---
title: "Making Your B and C Players Sell Like A-Players"
episode: 44
podcast: "The LeanScale Podcast"
publisher: "LeanScale"
guest: "Adam Roberts"
guest_title: "Commercial Leader, Ebsta"
date_published: 2025-10-29
date_modified: 2026-07-22
duration: 00:39:52
word_count: 7107
topics: ["revenue-operations", "forecasting", "ai-in-gtm", "sales-enablement", "sales-leadership"]
canonical_url: https://leanscale-knowledge-hub.netlify.app/podcast/adam-roberts-ebsta-platform-deep-dive/
source: "LeanScale Podcast Knowledge Hub — https://leanscale-knowledge-hub.netlify.app"
license: "Free to quote and cite with attribution to The LeanScale Podcast."
---

# Making Your B and C Players Sell Like A-Players — Full Transcript

> Episode 44 of The LeanScale Podcast, with Adam Roberts.
> Published October 29, 2025 · 00:39:52 · 7,107 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/adam-roberts-ebsta-platform-deep-dive/

## 00:00 — Intro: Ebsta, a revenue intelligence platform

**[0:00]** I'm really, really pumped to go into the Ebsta platform today. Ebsta is my favorite revenue intelligence platform. We've been working with them for a few years and we have Adam Roberts here today to go through it. A few weeks ago we had their founder CEO Guy on talking about the state of B2B sales and all of the opportunities for improvement and a lot of that data is coming from the platform itself and today we have an opportunity to dive into the product and see exactly how Ebsta is helping sellers become the best versions of themselves. Adam, thanks

**[0:39]** for being here. Really excited to go into the Ebsta platform and appreciate you taking out some time to do this. Yeah, thank you Anthony. It's great to be here. I'm very excited. Guy, guys told me how much fun he had on the show last time he was here. So yeah, really looking forward to diving into it. Shall I give a bit of an introduction for the audience for myself? Please, please do. Yeah, so I'm Adam. I'm the commercial leader here at Ebsta. I'm reporting to Guy. I've been in and around the Saas ecosystem for the last decade in various contributor manager leadership positions. I've always been in a position where I've helped

## 01:20 — Adam Roberts: a data geek at heart

**[1:20]** businesses do more with their data. I'm a bit of a data geek at heart and really kind of hope that hopefully we can kind of share Ebsta in a really positive light today. So if you're happy, Anthony, I'll dive straight in. Yeah, let's do it. And there's a lot of powerful data that comes out of Ebsta. So I'm sure you have a lot of fun with it as well as leading the sales team with Ebsta. It's very meta to be able to do that. But yeah, excited to see what we have today. Yeah, absolutely. Absolutely. Look, you said yourself there's a ton of data, and I think that's probably a really

## 01:55 — The state of B2B sales: 80% of reps miss quota

**[1:55]** good starting place. If I can just bring up some slides here for a second, hopefully it won't bore everybody by PowerPoint. That is my least favorite thing to do. What Guy would have talked about on the last show was the state of the market and the problems in the market. And we see that 80% of reps, nearly a missing quota. There's a huge delta in velocity between your top and your bottom performers. And even though the number of deals that are slipping across the market has dropped, we're still at like 36%, nearly 40% of deals, all deals slipping in the pipeline. And we see that

**[2:29]** there's this huge inconsistency. That delta in velocity is an inconsistency of seller performance. You know, our top 14% of sellers, our A players are delivering 80% of the revenue. And that isn't sustainable. And while there are tons of tools out there in the market that provide lots of features and functionality, there are businesses that are spending money on lots of tech and lots of platforms, and yet this is still the case. There is still a huge disparity between top and bottom performers. And the way that Edster thinks about this is really more about, we see huge gaps. We see huge

## 03:08 — Closing the visibility gap inside Salesforce

**[3:08]** gaps in the visibility and insight that sellers, managers and leaders have into their pipeline. They're able to understand signals, but they really don't have a real grasp and a handle on all of the data points that are influencing revenue. And what we strive to do here at Edster is bring all of that together into one place. We connect to multiple different data sources, and we firmly believe that Salesforce is and should be the single source of truth. And so what we've created in its rawest form is visibility and transparency in a single place inside Salesforce that is designed to transform the way that people view the risk and the health

