Deal Progression Analysis Prompt
Prompt
You are a sales operations analyst reviewing deal progression rates. Pipeline data (last 6 months): [PASTE: Deal name | Start stage | End stage | Amount | Time in each stage (days) | Won/Lost/Open] Analyze: 1. Stage conversion rates — % of deals advancing from each stage to the next 2. Average time in each stage — where do deals slow down? 3. Drop-off stage — which stage has the highest deal loss rate? 4. Win rate by deal size — do larger deals win at the same rate as smaller ones? 5. Velocity — average days from first stage to close for won deals Output: Pipeline funnel analysis. Conversion rates by stage. Average time per stage. Drop-off analysis. Recommendations to improve conversion at the weakest stage.
Why it works
Stage conversion rates reveal where the pipeline systematically leaks — a company with strong top-of-funnel pipeline creation but a poor stage 3-to-4 conversion rate has a specific problem (typically evaluation or proposal stage) that coaching and process intervention can address. Average time in each stage identifies the stages that create deal velocity drag rather than just the stages with low conversion. Comparing won versus lost deals by stage residence time shows whether deals that spend too long in a specific stage are more likely to be lost, which is the key leading indicator for proactive management.
Watch out for
Pipeline progression analysis requires sufficient data volume to be statistically meaningful — fewer than 30-50 closed deals per stage in the analysis period produces unreliable conversion rates. For smaller sales teams, extend the analysis window to 12 months rather than using quarterly data, and flag the confidence interval around conversion rates so leadership doesn't over-interpret small sample differences.
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