Executive summary
Five numbers. One leak total: €284,000 a year.
| Metric | Acme | Reference point | Gap |
|---|---|---|---|
| Avg. sales cycle | 47 days | 32 days | +47% slower |
| Proposal-to-close rate | 18% | 35% | -49% below |
| CRM data completeness | 34% | 78% | -56% below |
| Rep admin time | 8.2 hrs/week | 3.1 hrs/week | +165% excess |
| Leads lost to "no decision" | 41% | 22% | +86% above |
Illustrative example — not a benchmark or audit result. Reference points are illustrative, not cited industry data.
The findings
Three leaks. Each one proven, not asserted.
Follow-up failure
Signal: Pipeline graveyardEvidence
- →34 proposals sent in 90 days
- →23 (68%) received zero follow-up after Day 3
- →8 (24%) followed up once, then abandoned
- →3 (9%) had a structured follow-up sequence
Reps are sending proposals then moving to the next opportunity. No systematic follow-up means deals dying in the graveyard.
Root cause
No follow-up playbook. Each rep improvises. No CRM automation. No accountability.
Intervention
4-touch proposal follow-up sequence (call → email → LinkedIn voice → break-up). Automated CRM reminders at Day 1, 3, 7, 14.
Projected impact
+12% proposal-to-close rate = €127,000 additional ARR
CRM data decay
Signal: Blind pipelineEvidence
- →156 open opportunities
- →53 (34%) have no next step logged
- →89 (57%) have no stakeholder map
- →112 (72%) have no competitor intelligence
Management is forecasting from fiction. Pipeline reviews are theatre. Decisions made on gut, not data.
Root cause
CRM seen as admin burden, not sales tool. No enforcement. No consequence for bad data.
Intervention
Mandatory fields before stage advancement. Auto-enrichment. Weekly pipeline hygiene ritual (15 min).
Projected impact
+18% forecast accuracy → better resource allocation = €89,000 efficiency gain
Lead response latency
Signal: First-mover disadvantageEvidence
- →87 inbound leads in 90 days
- →Average first response time: 6.4 hours
- →31 (36%) never responded to at all
- →Best-performing rep responds in 23 minutes; worst in 18 hours
Speed-to-lead is random. No system. The top performer is 47x faster than the bottom. Luck, not process.
Root cause
No lead routing rules. No SLA. No auto-assignment. Reps check the CRM when they remember.
Intervention
Auto-routing by territory and company size. Slack alert + 2-hour SLA. Auto-personalised first-touch email template.
Projected impact
+22% lead-to-meeting rate = €68,000 additional pipeline
90-day roadmap
Every finding ends in a fix sequence, an owner, and a success metric.
The maths
The proof arrives before the implementation spend.
€7,500
Audit + roadmap
This diagnostic, on your data
€12,500
Build + implement
Only after you have the proof
€284K+
Projected annual return
Sum of the three leaks above
10.3x
Year-one ROI
Against €27,500 total investment
Illustrative example — not a benchmark or audit result. Real engagements report only what your data proves.
How this gets produced
Deterministic. Evidence-bound. No invented numbers.
This diagnostic is generated by the Revenue Diagnostic Delivery System (RDDS) — a deterministic workflow that ingests your pipeline data and outputs evidence-based proof-assets without hallucination or manual formatting.
All findings in a real engagement are derived from your actual CRM data. No generic benchmarks. No fabricated metrics. What you see is what your pipeline proved.
Start here
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