Why Most GTM Reporting is Useless

Speaker A: We had a customer, and I pulled up their pipeline report and looked at deals across their team, and I saw that some reps were closing at a 15% rate and other reps were closing at 85%. Both of these numbers are virtually impossible. What's happening is, is one rep is taking every single meeting that they have, and they're throwing it into the system and saying, this is my pipeline. I generated $3 million of pipeline. You're like, cool. All right, your quote is a million. You generated 3 million. That's 3X pipeline coverage. Great. You're in a good spot. Well, yeah, if Your close rate's 33%, but if your close rate's 15%, you don't even have half the pipeline that you need. You need to create a process that an average person on an average day can generate an average result. Meaning, like, if our entire engine is built around the idea that we have to hire a rainmaker and only a rainmaker can get us to the result that we need, then we have a fundamentally broken engine. Welcome to Go to Market Science. In this podcast, we share tangible, actionable playbooks from the trenches, working as Go to market strategy and RevOps consultants for our clients here at Union Square Consulting, and candid conversations with revenue leaders in the market that have been there. Now let's get into it.

Speaker B: If you're a revenue leader, chances are you've invested serious time and money into Salesforce dashboards that nobody looks at six months later. The problem isn't the reports. It's what's underneath them. Today, we're breaking down why most go to market reporting fails and what it actually takes to get you data you can trust. And I just realized that Salesforce Force Dashboards is a tongue twister.

Speaker A: Salesforce Dashboards, well, it's also HubSpot dashboards and Google Analytics and LinkedIn analytics and ad analytics, and just all kinds of data. Lots of data in Go to Market. Um, yeah, I'm excited to dive into this with you, Rachel. And by the way, like, great intro. Like, it's amazing to see the evolution from the very first podcast where I was like, get on camera. And now you're like, 50 podcasts in a seasoned veteran.

Speaker B: Absolutely. Yeah. We haven't done a podcast together in a while, too. Right. It's been. It's been a few months, I think.

Speaker A: Has it? Yeah, I've been doing my own. You've been doing your own? Yeah, you know.

Speaker B: All right, Eddie, so start us off. In your experience working with executive teams, how high does visibility into the Business rank on the C CEO's priority list.

Speaker A: I mean, at least in my world, it's number one. I mean, when I was working at Salesforce and I was selling Salesforce, whenever I talk to CEOs, the number one priority I always heard was visibility into the business. And as a CEO myself, and when I think about talking to other CEOs about their businesses, when I'm talking about nothing to do with Salesforce or even go to market, everybody wants to understand, where is my business trending, what's working, what's not working, where do I need to focus my attention? Some people operate more on gut feel and on verbal feedback, and other people want to be more data driven. I'm definitely the latter camp of being more data driven. I think that both approaches can be good in their own respect, but I think it's hard to maximize the performance of a business if you don't have a firm pulse on what's working and what's not working. And even more importantly, where you're most likely to land. And you know, like, for the CEO, like your. Your job ultimately is to grow the value of the company. And revenue is a major part of that.

Speaker B: And so what does that reporting journey typically look like?

Speaker A: Well, I think every company is all over the map in terms of their ability to report. Right. So I mean, let's just take like, accounting data to start. I don't know, a lot of companies at least passed like a couple million revenue that would have bad accounting data or like, absolutely trash accounting data. Right. Most companies would be able to report how much revenue they have, um, costs, profits, growth rates, things like this. To some extent, there's always this, like, argument over what counts as revenue, what doesn't count as revenue. Um, but I think that, you know, most companies can rely on accounting data to see, like, historic performance and kind of forecast out. But to me, it's like, woefully inadequate. And I remembered when I hired, um, our accountant who in the past had done like, public accounting and, uh, worked at one of the big accounting firms. You know, she immediately came in and created this forecast based off of all our historic data. Well, we sold this much last, uh, or we had this much revenue and this many expenses last quarter and the quarter before that and a year before that. And so this is what I'm going to project this year. And I just remember looking at that thinking, like, oh, okay, well, sure, like, we're not planning to, like, let go of any of the employees that we have, so that's fine. But you have no insight whatsoever into what we're going to sell. Like the fact that we sold or had this much revenue last quarter, this quarter, last year tells us nothing about what we're going to do this year. And so then I think like some companies come into the later, you know, later set of data and they get into this place where, or they are in this place where they can't trust Salesforce and they can't trust their other go to market systems, they can't trust their forecast. And since we work with companies Primarily in the 50 to 500 million dollar range, a lot of companies are past that, but many are still struggling with this. And this is probably the number one priority for the CRO to be able to forecast accurately and tell the CEO on the board where they're going to land with some level of confidence. And all of that comes down to how much you can trust your pipeline and how much you can trust how many leads marketing is going to generate and how that's going to convert into pipeline and how much you're going to be able to build with outbound and expansion sales, et cetera. And if you are guessing at that, then by definition it's impossible to forecast. And that's a really tough place to be as a mature business.

