The LeanScale Podcast · Episode 17

3 Sales Metrics You Need to Measure

Bernardo and Anthony Enrico on the three metrics that tell you if you'll hit your number — and how to calculate them without fooling yourself

Bernardo Alves · Engagement Manager, LeanScale · LeanScale Hosted by Anthony Enrico
Published Updated 00:07:43 7 min read 1,410 words
Revenue Operations Forecasting Sales Leadership GTM Strategy
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Executive Summary

The one-paragraph brief, extended

Why this conversation matters — and who should spend the hour.

In this short, tactical early episode of The LeanScale Podcast, host Anthony Enrico brings on LeanScale's Bernardo to answer a deceptively simple question: if you're running a sales team, which numbers actually tell you whether you're going to make it? Their answer is three metrics — weighted pipeline coverage, SQL-to-closed-won conversion, and win/loss reason analysis — and most of the value is in how you calculate each one so you don't fool yourself with raw, comfortable-looking data.

The first metric, weighted pipeline coverage, is a corrective to the most common self-deception in sales: opening the CRM, seeing a million dollars of pipeline against a $100,000 quota, and declaring victory — when 90% of it is unqualified stage-one deals. Bernardo's method is to weight each deal by a stage-based probability (ideally drawn from your own historical closed-won rates), then layer in a deal-health or 'subjective gut' adjustment that discounts finish-line risk like a compliance review you may not clear. Weighted honestly, that million-dollar pipeline might be $200,000 you can really count on.

The second metric, SQL-to-closed-won conversion, is the mid-period warning light and a planning baseline. Bernardo calculates it only on closed deals — never counting open ones — and segments it as far as the data allows, because enterprise and SMB (and different products, business units, and regions) convert at very different rates that a blended average hides. It only works if 'qualified' is defined tightly with reps: a qualified opportunity is one you genuinely could have won, so that a loss actually teaches you something. The third metric, win/loss reason analysis, is the feedback loop — and, as much as anything, a data-quality test. Bernardo hunts for close reasons that fail a common-sense check (a deal deep in legal review marked closed-lost as 'lost contact') and for trends that flag real problems, like losing most first calls on pricing. Anthony's closing summary ties the three together: coverage tells you if you'll hit the forecast, conversion warns you when something is going off the rails mid-quarter, and win/loss tells you how to get better next time.

Who should listen: RevOps leaders, sales managers, and revenue executives who want a clean, no-nonsense definition of the handful of metrics worth watching — plus founders early enough that they don't yet have the data to weight a pipeline and need to know how to start anyway.

Key Takeaways

10 things worth stealing

The load-bearing ideas, each with the business implication and who should care.

01

Raw pipeline lies — weight it before you trust your coverage

Opening the CRM and seeing $1M of pipeline against a $100k quota feels safe, but if 90% of it is unqualified stage-one deals you have far less real coverage than you think. Weighting each deal by stage-based probability turns that million into maybe $200k you can actually count on.

Why it matters: Never forecast off headline pipeline. Report weighted coverage to quota so leadership is reacting to countable pipeline, not a comforting illusion.

RevOps LeadersSales LeadersRevenue Executives
02

Weight by stage, then layer a deal-health discount

The base weight comes from stage — early-stage deals count for less than late-stage ones. On top of that, Bernardo adds a deal-health or 'subjective gut' component so you can tailor the forecast to the accuracy you actually want.

Why it matters: Treat weighting as two layers: a mechanical stage probability plus a human adjustment for the things the stage field can't see.

RevOps LeadersSales Leaders
03

Use your own historical closed-won rates as the weights

The best benchmark for how a deal will convert is your own past conversion rates by stage. If you don't have enough history yet, make informed estimates and iterate on them — the discipline still gives you predictability.

Why it matters: Build weights from your data, not generic percentages; early-stage companies should start rough and refine as closed deals accumulate.

RevOps LeadersFoundersSales Leaders
04

Discount finish-line risk even in late-stage deals

A deal in legal review looks like it's at the finish line, but if the customer has a robust compliance process you're not sure you'll clear, projecting it forward in full is a mistake. Known blockers can knock you out of contention at the last step.

