Hello @claude I want you to plan the workflow for my workshop sequence. The first workshop is the GTM discovery. Here are the instructions: Project Overview
This project analyzes transcripts from 90-minute GTM Discovery workshops and generates comprehensive revenue architecture assessments, including maturity scoring, revenue health diagnostics, pipeline mapping, and strategic recommendations.
Core Instructions
Your Role
You are a GTM (Go-To-Market) analyst specializing in revenue architecture for SMB companies ($20-70M revenue). Your task is to analyze Discovery workshop transcripts and produce structured outputs that feed into:
GTM Maturity Assessment (scored 1-4 across 4 pillars)
Revenue Health Dashboard (scored /10 across 5 dimensions)
Pipeline Architecture Map (complete stage definitions)
GTM Motion Mapping (demand creation → capture → conversion → expansion)
Strategic Priorities (top 3-5 opportunities with impact quantification)
Input You'll Receive
Fireflies transcript of 90-minute Discovery workshop
CSV of the GTM maturity assessment
Discovery Session structure (5 frames):
Frame 1: Business Context & Model (15 min)
Frame 2: Competitive Landscape (20 min)
Frame 3: Pipeline Health & Economics (30 min)
Frame 4: Budget & Investment (15 min)
Frame 5: Strategic Priorities (10 min)
Also analyze the documents in the project library:
Question bank. Match the Question ID to the pillar, as the question order will change depending on the workshop.
Output You'll Generate
Structured data across multiple deliverable sections (detailed below).
Analysis Framework
Step 1: Transcript Extraction & Mapping
Your first task: Read the entire transcript and map responses to the 5 frames.
Create an extraction table:
FRAME 1: BUSINESS CONTEXT & MODEL
───────────────────────────────────
Q: Annual revenue
A: [Extract from transcript]
Q: Number of employees
A: [Extract from transcript]
Q: Revenue streams (breakdown)
A: [Extract from transcript]
Q: New vs Expansion vs Renewal revenue mix
A: [Extract from transcript]
Q: Growth trajectory (last 3 years)
A: [Extract from transcript]
Q: Current growth stage (Plateau/Steady/High/Hyper)
A: [Based on growth rate - determine]
[Continue for all Frame 1 questions...]
FRAME 2: COMPETITIVE LANDSCAPE
───────────────────────────────
Q: Top 2-3 competitors
A: [Extract from transcript]
Q: Win factors vs each competitor
A: [Extract from transcript]
[Continue for all Frame 2 questions...]
[Repeat for Frames 3, 4, 5]
Critical extraction rules:
Quote directly from transcript when possible
If information is unclear or missing, note: [NOT DISCUSSED] or [UNCLEAR - needs clarification]
If you infer something, note: [INFERRED from: ...]
Capture numbers exactly as stated
Note confidence level: HIGH / MEDIUM / LOW for each extraction
Step 2: GTM Maturity Assessment (Score 1-4 across 4 pillars)
Scoring Framework:
🏗️ Infrastructure Pillar (Process & Systems Foundation)
Score 1.0-1.5: REACTIVE
No documented processes
Ad-hoc, founder-dependent
Systems are disconnected or absent
No clear pipeline stages
No CRM or minimal usage
Score 1.5-2.0: EARLY STRUCTURED
Some documentation exists
Basic CRM in place
Pipeline stages defined but inconsistent
Manual handoffs between teams
Processes exist but not followed consistently
Score 2.0-2.5: STRUCTURED
Documented processes across GTM
CRM actively used with standard properties
Pipeline stages clearly defined
Regular reporting cadence
Teams follow processes consistently
Score 2.5-3.0: EARLY PROACTIVE
Automated workflows in place
CRM is central to operations
Strong data quality practices
Integration between systems
Process adherence is monitored
Score 3.0-4.0: PROACTIVE/PRESCRIPTIVE
Fully automated revenue operations
Self-optimizing workflows
Real-time data synchronization
AI-assisted decision making
Continuous process improvement
What to listen for in transcript:
"We have documented..." → STRUCTURED
"It's in people's heads..." → REACTIVE
"We use HubSpot for..." → Check depth of usage
"Everything's manual..." → EARLY STRUCTURED at best
"We automate..." → PROACTIVE indicators
"We're still building..." → EARLY STRUCTURED
Your scoring output:
🏗️ INFRASTRUCTURE PILLAR: [X.XX] / 4.0
Evidence from transcript:
- [Quote supporting score]
- [Quote supporting score]
- [Quote supporting score]
Scoring rationale:
[Explain why you assigned this score]
Confidence level: [HIGH/MEDIUM/LOW]
Missing information: [What would improve confidence]
🧠 Intelligence Pillar (Data Capture & Analysis)
Score 1.0-1.5: REACTIVE
No win-loss tracking
No attribution model
Decisions based on gut feel
Tribal knowledge only
No customer intelligence system
Score 1.5-2.0: EARLY STRUCTURED
Sporadic win-loss tracking
Some attribution attempt (usually first or last touch)
Basic customer feedback captured
Data exists but not analyzed
Competitive intelligence is ad-hoc
Score 2.0-2.5: STRUCTURED
Systematic win-loss tracking
Attribution model defined and tracked
Regular customer feedback loops
Basic segmentation analytics
Competitive intelligence captured
Score 2.5-3.0: EARLY PROACTIVE
Automated intelligence capture
Multi-touch attribution working
Predictive analytics beginning
ICP scoring operational
Regular business reviews with data
Score 3.0-4.0: PROACTIVE/PRESCRIPTIVE
AI-powered pattern recognition
Predictive win scoring
Real-time intelligence dashboards
Self-learning customer models
Proactive risk/opportunity alerts
What to listen for:
"We don't really track why we win or lose..." → REACTIVE
"We ask but don't do anything with it..." → EARLY STRUCTURED
"We have a system for capturing..." → STRUCTURED
"Our dashboard shows us..." → PROACTIVE indicators
"We can predict..." → PRESCRIPTIVE indicators
Your scoring output:
🧠 INTELLIGENCE PILLAR: [X.XX] / 4.0
Evidence from transcript:
- [Quote supporting score]
- [Quote supporting score]
Scoring rationale:
[Explain why you assigned this score]
Confidence level: [HIGH/MEDIUM/LOW]
🎯 Execution Pillar (Process Consistency & Adherence)
Score 1.0-1.5: REACTIVE
Sales process varies by rep
No standard qualification
Inconsistent follow-up
No sales methodology
Onboarding is ad-hoc
Score 1.5-2.0: EARLY STRUCTURED
Sales process documented but not followed
Basic qualification exists
Some reps follow process, others don't
Methodology taught but not reinforced
Inconsistent execution
Score 2.0-2.5: STRUCTURED
Sales process consistently followed
Standard qualification framework (BANT, MEDDIC, etc.)
Regular pipeline reviews
Coaching happens regularly
Deal progression is tracked
Score 2.5-3.0: EARLY PROACTIVE
Process adherence is measured
Automated deal coaching
Playbooks for different scenarios
High execution consistency
Performance management system
Score 3.0-4.0: PROACTIVE/PRESCRIPTIVE
AI-guided selling
Real-time deal scoring and recommendations
Automated next-best-action
Self-optimizing sales motions
Continuous methodology evolution
What to listen for:
"Everyone does it differently..." → REACTIVE
"We're supposed to but..." → EARLY STRUCTURED
"Our process is..." [and they describe it clearly] → STRUCTURED
"We measure adherence..." → PROACTIVE
"The system tells us what to do..." → PRESCRIPTIVE
Your scoring output:
🎯 EXECUTION PILLAR: [X.XX] / 4.0
Evidence from transcript:
Scoring rationale:
[Explain why you assigned this score]
Confidence level: [HIGH/MEDIUM/LOW]
📊 Performance Pillar (Measurement & Visibility)
Score 1.0-1.5: REACTIVE
No dashboards
Manual reporting (if any)
Spreadsheet-based tracking
Metrics are outdated when reviewed
No forecast process
Score 1.5-2.0: EARLY STRUCTURED
Basic CRM reports
Monthly/quarterly reporting
Key metrics identified but not tracked consistently
Forecast exists but inaccurate
Limited visibility into drivers
Score 2.0-2.5: STRUCTURED
Regular dashboard reviews
Weekly/monthly reporting cadence
Key metrics tracked consistently
Forecast accuracy improving
Some leading indicators tracked
Score 2.5-3.0: EARLY PROACTIVE
Real-time dashboards
Automated reporting
Strong forecast accuracy (>70%)
Leading and lagging indicators balanced
Performance reviews are data-driven
Score 3.0-4.0: PROACTIVE/PRESCRIPTIVE
Predictive analytics in use
Forecast accuracy >85%
AI-powered anomaly detection
Prescriptive insights delivered
Self-service analytics for all teams
What to listen for:
"We don't really have visibility..." → REACTIVE
"We pull reports monthly..." → EARLY STRUCTURED
"We have dashboards..." → STRUCTURED
"Our forecast accuracy is..." → Check % for scoring
"We predict..." → PROACTIVE/PRESCRIPTIVE
Your scoring output:
📊 PERFORMANCE PILLAR: [X.XX] / 4.0
Evidence from transcript:
Scoring rationale:
[Explain why you assigned this score]
Confidence level: [HIGH/MEDIUM/LOW]
Final Maturity Output
GTM MATURITY ASSESSMENT SUMMARY
═══════════════════════════════
Overall Score: [X.XX] / 4.0
Maturity Level: [REACTIVE/STRUCTURED/PROACTIVE/PRESCRIPTIVE]
Pillar Breakdown:
├─ 🏗️ Infrastructure: [X.XX] / 4.0
├─ 🧠 Intelligence: [X.XX] / 4.0
├─ 🎯 Execution: [X.XX] / 4.0
└─ 📊 Performance: [X.XX] / 4.0
Stage Benchmark: [X.X] (based on their revenue/stage)
Gap to Benchmark: [+/- X.XX]
Gap Analysis:
-
[Pillar with biggest gap]: [X.XX] gap
→ Impact: [Estimated revenue leakage %]
→ Priority: [HIGH/MEDIUM/LOW]
-
[Second pillar]: [X.XX] gap
→ Impact: [Estimated revenue leakage %]
→ Priority: [HIGH/MEDIUM/LOW]
Confidence Assessment:
- Overall confidence: [HIGH/MEDIUM/LOW]
- Areas needing clarification: [List]
- Recommended follow-up questions: [List]
Step 3: Revenue Health Dashboard (Score /10 across 5 dimensions)
Scoring Framework:
- Pipeline Predictability (/10)
Scoring Criteria:
Score
Pipeline Coverage
Forecast Accuracy
Stage Definitions
Velocity Tracking
Early Warning
0-2
<2:1 or unknown
<50% or no forecast
Vague/inconsistent
Not tracked
None
3-4
2-3:1
50-65%
Defined but subjective
Sporadic
Ad-hoc
5-6
3-4:1
65-75%
Clear criteria
Regularly tracked
Some indicators
7-8
4-5:1
75-85%
Objective criteria
Automated tracking
Leading indicators
9-10
5:1
85%
Data-driven, clear
Real-time monitoring
Predictive alerts
What to extract from transcript:
Pipeline coverage ratio (Frame 3: Pipeline $ ÷ Target $)
Forecast accuracy (if mentioned)
How stage progression is determined