Speaker B: And then when they or uh, rev ops teams or go to market teams or whoever creates these dashboards, what ends up happening to them? You know, 1, 2, 3/4 out.

Speaker A: Again, I think it all depends on the organization. But I think one of the common problems I see, especially given the fact that the average CRO is in role for 18 months, they step into a situation where things are a mess, they try to clean it up, but they're also trying to hit a revenue number at the same time. Maybe they succeed, maybe they fail, they're out the door in 18 months, next year, steps in, tries to take over, we're back to square one. Um, one of the biggest mistakes that I see is people trying to boil the ocean, like getting everything right at once. Like let's build this massive dashboard, let's figure out what our lead conversion looks like, let's figure out what our pipeline looks like, let's figure out what outbound looks like and the data is only going to be as accurate as the inputs into the system. Now we can and do automate as much of this as possible. If you want to see how many meetings, uh, sales reps had, easy, like we can integrate calendars and then we can have accurate data at least on meetings booked, maybe not necessarily Meeting celled. Right. But if you want to see how much real pipeline you have, either you have to train your team and. Or really, really nail the AI to figure out which deals should be in pipeline and which deals should not, and make sure you've got all the deals that should be in pipeline actually in pipeline. Otherwise you can't trust your pipeline report. And then you can't forecast accurately because you're just taking a wild guess as to what you'll close because you don't have visibility into what you actually have in front of you and whether or not it's real and whether or not it has a real chance of closing. Um, and so I think that if you take each individual metric, what you'll see is there's this. There's this workflow process where you have to, like, first build the report, then you have to, like, get the team to follow the process that's supposed to feed the report, and you have to iterate on it again and again and again. And I'll turn the question back on you, Rachel, since you have been so heavily involved in doing this for our team, what have you seen from the first day that I asked you to run metrics? Um, to the last time? I mean, and I'll even preface this by saying I think we're pretty decent at doing this, but were we perfect?

Speaker B: Oh, God, no. Not at all. It takes so much trial and error and, like, figuring it out, you know, and, uh, so much of it can be a guess when you don't have a ton of historical data for some, uh, areas. So you just have to guess and do the best that you can for a long time until you can do better for sure.

Speaker A: But I think part of my question is specifically, like, what have you seen in terms of the quality of the data? Like, starting from day one, when I first asked you to put together metrics for our weekly team meeting, how accurate was the data? And how often did we, like, debate in the team meeting over where this number came from and whether or not it was right?

Speaker B: Yeah, I mean, that happened all the time. I think every meeting we had, um, some numbers that were confusing to me, and I was like, ah, uh, I see something different in Salesforce, or I see this thing in Salesforce, and I don't know why it's there and it shouldn't be in this other place. Um, and definitions. Like, we had a lot of disputes. I remember early on about, um, what a lead would be classified as because there was some gray area and it wasn't always Black and white between some of our lead source definitions. Um, so yeah, it's gotten a lot better since then.

Speaker A: This is exactly what we see with companies that we work with, right? So as an example, since you are in marketing here, you know, I'll touch on that. What is the definition of an mql? Love it or hate it, if you're going to track MQLs, we all have to agree on the definition, right? And so if we're sitting in a team meeting debating the definition and marketing sees an MQL M as one thing and sales season MQL M as another, and they each have their respective reports and you bring those reports to a team meeting, you, you're just going to end up in this like pointless debate, burning everybody's time debating over which number is right and where it came from. Once you have that definition in place, then you have to see like whether or not the data is accurate. So like, let's just use an example. Let's say that, you know, a lead comes in, it has to have a score of X which can be automated, and it has to fit the icp, right? Well, do we have a mechanism to make sure that it fits icp? I mean, there's been so many times where we've been in a meeting and because we have relatively big deals and few of them, it's kind of easy to memorize everything in your head. Where I've been presented with a number and I'm like, no, guys, hold on a second. What about this? What about that? And this is what we see with our clients as well, right? Especially CROs that are really have like a firm pulse on each deal. All of a sudden there's this debate about, well, was that deal qualified? Should that have gone into pipeline? Should that not have gone into pipeline? And this burns in a massive amount of time just trying to get to this end destination where we just have a report that we can all agree is accurate.