Why it matters: Late stage is not the same as safe. Haircut deals with real, identified finish-line risk instead of counting them at full value.

Sales LeadersRevOps Leaders
05

Calculate conversion on closed deals only

Bernardo only runs SQL-to-closed-won math on deals that have actually closed, excluding anything still open. Mixing in open pipeline contaminates the rate and makes it useless as a benchmark.

Why it matters: Freeze the denominator to closed outcomes so your conversion rate reflects reality, not in-flight optimism.

RevOps LeadersSales Leaders
06

Segment conversion rates or the average will lie to you

Blended conversion hides the truth. Enterprise and SMB — and different products, business units, and regions — convert at very different rates. Isolating the business into relevant pockets (where you have enough data) gives real visibility.

Why it matters: Segment SQL-to-closed-won by firmographics and unit; act on the segment rates, not the company-wide blend.

RevOps LeadersSales LeadersRevenue Executives
07

Define 'qualified' tightly with your reps

The metric only means something if a qualified opportunity is one you genuinely had a chance to win. Work with reps on clear definitions and requirements so that when you lose a qualified deal, there's a real learning in it.

Why it matters: Loose qualification poisons every downstream metric. Enforce a shared bar for what counts as qualified pipeline.

Sales LeadersRevOps Leaders
08

Track win reasons, not just loss reasons

There are usually more learnings on the loss side, but understanding why customers choose you over a competitor is genuinely valuable too. Analyzing both sides of the coin gives a fuller picture than a loss autopsy alone.

Why it matters: Instrument win reasons alongside loss reasons so you can double down on what's actually working, not just patch what's broken.

Sales LeadersRevOps Leaders
09

Win/loss analysis is really a data-quality test

Bernardo hunts for close reasons that fail the common-sense check — a deal deep in the pipeline marked closed-lost as 'we lost contact, they were unresponsive.' There's no reason a late-stage deal should have no better explanation than that.

Why it matters: Treat implausible close reasons as anomalies to fix, not data to trust. Clean reasons are the precondition for any real trend analysis.

RevOps LeadersSales Leaders
10

The three metrics work as a system, not a list

Weighted pipeline coverage tells you whether you'll hit the forecast and forms the basis for planning; SQL-to-closed-won conversion is the mid-quarter warning light that something is going off the rails; win/loss analysis is the feedback loop that makes you better next time.

Why it matters: Watch all three together — one predicts, one warns, one improves. Any single metric in isolation leaves you blind to part of the picture.

FoundersSales LeadersRevenue Executives
Frameworks Discussed

3 named models

Every framework Jimmy names, defined and time-stamped.

Weighted Pipeline Coverage

00:45

Coverage of pipeline to quota where each deal is discounted by a stage-based probability weight (ideally drawn from your own historical closed-won rates) plus a deal-health or subjective adjustment for finish-line risk.

It corrects the classic trap of seeing $1M of raw pipeline against a $100k quota and assuming you're safe when 90% sits in stage one. Weighted honestly, that pipeline might be ~$200k of countable coverage — and it doubles as the basis for future planning.

SQL-to-Closed-Won Conversion Rate

03:05

The rate at which sales-qualified opportunities become closed-won, calculated only on closed deals (never open ones) and segmented by product, business unit, region, and firmographic segment.

It's the mid-period early-warning light and a planning baseline. It only works if 'qualified' is defined tightly with reps, and segmenting matters because enterprise and SMB convert at very different rates a blended average hides.

Win/Loss Reason Analysis

04:55

Systematic review of why deals are won and lost, using close reasons that are relevant and actionable, then hunting for overall trends and anomalies that fail a common-sense check.

Beyond capturing why customers pick you or a competitor, it functions as a data-quality test — flagging nonsense like a deep-stage deal closed-lost as 'lost contact' — and surfaces problems such as losing most first calls on pricing.

Best Quotes

12 lines worth clipping

Pulled verbatim. Copy or share any of them.

