Customer Journey Gap Analyzer

When to invoke

• You have funnel/journey stage metrics and need to find the largest drop-offs.
• You want a simple prioritization framework for improvement ideas.
• You need a quick report for product, growth, or CX teams.

Inputs needed

• A CSV with at least these columns:
  • stage
  • entered (count entering stage)
  • completed (count completing stage)
• Optional columns:
  • avg_time_seconds
  • notes

Workflow

1. Load CSV and validate numeric columns.
2. Compute per-stage conversion rate: \(completed/entered\).
3. Compute drop-off count and drop-off rate.
4. Rank stages by impact using a simple score:
   • volume-weighted drop-off: \(entered - completed\)
   • plus penalty for very low conversion.
5. Generate recommended experiments based on common patterns:
   • clarify value proposition
   • reduce form friction
   • improve error messaging
   • add trust/privacy cues
6. Output a JSON report.

Output format

JSON to stdout:

• stages: metrics per stage
• top_gaps: ranked list with suggested experiments
• summary: overall conversion (first entered to last completed)

Guardrails

• Vendor-neutral: generic funnel analytics; no platform-specific assumptions.
• Recommendations are templates; validate with user research.

Reference code

• customer_journey_gap_analyzer.py