Speaker B: Yeah, absolutely. What do you think or uh, what do you see when you're working with other revenue teams? Um, what their first instinct usually is to fix reporting problems like this.

Speaker A: First instinct to fix reporting problems. I mean, the first instinct is to go in and build the report from a technical standpoint, like, let's log into Salesforce, let's build the report, add the fields, add the filter criteria. The second instinct is then to like kind of upload whatever data is necessary to feed that. Because these are the relatively easy things to do, right? Like you can just grab somebody from Revops and say Go do this thing for me. And that's fine. Like, this is all foundational work. That's great. But if you don't agree on the definitions and then drive the team to adopt the process behind those definitions, um, then you will never have accurate reporting. And so the easiest example of this is a pipeline report. And by that, to clarify what I mean is a report that shows us all of our sales or all of our sales qualified opportunities that are anywhere between whatever staged is the first stage in our, you know, qualified all the way to closed one or lost, right? That affects our close rate. It affects our, um, sales cycle, our asp, et cetera. Actually, let me back up. It doesn't really affect our ASP or sales cycle because we can just look at, like, the, um, the closed ones. It would affect sales cycle because we have to determine when do we create that opportunity, right? If we have the team not following the right process, then we have junk data. And I've seen this a million times, right? I'll give you a tangible example that comes to me off the top of my head. We had a customer, and I pulled up their pipeline report and looked at deals across their team, and I saw that some reps were closing at a 15% rate and other reps were closing at 85%. Both of these numbers are virtually impossible, right? I don't think that you can be that bad at sales to close 15% of your deals, and I don't think you can be that good at sales to close 85% of your deals. What's happening is, is one rep is taking every single meeting that they have, and they're throwing it into the system and saying, this is my pipeline. I generated $3 million of pipeline. You're like, cool, all right, your quote is a million. You generated 3 million. That's 3X pipeline coverage. Great. You're in a good spot. Well, yeah, if Your close rate's 33%, but if your close rate is 15%, you don't even have half the pipeline that you need. On the flip side, we've got a rep that's sandbagging everything. So they're closing 85% of their deals because they're waiting until they get a verbal before they put it in the system. And, you know, what's the math on 85%? One out of, like, only one out of, like, six deals or something like that doesn't close. I mean, like, they're waiting until the final hour. So then that means that the company doesn't have any visibility on how much pipeline they're actually generating, how many deals they're actually working. No ability to influence that deal. And so the fix for this is, is that you've got to go in and you've got to talk to reps and say this is what a qualified deal looks like. Let's look at your pipeline. Here are the deals that you have in pipeline. Are these actually qualified? Are they not? Hey, I see that you haven't generated a lot of deals. What's going on here with the 85% rep? Simple answer. I'm waiting until whatever point in time before I enter it into the system. Well, let's fix that. And then of course we can use tools like momentum and attention to grab call transcripts and start to populate this data in Salesforce now, which I think is really, really game changing to give management visibility into what's going on here and a greater ability to use AI and human intervention to coach the rep to say this deal should be qualified, this deal should not be qualified. Which does two things. Number one, it improves our ability to forecast accurately and number two, it improves our ability to make sure that our reps, especially reps that are still learning what deals to determine what deals they should be focused on when. One of the biggest wastes that I see in go to market is seeing reps spend a ton of time chasing a deal that they're never going to close when it could have just been pointed out to them after the first or second meeting that either they have no chance of closing this deal or they have no chance of closing this deal if they don't get X in place. And so instead of getting X in place, X could be. We need to get access to a decision maker. We need to understand how they approve budget. We need to understand what they need to see in a demo before providing a big customized demo, whatever it is. If we haven't figured that out, then the rep just ends up burning all this time chasing a deal that they're never going to close without in any way increasing their chance of closing it. Now we have bad forecast accuracy. We've got wasted time spent by our sales team. Higher CAC like it just creates problems that go all the way across the business.

Speaker B: And so that example you just shared, it's a pretty drastic example, right? It's pretty clear to see like oh

Speaker A: no, this is very common.