## 03:52 — Lifting B and C players toward A-player performance

**[3:52]** of their pipeline. It's really, really common for people to just notice the top A players, the top performers, the top closers, and really focus on just maybe making them better. But if you can get the 85% of your BEC players to just incrementally improve for an organization that has a decent sized sales team, that's massive improvement to overall performance. So I think it's a really strong mission to be on. And if we can just emulate a little bit of what those top performers are doing with the bottom performers, the results could be massive. Yeah, you're

**[4:29]** absolutely right, Anthony. And look, it's not like our BNC sellers are lazy or they don't want to win. We're all in sales to win and to close business and hit our numbers. It's a performance sport. And sometimes we just need the proper guidance and the proper understanding. And when that information is siloed or I have to log into 10 different systems to really get an understanding of what's going on every opportunity, it's a barrier. It's a barrier for people using the insight and using the information. So what we've done and what I'm about to show you is hopefully you can see my screen, is an opportunity record inside Salesforce.

## 05:07 — The enriched opportunity record

**[5:07]** And what episode has done in the background is it's connected to the mail server, it's connected to the conversations that we're having, and it's connected to CRM. It's looked back in time over the history of all the close won and close lost opportunities. And it's brought a ton of rich insight onto the opportunity record without anybody having to do anything that gives you real life visibility into risk, into relationship, into all the signals that are going on with this opportunity that are going to provide you insight into the health and the likelihood of

## 05:42 — Capturing the 70% of communications missing from CRM

**[5:42]** this deal to close. So what I'll do is I'll start by scrolling down and I'll start at the bottom and I'll work my way up. So I mentioned earlier that we connect to the mail server. The mail server is the single source of truth for all the communication and it's a pain in the proverbial to get reps to log in to log every activity and every contact. And we typically see that when we work with clients about 70% of that information is missing from CRM. So when it comes to understanding things like how multi-threaded you need to be, which is the right stakeholders to be involved in an opportunity,

**[6:17]** 70% of that information is missing, then businesses are blind. They're making decisions without the right information. And so we fix all that and we can fix all that historically within the first week of engaging. And what that looks like is we bring this onto the opportunity record, account record, lead and contact record inside Salesforce and you get this visual representation of all the communication that's taking place. You can see the contact, you can see the direction of communication. If you want to read the email, you can see what's going on. If you want to add that

**[6:48]** to Salesforce as an object, you can. Many of our clients used to use us to store all this information, but you can get a quick look as a manager, leader or seller as to all the communication that's taking place. You understand where your strongest relationships are. So of all the contacts that I'm talking with, I want to know who's the main contact there at a glance. I also want to make sure that my database, my marketing database is full. So if I want to add those contacts into Salesforce, I can. And I guess one of the key things that you'll see there, Anthony, is this

## 07:21 — Relationship score as a leading indicator

**[7:21]** idea of a relationship score. This is something that we're really proud of. And then one of the differentiators from us is that we were able to provide you that relationship score and that relationship trend over time. And it's one of the leading indicators of the health of an account, an opportunity and a relationship that really gives you the clearest indication of the likelihood of this deal to close. And not only that, because we look back historically over all of your close won, close lost opportunities, we're able to start setting things like benchmarks. And that brings me

## 08:02 — Benchmarking against won and lost deals

**[8:02]** really neatly on to the next set of information or suite of information that you'll probably see in front of you on the screen. Before you move on from that point, I want to highlight for those watching how important and foundational that is that not only are you saying, hey, we'll begin to capture emails and meetings that are coming in right now so you can start to begin to do a relationship score, but going back in time and going back in history and gathering all of that intelligence a lot. I know when you start an implementation, I don't know exactly what the