“I typically only do the calculation looking at things that are closed, so I don't take anything that's open into account, and I would segment it as much as I humanly can.”
Bernardo Alves 00:00
“Early stage deals are going to have less of an impact on overall pipeline than things that are further along. Then what you want to layer in is a deal health, or a subjective gut component, to tailor it to the forecast accuracy you're looking for.”
Bernardo Alves 00:45
“You're right at the finish line, but the company you're working with has a really robust process that might require compliance you're not sure you'll clear. You're not going to want to project that forward in full, because there are possible blockers that could get you out of contention right at the finish line.”
Bernardo Alves 01:18
“The weights are your best historical benchmark. One of the things we recommend is using your previous closed-won rates — looking at your conversion rates and using those to model what you're seeing in future performance.”
Bernardo Alves 01:50
“There's so many times where you look at a $100,000 quota, you open up your pipeline in Salesforce and see, 'Oh, we have a million dollars this quarter. No problem, we're going to make it.' Then you realize 90% of that is in stage one — it hasn't really been properly qualified yet.”
Anthony Enrico 02:30
“It's paramount that you're not just looking at any opportunity that's created. Work with your reps to have clear definitions over what you consider a qualified opportunity — and if you don't win, there's a learning opportunity that stems from it.”
Bernardo Alves 03:05
“Especially in firmographic segmentation, you'll find you have very different SQL-to-closed-won conversion rates between your enterprise segment and an SMB segment.”
Bernardo Alves 04:24
“You're going to have more learnings on the loss side than on the win side. But understanding why customers are signing with you and not somebody else — there's a lot of value in that too.”
Bernardo Alves 04:55
“One of the things that drives me insane is when we're in a deep stage in the pipeline and things are closed lost as 'we lost contact, they're unresponsive.' There should be no reason you're deep in a sales cycle and don't have a better reason why it didn't close.”
Bernardo Alves 05:28
“We break things down into closed-lost and win reasons that are relevant to your business and actionable, then look for overall trends and things that don't quite pass the common-sense check.”
Bernardo Alves 06:05
“Take a look at your weighted pipeline coverage. That's going to let you know if you're going to hit your forecast or not, and it becomes the basis for future planning.”
Anthony Enrico 06:38
“Go analyze why you won and why you lost, and use that data to get better in the future.”
Anthony Enrico 07:12
Practical Advice

What should you actually do?

The playbook, split by the seat you sit in.

RevOps Leaders

  • Build weighted pipeline coverage from your own historical closed-won rates by stage, then layer a deal-health / subjective adjustment for finish-line risk.
  • Calculate SQL-to-closed-won only on closed deals and segment it — by product, business unit, region, and enterprise vs. SMB — so blended averages don't hide the truth.
  • Audit closed-lost reasons for anomalies (a deep-stage deal marked 'lost contact') before you trust any win/loss trend.

Sales Leaders

  • Work with reps to define 'qualified' tightly — a qualified opportunity is one you genuinely could have won — so conversion data actually means something.
  • Track win reasons, not just loss reasons; understanding why you win is as instructive as why you lose.
  • Use the three metrics as a system: coverage to know if you'll hit the number, conversion as the mid-period warning light, win/loss as the feedback loop.

Revenue Executives

  • Don't take comfort from a big raw pipeline number; ask for weighted coverage to quota before you forecast.
  • Treat a spike in first-call pricing losses as a market signal to act on, not noise.

Founders

  • Early on you won't have enough closed data for reliable weights — make informed estimates and iterate on them rather than skipping the discipline entirely.
Operations Takeaways

By function

The same conversation, filtered for RevOps, pipeline/marketing ops, and customer ops.