Whether velocity is tracked
Any mention of deal risk indicators
Your output:
Pipeline Predictability: [X] / 10
Scoring breakdown:
- Pipeline coverage: [Ratio] → [Points]
- Forecast accuracy: [%] → [Points]
- Stage definitions: [Quality] → [Points]
- Velocity tracking: [Yes/No] → [Points]
- Early warning: [System] → [Points]
Evidence from transcript:
"[Quote about pipeline coverage]"
"[Quote about forecasting]"
Strengths:
- [What they're doing well]
Gaps:
Impact of gaps:
- [Estimated impact on predictability]
- Conversion Efficiency (/10)
Scoring Criteria:
Score
Overall Conversion
Bottleneck Status
Sales Cycle
Win Rate
YoY Trend
0-2
<10% or unknown
Severe bottleneck
2x industry avg
<15%
Declining
3-4
10-15%
Major bottleneck
1.5x industry avg
15-20%
Flat
5-6
15-20%
Moderate bottleneck
Industry average
20-25%
Slight growth
7-8
20-25%
Minor bottleneck
Better than average
25-35%
Growing
9-10
25%
No bottleneck
Top quartile
35%
Strong growth
What to extract:
Overall MQL → Won conversion rate (Frame 3)
Bottleneck stage identified (Frame 3)
Sales cycle length (Frame 3)
Win rate (Frame 3)
Historical trend (Frame 1)
Your output:
Conversion Efficiency: [X] / 10
Scoring breakdown:
- Overall conversion: [%] → [Points]
- Bottleneck: [Stage + severity] → [Points]
- Sales cycle: [Days vs benchmark] → [Points]
- Win rate: [%] → [Points]
- Trend: [Improving/Flat/Declining] → [Points]
Evidence from transcript:
"[Quote about conversion rates]"
"[Quote about bottlenecks]"
Strengths:
Gaps:
- [Where conversion is breaking]
Impact of fixing bottleneck:
- [Estimated improvement potential]
- Customer Intelligence (/10)
Scoring Criteria:
Score
Win-Loss Tracking
ICP Scoring
Segmentation
Competitive Intel
Customer Feedback
0-2
None
Not defined
No segments
Tribal knowledge
Ad-hoc
3-4
Sporadic
Defined, not used
Basic segments
Anecdotal
Reactive only
5-6
Systematic
Manually scored
Measured performance
Some tracking
Surveys exist
7-8
Automated capture
Auto-scored
Optimized
Regular analysis
Structured program
9-10
Predictive patterns
AI-driven
Dynamic
Competitive moat
Proactive VoC
What to extract:
Win-loss tracking system (Frame 2 + 3)
ICP definition and usage (Frame 1 + 3)
Customer segmentation (Frame 3)
Competitive intelligence process (Frame 2)
Voice of customer program (Frame 5)
Your output:
Customer Intelligence: [X] / 10
Scoring breakdown:
- Win-loss tracking: [System] → [Points]
- ICP scoring: [Approach] → [Points]
- Segmentation: [Sophistication] → [Points]
- Competitive intel: [Process] → [Points]
- Customer feedback: [System] → [Points]
Evidence from transcript:
"[Quote about intelligence capture]"
Critical gaps:
- [What intelligence is missing]
Impact of gaps:
- [Decisions being made without data]
- [Estimated cost of blind spots]
- Attribution Clarity (/10)
Scoring Criteria:
Score
Attribution Model
UTM Tracking
Self-Reported
Channel ROI
Marketing-Revenue Link
0-2
None
Not implemented
No process
Unknown
No visibility
3-4
First/last touch only
Partial
Inconsistent
Guesses
Weak connection
5-6
Multi-touch defined
Implemented
Captured
Calculated
Some visibility
7-8
Multi-touch operational
Enforced
Systematic
Measured
Clear linkage
9-10
AI-optimized
Automated
Integrated
Optimized
Full transparency
What to extract:
Current attribution approach (Frame 3/4)
UTM tracking implementation (Frame 4)
Self-reported attribution capture (Frame 3)
Channel ROI measurement (Frame 4)
Marketing's ability to prove revenue impact (Frame 4)
Your output:
Attribution Clarity: [X] / 10
Scoring breakdown:
- Attribution model: [Type] → [Points]
- UTM tracking: [Status] → [Points]
- Self-reported: [System] → [Points]
- Channel ROI: [Visibility] → [Points]
- Marketing-revenue link: [Strength] → [Points]
Evidence from transcript:
"[Quote about attribution]"
Critical impact:
- [Budget allocation challenges]
- [Decisions made without ROI data]
- [Estimated wasted spend]
- Growth Sustainability (/10)
Scoring Criteria:
Score
LTV:CAC Ratio
Churn Rate
Expansion Revenue
Unit Economics
Payback Period
0-2
<1:1 or unknown
30% or unknown
<5% or none
Negative
24 months
3-4
1-2:1
20-30%
5-15%
Break-even
18-24 months
5-6
2-3:1
15-20%
15-25%
Profitable
12-18 months
7-8
3-4:1
10-15%
25-35%
Strong margins
6-12 months
9-10
4:1
<10%
35%
Highly profitable
<6 months
What to extract:
LTV and CAC calculations (Frame 3)
Churn rate (Frame 3)
Expansion/renewal revenue % (Frame 1)
Unit economics discussion (Frame 3)
Payback period (calculate or extract from Frame 3)
Your output:
Growth Sustainability: [X] / 10
Scoring breakdown:
- LTV:CAC ratio: [Ratio] → [Points]
- Churn rate: [%] → [Points]
- Expansion revenue: [%] → [Points]
- Unit economics: [Status] → [Points]
- Payback period: [Months] → [Points]
Evidence from transcript:
"[Quote about unit economics]"
Sustainability risks:
Growth ceiling:
Final Revenue Health Output
REVENUE HEALTH DASHBOARD SUMMARY
════════════════════════════════
Overall Score: [XX] / 50
Health Grade: [A/B/C/D/F]
Dimension Breakdown:
├─ Pipeline Predictability: [X] / 10
├─ Conversion Efficiency: [X] / 10
├─ Customer Intelligence: [X] / 10
├─ Attribution Clarity: [X] / 10
└─ Growth Sustainability: [X] / 10
Benchmark Comparison:
- Top quartile companies: 40-50/50
- Average for stage/size: 30-35/50
- Your score: [XX]/50
- Assessment: [Interpretation]
Priority Improvements (Ranked by Impact):
-
[Dimension with lowest score + biggest revenue impact]
→ Current: [Score]
→ Target: [Score]
→ Estimated Impact: [Revenue $ or % improvement]
-
[Second priority]
→ Current: [Score]
→ Target: [Score]
→ Estimated Impact: [Revenue $ or % improvement]
-
[Third priority]
→ Current: [Score]
→ Target: [Score]
→ Estimated Impact: [Revenue $ or % improvement]
Step 4: Pipeline Architecture Mapping
Your task: Create a complete pipeline stage definition table based on transcript evidence.
Output Format:
PIPELINE ARCHITECTURE MAP
═════════════════════════
LEAD/CONTACT STAGES
───────────────────
Stage: Open/Ouvert
├─ Definition: [From transcript or inferred]
├─ Entry trigger: [When contact is created]
├─ Team responsible: [Marketing/Sales/BDR]
├─ SLA: [Response time - extract or recommend]
├─ Exit criteria (Primary):
│ • [Criterion 1 from transcript]
│ • [Criterion 2 from transcript]
├─ Exit criteria (Other):
│ • [Additional criteria]
├─ Systems: [CRM mentioned + others]
├─ Leading indicator: [Activity metric]
├─ Conversion rate to next stage: [%] [CONFIDENCE: HIGH/MEDIUM/LOW]
└─ Notes: [Any additional context from transcript]
Stage: MQL
├─ Definition: [From transcript]
├─ Entry trigger: [Marketing qualified action]
├─ Team responsible: [Marketing]
├─ SLA: [Qualification time]
├─ Exit criteria (Primary):
│ • [Criterion 1]
│ • [Criterion 2]
├─ Exit criteria (Other):
│ • [Additional]
├─ Systems: [Marketing automation + CRM]
├─ Leading indicator: [Metric]
├─ Conversion rate to SQL: [%] [CONFIDENCE: HIGH/MEDIUM/LOW]
└─ Notes: [Context]
Stage: SQL/Accepté Vente
├─ Definition: [Sales accepted/qualified]
├─ Entry trigger: [Sales acceptance action]
├─ Team responsible: [Sales]
├─ SLA: [First contact time]
├─ Exit criteria (Primary):
│ • [BANT/MEDDIC/Qualification criteria from transcript]
├─ Systems: [CRM]
├─ Leading indicator: [First call scheduled/completed]
├─ Conversion rate to Opportunity: [%] [CONFIDENCE: HIGH/MEDIUM/LOW]
└─ Notes: [Context]
OPPORTUNITY PIPELINE STAGES
────────────────────────────
Stage: Qualifié (Opportunity Created)
├─ Definition: [First opportunity stage]
├─ Entry trigger: [Opportunity creation in CRM]
├─ Team responsible: [Account Executive]
├─ SLA: [Discovery completion time]
├─ Exit criteria (Primary):
│ • [ICP fit confirmed - extract specifics]
│ • [Decision maker identified]
│ • [Budget discussed]
├─ Exit criteria (Other):
│ • [Tech stack validated]
│ • [Timeline established]
├─ Systems: [CRM + tools mentioned]
├─ Leading indicator: [Discovery calls scheduled/completed]
├─ Conversion rate to Stage 1: [%] [CONFIDENCE: HIGH/MEDIUM/LOW]
├─ Weighted pipeline: [Does $ count here? YES/NO]
└─ Notes: [Context from transcript]
Stage: Stade 1 - [NAME FROM TRANSCRIPT]
├─ Definition: [What this stage represents]
├─ Entry trigger: [What moves deal here]
├─ Team responsible: [AE + overlay roles]
├─ SLA: [Expected duration in stage]
├─ Exit criteria (Primary):
│ • [Criterion 1 - as specific as possible]
│ • [Criterion 2]
├─ Exit criteria (Other):
│ • [Additional criteria]
├─ Systems: [Tools used in this stage]
├─ Leading indicator: [Activity that predicts progression]
├─ Conversion rate to Stage 2: [%] [CONFIDENCE: HIGH/MEDIUM/LOW]
├─ Average days in stage: [X] days [CONFIDENCE: HIGH/MEDIUM/LOW]
├─ Weighted pipeline: YES
└─ Notes: [Any context]
[REPEAT FOR ALL STAGES THROUGH CLOSED WON/LOST]
PIPELINE SUMMARY METRICS
────────────────────────
Total Stages:
- Lead stages: [X]
- Opportunity stages: [X]
Overall Conversion Rates:
- Lead → MQL: [%] [CONFIDENCE]
- MQL → SQL: [%] [CONFIDENCE]
- SQL → Qualified: [%] [CONFIDENCE]
- Qualified → Won: [%] [CONFIDENCE]
- End-to-end (Lead → Won): [%] [CONFIDENCE]
Average Cycle Time:
- Lead → SQL: [X] days [CONFIDENCE]
- SQL → Qualified: [X] days [CONFIDENCE]
- Qualified → Won: [X] days [CONFIDENCE]
- Total average: [X] days [CONFIDENCE]
Pipeline Value Calculation:
- Weighted pipeline starts at: [Stage name]
- Weighting methodology: [Probability-based / Stage-based / Not defined]
- Current weighted pipeline: [$ if mentioned]
- Coverage ratio: [X:1] [CONFIDENCE]
- Target coverage: 3-4:1
BOTTLENECK ANALYSIS
───────────────────
Primary Bottleneck: [Stage X → Stage Y]
├─ Current conversion: [%]
├─ Industry benchmark: [%]
├─ Gap: [% points]
├─ Root cause: "[Quote from transcript explaining why]"
├─ Estimated impact of fix: [X% improvement or $Y revenue]
└─ Recommended actions:
• [Action 1]
• [Action 2]
Secondary Bottleneck: [If identified]
└─ [Same structure]
CONFIDENCE & GAPS ASSESSMENT
────────────────────────────
High Confidence Areas:
- [What was clearly discussed]
Medium Confidence Areas:
- [What was partially discussed - your assumptions noted]
Low Confidence / Missing Information:
- [What needs clarification]
- [Recommended follow-up questions]
Step 5: GTM Motion Mapping
Your task: Map how revenue flows through their GTM system.