**[8:36]** percentage is, but it's a high percentage of past contacts, past meetings, past activities that have happened that never made its way to the CRM, and you get to pull that in. It's absolutely game changer when you have that data finally enriched and finally in one place where you can benchmark it and see the relationship score too. Yeah, 100%. It is transformational. And I speak with lots of RevOps leaders about this, and they are trying to do these kinds of benchmarks. They're trying to understand who are the key stakeholders that we need at each stage and how long has this been in

**[9:14]** the pipeline for and what does good look like for our organization. They simply can't do it because they don't have enough data in the system, and that's one thing that we can fix immediately and hopefully provide RevOps leaders the opportunity to get back and do more critical work than trying to pull together all this information. Yeah. So yeah, really, really good point. Really good point. And look, this is just an example, right? But what we can see here is that, again, we're connected to the mail server. We're looking into the calendar. We can see that there's no

## 09:44 — Time-in-stage, deal age, and slippage

**[9:44]** future calls, meetings, or taskbooks. We've benchmarked this opportunity against historical business, and we can see that, look, it's been in the current stage for 24 days. And when we win business, the upper benchmark for us winning business is typically only 10 days. And we can also see that the opportunity is 44 days old, but typically the opportunities when we win, they're typically only 20 days old. Now, that doesn't mean that this is a disaster of an opportunity, but it does mean that as a seller, I need a narrative as to why this is taking longer

**[10:17]** to close than usual when it comes to my pipeline review. And I probably should have a good answer, because I know that my manager is going to see this. And probably one of the first points that he's going to ask me about is, well, why is this taking so long? And we know that slippage and when slippage deals slip beyond three to six months, win rates at like 3%, you can check out the benchmark report, go to the episode website and download it. If you want to find out exactly what that number was, but a huge impact on win rate by deal slipping. And so by having this visibility, we're surfacing all that risk in the pipeline and allowing sellers

**[10:58]** to make better decisions, make their next steps, have more constructive conversations with their managers. We can also see there's been no activities. And actually, the benchmark for Patrick here, his benchmark for closing deals is at 76, his relationship score is only 50. So he's got some things to work on. And really kind of benchmarking, understanding all of these factors that are influencing the deal is really, really important. And when you do this and you make it easy for reps to see what needs to be done and what's going on in their pipeline, it makes it much easier for them to take the necessary actions to self-serve, to fix these

## 11:36 — Qualification: MEDDIC and capturing data by stage

**[11:36]** problems really, really quickly. The next thing that I'm going to touch on, other kind of bits of visibility, you can get more context over the activity that's been going on over the duration of the opportunity, if you want to visualize that activity in a different way. And another thing that we're really hot on here at Ebster is qualification. We med pick for us as our gold standard, but this is just a method of capturing information. And so we have businesses that use spin, that use band, that use med pick, even have their own custom qualification capture fields

## 12:20 — Auto-capturing qualification from call intelligence

**[12:20]** baked into Ebster as well. I think a lot of the work we do at LeanScale too is helping people set up a qualification method, making sure capturing the data stage by stage. Is this where people will capture notes for this so the rep can easily enter in the information here? Yeah, exactly. So look, we believe in scoring, we believe in scoring med pick, right? And if you score it, you can measure it and therefore you can understand patterns and you can spot deficiencies across a large, broad group of sellers. And you can manually add data and tweak this as you want. One of the really cool things that Ebster provides with its core intelligence tool

**[13:06]** is the ability to auto capture qualification from the conversations that we're having without actually having to do much work at all. So let me just share a different tab briefly. So again, we can do this with gone recordings, we can do this with your Zoom calls, with Teams calls, bring everything inside Salesforce so you have that kind of single source of truth of all of your data and all of your insights. And we bring these kind of key moments onto the opportunity record. But to touch on that qualification, I mean, look, you get all of the things that you'd

**[13:43]** expect in terms of a call summary, in terms of the transcript, in terms of we can identify key topics. But the main use case here is really identifying and capturing that qualification status. So what the what the Ebster bots done here is it's analyzed the call. And it's recommending that look, metrics, we believe, you know, the AI believes that the metrics on this opportunity should be a three. And the rep can have a look at that, he can understand why. And it's also recommended some notes as to what's driving that score of a three. And the rep can update,

