E-commerce Funnel Optimization Engine
What You'll Get:
- Stage-by-stage funnel analysis with drop-off quantification
- Root cause analysis using CoT methodology
- A/B test recommendations with predicted impact
- Visual funnel flow with ASCII diagrams
- Revenue impact projections with confidence intervals
#### THE PROMPT:
```xml
<task>⟨🛒Conversion_Optimization_Architect⟩
Conduct comprehensive funnel optimization analysis for: [BUSINESS_TYPE]
Current performance: [CURRENT_METRICS]
Traffic scale: [TRAFFIC_VOLUME] × AOV: [AVG_ORDER_VALUE]
Target outcome: [REVENUE_GOAL]
Context: [KNOWN_ISSUES]
Technology: [TECH_STACK]
</task>
<framework>
⊳CoT_Chain × ⊳VoT_Visualization × ⊳Bayesian_Impact_Modeling
Enable: Quantitative_Analysis ∙ Root_Cause_Diagnosis ∙ A/B_Test_Design
<execution>
∇1→Funnel_Mapping:
⦿Core: Visualize complete customer journey
â—ˆASCII_Flow: Create visual funnel representation
→ Show: Each stage × conversion rate × visitor volume
→ Highlight: Drop-off points vs industry benchmarks
â—ˆQuantification:
→ Calculate: Absolute visitor loss per stage
→ Identify: Top 3 highest-impact drop-off points
→ Estimate: Revenue leakage per stage (visitors lost × AOV × expected conversion)
∇2→Root_Cause_Analysis:
⊳CoT_Systematic: For each major drop-off point:
Step 1: List potential causes (minimum 5 per stage)
Step 2: Categorize (UX, Technical, Content, Pricing, Trust)
Step 3: Reference [KNOWN_ISSUES] for validation
Step 4: Assess likelihood (High/Med/Low) based on:
→ Industry patterns
→ Data signals in [CURRENT_METRICS]
→ [TECH_STACK] limitations
Step 5: Estimate fix complexity (S/M/L/XL)
â—ˆPrioritization_Matrix:
→ Score: Impact × Likelihood × (1/Complexity)
→ Rank: All identified causes
→ Select: Top 8 for intervention design
∇3→Intervention_Design:
For top 8 causes:
â—ˆHypothesis_Formation:
→ "We believe [intervention] will improve [metric] because [reasoning]"
→ Include: Current baseline × expected lift × confidence level
â—ˆA/B_Test_Specification:
→ Control vs treatment design
→ Success metrics (primary + secondary)
→ Sample size calculation (statistical significance)
→ Test duration estimate
→ Technical implementation notes for [TECH_STACK]
â—ˆImpact_Modeling:
⊳Bayesian_Projection: For each test:
→ Conservative scenario (P25): Minimum expected lift
→ Base scenario (P50): Median expected lift
→ Optimistic scenario (P75): Maximum reasonable lift
→ Calculate: Revenue impact at each percentile
→ Account for: Interaction effects between stages
∇4→Prioritization_Roadmap:
â—ˆSequencing_Logic:
→ Consider: Implementation dependencies
→ Account for: Test duration + analysis time
→ Optimize: Cumulative revenue impact over 6 months
â—ˆPhase_Planning:
Phase 1 (Weeks 1-4): Quick wins + foundational tests
Phase 2 (Weeks 5-10): Medium complexity optimizations
Phase 3 (Weeks 11-24): Complex interventions + refinements
â—ˆResource_Requirements:
→ Development effort per test
→ Design resources needed
→ Analytics setup requirements
∇5→Revenue_Projection:
â—ˆFunnel_Modeling:
Current state: Calculate baseline revenue from [CURRENT_METRICS]
For each intervention: Model cumulative funnel improvement
Account for: Realistic adoption curves (not instant lifts)
â—ˆGoal_Validation:
→ Check: Can [REVENUE_GOAL] be achieved with proposed interventions?
→ If gap exists: Identify additional opportunities
→ Show: Month-by-month revenue progression
→ Include: Confidence intervals (P25-P75)
∇6→Quick_Win_Identification:
⦿Core: Highlight immediate actions (<2 weeks implementation)
◈Criteria: Low complexity × moderate impact × high confidence
â—ˆExamples: Copy changes, checkout friction reduction, trust signals
â—ˆExpected_Return: Conservative estimate of quick-win impact
∇7→Visualization_Layer:
⊳VoT_Enhanced: Throughout analysis:
â—ˆFunnel_Flow: ASCII visual showing current vs optimized state
◈Impact_Matrix: Visual grid (Impact × Complexity)
â—ˆTimeline_Gantt: Test sequencing with milestones
â—ˆRevenue_Curve: Projected growth trajectory
</execution>
<output_specification>
§Executive_Summary_μ350:
∙ Funnel health diagnosis (1-10 score)
∙ Top 3 opportunities × expected combined impact
∙ Path to [REVENUE_GOAL] with probability assessment
∙ Immediate actions (quick wins) × 30-day expected lift
§Funnel_Analysis_μ600:
∙ ASCII funnel visualization (current state)
∙ Stage-by-stage conversion rates vs benchmarks
∙ Quantified leakage: visitors lost × revenue impact per stage
∙ Comparative analysis: Where [BUSINESS_TYPE] under/overperforms
∙ Visual: Drop-off severity heat map
§Root_Cause_Deep_Dive_μ800:
∙ For each of top 8 causes:
→ CoT reasoning chain: Why this matters
→ Evidence from [CURRENT_METRICS] and [KNOWN_ISSUES]
→ Category (UX/Technical/Content/Pricing/Trust)
→ Likelihood score (1-10) with rationale
→ Complexity estimate (S/M/L/XL)
∙ Prioritization matrix visual (Impact × Complexity grid)
§Optimization_Roadmap_μ900:
∙ 8 prioritized A/B tests with full specifications:
→ Hypothesis statement (if/then format)
→ Control vs treatment descriptions
→ Success metrics (primary + guardrails)
→ Sample size + test duration calculations
→ Expected lift (P25/P50/P75 scenarios)
→ Revenue impact projection per percentile
→ Implementation notes for [TECH_STACK]
∙ Test sequencing with dependencies mapped
∙ Phase breakdown (Weeks 1-4, 5-10, 11-24)
§Revenue_Impact_Model_μ500:
∙ Baseline: Current state revenue calculation
∙ Optimized: Projected state with all interventions
∙ Month-by-month progression (realistic adoption curves)
∙ Cumulative impact: Path to [REVENUE_GOAL]
∙ Confidence intervals (P25-P75) around projections
∙ Sensitivity analysis: If tests underperform/overperform
∙ Visual: Revenue growth curve
§Quick_Wins_μ400:
∙ 5-7 immediate actions (<2 weeks implementation)
∙ Each with: Description × rationale × expected lift × effort
∙ Prioritized by ROI (impact/effort ratio)
∙ Technical implementation guidance for [TECH_STACK]
∙ 30-day expected combined impact
§Measurement_Framework_μ350:
∙ Analytics setup requirements
∙ KPI dashboard structure recommendation
∙ Statistical significance guidelines
∙ Reporting cadence (daily/weekly metrics)
∙ Decision criteria for test continuation/termination
⧉Quality_Standards:
∙ All revenue projections quantitative with methodology shown
∙ Statistical rigor: sample sizes, significance levels explicit
∙ Every recommendation evidence-based (tied to [CURRENT_METRICS])
∙ Realistic timelines (account for test duration + analysis)
∙ CRO professional standard (industry best practices applied)
</output_specification>
```
Created: 11/13/2025
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