Pipeline coverage explained
What pipeline coverage is, how to calculate it properly, why the 3x rule may be too high or too low for you and which mistakes make the figure useless.
Pipeline coverage is the ratio between the open pipeline for a period and the revenue target you still have to reach in that period. Coverage of 3x means there are three times as many open deals as you still need. How much coverage is enough depends on your own win rate: if you win a third of your pipeline you need roughly 3x, if you win a fifth, 5x. The figure is only useful if the pipeline is honest and the target contains only the revenue that genuinely has to come from new deals.
How do you calculate pipeline coverage?
The basic formula is simple:
Pipeline coverage = open pipeline with a close date in the period / remaining target for that period
The remaining target is the period target minus what has already been won. Halfway through the quarter, with EUR 400,000 of a EUR 1,000,000 target secured, the remaining target is EUR 600,000. If there is then EUR 1,800,000 of open deals with a close date this quarter, your coverage is 3x.
How much coverage you need follows from your win rate by value:
Required coverage = 1 / win rate
If you historically win 25 percent of the pipeline value due to close in a quarter, you need 4x. If you win 40 percent, 2.5x. The familiar 3x rule of thumb is therefore nothing more than the assumption that you win a third. For some businesses that holds, for many it does not.
Make sure you calculate your win rate by value, not by count. If you win many small deals and often lose the large ones, your win rate by value is considerably lower than by count, and you need more coverage than you think.
The most common mistake: the wrong target
In many B2B businesses a large share of revenue does not come from new deals, but from recurring revenue from existing customers: maintenance, licences, regular purchasing. That revenue is usually not recorded as a deal in the CRM. If you calculate coverage against the total revenue target, you are measuring something that does not exist.
Worked example: suppose your quarterly target is EUR 1,500,000. From your invoicing and contracts you expect EUR 900,000 of recurring revenue from existing customers. So EUR 600,000 has to come from new deals. Your open pipeline for the quarter is EUR 2,400,000.
- Against the total target: EUR 2,400,000 / EUR 1,500,000 = 1.6x. That looks tight.
- Against the part that has to come from deals: EUR 2,400,000 / EUR 600,000 = 4x.
If you historically win 25 percent of your pipeline, you need exactly 4x. Coverage is then just sufficient: not tight, not generous. The reverse happens too: a business that forgets its recurring base and worries about 1.6x may give unnecessary discounts to pull deals forward.
How to forecast that recurring base is covered in forecasting from historical revenue.
Other mistakes that make the figure useless
Counting dead deals. A pipeline full of deals that have not moved for months gives high coverage and low revenue. Filter on age and activity first, as described in how do you spot an unreliable pipeline. Coverage after those filters is the figure that counts.
Counting deals outside the period. Coverage for this quarter is only about deals with a realistic close date in this quarter. A deal that closes in eight months does not help your target.
Using the win rate of the wrong segment. If you win 15 percent of new customers and 50 percent of expansions with existing customers, an average win rate is misleading. Calculate coverage separately per segment, or at least for new and existing.
Mixing weighted and unweighted. Some CRMs show weighted pipeline (value multiplied by stage percentage). Coverage on weighted pipeline has an entirely different benchmark from coverage on unweighted pipeline. Weighted coverage of 1x in theory means exactly enough. Choose one definition and stick to it.
Coverage over time
Coverage is not a snapshot. At the start of a quarter, 4x is normal if you expect many deals to be created and closed within the quarter. By the end, coverage should be lower but more concentrated.
Three moments are worth measuring:
- Start of the quarter: coverage for this quarter and for the next. If coverage for next quarter is already low now, you still have time to act.
- Halfway: coverage on the remaining target, after filters. This is the moment to decide whether extra action is needed.
- Final month: here coverage matters less than the status of each deal. Five deals in the final stage say more than a ratio.
How quickly deals move through the pipeline determines how much coverage you need at the start of a period. If your cycle is shorter than your forecast period, part of the revenue will be won from deals that do not exist yet. That relationship is explained in pipeline velocity explained.
What coverage does not tell you
Coverage tells you whether there is enough in the funnel. It does not tell you whether the deals are good ones, whether they close on time, or whether won deals are actually invoiced. High coverage and a missed quarter can easily go together. That is why pipeline is not revenue: between an open deal and a paid invoice there are many steps where things can go wrong.
Coverage is therefore an early signal, not a forecast. It answers the question "do we need to create more pipeline?", not "how much revenue will we achieve?". For the second question you need a real forecast, with a range; see forecast confidence explained.
What is a good pipeline coverage ratio for your business?
Rather than adopting a rule of thumb, you can work out your own benchmark in an hour.
- Take the last four quarters.
- For each quarter, determine the pipeline on day one with a close date in that quarter, after filtering out dead deals. If your CRM keeps no history, start saving a snapshot every quarter now.
- For each quarter, determine how much revenue from new deals was actually won.
- Divide won revenue by the opening pipeline. That is your conversion per quarter.
- The required coverage at the start of a quarter is 1 divided by that conversion.
- Look at the spread between quarters. If conversion swings between 20 and 40 percent, your benchmark is uncertain and you want to be on the high side.
Do this separately for new customers and for expansions with existing customers, if your CRM lets you separate them.
Checklist for your next forecast meeting
- Is the target we measure against only the revenue that has to come from new deals?
- Have deals older than twice the cycle and without recent activity been filtered out?
- Do all counted deals have a realistic close date within the period?
- Are we using our own win rate by value, per segment?
- Have we also looked at coverage for next quarter?
- How much of the coverage sits in the three largest deals?
For the full picture of how coverage fits into a forecast, see what is revenue forecasting.
Frequently asked questions
Is 3x pipeline coverage enough?
Only if you win roughly a third of your pipeline. Calculate your own win rate by value and take 1 divided by that rate as your benchmark. For many businesses it lies between 2x and 5x.
Should you calculate coverage on weighted or unweighted pipeline?
Choose one and be consistent. Unweighted coverage with a benchmark based on your win rate is the easiest to explain. Weighted coverage depends on stage percentages that are often wrong themselves.
What do you do if coverage is too low?
First check that the figure is right: correct target, dead deals removed, correct period. If it is still too low, at the start of a quarter you have time to build extra pipeline. In the final month more pipeline rarely helps; focusing on the best deals is then the only thing that works.
Can coverage be too high?
Yes. Very high coverage often points to a pipeline full of deals that are not really alive, or to a target that is too low. Both are worth investigating.
More in this cluster
- What is revenue forecasting?Start here
- Why are sales forecasts so often wrong?
- CRM forecast vs actual revenue
- How do you build a reliable revenue forecast?
- Forecasting from CRM data
- Forecasting from historical revenue
- AI revenue forecasting explained
- AI forecasting vs traditional forecasting