send_team_briefing_to_ae

- launch_full_buying_committee_sequence

competitor_research:

trigger: "Account researching competitor topics"

actions:

- send_competitive_comparison_content

- add_to_competitive_ad_campaign

- alert_account_owner

```

---

Dynamic Content Generation

```yaml

personalization_variables:

account_level:

- Company name

- Industry

- Company size

- Recent news

- Technology stack

- Competitors used

contact_level:

- First name

- Title/role

- Department

- Seniority

- LinkedIn activity

- Content interests

behavioral:

- Pages visited

- Content downloaded

- Emails engaged

- Meeting history

content_templates:

email_subject_lines:

champion:

- "[Company] + [Our Company]: solving [pain point]"

- "[First name], quick question about [topic they researched]"

decision_maker:

- "How [Similar Company] achieved [result]"

- "[First name], ROI of [solution category] at [Company]"

email_body_frameworks:

pain_point_led:

opening: "I noticed [Company] is [signal/news/hiring]. Many [industry] companies face [pain point] when [situation]."

bridge: "We've helped [reference company] solve this by [solution approach]."

cta: "Worth a 15-minute call to see if we can help [Company] similarly?"

insight_led:

opening: "Based on [research/data point], [industry] companies are [trend]."

bridge: "[Company] is well-positioned to [opportunity] by [approach]."

cta: "I'd love to share how we're helping companies like [reference] capitalize on this."

```

---

Account Engagement Model

```yaml

engagement_scoring:

email_engagement:

open: 1

click: 3

reply: 10

meeting_booked: 25

website_engagement:

page_view: 1

pricing_page: 5

demo_page: 7

feature_page: 3

blog_post: 1

case_study: 4

content_engagement:

whitepaper_download: 5

webinar_registration: 7

webinar_attendance: 15

video_watch_50_percent: 3

video_watch_100_percent: 5

ad_engagement:

impression: 0.01

click: 2

sales_engagement:

meeting_held: 50

proposal_sent: 75

verbal_commit: 100

score_thresholds:

cold: "0-25"

engaged: "26-50"

marketing_qualified: "51-100"

sales_qualified: "101+"

```

---

Multi-Touch Attribution

```yaml

attribution_models:

first_touch:

description: "100% credit to first interaction"

use_case: "Understanding awareness channels"

last_touch:

description: "100% credit to last interaction before conversion"

use_case: "Understanding closing channels"

linear:

description: "Equal credit to all touchpoints"

use_case: "Balanced view of customer journey"

time_decay:

description: "More credit to recent touchpoints"

use_case: "Focus on conversion drivers"

position_based:

description: "40% first, 40% last, 20% middle"

use_case: "Balanced awareness + conversion focus"

data_driven:

description: "ML-based attribution"

use_case: "Most accurate but requires volume"

```

ABM Metrics Dashboard

```yaml

abm_metrics:

account_coverage:

- "% of target accounts reached"

- "% of buying committee engaged"

- "Average contacts engaged per account"

engagement_metrics:

- "Account engagement score trend"

- "Channel engagement breakdown"

- "Content performance by persona"

pipeline_metrics:

- "Target account pipeline generated"

- "Average deal size (ABM vs non-ABM)"

- "Win rate (ABM vs non-ABM)"

- "Sales cycle length (ABM vs non-ABM)"

efficiency_metrics:

- "Cost per engaged account"

- "Cost per opportunity"

- "Marketing influenced pipeline"

- "ABM ROI"

```

---

Tech Stack Integration

```yaml

abm_tech_stack:

crm:

primary: "Salesforce"

sync:

- Account scores

- Contact engagement

- Campaign membership

- Intent signals

marketing_automation:

primary: "Marketo / HubSpot"

sync:

- Lead scoring

- Email campaigns

- Landing pages

- Form submissions

abm_platform:

options: ["Demandbase", "6sense", "Terminus"]

capabilities:

- Account identification

- Intent data

- Advertising orchestration

- Analytics

sales_engagement:

options: ["Outreach", "Salesloft"]

sync:

- Sequence enrollment

- Activity logging

- Meeting scheduling

intent_data:

providers: ["Bombora", "G2", "TrustRadius"]

sync:

- Topic surge scores

- Research signals

- Review activity

enrichment:

providers: ["ZoomInfo", "Clearbit", "Apollo"]

data:

- Contact information

- Technographics

- Firmographics

```

---

1. Качество важнее количества — лучше 50 хорошо проработанных аккаунтов чем 500 поверхностных
2. Sales и Marketing alignment — совместное определение ICP и целевых аккаунтов
3. Персонализация по ролям — разный messaging для разных stakeholders
4. Multi-channel orchestration — координируй все каналы в единую journey
5. Intent-based prioritization — фокусируйся на аккаунтах с высоким intent
6. Измеряй account engagement, не только leads — ABM metric отличается от demand gen
7. Content по стадиям воронки — awareness → consideration → decision
8. Регулярный review target accounts — пересматривай список каждый квартал