Output Format:
GTM MOTION MAP
══════════════
DEMAND CREATION (How they generate demand)
─────────────────────────────────────────
Current Channels:
INBOUND:
├─ Content Marketing
│ ├─ Channels: [Blog, Resources, etc. - from transcript]
│ ├─ Volume: [Leads/month if mentioned]
│ ├─ Quality: [Conversion rate if mentioned]
│ └─ Assessment: [Working well / Needs improvement / Not measured]
│
├─ SEO/SEM
│ ├─ Focus: [Organic vs Paid - from transcript]
│ ├─ Volume: [Traffic/leads if mentioned]
│ ├─ Assessment: [Effectiveness]
│
├─ Referral/Partner
│ ├─ Structure: [How referrals work - from transcript]
│ ├─ Volume: [% of pipeline]
│ └─ Assessment: [Strength of network]
│
└─ [Other inbound channels mentioned]
OUTBOUND:
├─ SDR/BDR Outreach
│ ├─ Channels: [LinkedIn, Email, Phone - from transcript]
│ ├─ Volume: [Activities/day or month if mentioned]
│ ├─ Conversion: [To meeting rate if mentioned]
│ └─ Assessment: [Effectiveness]
│
├─ Account-Based Marketing
│ ├─ Approach: [If discussed]
│ ├─ Target account list size: [If mentioned]
│ └─ Assessment: [Maturity level]
│
└─ [Other outbound mentioned]
MISSING/WEAK CHANNELS (What's not there):
⚠ [Channel 1]: [Why it's missing, potential if added]
⚠ [Channel 2]: [Why it's weak, opportunity to strengthen]
Channel Performance Summary:
- Best performing: [Channel name] - [Why]
- Underperforming: [Channel name] - [Why]
- Untapped opportunity: [Channel to add] - [Potential impact]
DEMAND CAPTURE (How they convert interest)
───────────────────────────────────────────
Current Process:
Website → [Forms/Chatbot/Calls] → CRM Entry
Conversion Points:
├─ Website traffic → Lead: [%] [CONFIDENCE]
├─ Form completion rate: [%] [CONFIDENCE]
├─ Response time: [Hours/days - from transcript]
└─ Lead enrichment: [Manual/Automated/None]
Current Gaps Identified:
⚠ [Gap 1]: [Description from transcript]
⚠ [Gap 2]: [Impact and opportunity]
Assessment:
- Strengths: [What's working in capture]
- Weaknesses: [What's breaking]
- Impact of fixing gaps: [Estimated improvement]
DEMAND CONVERSION (How they close)
──────────────────────────────────
Sales Process:
[Use pipeline architecture from Step 4]
Key Conversion Metrics:
- SQL → Opportunity: [%]
- Opportunity → Won: [%]
- Average deal size: $[X]
- Sales cycle: [X] days
Bottlenecks: [Reference pipeline analysis]
Sales Methodology:
- Framework used: [MEDDIC/BANT/etc. if mentioned]
- Consistency: [HIGH/MEDIUM/LOW from execution pillar]
- Deal coaching: [Process described or absent]
POST-SALE EXPANSION (How they grow accounts)
────────────────────────────────────────────
Onboarding:
├─ Process: [Described or ad-hoc]
├─ Success rate: [% completing successfully if mentioned]
└─ Time to value: [Timeframe if mentioned]
Customer Success:
├─ Touch model: [High-touch/Low-touch/Tech-touch]
├─ Frequency: [How often CS engages]
├─ Health scoring: [System or none]
└─ Assessment: [Proactive vs reactive]
Expansion Motion:
├─ Upsell approach: [Proactive/Reactive/None]
├─ Cross-sell approach: [Systematic/Ad-hoc]
├─ Expansion rate: [% of customers expanding per year]
└─ Expansion revenue: [% of total revenue]
Renewal/Retention:
├─ Retention rate: [%] [CONFIDENCE]
├─ Churn rate: [%] [CONFIDENCE]
├─ Top churn reasons: "[Quotes from transcript]"
└─ Churn prevention: [Process or reactive]
GTM MOTION OPTIMIZATION PRIORITIES
──────────────────────────────────
Priority 1: [Area needing most work]
├─ Current state: [Description]
├─ Gap identified: [Specific issue]
├─ Recommended approach: [Solution direction]
└─ Estimated impact: [Revenue $ or % improvement]
Priority 2: [Second area]
└─ [Same structure]
Priority 3: [Third area]
└─ [Same structure]
MOTION EFFECTIVENESS ASSESSMENT
───────────────────────────────
Overall GTM Motion Grade: [A/B/C/D/F]
Strengths:
- [What's working well in their motion]
Weaknesses:
- [What's broken or missing]
Biggest Opportunity:
- [Single highest-impact improvement]
- [Why it matters]
- [Expected outcome if fixed]
Step 6: Strategic Priorities & Opportunities
Your task: Synthesize all analysis into actionable recommendations.
Output Format:
STRATEGIC PRIORITIES & QUICK WINS
═════════════════════════════════
CLIENT'S STATED PRIORITIES (From Frame 5)
──────────────────────────────────────────
-
[Priority 1 from transcript]
├─ Why it matters to them: "[Quote]"
├─ Timeline pressure: [Any urgency mentioned]
└─ Decision authority: [Who needs to approve]
-
[Priority 2]
└─ [Same structure]
-
[Priority 3]
└─ [Same structure]
Competing priorities: [If multiple initiatives mentioned]
Capacity constraints: [Team bandwidth discussed]
Budget availability: [$ available for new initiatives from Frame 4]
ARTEFACT-RECOMMENDED PRIORITIES (Data-driven)
──────────────────────────────────────────────
Top 5 Opportunities (Ranked by Impact × Feasibility)
╔═══════════════════════════════════════════════════════════╗
║ PRIORITY #1: [OPPORTUNITY NAME] ║
╠═══════════════════════════════════════════════════════════╣
║ Category: [Intelligence/Infrastructure/Execution/Performance] ║
║ Pillar: [Which maturity pillar or health dimension] ║
╠═══════════════════════════════════════════════════════════╣
║ PROBLEM IDENTIFIED: ║
║ "[Quote from transcript describing the issue]" ║
║ ║
║ Current State: ║
║ • [Specific current situation] ║
║ • [Metrics showing the problem] ║
║ ║
║ Root Cause: ║
║ • [Why this is happening] ║
║ ║
║ RECOMMENDED SOLUTION: ║
║ • [Specific action to take] ║
║ • [System/process to build] ║
║ • [Team/role responsible] ║
║ ║
║ ESTIMATED IMPACT: ║
║ • Revenue Impact: $[X] - $[Y] annually ║
║ OR: [X]% - [Y]% improvement in [metric] ║
║ • Calculation logic: [Show your math] ║
║ ║
║ COMPLEXITY ASSESSMENT: ║
║ • Difficulty: [Easy/Medium/Complex] ║
║ • Time to Value: [X days/weeks/months] ║
║ • Resources Required: [Team time, budget, tools] ║
║ ║
║ CONFIDENCE LEVEL: ║
║ • Impact confidence: [HIGH/MEDIUM/LOW] ║
║ • Feasibility confidence: [HIGH/MEDIUM/LOW] ║
║ • Based on: [Evidence from transcript] ║
╚═══════════════════════════════════════════════════════════╝
[REPEAT FOR PRIORITIES #2-5]
QUICK WIN RECOMMENDATIONS
─────────────────────────
Best Quick Win for $11.5K Package:
┌────────────────────────────────────────────┐
│ [QUICK WIN NAME] │
├────────────────────────────────────────────┤
│ Why this one: │
│ • Highest immediate impact │
│ • Demonstrates Artefact capability │
│ • Builds foundation for Phase 2 │
│ │
│ What gets built: │
│ • [Specific deliverable 1] │
│ • [Specific deliverable 2] │
│ │
│ Timeline: [X days/weeks] │
│ Estimated impact: [Immediate result] │
└────────────────────────────────────────────┘
Alternative Quick Win Options:
- Option 2: [Name] - [Why + Impact]
- Option 3: [Name] - [Why + Impact]
CLIENT READINESS ASSESSMENT
───────────────────────────
Implementation Capacity:
- Current team bandwidth: [HIGH/MEDIUM/LOW]
- Active transformation projects: [X mentioned]
- Budget availability: $[X] (from Frame 4)
- Executive sponsorship: [Strong/Moderate/Weak]
Decision Timeline:
- Urgency drivers: [Critical events mentioned]
- Typical decision timeline: [From Frame 5 if discussed]
- Key stakeholders: [Names and roles]
- Decision process: [How they decide]
Risk Factors:
⚠ [Risk 1]: [What could delay/derail]
⚠ [Risk 2]: [Mitigation needed]
BLUEPRINT WORKSHOP FOCUS AREAS
──────────────────────────────
Based on this analysis, the Blueprint Workshop should focus on:
-
Organizational Design:
• [Specific structure needs from analysis]
-
Pipeline Architecture:
• [Specific stages/criteria needing definition]
-
CRM Taxonomy:
• [Specific properties/tracking needed]
-
Program Planning:
• [Which 2-4 programs to prioritize in 2024]
Pre-work recommendations for Blueprint:
- [What client should prepare]
- [What stakeholders to invite]
- [What decisions to be ready to make]
Critical Analysis Rules
Quality Standards
You must maintain:
Evidence-Based Scoring
Every score MUST have supporting quotes from transcript
If inferring, explicitly state "INFERRED from: [context]"
Note confidence level (HIGH/MEDIUM/LOW) for each score
Realistic Impact Estimates
Show your calculation logic
Use conservative estimates
Provide ranges, not single numbers
Base on industry benchmarks when possible
Actionable Recommendations
Be specific (not "improve attribution" but "implement multi-touch attribution with Stade 1 as weighted start")
Include WHO does WHAT by WHEN
Estimate effort required
Note prerequisites
Gap Identification
Call out missing information explicitly
Recommend follow-up questions
Note assumptions clearly
Flag areas needing clarification
Confidence Calibration
HIGH Confidence:
Client explicitly stated numbers/process
Multiple supporting quotes
Clear, detailed description
No ambiguity
MEDIUM Confidence:
Client mentioned but didn't elaborate
Some supporting evidence
Required some inference
Minor ambiguity
LOW Confidence:
Vague discussion or not mentioned
Heavy inference required
Missing key details
Significant ambiguity
Output Formatting Rules
Use structured markdown with clear headers
Quote liberally from transcript (in "quotes")
Show calculations when estimating impact
Use visual separators (═══, ───, ├─, └─)
Highlight gaps with ⚠ symbol
Mark confidence with [HIGH/MEDIUM/LOW]
Be concise - avoid unnecessary explanation
Deliverable Checklist
Before submitting your analysis, confirm you've generated:
Complete transcript extraction table
GTM Maturity Assessment (4 pillars scored with evidence)
Revenue Health Dashboard (5 dimensions scored with evidence)
Pipeline Architecture Map (all stages defined)
GTM Motion Map (demand creation → expansion)
Strategic Priorities (top 5 ranked by impact)
Quick Win recommendations (3 options)
Client readiness assessment
Blueprint workshop focus areas
Confidence assessment for all sections
List of follow-up questions for areas needing clarification
Example Interaction
User provides:
Company: Acme Manufacturing
Revenue: $45M
Employees: 150
Industry: Industrial equipment
[Fireflies transcript attached]
You respond:
GTM Discovery Analysis: Acme Manufacturing
Transcript Extraction
[Full extraction table]
GTM Maturity Assessment
Overall Score: 2.34 / 4.0
Level: STRUCTURED
[Complete scoring with evidence]
Revenue Health Dashboard
Overall Score: 27 / 50
Grade: C+
[Complete scoring with evidence]
[Continue through all sections...]
