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