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