**[14:22]** or he can ignore it. You know, it's important to give the rep license, you know, we, the rep has to build this overall qualification over multiple calls, multiple emails. And so, you know, we do want to give we don't want to remove the the kind of the license from the rep, but we're just trying to make it as easy as possible for the rep to capture as much information as is humanly possible. And I think like one of one of the big advantages for businesses, particularly those mid market businesses that have got you know, anywhere from 20 to 150 sellers, right, like your individual

## 14:56 — Removing rep bias at scale

**[14:56]** managers, your leaders have got to interpret, you know, different, you know, biases and different, you know, different ways of doing things and capturing information from 150 different people, you know, some people lean negative, some people lean positive. What the AI does is when you're using this at scale is kind of remove all that, remove all the bias, remove all the, you know, in subtle interpretations and give you just blanket consistency of capturing information and of the quality of information you're getting. And it makes it it means it's really, really

**[15:30]** powerful for kind of then diving into, you know, which reps are under qualifying, which reps are not proficient, you know, maybe implication of pain, you know, all of those kind of things that we bring that make that really easy for people to see. Because we're capturing that data consistently at scale. No, that's incredible. So I just want to I just want to clarify. So any call recorder they're using, you have a native one in EPSTA, if they want to use that one, but if they're using Gong or something else, you'll capture the transcripts, and then you'll automatically populate qualification methods from the transcripts of the call. That's yeah,

**[16:09]** absolutely huge time saving. And like you mentioned, just getting it accurate and unbiased. Because who knows what reps are typing into these fields sometimes, or like, you know, I don't want to say anybody's doing anything with malintent, maybe they're just uninformed. But like, are they actually gathering the right qualification methods? Or the right qualification information? And are they putting in the right data? Or are they just thinking that they're qualifying and moving something along, when maybe they didn't actually have that conversation that needed to be had?

## 16:39 — Call intelligence: key moments and playback

**[16:39]** Exactly. That's exactly it. You've hit the nail on the head. Let me jump back to the opportunity record here. And I'll wrap this bit up. And then we'll get into some of the more sexy stuff. The final piece of the puzzle we've just touched on call intelligence. Yes, we we're looking for insight from the conversations that we're having, in order to be able to inform our insight into the health of the opportunity. And so, you know, every company, every company, every customer of Ebstas is different. And so these key moments that we pick out of the conversations, we work with our clients to define those prior to deployment. And they might be

**[17:16]** different from client to client, they also might be different from function to function. So, you know, those key moments, if you're, you're in CS, and you're working on a renewals basis, be very different to, you know, hunter gatherer in in net new. And so we can break it down right to that kind of really granular level. But what we're displaying here is the is the is the insight that we've picked out that matters most to the to the to the to the client on the opportunity. And so you can see here on the positive side, we've got a sense of urgency and readiness for

**[17:47]** implementation. We've talked about timeline and the fact that they want dealing place by the end of the quarter. And the same on the negative side, we can see that a couple of competitors have been mentioned. And so, again, we really try to bring this insight out to inform decision and inform leadership of, you know, where the risk is, what's the likely thing. And the beauty of this is that if I want to go as a manager and a leader and have a have a quick look, I can hit playback and write on the opportunity record inside Salesforce. I can view that, you know, a minute and a half,

**[18:25]** two minute clip on the key piece of information that I that I want to see. So that's really cool. That's so smooth, such a smooth process and giving information that you need right up front, organized really well. It's really, really intentional. Yeah, yeah. Now, honestly, like, you know, from a leader, the last, you know, I've used call intelligence tools before, you know, I think they're great. I one of the things that, you know, I just found I couldn't manage was like trying to scroll through, you know, managing six, seven, ten reps, scrolling through hundreds

**[19:00]** of gone calls or, you know, other calls to to really kind of find out what are the key bits, where's the bits that I'm going to have the most impact or make the most difference to me? Or how do I how do I identify what those what those where those where those where that gold is? And this really does that for me. And it means I don't have to kind of leave Salesforce, which is which is brilliant. So I guess that that that that wraps up the the opportunity stuff, right? We've we've we've gone through a lot of information, we've we've sucked data from the mail server, we've

