Do you have enough pipeline to hit your growth target? The coverage ratio answers that question — and why 1.0x is never enough.
The coverage ratio answers: "Do we have enough pipeline to hit our growth target?"
It's the total value of your pipeline divided by the revenue gap you need to fill.
Coverage Ratio = Pipeline Value / Revenue GapA ratio of 3.0x means you have $3 of pipeline for every $1 you need to win.
You won't win everything in your pipeline. Win rates in GovCon typically range from 20% to 40%, depending on the opportunity type. So if you need $5M in new revenue and your win rate is 25%, you need $20M of pipeline (4.0x coverage) just to break even on expectations.
| Coverage | Interpretation |
|---|---|
| < 2.0x | Danger zone—not enough opportunities to sustain growth |
| 2.0x – 3.0x | Tight—need to be winning at a high rate |
| 3.0x – 5.0x | Healthy for most GovCon firms |
| > 5.0x | Strong, but check if the pipeline is realistic |
These vary by company size, win rate, and how aggressively the pipeline is qualified.
Arcvue uses unweighted pipeline for coverage ratios. This is deliberate—it shows you the true size of your funnel without optimistic probability assumptions.
The denominator matters as much as the numerator. The revenue gap is:
Revenue Gap = Growth Target - Existing Backlog RevenueExisting backlog = revenue from contracts already won that will continue into the forecast period. The gap is what you need to win from new opportunities.
The Operator's View
One of the more sobering moments in running a GovCon firm—or an exciting one, depending on the outcome—is the day you internalize that every pipeline opportunity is binary. It either closes or it doesn’t. Wouldn’t it be convenient if a 30% PWIN actually delivered 30% of the revenue?
As a reformed investment banker, I learned this the hard way watching sell-side deals cycle through their predictable alive-dead-alive-again phases while the business absorbed its fourth consecutive pipeline loss on opportunities that were all sitting at 25% PWIN. Turns out 25% didn’t mean much. That’s when my mentor and I changed how we forecasted pipeline entirely. We kept the PWIN column in the materials—it has value as a relative positioning signal—but when it came to waterfall forecasting, we weighted the portfolio, not individual opportunities.
That’s the philosophy baked into Arcvue. We pull PWIN from your CRM and present weighted pipeline value, but it isn’t primary. The primary methodology is coverage ratio: how much well-qualified pipeline do you have, with conservative start dates and revenue spread over the period of performance, to cover your projected revenue targets?
PWIN was always designed to be read in aggregate across a large number of opportunities—that’s why large firms developed it and why it works for them. For most small and mid-sized firms, the pipeline simply isn’t deep enough for portfolio theory to bail you out. You might have eight qualified opportunities, not eighty. At that scale, a string of binary losses doesn’t average out—it creates a crisis. Build your pipeline discipline around coverage ratio, and let PWIN do the one job it was actually designed for: telling you where you’re better or worse positioned relative to other opportunities in your pipeline.