Revenue Operations

  • Weight before you trust. Raw CRM pipeline lies. Apply stage-based weights (from your own closed-won rates) plus a deal-health discount before you believe your coverage to quota.
  • Calculate conversion on closed only. Exclude open deals from SQL-to-closed-won math, and segment by product, business unit, region, and firmographics — enterprise and SMB convert very differently.
  • Define 'qualified' with reps. A qualified opp must be one you had a real chance to win; if not, the loss should teach you something. Loose definitions poison every downstream metric.
  • Win/loss is a data-quality test. A deep-stage deal closing lost as 'lost contact' is an anomaly, not a reason. Hunt for close reasons that fail the common-sense check before trusting any trend.

Pipeline & Marketing Ops

  • Coverage, not headline pipeline. Measure weighted coverage against quota; a $1M pipeline that's 90% stage-one is really about $200k of countable coverage.
  • Discount finish-line risk. Late-stage deals with known compliance or legal blockers shouldn't be projected forward in full, even at legal review.
  • Conversion is the early-warning light. SQL-to-closed-won conversion tells you mid-quarter whether something is going off the rails, and gives you a baseline for planning.
Metrics Mentioned

The numbers, with context

$100,000
Example quarterly quota

The coverage example: how much pipeline you need to have a real shot at hitting a $100k quarter.

$1M raw → ~$200K weighted
Raw vs. weighted pipeline

A pipeline that looks like a million dollars can be 90% stuck in stage one; weighting it honestly leaves roughly $200k you can actually count on.

90% in stage one
Unqualified share of 'safe' pipeline

The trap Anthony flags — most of that comfortable-looking coverage is unqualified early-stage pipeline.

~70%
First-call pricing losses

If you're losing about 70% of first calls on pricing, that's a clear market indicator you have something to fix.

Entities

Companies, people & tools mentioned

Auto-extracted and linked into the knowledge graph.

Companies

LeanScaleGTM Operations

The fractional RevOps firm behind the podcast; Bernardo is the LeanScale operator walking Anthony through the three sales metrics.

00:00Company →

People

Bernardo Alvesguest

Engagement manager at LeanScale; came up through customer success into operations and business intelligence, focused on attribution and usage-based tracking.

Engagement Manager, LeanScaleProfile →
Anthony Enricohost

Co-founder of LeanScale and host of The LeanScale Podcast.

Co-FounderProfile →

Tools & software

SalesforceCRM

Cited as where teams open their pipeline and get fooled by a big raw number — a $1M quarter that's really 90% unqualified stage-one pipeline.

Website ↗
Related LeanScale Services

Where this shows up in the work

Forecasting & ARR Definition

Define a raise-worthy ARR and build a reliable consumption forecast across finance, data science, and go-to-market.

Weighted pipeline coverage and conversion-rate benchmarks are the raw material of the reliable forecast LeanScale builds for customers.

Fractional RevOps

Embedded, field-facing RevOps leadership and execution — the operator model Jimmy describes, delivered as a service.

Defining qualified pipeline, weighting coverage, and instrumenting win/loss is exactly the field-facing RevOps work LeanScale embeds.

Frequently Asked Questions

Straight answers

Generated from the conversation, marked up for search and AI extraction.

What are the three sales metrics every team should measure?

Weighted pipeline coverage, SQL-to-closed-won conversion rate, and win/loss reason analysis. Coverage tells you whether you'll hit the forecast and forms the basis for planning; conversion is the mid-quarter warning light that something is going off the rails; win/loss analysis is the feedback loop you use to get better next time.

What is weighted pipeline coverage?

It's your pipeline-to-quota coverage after discounting each deal by a stage-based probability plus a deal-health adjustment, rather than counting raw pipeline at full value. It matters because a pipeline that looks like $1M against a $100k quota can be 90% unqualified stage-one deals — weighted honestly, it might be only about $200k you can actually count on.

How do you weight a pipeline?

Start with a stage-based probability weight — early-stage deals count for less than late-stage ones — ideally derived from your own historical closed-won conversion rates by stage. Then layer in a deal-health or subjective 'gut' component to account for risks the stage field can't see, like a late-stage deal facing a compliance review you may not clear. If you lack enough historical data, make informed estimates and iterate.

Why should you calculate conversion rates only on closed deals?