## 19:32 — Managing the pipeline at scale

**[19:32]** sucked data from the conversations, we've pulled historical opportunity data from Salesforce to provide you all this insight. And this is on an opportunity record. But how do how do people manage this at scale? And that's, that's what I want to dive into next. So what, hopefully, you can see my change of screen there, Anthony. But what what we're looking at here is a pipeline insight tool. And what we've built is a visual representation of every opportunity that exists in the business. And you can slice and dice this in information in any way you want. You can look at deals that are

**[20:08]** you know, that are current for this quarter, you can look at the height, you can log in by various different hierarchies or individual users, by product types, revenue types, you can use extra smart insights function, which will tell you all the deals that are stalling or they have warning signs. But it's a really easy way to manage a lot of opportunities at scale. We logged in as Wayne who's an individual contributor. And Wayne can manage his pipeline a lot, a lot more easily, right? He gets an initial glance of all his opportunities. And he can see, look,

**[20:39]** he's got a ton of opportunities where he's really high engagement, this relationship score kind of acts as that first gut check. And you can see how that relationship scores trending as well. On the flip side of that, if he wants to interrogate the lower end of his pipeline, he can see it's got a bunch of opportunities in here where there's no engagement or very little engagement. And these deals are realistically clogging up Wayne's pipeline, then they don't appear to be real. And he should probably just close, lose these deals and move on and focus where he's got high engagement and he's got a real chance of winning these deals.

## 21:19 — The deal score and warning signs

**[21:19]** That's kind of the first thing. The second thing you'll notice is this idea of a deal score. The deal score is, you know, a hundred deal score would be a close one opportunity, a zero deal score would be a close lost opportunity. And the deal score should increase as we move through the stages of the pipeline. But the deal score is impacted by all of those positive and negative factors. So all of those signals that we have identified by looking at all the data sources, by mapping those to how, you know, what the benchmarking of what good looks like when you

**[21:48]** win over the last 12 months worth of closed opportunities. All of those signals, all of those positive and negative factors are going to influence the deal score. So you can have a look, I've got a good relationship score, I've got a good deal score. However, I've got some warning signs and you can click on those and it takes you into the sidebar tab that pops up. And you can see, look, I can see all my negative factors on these deals. I can see the risk factors. I can see what I'm doing well, which is great. My relationship scores increased. I've had activities in the last

**[22:17]** seven days. However, I'm not multi-threaded. I'm not as multi-threaded as I should be. I need to get more colleagues involved. I've got closed days in the past. Unforgivable, if you ask me. But, you know, there's something that before a pipeline review, I'd probably going to want to address that immediately. And look, we've got no future calls, meetings or tasks, but again, that's a good next step for me as a rep on this opportunity. Go, look, get that next step in place. Get that, I mean, what they really should be doing is booking a meeting from a meeting. I spoke a lot about

**[22:48]** BAMFAM in my time. But look, we haven't got a meeting in. Let's go get our next steps in place. So again, as a seller, it's guiding me what to do. As a manager, it's guiding me, what questions do I need to ask? What do I need to be looking for? You know, here's all the risk. You can see your qualification. You can see the contacts that you've got on the opportunity. And again, you can see that activity timeline, all from the insights tab. Click on any of these opportunities and you get that visibility. Yeah, this is great just to be able to see, especially the intelligence of looking at what's being qualified, be able to slice and dice it.

