Ideal Customer Profile (ICP): AI-Powered Video Analysis Platform
Document Version: 1.0
Date: 2026-01-19
Owner: CODITECT Business Development
Status: Active
Executive Summary
The AI-powered video analysis platform targets mid-market to enterprise organizations (500+ employees) that process 100+ hours of video content monthly across learning & development, market research, compliance, or knowledge management functions. These organizations struggle with manual video processing bottlenecks, spending $150K-$2M annually on content analysis that could be automated for 99% cost reduction.
1. Firmographic Criteria
Company Size
| Criterion | Ideal | Acceptable | Why This Matters |
|---|---|---|---|
| Employees | 2,000-50,000 | 500-2,000 or 50,000+ | Sweet spot: Large enough to have video processing needs, small enough for agile adoption |
| Revenue | $500M-$10B | $100M-$500M or $10B+ | Budget for automation, ROI focus, not price-sensitive |
| Video Processing Team | 5-20 FTE | 2-5 FTE | Enough pain to justify solution, not too large to resist change |
Industry Verticals (Prioritized)
Tier 1: Highest Fit (Primary Targets)
-
Enterprise Learning & Development
- Companies: Fortune 500, large healthcare systems, pharma, financial services
- Pain Point: 1,000-10,000 hours of training content requiring indexing, searchability
- Budget: $20-50M annual L&D spend, 10-20% on content management
- Decision Maker: Chief Learning Officer, VP L&D
- Example: "Pharmaceutical company with 50,000 employees, 5,000 compliance training videos"
-
Market Research & Competitive Intelligence
- Companies: Investment firms, strategy consultancies, market research agencies
- Pain Point: Analyzing 500+ earnings calls, product demos, conference talks quarterly
- Budget: $50-100M annual research spend
- Decision Maker: Head of Research, CIO
- Example: "Investment research firm analyzing 2,000 earnings calls per year"
-
Legal Technology & eDiscovery
- Companies: Law firms (500+ attorneys), legal service providers, corporate legal departments
- Pain Point: Deposition videos, testimony, compliance reviews requiring searchable tranH.P.004-SCRIPTS
- Budget: $10-30M on eDiscovery and litigation support
- Decision Maker: Chief Legal Officer, eDiscovery Director
- Example: "Top 100 law firm with 200 video depositions per month"
Tier 2: Strong Fit (Secondary Targets)
-
Customer Success & Support
- Companies: SaaS companies (ARR >$100M), enterprise software vendors
- Pain Point: Product demos, support webinars, onboarding videos need knowledge base
- Budget: $5-15M customer success operations
- Decision Maker: VP Customer Success, Head of Support
- Example: "B2B SaaS company with 500 product demo videos, 1,000 support recordings"
-
Media & Broadcasting
- Companies: News organizations, media companies, content creators
- Pain Point: Thousands of hours of archive footage requiring cataloging
- Budget: $100M+ content operations
- Decision Maker: Head of Content Operations, CTO
- Example: "News network with 50,000 hours of archive footage"
-
Healthcare & Medical Education
- Companies: Medical schools, hospital systems, CME providers
- Pain Point: Surgical videos, lectures, case studies requiring detailed indexing
- Budget: $10-20M medical education budget
- Decision Maker: Dean of Medical Education, CME Director
- Example: "Academic medical center with 3,000 surgical procedure videos"
Tier 3: Moderate Fit (Opportunistic)
-
Government & Defense
- Pain Point: Training videos, briefings, surveillance footage requiring analysis
- Decision Maker: Procurement Officer, Program Manager
- Note: Long sales cycles (12-18 months), complex procurement
-
Manufacturing & Quality Assurance
- Pain Point: Training videos, quality inspection footage, safety compliance
- Decision Maker: VP Operations, Quality Director
- Note: Lower urgency, price-sensitive
2. Psychographic & Behavioral Criteria
Technology Adoption Profile
technology_maturity:
cloud_adoption: "Moderate to Advanced"
ai_ml_awareness: "High - actively exploring AI use cases"
automation_culture: "Strong - 'automate everything' mindset"
vendor_management: "Prefer best-of-breed over all-in-one"
digital_transformation_stage:
