Qualified Demand Loss Analyzer

Analyze the lifecycle-stage conversion data below to identify where qualified demand is being lost, distinguish likely process failures from data-quality problems, and prioritize the most actionable findings.

Lifecycle-stage definitions and sequence:
{{stage_definitions}}

Stage-level volume data for the analysis period:
{{stage_volume_data}}

Segmented conversion, aging, or time-in-stage data:
{{segment_data}}

Known CRM issues, process rules, handoff expectations, exclusions, and caveats:
{{data_quality_and_caveats}}

Instructions:
- Analyze the full supplied lifecycle path and calculate or report conversion and loss at every available transition. Show denominators and state the time period and stage definitions used.
- Account for re-entry, recycling, withdrawals, skipped stages, aging, incompatible cohorts, and mixed lead/opportunity populations when the supplied data indicates they matter.
- Separate observed facts, calculated metrics, hypotheses, and assumptions. Do not invent values, benchmarks, causes, sample-size thresholds, financial impact, or unsupported causal claims.
- Distinguish process failure, data-quality problem, and mixed or unclear findings using only supplied evidence such as timing, ownership, handoff completion, stage-history consistency, duplicate records, missing fields, stale records, or conflicting definitions.
- Report segment differences only where the supplied data supports a valid comparison, and flag insufficient volume or missing data.
- Rank findings by business impact and confidence.

Return exactly these sections in this order:
1. Executive finding: a concise statement identifying the largest qualified-demand loss points and the most important qualification or data caveat.
2. Lifecycle conversion table: use exactly these columns: Stage transition | Starting volume | Next-stage volume | Conversion rate | Loss rate or volume | Key segment differences | Likely issue type | Evidence | Confidence | Priority. Use N/A where a field cannot be calculated.
3. Finding classification: classify each material finding as Process failure, Data-quality problem, or Mixed or unclear, with a short rationale and supporting evidence.
4. Prioritized interventions: list the three to five most actionable interventions in priority order. For each, state the observed loss point, recommended process or data change, expected decision benefit, and confidence. Do not claim an expected uplift unless the inputs support it.
5. Missing data and validation checks: list the checks required before making high-confidence decisions.
6. Assumptions and limitations: state cohort, period, denominator, stage-definition, data-quality, and causality limitations that affect interpretation.

Use concise business language. Keep conclusions traceable to the supplied data or transparent calculations.