Is Pipeline Coverage Ratio simply the Inverse of Win Rate?

Live from Schenectady, New York, it's SAS Talk with the Metrics Brothers, Growth and CAC.

And I'm Growth, better known as Ray Reich, the founder and CEO of Benchmarkit.

And I'm CAC, better known as Dave Kellogg, independent consultant, EIR at Balderton Capital, and the author of Kellogg.

And together we are the Metrics Brothers.

And we go together like the Kwisatz Haderach and the Lisan al Gaib.

Wow, so we're doing a Dune theme today, huh, Kak?

We are, we are. We're gonna follow the one who points—

Well, the one who points the way is Muad'Dib, right?

Yes, named after the desert kangaroo mouse. But in the end, look, they're all the same guy anyway, right? It's all Paul Atreides.

Well, to think some people find Dune confusing.

Yeah, yeah, one would be surprised. Well, some people find pipeline coverage versus win rate confusing too, right?

Well, they won't after listening to today's podcast, CAC— I mean, Muradib. But before we chew into this topic, let's hear a word from our presenting sponsor. The Metrics Brothers is proudly sponsored by Benchmarkit, the B2B AI and software industry's largest, broadest, and most contextualized benchmarking index. Dave Kellogg and Ray Groth Wright have put the Metrics Brothers podcast on hiatus for remainder of 2026. All episodes we have ever recorded are still available on your favorite podcasting app. You can reach Dave at Kellogg and Ray on LinkedIn at Ray Reich. Thank you so much for listening to the Metrics Brothers podcast.

Okay, so let's jump in today, Ray. Um, this episode was prompted by last week's where we talked about the Bridge Group SaaS AE report. And if you look at slide 35 of that report, they, they make the following statement: win rates declining from 23% to 19% might not seem all that significant, but in terms of required pipeline, it represents a dramatic shift point from 4.3x to 5.3x coverage. So what they've done there is something a lot of people do, which is they just say, hmm, 1 over win rate equals pipeline coverage, right? A lot of people do that. I think it's a mistake. What do you think?

Well, if someone tells me they need 5.3 pipeline coverage ratio, I'm like, that seems really high. I lived in a world where 2x, 3x, you know, 4x was at the high end, Dave.

Yeah, right there. So, so Ray just followed what I call the first rule of math homework for all the parents out there, which if your kids have been Doing a problem that says calculate the summer temperature in Alaska and the answer is 468 degrees, you should go check your work, right? That's what I always taught the kids. And 5.3x pipeline coverage to me is a yellow flag kind of answer. So whatever math you did, and we'll talk about more math in a minute, but whatever math they're doing is saying, hey, you might need 5.3x pipeline coverage to start quarters. And, you know, I guess 2 things, Ray. In the warm-up you're saying, you know, the first thing that went through your mind is how's marketing going to do that? And that's what goes through CMO's mind too. Um, but to me, it's a red flag answer. Um, or in the— it's— I'm going to call it a yellow flag answer. A red flag— I literally saw a company once, Ray, that needed 90 times pipeline coverage. They looked at me with a straight face and said, we're carrying 90 times pipeline coverage. 90x. 9-0-x. And I just couldn't believe it. I said, you need 90x coverage?

Hey, Dave, I can tell you what happened that quarter. They missed their number.

You're a, you're a soothsayer, Ray. Absolutely. With 90x coverage, it wasn't enough. So the moral of that particular story is, as soon as you have a loss of pipeline discipline, you know, any number can pop out of these things. All of this begins with pipeline discipline. Is it a real opportunity? Does it have a real close date and a real value? And when you have kind of a real pipeline and you understand the difference between win rate, close rate, and pipeline coverage, you're not going to end up coming out with numbers like 5.3x, let alone 90x. Because to be honest, if a company tells me they need 5.3x pipeline coverage, my first question is why. First, I want to know about pipeline discipline, and I want to know about why. What's happening to all these deals? Because it's going to be very hard to have a cost-effective go-to-market model over time if we consistently need 5.3x.