ideal: "Phase 2-3: Scaling digital initiatives"
description: "Past proof-of-concept, ready to operationalize AI"
budget_allocation: "10-15% of IT budget to automation"
Pain Points & Triggers
Critical Pain Points (Must Have ≥2)
-
Manual Processing Bottleneck
- Symptom: "We have a 3-month backlog of videos to process"
- Quantified: >500 hours of unprocessed video
- Impact: Delayed insights, missed opportunities
-
High Labor Costs
- Symptom: "We spend $2M/year on analysts watching videos"
- Quantified: >$500K annual labor on video analysis
- Impact: Budget pressure, inefficient resource allocation
-
Lack of Searchability
- Symptom: "We can't find content in our 10,000 video library"
- Quantified: >5,000 videos without metadata
- Impact: Poor user experience, underutilized content
-
Compliance & Audit Requirements
- Symptom: "Regulators require timestamped tranH.P.004-SCRIPTS of all training"
- Quantified: Regulatory mandate with penalties
- Impact: Legal risk, audit failures
-
Time-to-Market Pressure
- Symptom: "Competitors analyze earnings calls before we do"
- Quantified: 2+ day lag vs. real-time analysis
- Impact: Competitive disadvantage
Trigger Events (Buying Signals)
- ✅ New regulation requiring video transcription (e.g., accessibility, compliance)
- ✅ Leadership mandate to "AI-enable all operations"
- ✅ Budget approved for automation/AI initiatives
- ✅ Hiring freeze forcing productivity gains
- ✅ New Learning Management System (LMS) implementation
- ✅ M&A activity requiring content consolidation
- ✅ Customer complaints about content discoverability
3. Buying Committee
Primary Economic Buyer (Signs Check)
| Role | Title | Budget Authority | Decision Criteria |
|---|---|---|---|
| C-Suite | CFO, CIO, Chief Learning Officer | $100K+ | ROI, strategic alignment, risk mitigation |
| VP-Level | VP L&D, VP Research, VP Operations | $50K-$250K | Business outcomes, team productivity |
Technical Buyer (Evaluates Solution)
| Role | Title | Influence | Decision Criteria |
|---|---|---|---|
| IT/Engineering | Director of Engineering, Solutions Architect | High - can veto | Technical feasibility, security, integration |
| Data/AI Team | Head of AI, ML Engineering Manager | Medium-High | Model quality, API reliability, cost efficiency |
End User (Day-to-Day User)
| Role | Title | Influence | Decision Criteria |
|---|---|---|---|
| Analysts | Research Analyst, Content Specialist | Low-Medium | Ease of use, accuracy, time savings |
| Admins | Learning Admin, Knowledge Manager | Medium | Workflow integration, reporting |
Champion (Internal Advocate)
Characteristics:
- Mid-level manager (Director, Senior Manager)
- Frustrated with status quo
- Tech-savvy, AI enthusiast
- Has influence with economic buyer
- Personal incentive: Promotion tied to efficiency gains
Finding Champions:
- LinkedIn: Posts about AI, automation, efficiency
- Industry events: Speakers on digital transformation
- Existing CODITECT users: Cross-sell opportunity
4. Qualifying Criteria (BANT Framework)
Budget
| Requirement | Ideal | Minimum |
|---|---|---|
| Annual Budget | $200K+ for automation | $100K |
| Video Processing Spend | $500K+ currently | $150K |
| Approval Level | VP or C-suite | Director |
Qualifying Questions:
- "What's your current annual spend on video content management?"
- "Do you have budget allocated for AI/automation initiatives this year?"
- "What's the approval process for a $100K+ project?"
Authority
Key Indicators:
- ✅ Speaking with VP-level or above
- ✅ Champion has access to economic buyer
- ✅ Budget authority confirmed ($100K+)
- ✅ Can convene buying committee
Red Flags:
- ❌ Only analyst-level contacts
- ❌ "I need to ask my boss's boss"
- ❌ No budget authority confirmed
Need
Must-Have Indicators (≥3 Required):
- Processing >100 hours video/month
- Manual process taking >500 hours/month
- Budget allocated for automation
- Regulatory/compliance driver
- Executive mandate for AI adoption
- Content searchability complaints
Pain Severity Score:
pain_score = {
'critical': {
'description': 'Urgent: Regulatory deadline, executive mandate',
'timeline': '<3 months',
'probability': '80%+ close rate'
},
'high': {
