Pipeline Velocity Analysis: Identify Revenue Leakage Points
Analytics & ReportingintermediateClaude 3.5 Sonnet or GPT-4o. Claude excels at multi-step financial analysis and can handle complex pipeline data structures with fewer hallucinations. GPT-4o is faster for real-time analysis if you're working with live CRM exports. Both handle the revenue impact calculations accurately.
When to Use This Prompt
Use this prompt when you need to justify pipeline acceleration investments to your CFO or identify where operational debt is costing you revenue. It's especially valuable when you're deciding between multiple AI implementation opportunities and need to quantify which one moves the needle fastest on revenue.
The Prompt
You are a revenue operations analyst helping a B2B marketing leader identify where deals slow down in the sales pipeline.
## Context
I manage a [SALES_CYCLE_LENGTH]-day sales cycle across [NUMBER_OF_REPS] sales reps. Our average deal size is [AVG_DEAL_SIZE]. Last quarter, we had [TOTAL_OPPORTUNITIES] opportunities enter the pipeline.
## Pipeline Data
Provide analysis based on these stage metrics:
- Stage 1 (Prospecting): [STAGE1_COUNT] deals, [STAGE1_AVG_DAYS] avg days
- Stage 2 (Discovery): [STAGE2_COUNT] deals, [STAGE2_AVG_DAYS] avg days
- Stage 3 (Proposal): [STAGE3_COUNT] deals, [STAGE3_AVG_DAYS] avg days
- Stage 4 (Negotiation): [STAGE4_COUNT] deals, [STAGE4_AVG_DAYS] avg days
- Stage 5 (Closed Won): [STAGE5_COUNT] deals, [STAGE5_AVG_DAYS] avg days
## Analysis Task
Identify the top 3 velocity bottlenecks in our pipeline. For each bottleneck:
1. Calculate the revenue impact (deals stuck × avg deal size × cost of delay)
2. Diagnose likely root causes (operational friction, qualification gaps, internal approvals, etc.)
3. Recommend one high-leverage intervention to accelerate that stage
4. Estimate the time savings and revenue recovery if the intervention succeeds
## Output Format
Structure your response as:
- Executive Summary (1-2 sentences on total revenue at risk)
- Bottleneck #1: [Stage Name]
- Bottleneck #2: [Stage Name]
- Bottleneck #3: [Stage Name]
For each bottleneck, use this format:
**Revenue Impact:** $X at risk
**Root Cause Analysis:** [2-3 sentences]
**Intervention:** [Specific, actionable recommendation]
**Expected Outcome:** [Time saved + revenue recovered]
Focus on operational friction that AI or process automation can address—not just sales coaching.
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Tips for Best Results
- 1.Replace all [BRACKETS] with actual numbers from your CRM. Estimates won't produce credible ROI. Export stage velocity data from Salesforce or HubSpot for accuracy.
- 2.If you don't have exact stage times, ask your VP of Sales for the last 90 days of closed deals. This grounds the analysis in real behavior, not wishful thinking.
- 3.Run this analysis quarterly and compare bottleneck rankings. If the same stage stays #1, your previous intervention didn't work—dig deeper into root cause.
- 4.Share the revenue-at-risk number with your CFO before proposing solutions. This justifies the investment in pipeline acceleration tools and removes the 'nice-to-have' objection.
Example Output
**Executive Summary**
Your pipeline has approximately $2.4M in revenue at risk due to velocity bottlenecks, with the largest leakage occurring in the Discovery and Proposal stages.
**Bottleneck #1: Discovery Stage**
**Revenue Impact:** $1.2M at risk
**Root Cause Analysis:** Deals are spending 18 days in Discovery vs. your 12-day target. This suggests reps are manually gathering prospect research, conducting redundant discovery calls, or waiting for internal stakeholders to provide competitive intelligence. The 6-day overage across 40 deals represents significant revenue delay.
**Intervention:** Implement an AI-powered prospect research tool that auto-populates company intelligence, decision-maker profiles, and competitive context before the first call. Reps spend 2 hours on research prep instead of 6.
**Expected Outcome:** Reduce Discovery stage by 4 days (33% improvement). Recover $800K in revenue velocity over next quarter.
**Bottleneck #2: Proposal Stage**
**Revenue Impact:** $900K at risk
**Root Cause Analysis:** 22-day average vs. 10-day target indicates proposal creation and internal approval cycles are sequential rather than parallel. Reps likely wait for marketing to customize decks, legal to review terms, and finance to approve pricing.
**Intervention:** Deploy an AI proposal generator that creates customized, compliant decks in 2 hours using your template library and deal data. Eliminate the handoff bottleneck.
**Expected Outcome:** Reduce Proposal stage by 8 days. Accelerate 35 deals by 1 week each, recovering $630K in quarterly revenue.
**Bottleneck #3: Prospecting Stage**
**Revenue Impact:** $300K at risk
**Root Cause Analysis:** 14-day average entry-to-qualification suggests leads aren't pre-qualified before handoff to sales, forcing reps to spend time on disqualification calls.
**Intervention:** Use AI lead scoring to automatically qualify inbound leads against your ideal customer profile before sales assignment.
**Expected Outcome:** Reduce Prospecting stage by 3 days, improve rep productivity by 15%.
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