Speaker B: Oh no, I'm not saying it's not common but um, um, it's a very in your face red flag that there's an issue like seeing 15% close rate on some reps and 85% on other reps. But what if some of your data is, um, it seems normal, but you don't realize that there are actually underlying process issues that are affecting the accuracy of that data, Even though it seems okay? Do you ever encounter that?

Speaker A: I mean, I would argue that that example is what you're describing. I think that you don't necessarily see the close rate by reps if you don't look, and if you know that reps are not following a process, um, then why look? Because here's the problem with this, right? So let's say that I, and I think this is the heart of what we're getting on this podcast. Let's say I'm the VP of RepOps in this company that I just um, shared and like that's what we were doing, is effectively serving in that role. And I go to the CRO and I'm like, hey, I've got some mind blowing news for you. I want to share with you that Bob over here is only closing 15% of his deals and Sarah over here is closing 85% of her deals. And the reason for that is because Bob doesn't know what a qualified deal looks like. And Sarah is sandbagging. What is CR going to say? Especially if it's a small team, they're going to be like, yeah, duh, uh, I know, so what? And, and that's the ultimate problem. Like this isn't new information, right? They know that these reps are doing these things, but they're focused on, well, okay, like Sarah may have an 85 close rate because she sandbags, but at least she's sitting quota. Okay, cool. The problem isn't exactly that Sarah's sandbagging, not putting deals into Salesforce. The problem is, is like, how could we help Sarah close more deals? Maybe Sarah's our best rep, but we don't have insight into what's going on there. Maybe Sarah is our best rep because she has the best territory and she's also a great salesperson and she's. So she's just uh, going in there and she's creating opportunities and she's closing things and she's a machine and she's amazing, but she's ignoring half of her territory because she's just got this huge rich territory. And by the way, this is real world example I've seen a million times. She's ignoring her territory and she doesn't log her calls and we don't have any integrations with email and things like that. So we don't actually know that she hasn't even reached out to all these amazing accounts in her territory because she's too busy working the other accounts. Meanwhile, Bob over here maybe isn't as good of a sales rep, but wouldn't it be better for Bob to reach out to, um, Acme versus having Sarah just ignore them for a year? And this is a real problem I see all the time. If we don't have a firm grip on our data, we can't see these things. We can't figure out, like, what can we do to move the needle. And we're stuck with CRO's just like coaching reps one on one and working deal by deal and then just ultimately determining, okay, Sarah's hitting quota and Bob is not. So we gotta let Bob go instead of figuring out, like, how can we orchestrate this entire thing to generate more revenue.

Speaker B: Yeah, it comes right back to visibility, which is funny because you said at the beginning that, you know, most CEOs would say visibility is their number one concern, but they say that and then, you know, they're, they're execs are saying like, well, it's, we don't need to get the visibility. We don't need to look any deeper because, you know, we're hitting revenue where our, uh, sales reps are hitting quota. So we don't need, you know, that data broken out.

Speaker A: Well, let me clarify. I don't think anybody is saying that. I think it's case by case. Right. So there's a lot of gray area in here. A CRO might say, hey, I know the forecast. Maybe I don't have perfect data in sales source, but I'm in every single deal. I talk to my reps about every single deal. And I can get an accurate forecast by going deal by deal by deal and figuring out which are going to close, which are not going to close, how much they are, and where we're going to land. Now that might work when you have 10 reps, maybe even 20 reps, but at a certain point that breaks down its scale. And, uh, now you've got too many reps and too many deals to do this manual review. Right. And I actually really like manual reviews, but I think you need to have like some kind of data behind it to know where to look and what questions to ask. At scale, that's cool for our qualified pipeline. But what about outbound? What about inbound? What about expansion? They may be in a place where they feel like they have a pretty firm grip on the deals that they're working and trying to close, but maybe not have as much visibility into what's going on with Outbound or how well a rep is covering their territory. And um, I'm fine with that. Right. Like you're not going to have perfect data ever because this always requires a pretty heavy lift. I shouldn't say always. There's a lot that we can do with automation and AI, but ultimately in most instances we need some level of human effort to really trust the reports. So if we want to see whether or not Sarah is covering her accounts, yeah, we can integrate email, we can integrate calendar, we can integrate call recording, we can populate fields in Salesforce course with AI tools and we can get a lot of visibility. But even if we have that data, somebody's got to go look at it and somebody has to determine. And maybe we can have AI do that too. Not maybe, we definitely can, but all this requires work. And why are we doing this? Well, one reason to do this is to make sure that we're covering our best accounts because that's a really great opportunity for us as an organization to generate more revenue by making sure that Sarah isn't crushing it by having this amazing territory where she ignores all these amazing accounts that we could give to another rep. This is a really serious problem in many organizations. Right. These are the kind of things that I think about where these are these little knobs that you can just tweak just a little bit and say, okay, this might not double our revenue, but if we tweak the knob here and get 1% and tweak this knob here and get 1%, it can add up to a lot.