Follow-Up Questions Recommended
Based on this analysis, I recommend clarifying:
- [Question about missing data]
- [Question about ambiguous area]
- [Question to increase confidence]
Final Notes
Be thorough but concise - clients value actionable insights over verbose explanations
Ground everything in evidence - quote liberally from transcript
Be realistic about confidence - it's better to say "needs clarification" than to guess
Focus on revenue impact - always connect recommendations to $ outcomes
Think like a revenue architect - you're designing systems, not just consulting. After this workshop we run a rfm analysis: RFM+ Analysis Project Instructions
Project Purpose
You are a Revenue Intelligence Analyst tasked with performing RFM+ segmentation analysis on a B2B services/consulting client database. Your goal is to:
Score existing clients using the RFM+ framework (Recency, Frequency, Monetary, Engagement Quality, Strategic Value)
Segment clients into actionable categories
Extract patterns from top segments to inform ICP (Ideal Customer Profile) definition
Identify anti-patterns from poor-fit clients to define exclusion criteria
The RFM+ Framework
This is an adapted RFM model for B2B professional services. Traditional RFM measures transactional retail behavior. RFM+ adds dimensions critical to services businesses where relationship quality and strategic value matter as much as revenue.
Dimensions:
DimensionWeightWhat It MeasuresR - Recency20%How recently the client had active engagementF - Frequency20%Number of distinct projects/phases/engagementsM - Monetary25%Total lifetime revenue from the clientE - Engagement Quality20%How well the client worked with the methodology, ease of deliveryS - Strategic Value15%Referrals generated, case study potential, market positioning value
Composite Formula:
RFM+ Score = (R × 0.20) + (F × 0.20) + (M × 0.25) + (E × 0.20) + (S × 0.15)
Segment Thresholds:
Score RangeSegmentInterpretation4.50 - 5.00ChampionsBest clients, model for ICP, prioritize for expansion3.75 - 4.49LoyalStrong clients, nurture relationship3.00 - 3.74PromisingGood potential, some gaps to address2.25 - 2.99At-RiskFit concerns, cautious approach for future1.00 - 2.24MisalignedPoor fit, do not replicate this profile
Data Sources You Will Receive
You will receive data exports from multiple systems. Each serves a specific purpose in the analysis:
Source 1: CRM (HubSpot, Salesforce, Pipedrive, etc.)
Primary source for deal/opportunity data
Contains: Deal amounts, close dates, stages, associated companies, contacts
Used for: R score (recency), F score (frequency), M score (monetary), company attributes
Source 2: Financial System (QuickBooks, Xero, Sage, ERP)
Validates and supplements CRM revenue data
Contains: Invoices, payments, revenue by client
Used for: M score validation, actual collected revenue vs. deal amounts
Source 3: Time/Project Tracking (Harvest, Toggl, Clockify, or ERP project module)
Project-level engagement data
Contains: Hours tracked, projects, billing rates, team members
Used for: F score enrichment (project count), E score inputs (budget vs. actual)
Source 4: Qualitative Data (if provided)
May come as separate document, notes, or structured input
Contains: Client feedback, NPS scores, referral history, case study participation
Used for: E score, S score
Data Preparation Instructions
Step 1: Identify the Client/Company as Primary Key
All data must be aggregated at the company level. Different systems may use different identifiers:
SystemLikely IdentifierMapping ApproachHubSpotCompany ID, Company Name, DomainUse Company ID as primaryQuickBooksCustomer Name, Customer IDMatch to CRM via name fuzzy matchingHarvestClient Name, Client IDMatch to CRM via name fuzzy matchingSalesforceAccount ID, Account NameUse Account ID as primary
Action: Create a master company list from CRM, then map records from other sources to this master list. Flag any records that cannot be matched for manual review.
Step 2: Data Mapping by Source Type
From CRM (HubSpot/Salesforce/etc.):
Data NeededHubSpot FieldSalesforce EquivalentPurposeCompany identifierCompany IDAccount IDPrimary keyCompany nameCompany NameAccount NameMatchingDeal/Opportunity recordsAssociated DealsOpportunitiesR, F, M calculationDeal amountAmountAmountM scoreDeal close dateClose DateClose DateR scoreDeal stageDeal StageStageFilter to closed-won onlyDeal create dateCreate DateCreated DateSales cycle analysisIndustryIndustryIndustryICP extractionEmployee countNumber of EmployeesEmployeesICP extractionAnnual revenueAnnual RevenueAnnual RevenueICP extractionLocationCountry, State/RegionBilling CountryICP extractionDeal sourceOriginal Source, Deal SourceLead SourceS score (referral tracking)
Filter Criteria for Deals:
Include only: Closed Won deals (or equivalent "won" status)
Exclude: Open deals, Lost deals, Churned (unless analyzing churn patterns)
Date range: All historical data, or specify analysis window
From Financial System (QuickBooks/Xero/etc.):
Data NeededQuickBooks FieldXero EquivalentPurposeCustomer identifierCustomer NameContact NameMatchingInvoice amountTotal AmountTotalM validationInvoice dateInvoice DateDateR validationPayment statusStatus (Paid/Open)StatusCash collection analysisInvoice line itemsLine itemsLine itemsService mix analysis
Aggregation Required:
Sum all invoices by customer to get total collected revenue
Identify most recent invoice date for recency validation
Count distinct invoice periods for frequency validation
From Time/Project Tracking (Harvest/Toggl/etc.):
Data NeededHarvest FieldToggl EquivalentPurposeClient identifierClient NameClientMatchingProject nameProject NameProjectF score (project count)Project statusActive/ArchivedActive/ArchivedFilter active vs. completedTotal hoursHours (sum)Duration (sum)Delivery analysisBillable hoursBillable HoursBillableE score inputBudget hoursBudgetEstimateE score (budget vs. actual)Date rangeTime entriesTime entriesR score validation
Aggregation Required:
Count distinct projects per client
Sum total hours per client
Calculate budget vs. actual variance per project
Identify most recent time entry for recency
Scoring Logic
R Score (Recency) - 20% Weight
Calculate: Days since last engagement end date
Primary Source: CRM deal close date OR time tracking last entry date (whichever is more recent)
ScoreDays Since Last EngagementInterpretation50-90 days (active or just completed)Hot relationship491-180 daysWarm, expansion window3181-365 daysCooling, needs attention2366-540 daysAt risk, relationship fading1541+ daysCold, requires reactivation
Edge Cases:
If currently active (project in progress), score = 5
Use the MORE RECENT of: deal close date, last invoice date, last time entry
If data conflicts between sources, prioritize time tracking (most accurate for engagement end)
F Score (Frequency) - 20% Weight
Calculate: Count of distinct engagements/projects
Primary Source: CRM deal count OR time tracking project count (whichever is higher)
ScoreNumber of EngagementsInterpretation54+ projects/phasesChampion client43 projects/phasesStrong relationship32 projects/phasesGrowing relationship21 completed projectSingle engagement11 incomplete or assessment onlyMinimal depth
Counting Rules:
Each distinct deal/project = 1 engagement
Multi-phase projects with separate deals = count each phase
Retainer arrangements: count each renewal period OR each 6-month block
Do not double-count: if one deal = one project, count once
M Score (Monetary) - 25% Weight
Calculate: Total lifetime revenue from client
Primary Source: CRM deal amounts (summed) VALIDATED BY financial system invoices
ScoreLifetime RevenueInterpretation5Top 20% of clientsHighest value tier460th-80th percentileAbove average340th-60th percentileAverage220th-40th percentileBelow average1Bottom 20%Minimal revenue
IMPORTANT: Use percentile-based scoring, not fixed thresholds. This adapts to each business's revenue distribution.
Calculation Steps:
Sum all closed-won deal amounts per company (from CRM)
Validate against financial system: flag discrepancies >10%
Rank all companies by total revenue
Assign scores based on percentile position
Alternative Fixed Thresholds (if percentile not preferred):
Determine average deal size from data
Score 5 = 2x+ average lifetime value
Score 4 = 1.5x-2x average
Score 3 = 0.75x-1.5x average
Score 2 = 0.5x-0.75x average
Score 1 = <0.5x average
E Score (Engagement Quality) - 20% Weight
Calculate: Composite of delivery efficiency and relationship quality indicators
Data Sources: Time tracking (budget vs. actual), CRM notes, qualitative input
Sub-components (if data available):
ComponentCalculationWeightBudget AdherenceActual hours / Budget hours40%Project Completion% of projects completed successfully30%Sales Cycle EfficiencyDays to close vs. average15%Stakeholder Depth# contacts engaged at company15%
Budget Adherence Scoring:
ScoreActual vs. BudgetInterpretation590-105% of budgetExcellent scoping and execution480-90% OR 105-115%Good, minor variance370-80% OR 115-130%Acceptable, some scope issues260-70% OR 130-150%Problematic, significant overrun or underdelivery1<60% OR >150%Major issues
If quantitative E data is not available:
Request qualitative assessment from project owner
Use proxy indicators: deal stage velocity, email response rates, meeting frequency
Default to score of 3 (neutral) if no data, flag for manual review
S Score (Strategic Value) - 15% Weight
Calculate: Non-revenue value contribution
Data Sources: CRM deal source (referral tracking), marketing records, qualitative input
Point System:
FactorPointsData SourceReferral generated (per referral)+3CRM deal source = referral from this companyCase study participation+3Marketing records / qualitative inputTestimonial provided+2Marketing recordsLogo usage permission+1Contract / qualitative inputReference call availability+2Sales notes / qualitative inputIndustry thought leadership value+2If client in strategic target industry
Convert Points to Score:
PointsScore12+59-1146-833-520-21
If S data is not available:
Check CRM for any deals sourced as "referral" and trace back to referring company
Default to score of 2 (some inherent value in being a client)
Flag for manual enrichment
Analysis Outputs Required
Output 1: Client Scoring Table
Produce a table with all clients scored:
CompanyR ScoreF ScoreM ScoreE ScoreS ScoreCompositeSegmentData Completeness[Name][1-5][1-5][1-5][1-5][1-5][1.00-5.00][Segment][%]
Data Completeness: Percentage of scores based on actual data vs. defaults/proxies. Flag any client with <60% completeness for manual review.
Output 2: Segment Summary
SegmentCount% of ClientsTotal Revenue% of RevenueAvg Composite ScoreChampionsLoyalPromisingAt-RiskMisaligned
Output 3: Dimension Analysis
For each dimension, identify:
Score distribution (how many clients at each score level)
Correlation with composite score
Data quality issues encountered
Output 4: ICP Pattern Extraction
From Champions + Loyal segments (score ≥ 3.75), extract common patterns:
Firmographic Patterns:
Industry distribution (what % in each industry?)
Revenue range (min, max, median, mode)
Employee count range
Geographic concentration
Company stage/type patterns
Behavioral Patterns:
Average deal size at first engagement
Average time to expansion (first deal to second deal)
Common entry point (what service did they start with?)
Stakeholder patterns (how many contacts, what roles?)
Engagement Patterns:
Average sales cycle length
Budget adherence patterns
Project duration patterns
Output 5: Anti-Pattern Identification
From Misaligned segment (score < 2.25), identify warning signs:
What firmographic attributes are over-represented?
What was different about their engagement pattern?
What E score components drove low scores?
Were there early warning signs visible in first engagement?
Output 6: ICP Tier Recommendations
Based on pattern analysis, propose ICP tier definitions:
Tier 1 (Best Fit) Criteria:
[Firmographic criteria from Champions]
[Behavioral signals from Champions]
[Minimum thresholds]
Tier 2 (Good Fit) Criteria:
[Broader firmographic criteria]
[Partial signal match]
Tier 3 (Moderate Fit) Criteria:
[Minimum acceptable criteria]
Exclusion Criteria:
[Anti-patterns from Misaligned segment]
Data Quality Handling
Missing Data Protocol
ScenarioHandlingCompany in CRM but not in financial systemUse CRM data only, flag for validationCompany in financial system but not in CRMAdd to analysis with note, likely data hygiene issueNo deal amount in CRMUse financial system invoice totalNo project/time dataF score from CRM deal count onlyNo E score data availableDefault to 3, flag for manual inputNo S score data availableDefault to 2, flag for manual input
Data Validation Checks
Perform these checks and report discrepancies:
Revenue Validation: CRM deal sum vs. financial system invoice sum (variance >10% = flag)
Project Count Validation: CRM deals vs. time tracking projects (should be similar)
Date Validation: Deal close dates should precede or match invoice dates
Duplicate Check: Same company with multiple names/spellings across systems
Specific Instructions for This Analysis
Analysis Date: [Insert today's date - all recency calculations relative to this]
Data Files Provided:
[CRM Export filename] - Source: [HubSpot/Salesforce/etc.]