Because including open, still-in-flight deals contaminates the rate and makes it unreliable as a benchmark. Freezing the calculation to closed outcomes means your SQL-to-closed-won conversion reflects what actually happened, not optimism about deals that haven't resolved yet.

Why should you segment SQL-to-closed-won conversion rates?

Because a blended, company-wide average hides big differences. Enterprise and SMB segments — and different products, business units, and regions — convert at very different rates. Isolating the business into relevant pockets (where you have enough data) gives you real visibility and lets you act on the segment rate instead of a misleading blend.

What should you look for in win/loss analysis?

Track both win and loss reasons, using close reasons that are relevant and actionable for your business, then look for overall trends and anomalies that fail a common-sense check. A deal deep in the pipeline closed-lost as 'we lost contact' is a red flag that the data is wrong, and losing most first calls on pricing is a market indicator you have something to fix.

Why can a large pipeline still mean you'll miss quota?

Because raw pipeline volume says nothing about quality. If most of that pipeline is early-stage and unqualified, very little of it will convert. Weighting coverage by stage probability and deal health exposes how much you can actually count on — often a fraction of the headline number — so you react to real coverage instead of a comforting illusion.

Timeline

Jump through the conversation

Click any chapter to open it in the transcript.

00:00Cold open + intro: the three sales metrics 00:45Metric 1: weighted pipeline coverage 01:18Deal health: discounting late-stage finish-line risk 01:50Why weight? Coverage to quota and historical rates 03:05Metric 2: SQL-to-closed-won conversion 03:46Calculating conversion: closed-only and segmented 04:55Metric 3: win/loss reason analysis 05:28Hunting anomalies in close reasons 06:38Recap: the three metrics that matter
Full Transcript

The whole conversation

Broken into chapters, searchable, verbatim from the audio. Speakers inferred (not diarized).

Transcript as markdown ↓ Whole episode as markdown ↓
00:00Cold open + intro: the three sales metrics

0:00 I typically only do the calculation looking at things that are closed, so I don't take anything that's open into account, and I would segment it as much as I humanly can. Welcome to The LeanScale Podcast where we talk about everything RevOps. Thank you for listening. Today we're going to be talking about the three metrics that matter for sales teams. I got Bernardo here to walk me through it. Bernardo, what's the first metric people need to be looking at for sales? The first metric you should be looking at is weighted pipeline coverage. Weighted pipeline coverage. There's two components to that there. Walk

00:45Metric 1: weighted pipeline coverage

0:45 me through the weighted aspect. How do you weight your pipeline and what do you recommend? The starting basics is you're going to assign a percentage weight of that pipeline that will be forecasted into the future. Early stage deals are going to have less of an impact on overall pipeline than things that are further along. Then what you're going to want to layer in is a deal health or a subjective gut component in order to help tailor that to be exactly what you're looking for in terms of forecast accuracy. What would be some examples of deal health and how would you use it to discount?

01:18Deal health: discounting late-stage finish-line risk

1:18 One of the things that you can look at is, for example, let's say that you have a deal that's in a late stage. Let's say legal review, for example, right? You're right at the finish line, but you know that the company that you're working with has a really robust process that might require some kind of compliance that you're not entirely sure you're going to clear or something like that. You're not going to want to project that forward in full because you know that there are possible blockers in there that could get you out of the contention right at the finish line, so being able to have an eye on that is really important.

01:50Why weight? Coverage to quota and historical rates

1:50 Makes a lot of sense. Why wait in general? I'll open up with, usually you want to look at the coverage that you have to a quota. Let's say you have a $100,000 quota for a quarter and you're looking at what pipeline you have to cover it to maybe achieve that. Why look at the waits? The waits are going to be your best historical benchmark that you can use in order to move things along, so one of the things that we recommend with waits is using your previous close one rates if you have those in the past, so looking at your conversion rates and using those to model what you're seeing in terms of future performance. If you don't have those