Totally agree. But Dave, you know, as the co-founder of the SaaS Metric Standards Board, I love math and calculation formulas. So let's cut to the math. Where do you find the flaw here in win rate not being the inverse of pipeline coverage?

Yeah, well, there's a couple things here, Wei, uh, that, that the risk in inverting 2 metrics is if they aren't inverses, simply put. So, so let's go look at these 2 metrics, and I'm going to argue that both can be ill-defined. That just saying pipeline coverage alone Like, for what period? This quarter, next quarter, all quarters? We're probably okay here. Most people in the context of Bridge Group was talking about it, we're probably talking about current quarter pipeline. Okay, well, that's fine. So we're talking about current quarter pipeline. In general, more adjectives are better. Then of course that changes every day in the quarter, right? So when in the quarter? Are we talking about day 89 where you can get by with 1x, hopefully, or maybe even 0x if you've already hit your number on a to-go basis, right? Or are we talking about day 1 when there's still a lot of junk in there from last quarter that hasn't been cleaned up yet? Or are we talking about day 10, week 3, entering week 3 when things should be more stable? So, so whenever we're going to look at a metric like this, we're going to need to tell me what period are we covering and when are we taking the snapshot.

Yeah, and you mentioned a couple things there. So, and I know we've talked about this in a previous episode, Like in a 90-day sales cycle, let's just use that for this example. You know, at what point do you really measure pipeline coverage ratio? And to me, that's at least 2, maybe 3 weeks into the new quarter where you've had a chance to clean out a lot of the crap pipeline, etc. And to me, that's their first point of measurement. Let's say it's a $5 million at day 21. Then that's what I'll use to divide by the pipeline coverage ratio to give my predicted ARR. What do you think of that? Waiting until like week 2 or week 3 to start?

Yeah, as you mentioned, we've covered this before. I think it's the best practice. In enterprise, I do day 1, week 3, which is the same as you. So it gives them all the first 2 weeks to clean stuff up. So basically Sunday night before week 3, that's when I take my snapshots for kind of quarterly analytics. I would say if you happen to be in a monthly cadence, I use day 5 pipeline. It's the same principle. Sales needs a little time. at the pipeline, it's reasonable. So let's establish a date. Let's make sure every quarter or every month it's cleaned up by then. Then we can do our snapshots for kind of longer-term analytics, monthly or quarterly analytics. So, so first, when we say pipeline coverage, we need to know really 2 things. What period? Current quarter. And when in the quarter? And we're just going to say day 1, week 3. Okay, got it. So if you add those, provide those, at least we know what we're talking about. But what's win rate? Let's look at the other metric here, because the assertion is kind of that pipeline coverage, 1 over pipeline coverage equals win rate, or 1 over win rate equals pipeline coverage. And are these in fact metrics that are inverses? Are they defined identically? Well, when most people say win rate, do they mean opportunities at the start of the quarter or opportunities in the week 3, day 1 pipeline? Actually, no. Win rate is typically not done at a start of quarter snapshot, right? Close rates are done on a cohort basis. Tell me the, the close rate of all the opties generated in October. Right. So that's done by anchoring the starting point, all opties generated in October. Pipeline coverage is typically done in a week 3 snapshot. Win rate is done at the other end, which is all opportunities that hit a terminal state in the quarter. Right. And in most companies, there's 3 terminal states, won, lost, and no decision, also known as derailed. Those are the 3 terminal states. And of course, the other way a pipeline— an opportunity can get out of the pipeline You don't, you don't put it in a terminal state, you just slide it, right? So you slide the close date out and, and you avoid having to put it in a terminal state. But anyway, when most people calculate— therefore, when most people calculate WACC, they're excluding stuff that slipped out. They actually will include stuff that got pulled forward, right? If it hit a terminal state in that quarter even though the close date was next quarter, that typically gets included. And then they'll typically Divide the number 1 by something to get a win rate. So already you can see that these 2 metrics are not— that one is just not the other one upside down.

Yeah. So even with what you just said and kind of being a student of this, I'm a little confused. So it tells you how other people are going to get confused because there's a couple other variables, I believe.