'description': 'Significant: Large budget waste, competitive pressure',
'timeline': '3-6 months',
'probability': '50-60% close rate'
},
'medium': {
'description': 'Moderate: Nice-to-have, efficiency play',
'timeline': '6-12 months',
'probability': '20-30% close rate'
},
'low': {
'description': 'Exploratory: No clear urgency',
'timeline': '>12 months or never',
'probability': '<10% close rate'
}
}
Timeline
| Urgency | Timeline to Decision | Probability | Qualifying Questions |
|---|---|---|---|
| Urgent | <30 days | 80% | "When is your regulatory deadline?" |
| Active | 1-3 months | 50% | "What's driving the timeline?" |
| Planned | 3-6 months | 30% | "What needs to happen before you can start?" |
| Exploring | 6-12 months | 10% | "What would make this a priority?" |
5. Disqualification Criteria (Fast No's)
Immediate Disqualifiers
- ❌ <100 hours video/month: Too small for ROI
- ❌ <500 employees: Unlikely to have dedicated video processing
- ❌ No budget for 2026: Can't close this year
- ❌ No economic buyer access: Can't get to decision maker
- ❌ DIY mentality: "We'll build it ourselves"
- ❌ Price-shopping: Only cares about cost, not value
- ❌ No clear pain: "Just exploring options"
Red Flag Indicators
- ⚠️ RFP with 20 vendors: Procurement exercise, not buying intent
- ⚠️ "Send us a proposal": No discovery, no qualification
- ⚠️ Multi-year decision process: Government, academia (long sales cycles)
- ⚠️ Analyst-only contacts: No executive sponsorship
- ⚠️ "We're happy with current process": No urgency
6. Ideal Customer Profile Summary
The Perfect Customer
Company:
- Mid-market enterprise (2,000-10,000 employees)
- $500M-$5B revenue
- Fortune 500 or fast-growing private company
- Industry: L&D, Market Research, Legal, SaaS
Situation:
- Processing 500+ hours video/month
- 10-20 FTE manually analyzing content
- $1M+ annual labor cost on video processing
- Regulatory/compliance driver or executive mandate
Buying Committee:
- Economic buyer: VP or C-suite (confirmed)
- Technical buyer: Director of Engineering (engaged)
- Champion: Mid-level manager (AI enthusiast)
- Timeline: 1-3 months to decision
Psychographic:
- "Automate everything" culture
- 2nd/3rd wave of AI adoption (past PoC stage)
- Prefers best-of-breed over all-in-one
- ROI-driven, not price-sensitive
Example: "Fortune 500 pharmaceutical company with 20,000 employees. VP of Learning & Development seeking to automate indexing of 3,000 compliance training videos (currently taking 5 FTE analysts 6 months). Budget: $500K. Timeline: 2 months (regulatory deadline). Champion: Director of Digital Learning (reports to VP)."
7. Account Scoring Model
Lead Scoring Matrix
lead_score = {
'firmographic': {
'company_size': {
'2000-10000_employees': 25,
'500-2000_employees': 15,
'10000+_employees': 20,
'<500_employees': 0
},
'industry': {
'learning_development': 25,
'market_research': 25,
'legal_tech': 20,
'customer_success': 15,
'other': 5
},
'revenue': {
'500m_5b': 15,
'100m_500m': 10,
'5b+': 12,
'<100m': 0
}
},
'behavioral': {
'video_volume': {
'500+_hours_month': 20,
'200-500_hours': 15,
'100-200_hours': 10,
'<100_hours': 0
},
'pain_severity': {
'critical': 20,
'high': 15,
'medium': 5,
'low': 0
},
'timeline': {
'urgent_<30d': 15,
'active_1-3mo': 10,
'planned_3-6mo': 5,
'exploring_6mo+': 0
}
},
'engagement': {
'buyer_access': {
'c_suite_confirmed': 20,
'vp_level_engaged': 15,
'director_only': 5,
'analyst_only': 0
},
'budget_confirmed': {
'yes_200k+': 15,
'yes_100k+': 10,
'allocated_exploring': 5,
'no_budget': 0
}
}
}
# Total score range: 0-180
# Score >100: Hot lead (immediate sales action)
# Score 70-100: Warm lead (nurture, demo)
# Score 40-70: Cold lead (education, content)
# Score <40: Disqualify or long-term nurture
Scoring Example
example_lead = {
'company': 'Acme Pharmaceuticals',
'employees': 5000,
'revenue': 2_000_000_000,
'industry': 'learning_development',
'video_hours_month': 600,
'pain_severity': 'critical',
'timeline': 'active_1-3mo',
'buyer_access': 'vp_level_engaged',
'budget_confirmed': 'yes_200k+',
'calculated_score': (
25 + # Company size
25 + # Industry
15 + # Revenue
20 + # Video volume
20 + # Pain severity
10 + # Timeline
15 + # Buyer access
15 # Budget
), # Total: 145 (Hot lead!)