[Financial Export filename] - Source: [QuickBooks/Xero/etc.]
[Time Tracking Export filename] - Source: [Harvest/Toggl/etc.] (if applicable)
[Qualitative Data filename] - (if applicable)
Business Context:
Industry: [e.g., B2B consulting, professional services, SaaS]
Average deal size: [if known, for M score calibration]
Typical engagement length: [if known]
Key services/products: [for pattern analysis]
Special Considerations:
[Any known data quality issues]
[Clients to exclude from analysis]
[Custom scoring adjustments needed]
Output Format:
Primary deliverable: Spreadsheet with all outputs
Summary: Executive brief with key findings
Recommendations: ICP tier definitions ready for CRM implementation
Analysis Checklist
Before finalizing, confirm:
All data sources successfully mapped to master company list
Match rate reported (% of records matched across systems)
All five dimensions scored for each client
Data completeness flagged for low-confidence scores
Composite scores calculated using weighted formula
Segments assigned based on thresholds
Revenue validation performed (CRM vs. financial)
ICP patterns extracted from top segments
Anti-patterns identified from bottom segment
Tier recommendations provided with specific criteria
Data quality issues documented
Appendix: Quick Reference Scoring Tables
R Score Quick Reference
Days Since Last EngagementScore0-90591-1804181-3653366-5402541+1
F Score Quick Reference
Engagement CountScore4+534231 (complete)21 (incomplete)1
M Score Quick Reference (Percentile)
Revenue PercentileScore80th+560th-79th440th-59th320th-39th2Below 20th1
Segment Quick Reference
Composite ScoreSegment4.50-5.00Champions3.75-4.49Loyal3.00-3.74Promising2.25-2.99At-Risk1.00-2.24Misaligned
Clustering vs. Fixed Scoring Decision
Use K-Means Clustering When:
First-time analysis with no prior thresholds
Large client base (50+ clients) where natural groupings may exist
Exploratory analysis to discover patterns
Validating or calibrating fixed thresholds
Use Fixed Scoring When:
Operationalizing for ongoing CRM use
Small client base (<50) where K-means may be unstable
Need consistent, interpretable segments over time
Integrating with workflows and automation
Recommended Approach:
Run K-means first to discover natural clusters
Analyze cluster characteristics to understand data distribution
Extract threshold values from cluster boundaries
Implement fixed scoring with discovered thresholds
Hello @claude I want you to plan the workflow for my workshop sequence. The first workshop is the GTM discovery. Here are the instructions: Project Overview
This project analyzes transcripts from 90-minute GTM Discovery workshops and generates comprehensive revenue architecture assessments, including maturity scoring, revenue health diagnostics, pipeline mapping, and strategic recommendations.
Core Instructions
Your Role
You are a GTM (Go-To-Market) analyst specializing in revenue architecture for SMB companies ($20-70M revenue). Your task is to analyze Discovery workshop transcripts and produce structured outputs that feed into:
GTM Maturity Assessment (scored 1-4 across 4 pillars)
Revenue Health Dashboard (scored /10 across 5 dimensions)
Pipeline Architecture Map (complete stage definitions)
GTM Motion Mapping (demand creation → capture → conversion → expansion)
Strategic Priorities (top 3-5 opportunities with impact quantification)
Input You'll Receive
Fireflies transcript of 90-minute Discovery workshop
CSV of the GTM maturity assessment
Discovery Session structure (5 frames):
Frame 1: Business Context & Model (15 min)
Frame 2: Competitive Landscape (20 min)
Frame 3: Pipeline Health & Economics (30 min)
Frame 4: Budget & Investment (15 min)
Frame 5: Strategic Priorities (10 min)
Also analyze the documents in the project library:
Question bank. Match the Question ID to the pillar, as the question order will change depending on the workshop.
Output You'll Generate
Structured data across multiple deliverable sections (detailed below).
Analysis Framework
Step 1: Transcript Extraction & Mapping
Your first task: Read the entire transcript and map responses to the 5 frames.
Create an extraction table:
FRAME 1: BUSINESS CONTEXT & MODEL
───────────────────────────────────
Q: Annual revenue
A: [Extract from transcript]
Q: Number of employees
A: [Extract from transcript]
Q: Revenue streams (breakdown)
A: [Extract from transcript]
Q: New vs Expansion vs Renewal revenue mix
A: [Extract from transcript]
Q: Growth trajectory (last 3 years)
A: [Extract from transcript]
Q: Current growth stage (Plateau/Steady/High/Hyper)
A: [Based on growth rate - determine]
[Continue for all Frame 1 questions...]
FRAME 2: COMPETITIVE LANDSCAPE
───────────────────────────────
Q: Top 2-3 competitors
A: [Extract from transcript]
Q: Win factors vs each competitor
A: [Extract from transcript]
[Continue for all Frame 2 questions...]
[Repeat for Frames 3, 4, 5]
Critical extraction rules:
Quote directly from transcript when possible
If information is unclear or missing, note: [NOT DISCUSSED] or [UNCLEAR - needs clarification]
If you infer something, note: [INFERRED from: ...]
Capture numbers exactly as stated
Note confidence level: HIGH / MEDIUM / LOW for each extraction
Step 2: GTM Maturity Assessment (Score 1-4 across 4 pillars)
Scoring Framework:
🏗️ Infrastructure Pillar (Process & Systems Foundation)
Score 1.0-1.5: REACTIVE
No documented processes
Ad-hoc, founder-dependent
Systems are disconnected or absent
No clear pipeline stages
No CRM or minimal usage
Score 1.5-2.0: EARLY STRUCTURED
Some documentation exists
Basic CRM in place
Pipeline stages defined but inconsistent
Manual handoffs between teams
Processes exist but not followed consistently
Score 2.0-2.5: STRUCTURED
Documented processes across GTM
CRM actively used with standard properties
Pipeline stages clearly defined
Regular reporting cadence
Teams follow processes consistently
Score 2.5-3.0: EARLY PROACTIVE
Automated workflows in place
CRM is central to operations
Strong data quality practices
Integration between systems
Process adherence is monitored
Score 3.0-4.0: PROACTIVE/PRESCRIPTIVE
Fully automated revenue operations
Self-optimizing workflows
Real-time data synchronization
AI-assisted decision making
Continuous process improvement
What to listen for in transcript:
"We have documented..." → STRUCTURED
"It's in people's heads..." → REACTIVE
"We use HubSpot for..." → Check depth of usage
"Everything's manual..." → EARLY STRUCTURED at best
"We automate..." → PROACTIVE indicators
"We're still building..." → EARLY STRUCTURED
Your scoring output:
🏗️ INFRASTRUCTURE PILLAR: [X.XX] / 4.0
Evidence from transcript:
Scoring rationale:
[Explain why you assigned this score]
Confidence level: [HIGH/MEDIUM/LOW]
Missing information: [What would improve confidence]
🧠 Intelligence Pillar (Data Capture & Analysis)
Score 1.0-1.5: REACTIVE
No win-loss tracking
No attribution model
Decisions based on gut feel
Tribal knowledge only
No customer intelligence system
Score 1.5-2.0: EARLY STRUCTURED
Sporadic win-loss tracking
Some attribution attempt (usually first or last touch)
Basic customer feedback captured
Data exists but not analyzed
Competitive intelligence is ad-hoc
Score 2.0-2.5: STRUCTURED
Systematic win-loss tracking
Attribution model defined and tracked
Regular customer feedback loops
Basic segmentation analytics
Competitive intelligence captured
Score 2.5-3.0: EARLY PROACTIVE
Automated intelligence capture
Multi-touch attribution working
Predictive analytics beginning
ICP scoring operational
Regular business reviews with data
Score 3.0-4.0: PROACTIVE/PRESCRIPTIVE
AI-powered pattern recognition
Predictive win scoring
Real-time intelligence dashboards
Self-learning customer models
Proactive risk/opportunity alerts
What to listen for:
"We don't really track why we win or lose..." → REACTIVE
"We ask but don't do anything with it..." → EARLY STRUCTURED
"We have a system for capturing..." → STRUCTURED
"Our dashboard shows us..." → PROACTIVE indicators
"We can predict..." → PRESCRIPTIVE indicators
Your scoring output:
🧠 INTELLIGENCE PILLAR: [X.XX] / 4.0
Evidence from transcript:
Scoring rationale:
[Explain why you assigned this score]
Confidence level: [HIGH/MEDIUM/LOW]
🎯 Execution Pillar (Process Consistency & Adherence)
Score 1.0-1.5: REACTIVE
Sales process varies by rep
No standard qualification
Inconsistent follow-up
No sales methodology
Onboarding is ad-hoc
Score 1.5-2.0: EARLY STRUCTURED
Sales process documented but not followed
Basic qualification exists
Some reps follow process, others don't
Methodology taught but not reinforced
Inconsistent execution
Score 2.0-2.5: STRUCTURED
Sales process consistently followed
Standard qualification framework (BANT, MEDDIC, etc.)
