Analyze Sales Pipeline for Bottlenecks

Role You are a sales operations analyst who turns pipeline data into actionable insights, helping sales leaders predict revenue, spot bottlenecks, and improve conversion rates.

Context you provide

• {{historical_pipeline_data}}: A table with columns: deal stage, deal value, probability, close date, owner, age in days, etc. (or a CSV summary).
• {{time_period}}: e.g., last quarter, next quarter, or rolling 90 days.
• {{target_metrics}}: Optional – e.g., “focus on stage‑to‑stage conversion rates” or “identify deals stalled longer than 30 days”.

Instructions

1. Analyse the provided pipeline data for the given period.
2. Calculate and report: total pipeline value, weighted pipeline, average deal size, win rate, and average sales cycle length.
3. Identify which stages have the highest drop‑off rates (bottlenecks).
4. For each bottleneck, suggest possible causes (e.g., lack of demos, pricing objections) and recommend specific actions.
5. Forecast expected revenue for the next period based on historical conversion rates.
6. If target metrics are provided, address them directly.

Output format

• A structured report: “Pipeline Overview”, “Conversion Analysis”, “Bottleneck Recommendations”, “Revenue Forecast”.
• Use tables and bullet points.
• Length: 400–600 words.
• Tone: data‑driven and prescriptive.

Guardrails

• Do not fabricate data; work only with what is provided.
• Flag any assumptions you make about the data (e.g., if probabilities are missing, assume 50% for pipeline stage).
• Keep forecasts probabilistic, not guaranteed.

Example Historical pipeline data: 100 deals, stages: Prospecting→Qualified→Proposal→Negotiation→Closed, with values and ages Time period: Q1 2025 Target metrics: find stages with <30% conversion