**[23:25]** And this is such a better view for a rep to go through their own pipeline before getting ready for that pipeline review. Hopefully they catch opportunities with close dates in the past and things like that before they get in front of their manager. But it just gives you all the information you need right up front. Yeah, absolutely. And look at that. We've built, we've built this in mind for the rep, right? You click on the smart insights tab, you click on has a closed date in the past, and it's going to show you all the opportunities that have a closed date in the past. There's nine of them. Yeah. And if you're managing a

**[23:57]** high volume of opportunities, sometimes it's not about negligence or something you just may not have realized like, Oh, it has been two weeks since I've reached out to them. Or, Oh, I forgot to book a call on the last call that we had. So just empowering them to manage a bigger book of opportunities to help expand their level of effectiveness. It's really, really important to help them close as many deals as possible. Yeah, absolutely. Couldn't agree more. So how does all this roll up into forecasting, right? Like we've provided you with this data. You can

## 24:34 — Bottoms-up forecasting with manager overrides

**[24:34]** slice and dice it at scale. You can look at it, you know, it maps to the hierarchy that sits inside Salesforce. So if I'm a manager, I'm going to get a roll up of all my reps opportunities. I guess that what we say is that look, we believe in bottoms up forecasting, right? If you take this approach from bottoms up, we get the reps involved and committed to submitting a forecast week in week out, backed by data, backed by information. The managers will then come and submit their forecast. If we do it bottoms up, we get a bigger number, right? Our job as revenue leaders is

**[25:13]** to make sure that we're forecasting accurately, but that number's as big as possible. And there are tons of tools in the market that will look at signals and go, you're going to hit that number. But if you do it this way, if you give everybody in the organization access to the data and the metrics that matter, we drive better performance. And we drive, we, you know, here at Epstein, we're able to guarantee that we guarantee that we will improve quota attainment. We guarantee that we will improve your forecasting accuracy. So yes, we want to get you to that accurate number,

**[25:39]** but we want that number to be as big as possible. We want you to win more deals. We want your reps to achieve more of their quota. And we built this tool to help them do that. So as we segue from kind of that to forecasting, the idea here is that you come in as a rep, you have a weekly forecast cadence. I can check out my deals. I can see that I've got a good relationship score. I've got a reasonable deal score. I can see the warning signs. I can see how well qualified my deal is. And I'm going to make a forecast submission, right? I'm going to put this, this is going to stay in

**[26:15]** pipeline. I'm going to move it to upside. I'm going to move it to commit. And on a weekly basis, we'd expect our reps to be making a forecast submission. And they can submit their forecast by very, very, very, very easily. They can submit their commit forecast. They can, they can make some notes. They can adjust the numbers. They can, you know, they can account for a pipeline that's created and closed in period or all of that kind of good stuff. But what we also say is that the manager needs to have license to forecast as well, right? So the manager might come in here and say,

**[26:47]** look, you know, Wayne here, he's got decent relationship score. He's got a great deal score. But he's not, he's not qualified enough on the paper process. And he's got this deal in commit. So as a manager, I'm going to hedge that. And I think that's an upside deal. I'm going to do everything that I can to help Wayne close that, but I can't forecast that yet. And then the manager will submit their forecast at the same time. So how do we then view what, you know, view the forecast? How do we view the performance of the business as a whole? What you're looking at here is the

## 27:21 — The forecasting tab: coverage and pacing

**[27:21]** Exodus forecasting tab. Again, everything's inside Salesforce. I'm logged in here as James Worthington, who's the VP of Sales. So he's responsible for the whole business. He can see his Q2 quota. He can see his attainment to quota today. He can see his all of his team members. And you can drill into this information. You can see how Helen's team members are performing, Jacob's and Josh's. James is going to look at this and say, hey, look, Helen is smashing it. I need to spend most of my time with Jacob and Josh and figure out how we get them to quota. You can see the commit submission for

**[27:55]** the business. You can see the individual team commits submissions. You get two numbers here. So one is the rep submission and then one is the manager's adjusted submission. You can see the upside submission. And we pull some really cool insights around things like, you know, your required coverage and your pipeline coverage. So we've done some analysis historically because we know the pipeline really well. We can see that Josh here has got 6.8x coverage. And he typically, based on historical performance, he requires 3.6x to hit his target. So what the message to Josh is,

**[28:29]** look, you're behind on Q2 relative to the rest of the team. You've got enough pipeline. I want you to focus on closing deals because we know that you've got enough pipeline to cover your gap. So really a quick glance, leaders in the business can see exactly how the business is pacing. Once you've got a visualization of exactly how the business is pacing, as a leader or a manager, you want to focus on what's going to move the needle, what's going to help us get there. And that typically we find is focusing on the problem deals. So I'm going to switch to our pipeline