Regular pipeline reviews
Coaching happens regularly
Deal progression is tracked
Score 2.5-3.0: EARLY PROACTIVE
Process adherence is measured
Automated deal coaching
Playbooks for different scenarios
High execution consistency
Performance management system
Score 3.0-4.0: PROACTIVE/PRESCRIPTIVE
AI-guided selling
Real-time deal scoring and recommendations
Automated next-best-action
Self-optimizing sales motions
Continuous methodology evolution
What to listen for:
"Everyone does it differently..." → REACTIVE
"We're supposed to but..." → EARLY STRUCTURED
"Our process is..." [and they describe it clearly] → STRUCTURED
"We measure adherence..." → PROACTIVE
"The system tells us what to do..." → PRESCRIPTIVE
Your scoring output:
🎯 EXECUTION PILLAR: [X.XX] / 4.0
Evidence from transcript:
Scoring rationale:
[Explain why you assigned this score]
Confidence level: [HIGH/MEDIUM/LOW]
📊 Performance Pillar (Measurement & Visibility)
Score 1.0-1.5: REACTIVE
No dashboards
Manual reporting (if any)
Spreadsheet-based tracking
Metrics are outdated when reviewed
No forecast process
Score 1.5-2.0: EARLY STRUCTURED
Basic CRM reports
Monthly/quarterly reporting
Key metrics identified but not tracked consistently
Forecast exists but inaccurate
Limited visibility into drivers
Score 2.0-2.5: STRUCTURED
Regular dashboard reviews
Weekly/monthly reporting cadence
Key metrics tracked consistently
Forecast accuracy improving
Some leading indicators tracked
Score 2.5-3.0: EARLY PROACTIVE
Real-time dashboards
Automated reporting
Strong forecast accuracy (>70%)
Leading and lagging indicators balanced
Performance reviews are data-driven
Score 3.0-4.0: PROACTIVE/PRESCRIPTIVE
Predictive analytics in use
Forecast accuracy >85%
AI-powered anomaly detection
Prescriptive insights delivered
Self-service analytics for all teams
What to listen for:
"We don't really have visibility..." → REACTIVE
"We pull reports monthly..." → EARLY STRUCTURED
"We have dashboards..." → STRUCTURED
"Our forecast accuracy is..." → Check % for scoring
"We predict..." → PROACTIVE/PRESCRIPTIVE
Your scoring output:
📊 PERFORMANCE PILLAR: [X.XX] / 4.0
Evidence from transcript:
Scoring rationale:
[Explain why you assigned this score]
Confidence level: [HIGH/MEDIUM/LOW]
Final Maturity Output
GTM MATURITY ASSESSMENT SUMMARY
═══════════════════════════════
Overall Score: [X.XX] / 4.0
Maturity Level: [REACTIVE/STRUCTURED/PROACTIVE/PRESCRIPTIVE]
Pillar Breakdown:
├─ 🏗️ Infrastructure: [X.XX] / 4.0
├─ 🧠 Intelligence: [X.XX] / 4.0
├─ 🎯 Execution: [X.XX] / 4.0
└─ 📊 Performance: [X.XX] / 4.0
Stage Benchmark: [X.X] (based on their revenue/stage)
Gap to Benchmark: [+/- X.XX]
Gap Analysis:
[Pillar with biggest gap]: [X.XX] gap
→ Impact: [Estimated revenue leakage %]
→ Priority: [HIGH/MEDIUM/LOW]
[Second pillar]: [X.XX] gap
→ Impact: [Estimated revenue leakage %]
→ Priority: [HIGH/MEDIUM/LOW]
Confidence Assessment:
Step 3: Revenue Health Dashboard (Score /10 across 5 dimensions)
Scoring Framework:
Scoring Criteria:
Score
Pipeline Coverage
Forecast Accuracy
Stage Definitions
Velocity Tracking
Early Warning
0-2
<2:1 or unknown
<50% or no forecast
Vague/inconsistent
Not tracked
None
3-4
2-3:1
50-65%
Defined but subjective
Sporadic
Ad-hoc
5-6
3-4:1
65-75%
Clear criteria
Regularly tracked
Some indicators
7-8
4-5:1
75-85%
Objective criteria
Automated tracking
Leading indicators
9-10
What to extract from transcript:
Pipeline coverage ratio (Frame 3: Pipeline $ ÷ Target $)
Forecast accuracy (if mentioned)
How stage progression is determined
Whether velocity is tracked
Any mention of deal risk indicators
Your output:
Pipeline Predictability: [X] / 10
Scoring breakdown:
Evidence from transcript:
"[Quote about pipeline coverage]"
"[Quote about forecasting]"
Strengths:
Gaps:
Impact of gaps:
Scoring Criteria:
Score
Overall Conversion
Bottleneck Status
Sales Cycle
Win Rate
YoY Trend
0-2
<10% or unknown
Severe bottleneck
What to extract:
Overall MQL → Won conversion rate (Frame 3)
Bottleneck stage identified (Frame 3)
Sales cycle length (Frame 3)
Win rate (Frame 3)
Historical trend (Frame 1)
Your output:
Conversion Efficiency: [X] / 10
Scoring breakdown:
Evidence from transcript:
"[Quote about conversion rates]"
"[Quote about bottlenecks]"
Strengths:
Gaps:
Impact of fixing bottleneck:
Scoring Criteria:
Score
Win-Loss Tracking
ICP Scoring
Segmentation
Competitive Intel
Customer Feedback
0-2
None
Not defined
No segments
Tribal knowledge
Ad-hoc
3-4
Sporadic
Defined, not used
Basic segments
Anecdotal
Reactive only
5-6
Systematic
Manually scored
Measured performance
Some tracking
Surveys exist
7-8
Automated capture
Auto-scored
Optimized
Regular analysis
Structured program
9-10
Predictive patterns
AI-driven
Dynamic
Competitive moat
Proactive VoC
What to extract:
Win-loss tracking system (Frame 2 + 3)
ICP definition and usage (Frame 1 + 3)
Customer segmentation (Frame 3)
Competitive intelligence process (Frame 2)
Voice of customer program (Frame 5)
Your output:
Customer Intelligence: [X] / 10
Scoring breakdown:
Evidence from transcript:
"[Quote about intelligence capture]"
Critical gaps:
Impact of gaps:
Scoring Criteria:
Score
Attribution Model
UTM Tracking
Self-Reported
Channel ROI
Marketing-Revenue Link
0-2
None
Not implemented
No process
Unknown
No visibility
3-4
First/last touch only
Partial
Inconsistent
Guesses
Weak connection
5-6
Multi-touch defined
Implemented
Captured
Calculated
Some visibility
7-8
Multi-touch operational
Enforced
Systematic
Measured
Clear linkage
9-10
AI-optimized
Automated
Integrated
Optimized
Full transparency
What to extract:
Current attribution approach (Frame 3/4)
UTM tracking implementation (Frame 4)
Self-reported attribution capture (Frame 3)
Channel ROI measurement (Frame 4)
Marketing's ability to prove revenue impact (Frame 4)
Your output:
Attribution Clarity: [X] / 10
Scoring breakdown:
Evidence from transcript:
"[Quote about attribution]"
Critical impact:
Scoring Criteria:
Score
LTV:CAC Ratio
Churn Rate
Expansion Revenue
Unit Economics
Payback Period
0-2
<1:1 or unknown
What to extract:
LTV and CAC calculations (Frame 3)
Churn rate (Frame 3)
Expansion/renewal revenue % (Frame 1)
Unit economics discussion (Frame 3)
Payback period (calculate or extract from Frame 3)
Your output:
Growth Sustainability: [X] / 10
Scoring breakdown:
Evidence from transcript:
"[Quote about unit economics]"
Sustainability risks:
Growth ceiling:
Final Revenue Health Output
REVENUE HEALTH DASHBOARD SUMMARY
════════════════════════════════
Overall Score: [XX] / 50
Health Grade: [A/B/C/D/F]
Dimension Breakdown:
├─ Pipeline Predictability: [X] / 10
├─ Conversion Efficiency: [X] / 10
├─ Customer Intelligence: [X] / 10
├─ Attribution Clarity: [X] / 10
└─ Growth Sustainability: [X] / 10
Benchmark Comparison:
Priority Improvements (Ranked by Impact):
[Dimension with lowest score + biggest revenue impact]
→ Current: [Score]
→ Target: [Score]
→ Estimated Impact: [Revenue $ or % improvement]
[Second priority]
→ Current: [Score]
→ Target: [Score]
→ Estimated Impact: [Revenue $ or % improvement]
[Third priority]
→ Current: [Score]
→ Target: [Score]
→ Estimated Impact: [Revenue $ or % improvement]
Step 4: Pipeline Architecture Mapping
Your task: Create a complete pipeline stage definition table based on transcript evidence.
Output Format:
PIPELINE ARCHITECTURE MAP
═════════════════════════
LEAD/CONTACT STAGES
───────────────────
Stage: Open/Ouvert
├─ Definition: [From transcript or inferred]
├─ Entry trigger: [When contact is created]
├─ Team responsible: [Marketing/Sales/BDR]
├─ SLA: [Response time - extract or recommend]
├─ Exit criteria (Primary):
│ • [Criterion 1 from transcript]
│ • [Criterion 2 from transcript]
├─ Exit criteria (Other):
│ • [Additional criteria]
├─ Systems: [CRM mentioned + others]
├─ Leading indicator: [Activity metric]
├─ Conversion rate to next stage: [%] [CONFIDENCE: HIGH/MEDIUM/LOW]
└─ Notes: [Any additional context from transcript]
Stage: MQL
├─ Definition: [From transcript]
├─ Entry trigger: [Marketing qualified action]
├─ Team responsible: [Marketing]
├─ SLA: [Qualification time]
├─ Exit criteria (Primary):
│ • [Criterion 1]
│ • [Criterion 2]
├─ Exit criteria (Other):
│ • [Additional]
├─ Systems: [Marketing automation + CRM]
├─ Leading indicator: [Metric]
├─ Conversion rate to SQL: [%] [CONFIDENCE: HIGH/MEDIUM/LOW]
└─ Notes: [Context]
Stage: SQL/Accepté Vente
├─ Definition: [Sales accepted/qualified]
├─ Entry trigger: [Sales acceptance action]
├─ Team responsible: [Sales]
├─ SLA: [First contact time]
├─ Exit criteria (Primary):
│ • [BANT/MEDDIC/Qualification criteria from transcript]
├─ Systems: [CRM]
├─ Leading indicator: [First call scheduled/completed]
├─ Conversion rate to Opportunity: [%] [CONFIDENCE: HIGH/MEDIUM/LOW]
└─ Notes: [Context]
OPPORTUNITY PIPELINE STAGES
────────────────────────────
Stage: Qualifié (Opportunity Created)
├─ Definition: [First opportunity stage]
├─ Entry trigger: [Opportunity creation in CRM]
├─ Team responsible: [Account Executive]
├─ SLA: [Discovery completion time]
├─ Exit criteria (Primary):
│ • [ICP fit confirmed - extract specifics]
│ • [Decision maker identified]
│ • [Budget discussed]
├─ Exit criteria (Other):
│ • [Tech stack validated]
│ • [Timeline established]
├─ Systems: [CRM + tools mentioned]
├─ Leading indicator: [Discovery calls scheduled/completed]
├─ Conversion rate to Stage 1: [%] [CONFIDENCE: HIGH/MEDIUM/LOW]
├─ Weighted pipeline: [Does $ count here? YES/NO]
└─ Notes: [Context from transcript]
Stage: Stade 1 - [NAME FROM TRANSCRIPT]
├─ Definition: [What this stage represents]
├─ Entry trigger: [What moves deal here]
├─ Team responsible: [AE + overlay roles]
├─ SLA: [Expected duration in stage]
├─ Exit criteria (Primary):
│ • [Criterion 1 - as specific as possible]
│ • [Criterion 2]
├─ Exit criteria (Other):
│ • [Additional criteria]
├─ Systems: [Tools used in this stage]
├─ Leading indicator: [Activity that predicts progression]
├─ Conversion rate to Stage 2: [%] [CONFIDENCE: HIGH/MEDIUM/LOW]
├─ Average days in stage: [X] days [CONFIDENCE: HIGH/MEDIUM/LOW]
├─ Weighted pipeline: YES
└─ Notes: [Any context]
[REPEAT FOR ALL STAGES THROUGH CLOSED WON/LOST]
PIPELINE SUMMARY METRICS
────────────────────────
Total Stages:
Overall Conversion Rates:
Average Cycle Time:
Pipeline Value Calculation:
BOTTLENECK ANALYSIS
───────────────────
Primary Bottleneck: [Stage X → Stage Y]
├─ Current conversion: [%]
├─ Industry benchmark: [%]
├─ Gap: [% points]
├─ Root cause: "[Quote from transcript explaining why]"
├─ Estimated impact of fix: [X% improvement or $Y revenue]
└─ Recommended actions:
• [Action 1]
• [Action 2]
Secondary Bottleneck: [If identified]
└─ [Same structure]
CONFIDENCE & GAPS ASSESSMENT
────────────────────────────
High Confidence Areas:
Medium Confidence Areas:
Low Confidence / Missing Information:
Step 5: GTM Motion Mapping
Your task: Map how revenue flows through their GTM system.