Speaker B: Yeah, we were talking about this in um, the ROI of go to market ops. Like just tiny little percentages of not even adding resources or headcount or changing anything about your budget. You can add like millions of dollars in revenue just by creating these tiny little tweaks and these things in your processes and your reporting and the way that you're making decisions and ah, allocating, um, resources.

Speaker A: Yeah, absolutely. I think that that is easier than trying to find the next rainmaker ae. Uh, I saw a post the other day that I thought like really kind of blew my mind, um, and a lot of other people's cuz it went really viral and this person was talking about like the 60% of AES in the middle, right? It's like, okay, you got like the bottom tranche that you know those people are just gonna get let go. Uh, sorry guys, it's a tough, tough job. You have the rainmakers at the top that are just crushing it no matter what, and they get all the attention. And we're constantly looking for these rainmakers. And a friend of mine that runs a much larger business than mine, that built this business and sold it, um, shared this with me, and it really hit me. He said, you need to create a process that an average person on an average day can generate an average result. Right. Meaning, like, if our entire engine is built around the idea that we have to hire a rainmaker and only a rainmaker can get us to the result that we need, then we have a fundamentally broken engine. Right. And what this CRO is saying on LinkedIn is that you've got these, like, AES in the middle of the pack that make up 60% of the AES and, and maybe make up a smaller percentage of the revenue. But those are the workhorses. Those are not the people that want to work 100 hours a week and kill themselves to be number one. But they come in every day and give a good effort and produce real results. And that is like the backbone of go to market. But we, like, we need to build an engine around those people so that an average rep can perform an average amount such that our company can achieve our goals. And if we can't do that, then we are reliant on rainmakers and whale deals to get to our number. And that's a bad place to be.

Speaker B: Yeah. It's not a great business model, unfortunately.

Speaker A: It's a common one.

Speaker B: Yeah. What does it look like to build that engine and build these processes that need to exist before reporting can be trusted and actually useful in decision making?

Speaker A: One of the reasons why we've done a weekly metrics review, we take half of our team meeting to go through metrics every single week. As you're well aware, Rachel, since you're building this, I'm saying this for the audience. It feels like such overkill for a business of our size with one person in marketing and zero in sales. But the reason that I do it is because, A, I want to give everybody in the company transparency on where we're, uh, at the challenges we're facing and, like, what we need to do to win. And B, I want to exercise this muscle. And what I've seen, and I'd love your interpretation of this as well, is that if we say, okay, like, we generated this many MQLs and this much qualified pipeline from marketing, and we see it every single week, the first week, the number is Wrong. And we argue about the definition. The second week the number's wrong and we argue about the definition. The third week, you know, the number's wrong, we argue about the definition and we go back in and we clean up the data and we change this and we change that. Like, we keep working on it and week after week after week of doing this. And keep in mind, like, we all have other jobs. Like, none of us is like full time rev ops inside of usc. So, you know, we're all distracted with other things. Like, like many companies. And it's this like, repetition of measuring it that eventually gets us to the promised land where we can say, this is how many MQLs we generated, this is how much pipeline we generated. These are the sources that it came from, this is how much revenue we generated from it. And then I can make investment decisions on, for example, as we recently did, doubling our spend on marketing based on that data. But it took a while to trust that data. I mean, what did you see in this, in this journey going from like the first time I asked you to report how many MQLs we generated and also revenue to today

Speaker B: the changes that we had made to our processes and stuff throughout that journey?

Speaker A: Or just what did you see in general? I mean, what did it feel like the first day that you presented all this data versus today?

Speaker B: Oh, I felt like, uh, a baby deer on ice and

Speaker A: like a baby

Speaker B: deer on ice and like presenting this data and like looking at all this stuff and then, um, seeing things that were wrong and definitions that didn't quite make sense or didn't match up to what the numbers should be and being like, I don't know what's going on here, but, uh, um, a lot more confident in it now and being able to see the numbers and being able to say like, well, we know that this number is this way because this other number indicated that weeks before. So we knew that this would happen. And I feel more confident being able to say, like, we expect to get this many leads or expect that we won't get very many leads this month because of this and this. Um, and also being able to see, like, trends in the way. I don't want to get too into the weeds, but trends in the way different channels act and how they've changed over the years. Um, and that means different pivots to our inbound strategy and like, what we focus on more, what we focus on less and where we allocate our time. So, um, in the beginning it was very like, there wasn't enough Data to truly make very informed decisions about where we spent most of our time and efforts. And now I feel like we're in a place where we can do that.