One is— So this is going to get more confusing? Hang on, Ray. More confusing.

At day 1, week 3, right? If you have all the pipeline that's scheduled to close that quarter, they're at different stages in the pipeline. Maybe one's at day 30 and another one's at day 75. Win rates change every day of an opportunity's lifecycle. The win rate on day 38 is very different than the win rate on day 82, Dave. And most people don't have the automation or data intelligence to do that math. That's one of the reasons I do love funnel management automation, because you can actually look at the date in the sales cycle and put some predictive logic. But maybe that's getting us derailed.

Well, it will do. Careful the word derail, because I'm going to use that as terminal state. But I think you're bringing up 2 additional points there, Ray, not one, which is all this varies as a function of time. And it varies as a function of time and the maturity of an opportunity, i.e., as it goes through the various stages. And it also varies almost independent. You can have a week 10 close rate, a week 11 close rate, and that is a mix of opportunities at different stages. Typically, as you go through a quarter, that's, you know, they all trend to later stages. If it's week 12 and you've got a stage 2 opportunity or even a stage 3 or stage 4 opportunity, it's like, how in the world is this possibly going to close? Right? It has much more maturation to do. So those are all good points, but I tend to think they can come out in the wash. I think it's great if you have an app that'll manage that for you and analyze it. But my point here is simpler, Ray. It's just that pipeline coverage is a week 3 snapshot. What's the total value of opportunities with a close date in the current quarter divided by the target? That's pipeline coverage. Win rate is calculated completely differently. It's of all the opportunities that hit a terminal state in the quarter, let's take the number we won divided by something. And let me define something. Something is either wins plus losses, Or wins plus losses plus slips. And I call the former narrow win rate and the latter broad win rate. But once again, because my main point here, Ray, is that these terms are kind of ill-defined and people go, oh, 1 over win rate equals pipeline coverage. Or hopefully right now we've already convinced the audience that this is just plain not true because they're not defined inversely. And the other is it's just sloppy because which win rate? Most importantly, which win rate?

So however, if you looked at day 1, week 3, and look at that qualified pipeline to close in that quarter, right? And you know that your traditional win rate— and maybe it should be close rate, so correct me here.

I call this a conversion rate. I call this week 3 pipeline conversion rate. I think you're building to what I call week 3 pipeline conversion rate. Go ahead.

Then you'd say your pipeline coverage ratio is 5x.

If you— yes, I would. So you're skipping, Ray, cheating ahead as always. Um, if you want to invert something to get a pipeline coverage ratio, kind of a required pipeline coverage, it should be the inverse of your week 3 pipeline conversion rate. What does that mean? Well, week 3 pipeline conversion rate is week 3 current quarter pipeline, right, on the bottom, the denominator. The numerator would be ARR closed in the quarter. Right. So you're saying you— I call this an extraction ratio. How much do we extract from the pipeline or how much do we convert? Someone called it conversion ratio of that pipeline. So if you're converting through 33%, then that's— then that means you need 3x coverage. So it actually has nothing to do with win rate. To be clear, it has nothing to do with win rate.

But even with that week 3, day 1 conversion rate to get to your pipeline coverage ratio, That has to assume some additional constants, right? If not, there's going to be errors. And I'll give you one. To me, I find a lot of times deals will pull in that last month of a quarter that aren't even in the pipeline. They just came in through referral. That inherently is going to change your pipeline coverage ratio. So you need to track that every quarter to see what percent of revenue AR1 actually comes from last-minute points. What do you think of that?