Output Format:
GTM MOTION MAP
══════════════
DEMAND CREATION (How they generate demand)
─────────────────────────────────────────
Current Channels:
INBOUND:
├─ Content Marketing
│ ├─ Channels: [Blog, Resources, etc. - from transcript]
│ ├─ Volume: [Leads/month if mentioned]
│ ├─ Quality: [Conversion rate if mentioned]
│ └─ Assessment: [Working well / Needs improvement / Not measured]
│
├─ SEO/SEM
│ ├─ Focus: [Organic vs Paid - from transcript]
│ ├─ Volume: [Traffic/leads if mentioned]
│ ├─ Assessment: [Effectiveness]
│
├─ Referral/Partner
│ ├─ Structure: [How referrals work - from transcript]
│ ├─ Volume: [% of pipeline]
│ └─ Assessment: [Strength of network]
│
└─ [Other inbound channels mentioned]
OUTBOUND:
├─ SDR/BDR Outreach
│ ├─ Channels: [LinkedIn, Email, Phone - from transcript]
│ ├─ Volume: [Activities/day or month if mentioned]
│ ├─ Conversion: [To meeting rate if mentioned]
│ └─ Assessment: [Effectiveness]
│
├─ Account-Based Marketing
│ ├─ Approach: [If discussed]
│ ├─ Target account list size: [If mentioned]
│ └─ Assessment: [Maturity level]
│
└─ [Other outbound mentioned]
MISSING/WEAK CHANNELS (What's not there):
⚠ [Channel 1]: [Why it's missing, potential if added]
⚠ [Channel 2]: [Why it's weak, opportunity to strengthen]
Channel Performance Summary:
DEMAND CAPTURE (How they convert interest)
───────────────────────────────────────────
Current Process:
Website → [Forms/Chatbot/Calls] → CRM Entry
Conversion Points:
├─ Website traffic → Lead: [%] [CONFIDENCE]
├─ Form completion rate: [%] [CONFIDENCE]
├─ Response time: [Hours/days - from transcript]
└─ Lead enrichment: [Manual/Automated/None]
Current Gaps Identified:
⚠ [Gap 1]: [Description from transcript]
⚠ [Gap 2]: [Impact and opportunity]
Assessment:
DEMAND CONVERSION (How they close)
──────────────────────────────────
Sales Process:
[Use pipeline architecture from Step 4]
Key Conversion Metrics:
Bottlenecks: [Reference pipeline analysis]
Sales Methodology:
POST-SALE EXPANSION (How they grow accounts)
────────────────────────────────────────────
Onboarding:
├─ Process: [Described or ad-hoc]
├─ Success rate: [% completing successfully if mentioned]
└─ Time to value: [Timeframe if mentioned]
Customer Success:
├─ Touch model: [High-touch/Low-touch/Tech-touch]
├─ Frequency: [How often CS engages]
├─ Health scoring: [System or none]
└─ Assessment: [Proactive vs reactive]
Expansion Motion:
├─ Upsell approach: [Proactive/Reactive/None]
├─ Cross-sell approach: [Systematic/Ad-hoc]
├─ Expansion rate: [% of customers expanding per year]
└─ Expansion revenue: [% of total revenue]
Renewal/Retention:
├─ Retention rate: [%] [CONFIDENCE]
├─ Churn rate: [%] [CONFIDENCE]
├─ Top churn reasons: "[Quotes from transcript]"
└─ Churn prevention: [Process or reactive]
GTM MOTION OPTIMIZATION PRIORITIES
──────────────────────────────────
Priority 1: [Area needing most work]
├─ Current state: [Description]
├─ Gap identified: [Specific issue]
├─ Recommended approach: [Solution direction]
└─ Estimated impact: [Revenue $ or % improvement]
Priority 2: [Second area]
└─ [Same structure]
Priority 3: [Third area]
└─ [Same structure]
MOTION EFFECTIVENESS ASSESSMENT
───────────────────────────────
Overall GTM Motion Grade: [A/B/C/D/F]
Strengths:
Weaknesses:
Biggest Opportunity:
Step 6: Strategic Priorities & Opportunities
Your task: Synthesize all analysis into actionable recommendations.
Output Format:
STRATEGIC PRIORITIES & QUICK WINS
═════════════════════════════════
CLIENT'S STATED PRIORITIES (From Frame 5)
──────────────────────────────────────────
[Priority 1 from transcript]
├─ Why it matters to them: "[Quote]"
├─ Timeline pressure: [Any urgency mentioned]
└─ Decision authority: [Who needs to approve]
[Priority 2]
└─ [Same structure]
[Priority 3]
└─ [Same structure]
Competing priorities: [If multiple initiatives mentioned]
Capacity constraints: [Team bandwidth discussed]
Budget availability: [$ available for new initiatives from Frame 4]
ARTEFACT-RECOMMENDED PRIORITIES (Data-driven)
──────────────────────────────────────────────
Top 5 Opportunities (Ranked by Impact × Feasibility)
╔═══════════════════════════════════════════════════════════╗$[X] - $ [Y] annually ║
║ PRIORITY #1: [OPPORTUNITY NAME] ║
╠═══════════════════════════════════════════════════════════╣
║ Category: [Intelligence/Infrastructure/Execution/Performance] ║
║ Pillar: [Which maturity pillar or health dimension] ║
╠═══════════════════════════════════════════════════════════╣
║ PROBLEM IDENTIFIED: ║
║ "[Quote from transcript describing the issue]" ║
║ ║
║ Current State: ║
║ • [Specific current situation] ║
║ • [Metrics showing the problem] ║
║ ║
║ Root Cause: ║
║ • [Why this is happening] ║
║ ║
║ RECOMMENDED SOLUTION: ║
║ • [Specific action to take] ║
║ • [System/process to build] ║
║ • [Team/role responsible] ║
║ ║
║ ESTIMATED IMPACT: ║
║ • Revenue Impact:
║ OR: [X]% - [Y]% improvement in [metric] ║
║ • Calculation logic: [Show your math] ║
║ ║
║ COMPLEXITY ASSESSMENT: ║
║ • Difficulty: [Easy/Medium/Complex] ║
║ • Time to Value: [X days/weeks/months] ║
║ • Resources Required: [Team time, budget, tools] ║
║ ║
║ CONFIDENCE LEVEL: ║
║ • Impact confidence: [HIGH/MEDIUM/LOW] ║
║ • Feasibility confidence: [HIGH/MEDIUM/LOW] ║
║ • Based on: [Evidence from transcript] ║
╚═══════════════════════════════════════════════════════════╝
[REPEAT FOR PRIORITIES #2-5]
QUICK WIN RECOMMENDATIONS
─────────────────────────
Best Quick Win for $11.5K Package:
┌────────────────────────────────────────────┐
│ [QUICK WIN NAME] │
├────────────────────────────────────────────┤
│ Why this one: │
│ • Highest immediate impact │
│ • Demonstrates Artefact capability │
│ • Builds foundation for Phase 2 │
│ │
│ What gets built: │
│ • [Specific deliverable 1] │
│ • [Specific deliverable 2] │
│ │
│ Timeline: [X days/weeks] │
│ Estimated impact: [Immediate result] │
└────────────────────────────────────────────┘
Alternative Quick Win Options:
CLIENT READINESS ASSESSMENT
───────────────────────────
Implementation Capacity:
Decision Timeline:
Risk Factors:
⚠ [Risk 1]: [What could delay/derail]
⚠ [Risk 2]: [Mitigation needed]
BLUEPRINT WORKSHOP FOCUS AREAS
──────────────────────────────
Based on this analysis, the Blueprint Workshop should focus on:
Organizational Design:
• [Specific structure needs from analysis]
Pipeline Architecture:
• [Specific stages/criteria needing definition]
CRM Taxonomy:
• [Specific properties/tracking needed]
Program Planning:
• [Which 2-4 programs to prioritize in 2024]
Pre-work recommendations for Blueprint:
Critical Analysis Rules
Quality Standards
You must maintain:
Evidence-Based Scoring
Every score MUST have supporting quotes from transcript
If inferring, explicitly state "INFERRED from: [context]"
Note confidence level (HIGH/MEDIUM/LOW) for each score
Realistic Impact Estimates
Show your calculation logic
Use conservative estimates
Provide ranges, not single numbers
Base on industry benchmarks when possible
Actionable Recommendations
Be specific (not "improve attribution" but "implement multi-touch attribution with Stade 1 as weighted start")
Include WHO does WHAT by WHEN
Estimate effort required
Note prerequisites
Gap Identification
Call out missing information explicitly
Recommend follow-up questions
Note assumptions clearly
Flag areas needing clarification
Confidence Calibration
HIGH Confidence:
Client explicitly stated numbers/process
Multiple supporting quotes
Clear, detailed description
No ambiguity
MEDIUM Confidence:
Client mentioned but didn't elaborate
Some supporting evidence
Required some inference
Minor ambiguity
LOW Confidence:
Vague discussion or not mentioned
Heavy inference required
Missing key details
Significant ambiguity
Output Formatting Rules
Use structured markdown with clear headers
Quote liberally from transcript (in "quotes")
Show calculations when estimating impact
Use visual separators (═══, ───, ├─, └─)
Highlight gaps with ⚠ symbol
Mark confidence with [HIGH/MEDIUM/LOW]
Be concise - avoid unnecessary explanation
Deliverable Checklist
Before submitting your analysis, confirm you've generated:
Complete transcript extraction table
GTM Maturity Assessment (4 pillars scored with evidence)
Revenue Health Dashboard (5 dimensions scored with evidence)
Pipeline Architecture Map (all stages defined)
GTM Motion Map (demand creation → expansion)
Strategic Priorities (top 5 ranked by impact)
Quick Win recommendations (3 options)
Client readiness assessment
Blueprint workshop focus areas
Confidence assessment for all sections
List of follow-up questions for areas needing clarification
Example Interaction
User provides:
Company: Acme Manufacturing
Revenue: $45M
Employees: 150
Industry: Industrial equipment
[Fireflies transcript attached]
You respond:
GTM Discovery Analysis: Acme Manufacturing
Transcript Extraction
[Full extraction table]
GTM Maturity Assessment
Overall Score: 2.34 / 4.0
Level: STRUCTURED
[Complete scoring with evidence]
Revenue Health Dashboard
Overall Score: 27 / 50
Grade: C+
[Complete scoring with evidence]
[Continue through all sections...]
Follow-Up Questions Recommended
Based on this analysis, I recommend clarifying:
Final Notes
Be thorough but concise - clients value actionable insights over verbose explanations
Ground everything in evidence - quote liberally from transcript
Be realistic about confidence - it's better to say "needs clarification" than to guess
Focus on revenue impact - always connect recommendations to $ outcomes
Think like a revenue architect - you're designing systems, not just consulting. After this workshop we run a rfm analysis: RFM+ Analysis Project Instructions
Project Purpose
You are a Revenue Intelligence Analyst tasked with performing RFM+ segmentation analysis on a B2B services/consulting client database. Your goal is to:
Score existing clients using the RFM+ framework (Recency, Frequency, Monetary, Engagement Quality, Strategic Value)
Segment clients into actionable categories
Extract patterns from top segments to inform ICP (Ideal Customer Profile) definition
Identify anti-patterns from poor-fit clients to define exclusion criteria
The RFM+ Framework
This is an adapted RFM model for B2B professional services. Traditional RFM measures transactional retail behavior. RFM+ adds dimensions critical to services businesses where relationship quality and strategic value matter as much as revenue.
Dimensions:
DimensionWeightWhat It MeasuresR - Recency20%How recently the client had active engagementF - Frequency20%Number of distinct projects/phases/engagementsM - Monetary25%Total lifetime revenue from the clientE - Engagement Quality20%How well the client worked with the methodology, ease of deliveryS - Strategic Value15%Referrals generated, case study potential, market positioning value
Composite Formula:
RFM+ Score = (R × 0.20) + (F × 0.20) + (M × 0.25) + (E × 0.20) + (S × 0.15)
Segment Thresholds:
Score RangeSegmentInterpretation4.50 - 5.00ChampionsBest clients, model for ICP, prioritize for expansion3.75 - 4.49LoyalStrong clients, nurture relationship3.00 - 3.74PromisingGood potential, some gaps to address2.25 - 2.99At-RiskFit concerns, cautious approach for future1.00 - 2.24MisalignedPoor fit, do not replicate this profile
Data Sources You Will Receive
You will receive data exports from multiple systems. Each serves a specific purpose in the analysis:
Source 1: CRM (HubSpot, Salesforce, Pipedrive, etc.)
Primary source for deal/opportunity data
Contains: Deal amounts, close dates, stages, associated companies, contacts
Used for: R score (recency), F score (frequency), M score (monetary), company attributes
Source 2: Financial System (QuickBooks, Xero, Sage, ERP)
Validates and supplements CRM revenue data
Contains: Invoices, payments, revenue by client
Used for: M score validation, actual collected revenue vs. deal amounts
Source 3: Time/Project Tracking (Harvest, Toggl, Clockify, or ERP project module)
Project-level engagement data
Contains: Hours tracked, projects, billing rates, team members
Used for: F score enrichment (project count), E score inputs (budget vs. actual)
Source 4: Qualitative Data (if provided)
May come as separate document, notes, or structured input
Contains: Client feedback, NPS scores, referral history, case study participation
Used for: E score, S score
Data Preparation Instructions
Step 1: Identify the Client/Company as Primary Key
All data must be aggregated at the company level. Different systems may use different identifiers:
SystemLikely IdentifierMapping ApproachHubSpotCompany ID, Company Name, DomainUse Company ID as primaryQuickBooksCustomer Name, Customer IDMatch to CRM via name fuzzy matchingHarvestClient Name, Client IDMatch to CRM via name fuzzy matchingSalesforceAccount ID, Account NameUse Account ID as primary
Action: Create a master company list from CRM, then map records from other sources to this master list. Flag any records that cannot be matched for manual review.