'recommendation': 'Priority: Executive demo within 48 hours'
}
8. Anti-Personas (Who NOT to Target)
Anti-Persona 1: "The Hobbyist"
Profile:
- Individual content creator, YouTuber, course creator
- <10 videos/month
- $0 budget for tools
- Looking for free solution
Why Not: No budget, volume too low, price-sensitive
Anti-Persona 2: "The DIY Builder"
Profile:
- Tech company with strong ML team
- "We'll build it ourselves" mentality
- Wants to own IP, not buy software
Why Not: Will never buy, sees tools as competitive advantage
Anti-Persona 3: "The Tire Kicker"
Profile:
- Generic "exploring AI" inquiry
- No specific pain point
- No timeline, no budget
- Collecting vendors for future RFP
Why Not: No urgency, wastes sales time, low close rate
Anti-Persona 4: "The Price Shopper"
Profile:
- Only cares about price, not value
- Demands lowest cost option
- Compares to free tools
- No appreciation for ROI
Why Not: Will churn, not profitable, high support burden
9. Go-to-Market Implications
Sales Approach by ICP Segment
Tier 1 (Learning & Development)
- Entry Point: VP Learning & Development, Chief Learning Officer
- Value Prop: "20x ROI by automating training content indexing"
- Demo Focus: SharePoint integration, compliance tracking
- Sales Cycle: 1-3 months
- Average Deal Size: $150K first year
Tier 2 (Market Research)
- Entry Point: Head of Research, Investment Committee
- Value Prop: "Real-time competitive intelligence from 1000s of earnings calls"
- Demo Focus: Sentiment analysis, quote extraction, timeline
- Sales Cycle: 2-4 months
- Average Deal Size: $200K first year
Tier 3 (Legal Tech)
- Entry Point: eDiscovery Director, Chief Legal Officer
- Value Prop: "Searchable deposition tranH.P.004-SCRIPTS, 10x faster review"
- Demo Focus: Timestamp accuracy, keyword search, redaction
- Sales Cycle: 3-6 months (procurement heavy)
- Average Deal Size: $250K first year
Marketing Strategy by ICP
content_marketing:
tier_1_learning:
- roi_calculator_landing_page
- case_study_pharmaceutical_training
- webinar_automating_compliance_content
tier_2_research:
- earnings_call_analysis_demo
- competitive_intelligence_ebook
- analyst_productivity_benchmark
tier_3_legal:
- deposition_analysis_white_paper
- legal_tech_conference_sponsorship
- law_firm_partnership_program
lead_generation:
paid_channels:
- linkedin_ads_targeting_vp_learning
- google_ads_earning_call_analysis
- sponsored_content_in_clomedia
organic_channels:
- seo_for_video_transcription_tools
- linkedin_thought_leadership
- industry_conference_speaking
partnerships:
- lms_vendors_sharepoint_confluence
- system_integrators_accenture_deloitte
- professional_associations_atd_shrm
10. Success Metrics
ICP Validation Metrics
icp_metrics = {
'deal_velocity': {
'target_close_rate_icp': 0.50, # 50% of qualified ICPs close
'target_close_rate_non_icp': 0.10, # Only 10% of non-ICPs
'current_close_rate': 0.35
},
'sales_cycle': {
'target_icp': 60, # 60 days
'target_non_icp': 180, # 180 days
'current_average': 90
},
'deal_size': {
'target_icp_acv': 150000,
'target_non_icp_acv': 50000,
'current_average_acv': 100000
},
'customer_lifetime_value': {
'target_icp_ltv': 500000, # 3+ years
'target_non_icp_ltv': 100000, # Churn after year 1
'target_ltv_cac_ratio': 5.0
}
}
Quarterly ICP Review
- Q1: Analyze closed deals, validate ICP criteria
- Q2: Refine scoring model based on win/loss analysis
- Q3: Update messaging and positioning per segment
- Q4: Set next year's ICP evolution
Appendix: Account Examples
Example 1: Perfect ICP Match
Company: MedLearn University
Industry: Healthcare Education
Size: 5,000 employees
Revenue: $1.2B
Pain: 4,000 medical education videos, no searchability
Contact: Dr. Sarah Chen, Dean of Medical Education (C-suite)
Budget: $300K approved for digital transformation
Timeline: 3 months (accreditation deadline)
Score: 152 (Hot lead)
Example 2: Good ICP Match
Company: InvestCo Partners
Industry: Market Research
Size: 800 employees
Revenue: $450M
Pain: 1,500 earnings calls/year, 2-day analysis lag
Contact: James Martinez, Director of Research (VP reports to)
Budget: $150K (needs VP approval)
Timeline: 4 months (Q2 planning)
Score: 88 (Warm lead)
Example 3: Poor ICP Match (Disqualify)
Company: StartupTV
Industry: Media
Size: 50 employees
Revenue: $5M
Pain: "Just exploring AI options"
Contact: Jane Doe, Junior Analyst
Budget: "Need to justify ROI first"
Timeline: "Maybe next year"
Score: 15 (Disqualify)