2:30 set up or you just don't have enough data or you're early in your company's lifecycle in terms of getting deals across the finish line, being able to make informed decisions and iterating on those is going to be really important to maintain that accuracy but it just gives you predictability. I also think it helps you avoid, there's so many times where you look at a $100,000 quota, you go open up your pipeline in Salesforce and you see, "Oh, we have a million dollars this quarter in pipeline. No problem. We're going to make it." Then you realize, "Oh, 90% of that is in stage one. It hasn't really been properly qualified quite yet," so this

03:05Metric 2: SQL-to-closed-won conversion

3:05 is where when you look at the weighting of your pipeline, maybe you have $200,000 of actual weighted that you could probably count on. Exactly. What's the next metric we need to be looking at? Yeah, the next one that's really important is SQL to close one conversion rates. Tell me why. Yeah, I think the big thing here is just understanding what's working and what isn't, so one of the critical aspects of this is understanding what we consider qualified pipeline, right? It's paramount here that you're not just looking at any opportunity that's created, so working with your reps to have clear definitions and requirements over what are we considering

03:46Calculating conversion: closed-only and segmented

3:46 at a qualified opportunity and making sure that that reflects something that you guys have a chance to win and if you don't win, there's a learning opportunity that stems from it. One thing I find when people are measuring conversion rates, especially when you're looking at sales-qualified lead to close one, you may have different sales cycles. Any recommendations you have, how do you actually do the calculation and what do you need to consider when you do that? Yeah, I typically only do the calculation looking at things that are closed, so I don't take anything that's open into account and I would segment it as much as I humanly can,

4:24 right? I think it's important to have your sense of what the overall business is doing, but if you have different products, different business units, different regions that you're servicing, isolating that into relevant pockets provided that you have enough data to make informed business decisions on those is going to give you a lot of flexibility and visibility into how the business is performing. I would say especially in the firmographic segmentation, you'll find you'll have very different SQL to close one conversion rates between your enterprise segment and an SMB

04:55Metric 3: win/loss reason analysis

4:55 segment. Taking that into account is going to be really important to make sure you have accurate data. Of course. All right, what's the last thing if you're managing a sales team, what are the metrics you need to be looking at? I think the most important thing here to call out is kind of related to the one that we just talked about, but it's going one step deeper in terms of your close rates and understanding your win-loss reasons in there. I think it's important to look at both sides of the coin. I think you're going to have more learnings on the loss side than on the win side, but understanding why customers

05:28Hunting anomalies in close reasons

5:28 are signing with you and not somebody else, there's a lot of value in that too. How would you look at analysis like that? Are there certain reasons you're looking for? What are the typical reasons? One, are companies even tracking those reasons sometimes? Yes, but not well, right? I think the big thing here is breaking things down into close loss reasons and win-loss and win reasons that are relevant to your business and actionable. One of the things that we look for in these kinds of analyses are overall trends and then things that don't quite pass the common sense check, right?

6:05 One of the things that drives me insane, for example, is if we are in a deep stage in the pipeline and things are being closed lost as we lost contact and they're unresponsive. There should be no reason you're deep in a sales cycle and you don't have any better reason as to why things didn't close. Looking for those kinds of anomalies and having a clear understanding of when things are becoming problems, right? For example, if in your first call you're closing 70% of things on pricing, that's a pretty clear market indicator that you might have something to work on.

06:38Recap: the three metrics that matter

6:38 It makes sense. Yeah, I like when something's in the legal review stage and then it's, "Oh, we lost contact." How did you get to legal review stage? Well, Bernardo, these are great I think just to sum it up for everybody who's listening. If you're running a sales team, three metrics that matter the most, take a look at your way to pipeline coverage. That's going to let you know if you're going to hit your forecast or not and also becomes the basis for future planning. Take a look at those SQL to closed one conversion rates. This is going to let you know if anything is going off the rails in the middle of a

7:12 quarter, in the middle of a year and will also be a good baseline for planning as well. And then go analyze why you won and why you lost and use that data to get better in the future. Bernardo, thank you. Thank you, Anthony. Thank you for listening to this episode. If you liked the discussion, please like, share, and subscribe to wherever you listen to podcasts so you never miss a new episode.