Yes and no, Ray. Yes and no. So, so, so, so I'm a big believer kind of first-order calculations, then second-order calculations, right? Because we can explode this stuff arbitrarily deep. Um, I do think the point you've raised is a really good one because a lot of sales managers have a lot of trouble. I mean, you kind of represented the typical sales viewpoint of, wait a minute, there's stuff that we closed that wasn't in there, so how do we deal with that? And their minds kind of explode. Right, it wasn't in the Q2 pipeline because it had a Q3 close date and we want it early, so that's going to screw the ratio. Shouldn't we exclude it? Or like, how do we calculate for that? And the answer to me is, first, I mean, 2 Latin words economists use a lot: ceteris paribus, all other things being equal. So if every quarter you pull forward 20% of the wins, then that should largely come out in the wash. Some of this stuff doesn't work when you're growing very quickly. Right? Because 20% of next quarter's pipeline might be a lot bigger, right? So some of the— I always think of it as a tipped growth curve. But, but my first order calculation is just show me— and most companies can't even do this— show me your week 3 pipeline conversion rate, um, and then invert it, and let's see how good a metric that is. And if we see it kind of, you know, jumping all over the place, then maybe we have to do the next layer and say, okay, what's the first level of correction we want to do this? But I kind of start by saying, hey, assume it all comes out in the wash. Is there any reason to believe we're better at putting the right close date in? Is there any reason to believe we're worse? Um, and if not, and then we can just go look and say, okay, so I don't disagree with your conclusion. We just go track it to see if it's not ceteris paribus, right? If it is jumping all over the place. But, but the— I kind of do the first calculation first, which many people can't do, and then, uh, I try to analyze that.

Gotcha. So using that concept, things like Things that close before week 3, day 1, things that get pulled in, you just exclude those because that's noise and they're going to bounce out on par, basically.

Well, I mean, there are some assumptions to this. Let's just go through this. So this formula works great on a couple of things. One, assumes long sales cycles, right? So we're talking about quarterly pipeline coverage, week 3 conversion rate. That is a great metric provided you have long sales cycles. Like, pretty much anything that has a chance of closing is already in the pipeline even somewhere in week 3. Might be in the wrong quarter, but at least it's somewhere, uh, even though it'll be excluded from the math. Long cycle sales cycles are included. Like, we're not assuming a lot of stuff gets generated halfway through the quarter and closed within the quarter, right? That kind of breaks this model. Uh, I did a whole post on this about monthly cadence and SaaS, and a lot of the metrics you and I talk about just don't work with monthly cadence. In this particular case, I would actually do kind of a day 5 monthly pipeline conversion and try and forecast it by month. Right? Because you basically have— you're looking out and view yourself as driving in fog, and you have 1 month ahead of you in a monthly cadence, and you have 1 quarter ahead of you in a quarterly cadence. So, go ahead.

I hate doing this to you, but I just always go back to my own experience. And I was in a lot of times where we had sales cycles of 120 days to 180, right? Still not classic 1-year enterprise, but we always had to look at pipeline, not only current quarter, but current quarter plus 1, and current quarter plus 2. I don't even know if that's something we should dive into today.

No, we shouldn't, because it's a different topic. Remember, our topic today, Ray, is just, is, is, is win rate 1 over pipeline coverage, or is pipeline coverage 1 over win rate? Same question. That's the question we're trying to drill into, because a lot of people do what Bridge Group did, and it's wrong. Because you've done this for a long time, there are a ton of other things we could talk about, and we could Basically, we talk about what I call time-based close rates, which is actually looking over every quarter. You know, if, if our eventual close rate is 25%, how much of it comes in the current quarter, in the, the next quarter, and the next quarter after that, right? Um, so again, if you're interested in this topic, guys, go look at time-based close rates on Kellogg because it's a great topic. But, but today I'm just trying to keep it simple, Ray, which is a lot of people walk around going, our win rate is, uh, 20%, therefore we need 5x pipeline coverage. And they're not even asking the question, what goes into those 2 calculations? And win rate most of the time is just wins divided by wins plus losses in a period. It has no notion of when the opti was created. Could be this quarter, last quarter, 10 quarters ago. It's not in the math.

Actually, I got to ask this question because win rate, you just mentioned about how to calculate it, right? And it was number of opportunities won and lost. But then there's the whole, what's the value of each opportunity? Because to me, I always did it against the value, not the number of oppies.

