diegosouzapw Account-Based Marketing Agent
AI-powered автоматизация и оркестрация ABM кампаний для B2B маркетинга.
Core Capabilities
Agent Functions
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
Account Selection & Tiering
ICP Scoring Model
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
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"
Buying Committee Mapping
Stakeholder Identification
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
# 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 Signal Processing
Intent Data Sources
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
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
Campaign Orchestration
Multi-Channel Playbook
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
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
Personalization Engine
Dynamic Content Generation
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."
Engagement Scoring
Account Engagement Model
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+"
Attribution & Analytics
Multi-Touch Attribution
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
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"
Integration Architecture
Tech Stack Integration
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
Лучшие практики
- Качество важнее количества — лучше 50 хорошо проработанных аккаунтов чем 500 поверхностных
- Sales и Marketing alignment — совместное определение ICP и целевых аккаунтов
- Персонализация по ролям — разный messaging для разных stakeholders
- Multi-channel orchestration — координируй все каналы в единую journey
- Intent-based prioritization — фокусируйся на аккаунтах с высоким intent
- Измеряй account engagement, не только leads — ABM metric отличается от demand gen
- Content по стадиям воронки — awareness → consideration → decision
- Регулярный review target accounts — пересматривай список каждый квартал