Speaker A: Yeah. And I think the funny thing is, is that we had the systems in place, right? Like, we had the rigor to know when a deal was qualified, much more so than many of the companies that we work with. We had all the data in Salesforce. We. We had all the meetings in Salesforce. We had HubSpot built out. We had forms on our website that integrated with HubSpot. We've been too cheap to integrate HubSpot with Salesforce. Anybody listening to this? Like, don't judge me, but, uh, you know, quadrupling my spend on HubSpot just to save some manual entry was just not something I personally wanted to do. Given our low volume of leads, um, that was kind of the only, like, breaking point. But, like, we had a, uh, very, very good process to make sure that we were taking the leads from HubSpot and entering them in Salesforce. There was no breakdown. There was, but what I saw was, like, this debate of, like, is this a lead? Is this not a lead? Like, here's a good example, right? Like, we don't really have a definition of an MQL just because it wasn't really a problem until it was. And then we changed our content, and then that problem went away. And we're closing a large percentage of our leads, including the ones that are kind of, like, out of outside of icp. So, like, if it ain't broke, don't fix it. But we did have, like, leads where it was just, like, pure spam, right? Somebody's just, like, purely spamming us. And then we'd get into this meeting and debate, like, do we delete the lead? Do we save the lead? But call it unqualified, qualified, and like, sure, like, these are easy things to answer, but it burns time and it changes the number. It's like, did we generate 10 leads and close one, or do we generate 13 leads and close one? And therefore our. Our close. Our lead to win rate is 10 versus 7%. I mean, that's a, like, large difference, right? We're talking about a 30% difference in outcomes. Well, if we pick these three leads that are just pure spam and define that that's not a lead, or if we had a bigger problem with leads, what I would say is, company has to have a certain level of revenue. We have to be talking to a certain buyer. There has to be a certain level of intent Et cetera, et cetera. These things are super, super important, right? And not only that, but, like, once we had a grip on those leads and we really, really looked at it. And I'm trying to remember when we started doing this, because I feel like we weren't generating revenue and marketing when we started it and started the analytics. We were generating leads, but not revenue. And then I remember, like, this sort of come to Jesus moment where I sat down with you, and it's like, rachel, we've generated all these leads and none of them have con or closed. Why is that? And what we realized was that we were attracting the wrong person in the wrong type of company. And then we changed our content to talk to the right person in the right company. And that solved the problem overnight, which is I still, like, I would never have, like, imagined that it would work out that easily, but it did. And then it was like, okay, well, we don't have to, like, over complicate this. Let's just keep making content for the right person in the right company, and then we'll win 10% of those leads, um, as customers, and let's keep cranking. And then that enabled us to then double down on that content, generate a bunch of revenue last year, and then come into this year and go, well, what if we double our spend on marketing? What if we hire this agency to produce this podcast? What if we generate five times as many podcasts? Or what if we do as many podcasts this year as we've done the last five years combined? And I needed data to make that decision. And that might sound trivial to somebody in a $200 million company, but this money comes directly out of my pocket. And so, like, you know, I drive a used Subaru and I'm throwing money around on marketing that's like an order of magnitude larger. And it's like, I don't know. For me, it's hard to do that if I don't have some data I can trust.

Speaker B: Quick pause. Everything we talk about on this show. Diagnosing go to market ops. Prioritizing projects for revenue impact, processes, metrics, insights, building a predictable go to market engine. We've built frameworks for all of it. They're free and ungated on our website, unionsquareconsulting.com frameworks. The link will also be in the show notes, so make sure you check that out. All right, back to the episode. Let's get back to building the process to get this data that we can trust. How do you recommend that companies, you know, uh, People listening to this, 30 million, 50 million higher revenue companies, um, they already have processes for most of these things. Probably sometimes not, but usually yeah. But what would you recommend they. Where would you recommend they start when they need to like, look at the processes they already have or look at what's missing and start tweaking and fine tuning to get data that they can trust more and forecast better with.