## 29:05 — Pipeline change: focus on the problem deals

**[29:05]** change tool. So the pipeline change tool here is really a visualization of what's trending in the pipeline. We can see that we started the period and where we're predicting on ending the period. And we can see that as a leader, certainly, I'm looking at this first up. I'm looking at the deals that I've got in commit. They're in commit for Q2. It's great that we've won some deals. It's great that we've got some deals that are trending up. So there's quite a few deals here that are idle. And that's probably my second priority. But first and foremost, I want to focus

**[29:42]** on these deals. They're in commit for this quarter. There's 11 deals valued at $300,000 and they're trending down. And what I can do is I can click on that number. I can see immediately that I've got a bunch of these commit deals that have very low relationship score. There's a bunch of warning signs. We've got some OK deal scores and there are some good relationship scores. But what I want to do is I want to expand those deals out and then I can interrogate the pipeline in exactly the same way. So I can see that I probably need to be having a conversation with Lucy, Neil and James

**[30:17]** because these deals are in commit. We've got a bunch of warning signs. There's no engagement. They need to come out and be closed, lost immediately. And then I would probably start with these deals. They might need some support and engagement. We might need to move those to upside and work on an engagement plan. But really, as a leader within the space of 30 seconds, I've identified exactly how the business is pacing. I've identified exactly which opportunities are going to move the needle for me, which are the problem opportunities. And I've now got a line

**[30:53]** of sight to exactly the conversations that I need to address that ASAP. So easy for a manager to just lie in high level, see exactly where the forecast is, see where opportunities are moving, target. They have very limited time. So target the opportunities they need to and go figure out how they can help their reps. And this is what I think we're talking about at the beginning. How do we get those BC players to just perform a little bit better, get a little bit closer to that A player performance and the results can be massive if you can focus that manager's time on

## 31:24 — Funnel analytics and time-in-stage

**[31:24]** where they should be. Yeah, absolutely. Absolutely. This is something that we really care about. So it's a really good segue actually to our funnel analytics tool. So we talked about benchmarking time in stage, time in pipeline. And you said there, how do we improve those B and those C sellers? This is one example of how we do that, right? But what you see here is basically the funnel performance for the business. We can see, and again, you can slice that by quarter, by opportunity type, by revenue type, but you get a very quick view of the velocity or team's

**[32:05]** velocity, opportunities close, win rate, sales cycle, average order value in a very quick snapshot. And you can filter this by, you can drill down to individuals, but you can see what's moving through the pipeline and how things move through the pipeline. And obviously this is dummy data, right? This looks like a beautiful waterfall. I have never seen a client funnel look like this, look this pretty. Obviously mine looks like this, mine's perfect, but that's for another story. But look, one of those things is, look, again, you can drill into all of this information and

**[32:39]** you can drill into pipeline insight so you can see those opportunities that slipped or dropped out. But one of the things that we're talking about around that benchmarking is look, time in stage. So immediately what we've done is we've got visibility of who's lagging, who's dragging, we see Jacob here, his average time in stage is 24, in average time in the value proposition stage is 24 days. However, when he close wins deals, his time in stage is only 14 days. So that tells me that Jacob's team is hanging on too much, hanging on too long to deals, because anything that's

**[33:17]** over that long time in stage, anything that's over like 15, 16 days is probably not going to close. So that might mean that I need to work with Jacob and his team on better crafting value propositions, better communication of business impact and outcomes to clients. And it's really just a really good way of spotting trends and patterns in big data sets across the funnel and helping you kind of root out those problems. Before we wrap up Anthony, there's probably just one more thing that I think I would like to show you, but it's this forecast change. So this is

## 33:52 — Forecast change: where leadership lives

**[33:52]** where a lot of the C level, a lot of the leadership will spend their time. They want to know what's happening to their forecast over the period and they want to know why. They want to understand what's been submitted at the start of the period versus at the end of the period and what happened in between. And again, you can see that in this case, we've pulled in a chunk from future quarters. We've got some deals that are new in forecast. We won a load that wasn't committed. At the same time, we've had 181K value slip. And again, all of this data is drillable. And within a very