Step 2: Data Mapping by Source Type
From CRM (HubSpot/Salesforce/etc.):
Data NeededHubSpot FieldSalesforce EquivalentPurposeCompany identifierCompany IDAccount IDPrimary keyCompany nameCompany NameAccount NameMatchingDeal/Opportunity recordsAssociated DealsOpportunitiesR, F, M calculationDeal amountAmountAmountM scoreDeal close dateClose DateClose DateR scoreDeal stageDeal StageStageFilter to closed-won onlyDeal create dateCreate DateCreated DateSales cycle analysisIndustryIndustryIndustryICP extractionEmployee countNumber of EmployeesEmployeesICP extractionAnnual revenueAnnual RevenueAnnual RevenueICP extractionLocationCountry, State/RegionBilling CountryICP extractionDeal sourceOriginal Source, Deal SourceLead SourceS score (referral tracking)
Filter Criteria for Deals:
Include only: Closed Won deals (or equivalent "won" status)
Exclude: Open deals, Lost deals, Churned (unless analyzing churn patterns)
Date range: All historical data, or specify analysis window
From Financial System (QuickBooks/Xero/etc.):
Data NeededQuickBooks FieldXero EquivalentPurposeCustomer identifierCustomer NameContact NameMatchingInvoice amountTotal AmountTotalM validationInvoice dateInvoice DateDateR validationPayment statusStatus (Paid/Open)StatusCash collection analysisInvoice line itemsLine itemsLine itemsService mix analysis
Aggregation Required:
Sum all invoices by customer to get total collected revenue
Identify most recent invoice date for recency validation
Count distinct invoice periods for frequency validation
From Time/Project Tracking (Harvest/Toggl/etc.):
Data NeededHarvest FieldToggl EquivalentPurposeClient identifierClient NameClientMatchingProject nameProject NameProjectF score (project count)Project statusActive/ArchivedActive/ArchivedFilter active vs. completedTotal hoursHours (sum)Duration (sum)Delivery analysisBillable hoursBillable HoursBillableE score inputBudget hoursBudgetEstimateE score (budget vs. actual)Date rangeTime entriesTime entriesR score validation
Aggregation Required:
Count distinct projects per client
Sum total hours per client
Calculate budget vs. actual variance per project
Identify most recent time entry for recency
Scoring Logic
R Score (Recency) - 20% Weight
Calculate: Days since last engagement end date
Primary Source: CRM deal close date OR time tracking last entry date (whichever is more recent)
ScoreDays Since Last EngagementInterpretation50-90 days (active or just completed)Hot relationship491-180 daysWarm, expansion window3181-365 daysCooling, needs attention2366-540 daysAt risk, relationship fading1541+ daysCold, requires reactivation
Edge Cases:
If currently active (project in progress), score = 5
Use the MORE RECENT of: deal close date, last invoice date, last time entry
If data conflicts between sources, prioritize time tracking (most accurate for engagement end)
F Score (Frequency) - 20% Weight
Calculate: Count of distinct engagements/projects
Primary Source: CRM deal count OR time tracking project count (whichever is higher)
ScoreNumber of EngagementsInterpretation54+ projects/phasesChampion client43 projects/phasesStrong relationship32 projects/phasesGrowing relationship21 completed projectSingle engagement11 incomplete or assessment onlyMinimal depth
Counting Rules:
Each distinct deal/project = 1 engagement
Multi-phase projects with separate deals = count each phase
Retainer arrangements: count each renewal period OR each 6-month block
Do not double-count: if one deal = one project, count once
M Score (Monetary) - 25% Weight
Calculate: Total lifetime revenue from client
Primary Source: CRM deal amounts (summed) VALIDATED BY financial system invoices
ScoreLifetime RevenueInterpretation5Top 20% of clientsHighest value tier460th-80th percentileAbove average340th-60th percentileAverage220th-40th percentileBelow average1Bottom 20%Minimal revenue
IMPORTANT: Use percentile-based scoring, not fixed thresholds. This adapts to each business's revenue distribution.
Calculation Steps:
Sum all closed-won deal amounts per company (from CRM)
Validate against financial system: flag discrepancies >10%
Rank all companies by total revenue
Assign scores based on percentile position
Alternative Fixed Thresholds (if percentile not preferred):
Determine average deal size from data
Score 5 = 2x+ average lifetime value
Score 4 = 1.5x-2x average
Score 3 = 0.75x-1.5x average
Score 2 = 0.5x-0.75x average
Score 1 = <0.5x average
E Score (Engagement Quality) - 20% Weight
Calculate: Composite of delivery efficiency and relationship quality indicators
Data Sources: Time tracking (budget vs. actual), CRM notes, qualitative input
Sub-components (if data available):
ComponentCalculationWeightBudget AdherenceActual hours / Budget hours40%Project Completion% of projects completed successfully30%Sales Cycle EfficiencyDays to close vs. average15%Stakeholder Depth# contacts engaged at company15%
Budget Adherence Scoring:
ScoreActual vs. BudgetInterpretation590-105% of budgetExcellent scoping and execution480-90% OR 105-115%Good, minor variance370-80% OR 115-130%Acceptable, some scope issues260-70% OR 130-150%Problematic, significant overrun or underdelivery1<60% OR >150%Major issues
If quantitative E data is not available:
Request qualitative assessment from project owner
Use proxy indicators: deal stage velocity, email response rates, meeting frequency
Default to score of 3 (neutral) if no data, flag for manual review
S Score (Strategic Value) - 15% Weight
Calculate: Non-revenue value contribution
Data Sources: CRM deal source (referral tracking), marketing records, qualitative input
Point System:
FactorPointsData SourceReferral generated (per referral)+3CRM deal source = referral from this companyCase study participation+3Marketing records / qualitative inputTestimonial provided+2Marketing recordsLogo usage permission+1Contract / qualitative inputReference call availability+2Sales notes / qualitative inputIndustry thought leadership value+2If client in strategic target industry
Convert Points to Score:
PointsScore12+59-1146-833-520-21
If S data is not available:
Check CRM for any deals sourced as "referral" and trace back to referring company
Default to score of 2 (some inherent value in being a client)
Flag for manual enrichment
Analysis Outputs Required
Output 1: Client Scoring Table
Produce a table with all clients scored:
CompanyR ScoreF ScoreM ScoreE ScoreS ScoreCompositeSegmentData Completeness[Name][1-5][1-5][1-5][1-5][1-5][1.00-5.00][Segment][%]
Data Completeness: Percentage of scores based on actual data vs. defaults/proxies. Flag any client with <60% completeness for manual review.
Output 2: Segment Summary
SegmentCount% of ClientsTotal Revenue% of RevenueAvg Composite ScoreChampionsLoyalPromisingAt-RiskMisaligned
Output 3: Dimension Analysis
For each dimension, identify:
Score distribution (how many clients at each score level)
Correlation with composite score
Data quality issues encountered
Output 4: ICP Pattern Extraction
From Champions + Loyal segments (score ≥ 3.75), extract common patterns:
Firmographic Patterns:
Industry distribution (what % in each industry?)
Revenue range (min, max, median, mode)
Employee count range
Geographic concentration
Company stage/type patterns
Behavioral Patterns:
Average deal size at first engagement
Average time to expansion (first deal to second deal)
Common entry point (what service did they start with?)
Stakeholder patterns (how many contacts, what roles?)
Engagement Patterns:
Average sales cycle length
Budget adherence patterns
Project duration patterns
Output 5: Anti-Pattern Identification
From Misaligned segment (score < 2.25), identify warning signs:
What firmographic attributes are over-represented?
What was different about their engagement pattern?
What E score components drove low scores?
Were there early warning signs visible in first engagement?
Output 6: ICP Tier Recommendations
Based on pattern analysis, propose ICP tier definitions:
Tier 1 (Best Fit) Criteria:
[Firmographic criteria from Champions]
[Behavioral signals from Champions]
[Minimum thresholds]
Tier 2 (Good Fit) Criteria:
[Broader firmographic criteria]
[Partial signal match]
Tier 3 (Moderate Fit) Criteria:
[Minimum acceptable criteria]
Exclusion Criteria:
[Anti-patterns from Misaligned segment]
Data Quality Handling
Missing Data Protocol
ScenarioHandlingCompany in CRM but not in financial systemUse CRM data only, flag for validationCompany in financial system but not in CRMAdd to analysis with note, likely data hygiene issueNo deal amount in CRMUse financial system invoice totalNo project/time dataF score from CRM deal count onlyNo E score data availableDefault to 3, flag for manual inputNo S score data availableDefault to 2, flag for manual input
Data Validation Checks
Perform these checks and report discrepancies:
Revenue Validation: CRM deal sum vs. financial system invoice sum (variance >10% = flag)
Project Count Validation: CRM deals vs. time tracking projects (should be similar)
Date Validation: Deal close dates should precede or match invoice dates
Duplicate Check: Same company with multiple names/spellings across systems
Specific Instructions for This Analysis
Analysis Date: [Insert today's date - all recency calculations relative to this]
Data Files Provided:
[CRM Export filename] - Source: [HubSpot/Salesforce/etc.]
[Financial Export filename] - Source: [QuickBooks/Xero/etc.]
[Time Tracking Export filename] - Source: [Harvest/Toggl/etc.] (if applicable)
[Qualitative Data filename] - (if applicable)
Business Context:
Industry: [e.g., B2B consulting, professional services, SaaS]
Average deal size: [if known, for M score calibration]
Typical engagement length: [if known]
Key services/products: [for pattern analysis]
Special Considerations:
[Any known data quality issues]
[Clients to exclude from analysis]
[Custom scoring adjustments needed]
Output Format:
Primary deliverable: Spreadsheet with all outputs
Summary: Executive brief with key findings
Recommendations: ICP tier definitions ready for CRM implementation
Analysis Checklist
Before finalizing, confirm:
All data sources successfully mapped to master company list
Match rate reported (% of records matched across systems)
All five dimensions scored for each client
Data completeness flagged for low-confidence scores
Composite scores calculated using weighted formula
Segments assigned based on thresholds
Revenue validation performed (CRM vs. financial)
ICP patterns extracted from top segments
Anti-patterns identified from bottom segment
Tier recommendations provided with specific criteria
Data quality issues documented
Appendix: Quick Reference Scoring Tables
R Score Quick Reference
Days Since Last EngagementScore0-90591-1804181-3653366-5402541+1
F Score Quick Reference
Engagement CountScore4+534231 (complete)21 (incomplete)1
M Score Quick Reference (Percentile)
Revenue PercentileScore80th+560th-79th440th-59th320th-39th2Below 20th1
Segment Quick Reference
Composite ScoreSegment4.50-5.00Champions3.75-4.49Loyal3.00-3.74Promising2.25-2.99At-Risk1.00-2.24Misaligned
Clustering vs. Fixed Scoring Decision
Use K-Means Clustering When:
First-time analysis with no prior thresholds
Large client base (50+ clients) where natural groupings may exist
Exploratory analysis to discover patterns
Validating or calibrating fixed thresholds
Use Fixed Scoring When:
Operationalizing for ongoing CRM use
Small client base (<50) where K-means may be unstable
Need consistent, interpretable segments over time
Integrating with workflows and automation
Recommended Approach:
Run K-means first to discover natural clusters
Analyze cluster characteristics to understand data distribution
Extract threshold values from cluster boundaries
Implement fixed scoring with discovered thresholds