Speaker A: The first thing I would do is I would document the definitions and process what is an mql? What is it, you know, like a marketing qualified lead? What is a sales qualified opportunity? Are we using sales accepted leads and sales qualified leads? What is a. Like, what is stage zero, stage one, stage two, stage three, etc. What are the entry and exit criteria? Write that down on a piece of paper, share it around with all the powers that be in the organization and make sure you get alignment and agreement on that. Right? That's step number one. Step number two is go build the reporting structure. Right? What is our pipeline report going to look like? What fields are we going to ask for? Like, it's not going to just be how many opportunities do we have in each stage that goes from stage X to stage Y? It's going to be what information do we want to see about these opportunities? Do we want to see the decision making criteria? Uh, who the key decision maker is, Things like this, Right? So we build out that reporting structure, then we look to automate whatever we can. Right? So if we're taking leads from our website and manually entering them into the system, like that's a pretty obvious one. Like let's automate that. Unless you're like me and you're too cheap to upgrade HubSpot to integrate with Salesforce, which I don't recommend for a larger company. Automate this, right, otherwise you're going to have this breakdown where like a human being makes a human mistake and then you have bad data for a really stupid reason. Um, so automate everything that you can. Automate the capture of emails, automate the capture of calendar invites, Automate, you know, call transcription. Use tools like attention, uh, or momentum to fill out fields in Salesforce so that you can get as much visibility as you can. This is all the easy stuff. The hard part is now taking it to the last mile and going to the sales team and saying, okay, here's the part that we can't automate. We can't automate you moving a deal from stage zero to stage one because you feel that it's qualified and stage one is our first qualified stage we can't automate that. We shouldn't automate that because that needs to be a human decision that, like, I feel like this deal is genuinely qualified, sure, the AI can flag it and say, we listen, we read through the call transcripts and here's all this stuff going on and you don't have access to the decision maker, but I think a human being should ultimately make that call. As to whether or not this enter is a qualified pipeline or not, you've got to train the rep on that. And then even that's not enough because now what you need to do is you need to implement a management process where you're going to review these reports and give people constant feedback and coaching and hold them accountable to make sure that these reports are accurate. So if, for example, we want to have a clean pipeline, guaranteed, without a doubt, full stop, you will not have a clean pipeline unless you inspect the pipeline and coach your sales reps on what should be in pipeline or what should not be in pipeline. You will not have accurate stages unless you coach your reps on what deals should be in each stage and when they should not be in those stages, full stop, right? If we then go and say, all right, like we've automated our MQLs and we're using some thing that we can automate to make them qualified, okay, great, they go into our system. But now we want to make sure that we have X number of follow ups before we mark a lead, like close, lost or dead, no response. We need to inspect that report and make sure that our team is actually following up X number of times before they do that. If we don't have that inspection process, I guarantee without any doubt that you'll have leads that will come in, get 1, 2, 3 follow ups and they get marked dead, no response. And then you'll be looking at the data six months later and saying, like, our lead conversion rate is this. And then marketing, you'll say, well, yeah, your lead conversion rate is terrible because your salespeople don't follow up. And who's to blame for that? Now if your salespeople do follow up, then you can easily point the finger back and say, we followed up with every single lead you gave us 15 times and our conversion rate is 1%. We need to change the definition of an MQL such that following up with them 15 times does not result in a 1% conversion. That's a perfectly fair pushback for marketing. But you can't do that if you don't have accurate data. And I'll kind, uh, of stay on the soapbox just for a moment. We've seen this with our customers where marketing and sales are pointing the finger at each other and we say, okay, let's implement this process and execute it consistently. And then you do that, and then you see that these leads don't convert. And then you say, okay, let's go take that same process and apply it somewhere else. In this particular example that I'm thinking of, it was applied to Outbound. Now all of a sudden they're generating all this pipeline because they're following up with all these target accounts 15 times. And you say, hey, marketing, we can call a cold prospect that doesn't know us from Adam and turn that into revenue or at least pipeline. But we can't convert your leads. We need to change the definition of a lead, because if your definition of a lead gives us this result, it's a bad definition. And all we're looking for here is a starting point, right? We're never gonna get things perfect. And I think the whole idea of, like, figuring out what a qualified lead or a qualified opportunity is is to reverse engineer, like, where do we have a halfway decent chance of winning?

Speaker B: And you were talking before about, um, coaching your team to actually execute on these processes once you have it all written down. So what does that coaching cadence look like in practice? How hands on does management really need to be and for how long?