**[34:41]** few short clicks, I can identify all of those deals that slipped. They're in upside, but they've slipped. Why have they slipped? We've got no engagement. We've got low deal scores. We've got a bunch of warning signs on the deal. So again, it's another way of slicing and dicing this data. So I think from my point of view, Anthony, it's been a whistle-stop tour, hopefully a meaningful one. And hopefully it's added a ton of value. No, I think you covered a ton in a short period of time, and especially because the EPSTA platform is so powerful and robust. But just to reiterate some of the things that

## 35:24 — Recap: start with the data foundation

**[35:24]** I think really stand out to me and our team, to remind people listening, we're doing revenue operations for high-growth, fast-selling companies all day long. You really hit the most important part, starting with the foundation, getting the data in, getting the emails, the meetings, the contacts, the relationships. Everything we do is relational. Sales is relationships. So understanding the health of those relationships, going back in time. You have lots of tools that can start picking up where you are today, but being able to go back in history and pull all

**[36:00]** of that in now, that's super powerful. And that data foundation just gives you the opportunity to get started and is immense value day one if you go with EPSTA. Then integrating the call intelligence, auto-populating the qualification methodologies, getting the right information that a busy manager or busy rep needs to see on an opportunity. Single-click into something positive going on with the deal. Single-click into something negative that's going on with the deal. Then seeing your whole pipeline in one place with your qualification, your contact relationship

**[36:32]** scores, being able to slice and dice your pipeline as a rep. So powerful. Then for managers and executives, the funnel analytics and funnel waterfall, huge insights packed into really, really easy to consume visualizations. And you make sure you have all the data there. So it's accurate and it's actionable. You can click right into any of those charts and start actioning any of that data. It's not living in some data warehouse plugged into a BI tool where the drill downs are tough to deal with. You can hop right in and start going from top level board room,

**[37:11]** executive level reporting, diving deep into a specific conversation, a rep had with a specific deal. So that's why when people are in market for a revenue intelligence platform, Epstein, I think you really have put together all the components you need where a lot of them maybe meet one or two, but you really cover that full journey. And I love the mission. Let's get those BNC players performing at least a little bit more like your A players and then see the results that'll happen in your business. Yeah, I love it. Absolutely. Amazing summary. And look, the point is that you can't do the last thing you said, which is improve your BNC sellers without

## 37:55 — Data as the foundation for an AI-first world

**[37:55]** data. And I'd also like as a side note, we're moving very, very quickly into an AI first world. And having that foundation of data is probably one of the most important things to getting businesses AI strategy, right? Because AI is only as good as the data that it has access to. And if your data's in different disparate silos all over the place, not structured, the AI is going to provide meaningless output. And it's one of the things that I think businesses really need to focus on in the next six to 12 months is to get that foundational data piece, right? Because if

**[38:32]** they don't, any AI that they layer over the top of their business, unless it's within specific tooling is going to have a really hard time. Oh, totally agree. And we talk about this all the time at LeanScale. You can swap in and out AI models, AI platforms all day long. Don't focus so much on which AI tools you should be getting. Focus more on, we really like the term ontology. So focus on the data accuracy and how you structure your data in order to train a model. If you do that really, really well, don't worry. There's going to be plenty of AI tools on the

## 39:08 — Close: where to find Adam

**[39:08]** market that will be able to read that data, but you have to start there. Yeah, couldn't agree more. Well, Adam, this has been awesome. Thank you so much for sharing so much in such a short period of time. We really appreciate having you on the LeanScale podcast, and we can't wait to see what you and the team at Epstein build next. Amazing. It's been an absolute pleasure, Anthony. If anybody would like to know more, find out. Hit me up on LinkedIn. I'm on there 97% of the time. So yeah, it's been great fun. I've really enjoyed it. And yeah, hopefully we can do something like this again. We'd love to. Thanks, Adam.