---
title: "3 Sales Metrics You Need to Measure"
episode: 17
podcast: "The LeanScale Podcast"
publisher: "LeanScale"
guest: "Bernardo Alves"
guest_title: "Engagement Manager, LeanScale"
date_published: 2023-08-02
date_modified: 2026-07-22
duration: 00:07:43
word_count: 1410
topics: ["revenue-operations", "forecasting", "sales-leadership", "gtm-strategy"]
canonical_url: https://leanscale-knowledge-hub.netlify.app/podcast/bernardo-3-sales-metrics-to-measure/
source: "LeanScale Podcast Knowledge Hub — https://leanscale-knowledge-hub.netlify.app"
license: "Free to quote and cite with attribution to The LeanScale Podcast."
---

# 3 Sales Metrics You Need to Measure — Full Transcript

> Episode 17 of The LeanScale Podcast, with Bernardo Alves.
> Published August 2, 2023 · 00:07:43 · 1,410 words.
> Machine-transcribed and **not diarized** — speaker attribution is inferred, so verify
> attribution against the audio before quoting a specific person.
> Structured breakdown: https://leanscale-knowledge-hub.netlify.app/podcast/bernardo-3-sales-metrics-to-measure/

## 00:00 — Cold 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

## 00:45 — Metric 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?

## 01:18 — Deal 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.

## 01:50 — Why 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

## 03:05 — Metric 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

## 03:46 — Calculating 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

## 04:55 — Metric 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

## 05:28 — Hunting 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.

## 06:38 — Recap: 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.