You could do it based on value. Again, to me, it's just kind of more complexity, and it, it may be required. You know my rule of thumb, Ray: the more your oppies are different from each other, the more you need to do that, right? The more every deal is a $10K deal and you're just a machine grinding those out, the more that count is a proxy for value, then the less that's true conversely. Just say you have a bimodal business, you do a bunch of $250K deals, they have longer sales cycles, you do a bunch of $25K deals. Say you have like 2 modes, one at $250K and one at $25K, and that does happen. Then in my mind, you should actually split that. I think you could still use count, but I would just count, you know, corporate sales and enterprise sales. And I think once again, because I've got a pretty tight distribution around some mean, Then I can use count as a proxy for value. The reason— look, I'm always in favor of calculating one more thing, Ray. And, and it— but I always like to start with, if I could, if I could only calculate one, which would it be? And I would do these on a count basis. If I had effectively multiple businesses, then I would try and separate them and say, in our SMB business, our win rate is X%, and in our enterprise business, our win rate is Y%. Just like I might say, in our SMB business, the ASP is $25K. In enterprise, it's $250K. And in an SMB, our sales cycle is 37 days. And in enterprise, it's, you know, 165 days.

Okay. So we're 20 minutes into the episode and we've answered the question. Pipeline coverage is not the inverse of win rate. I think we're pretty clear on that, right? It's not.

You think so? I think so. I think we've proven it's not. So required pipeline coverage should be 1 over your week 3 pipeline conversion rate. And there are some provisos that Ray was talking— there's a lot of provisos, right? We're always trying to keep it simple here, which means the possibility for error, but it assumes long sales cycles. It assumes nothing sold before week 3, so I would probably just add that back in, or I could do the pipeline coverage on a to-go basis. Again, there's a bunch of Kellogg's about that, but the hope on week 3 is that it's enough time to clean up the mess in the pipeline but not enough time to actually sell anything. Um, and if you've sold a bunch, then it can jam this math up and you'll have to correct for it.

What this episode tells me is we should consider doing another episode on how to determine what is the amount of pipeline you need to make your next quarter and maybe even next quarter plus one. You think it'd be a good episode?

You could do it. We could certainly do it. We could talk about it. Maybe we could talk. I don't know if we've done one, Ray, on a chart I call the pipeline progression chart. But that's where I would start to answer that question. Because remember, everything we talked about is this quarter only. We've not talked in this episode about next quarter. By the way, I had 2 more corrections I needed to add to the prior thing just for anyone who's going to start doing this. It does assume long sales cycles, assumes nothing sold before week 3. It assumes that, that, that we have offsetting errors on if there are some opportunities like expansion sold and created and closed in the quarter, if there's pull-forwards, if there's expansion and shrinkage because deal values could constantly change, right? This is a macro metric. If you want detail, use time-based close rates. But, but I do think, Ray, a lot of sales ops people and finance people get wrapped around the axle on all the corrections. And at least what I like to do is help them see the kind of forest for the trees and get up, get up above this and say, why wouldn't everything else offset? Um, because otherwise I'm signing up for tons of hideous math.

Well, I'm very glad that we brought CAC, also the desert kangaroo mouse known as MUA deep. So thank you, Dave.

Thank you, Ray. We'll see you next time.

Okay, bye-bye now.

The Metrics Brothers: AI, SaaS, and More is a production of The Metrics Brothers Growth and CAC and a member of the Benchmarkit Podcast Network. By accessing this podcast, you acknowledge that The Metrics Brothers make no warranty, guarantee, or representation as to the accuracy or sufficiency of the information provided or the humor content of the jokes told. Ray, information, opinions, and recommendations presented are, according to our spouses, probably wrong and provided for information purposes only. This podcast should not be considered professional or, for that matter, unprofessional advice. We disclaim any and all liability for any direct, indirect, indirect, misdirect, incidental, special, ordinary, consequential, inconsequential, or other damages arising out of any use of, or God help you, reliance upon the information presented here. Ray Grothreich is based in New York City and available on X @rayreich. Dave Kellogg is based in Silicon Valley and available on X @davekellogg. Kellogg. Schedectity, which is French for unspellable, is not our actual production location. Thanks for listing.