Speaker A: Personally, for me, like, uh, when I was at Salesforce, my manager and I always had a one on one every single week. Um, I've hired reps here that didn't necessarily want that. I always found it really valuable. I personally wanted more data and less, like, opinions. Um, I really love, like, what Kevin, Like, I, I remember Kevin Dorsey outlining this and I forget if it was like on his podcast or something like that. And he talked about, like, working with the rep to figure out how they get to their number in their own terms. So the example he shared is like, he's meeting with, uh, a rep, let's call her Sally. And he's like, hey, Sally. Like, this is how many calls you're making. This is how many meetings you're booking. This is how much pipeline you're generating. This is your close rate. This is what you're closing and winning. You're making 100 calls a day or whatever it is. And what this is resulting in is you being at, uh, like 30% of your quota. Assuming the same conversion rates, you could get to quota by making 300 calls a day of the same Quality, Right? It's really Easy to make 300 calls a day if you don't care about the quality, but we got to maintain the same quality. And Sally's like, I don't want to make 300 calls a day. That sounds awful. Okay, if you don't want to make 300 calls a day, here are the other options we have. We can improve your meeting conversion rate. We can improve your close rate. We can increase the size of the deal. We can shorten the sales cycle. There's all these things that we can work on together to improve your metrics so that you hit quota and you get your commission check and you get promoted and you have this really great bullet point in your resume for the rest of your life that you crushed it in this year in this job. And how would you like to get there? And I don't remember what the answer was, but it's easy to imagine Sally saying, like, well, I'd really like to improve my close rate. It's 15%. And I see these other reps are over here at 30%, which, by the way, I would volunteer that as a, as a manager. Here's how you stack ranking. Like, here's like, how many calls you make and how many meetings you book. This is what the number looks like for others. This is the conversion rate for meetings to, you know, and you go through that process and. And now Sally's like, yeah, I want to, I want to work on improving my close rate. Okay, cool. Let's drill into that. All right. It's hard to do that without data. Here's the deals that you're working. What is going to influence close rate? Well, it depends for every product and every organization. But like, commonly, like, we have Medic for a reason. Going to be like, do we have access to the decision maker? Do we know what their decision making criteria is? You know, do we know what their metrics are? Et cetera, et cetera. I won't, like, just regurgitate Medic on this podcast. Well, maybe that rep is at a 15% close rate because they don't really understand Medic or how to use it properly, especially for that company and that product and that customer. So let's focus on that. And if we have solid data and we have a, like, we have got the AI listening to the call transcripts and filling out salesforce, then we're in a much better position to go in and coach a replay on how to do that better so that they can improve their clothes. Right. But the, the, the opposite end of the spectrum is. Is that you don't have any of those Visibility that visibility. And you're just like, sally, well, where do you think you need to improve and how can I help you? And it's like, maybe Sally's not performing because she doesn't know.

Speaker B: So at what point do you know that you can transition from managing the process and managing, like, whether your reps are following the process and they're trained properly into having conf. Uh, baseline is all well and good, and then transitioning to managing the metrics themselves?

Speaker A: What do you mean by that?

Speaker B: Like, how do you know that the foundations of the process are not the problem anymore? And now you can really start looking at the metrics and managing those rather than looking deeper underneath them.

Speaker A: So I think, like, for an individual, a team, a company, you have this transitional period from 0 to 1. So let's imagine that, like, we've got the company and the team just dialed in, and then we hire a new rep. Well, you have to teach the rep all of these things. And this was the situation I walked into at Salesforce. And I remember, like, four hours into my job, my manager, like, pinging me, going, hey, I don't see any, like, calls log today. Like, are you having trouble figuring out how to log a call in Salesforce? It's like, no, I haven't made any calls yet. And he's like, you should get on that. And it's like, okay, I hear you loud and clear. Like, and this. I was. What I always thought was funny about Salesforce. They made it abundantly clear that, like, if you don't follow this playbook, you will not be employed here, full stop, period. A lot of organizations are afraid to do that, right? So what I experienced with that, especially given the fact that I'm literally sitting in a row next to all these other sales reps that are doing the same thing, is that, like, snap of a finger within a very short period of time, you adopt that entire process, and then you're just done. And then what this does is it really gives you freedom to open up and start spending the bulk of your time talking with management colleagues, ses, you know, executives, anybody that can help you either close the deal or figure, uh, out how to hit your number about the more nuanced things, because you're, like, not debating what a qualified deal is or, you know, who the decision maker is or any of this stuff, because it's just so ingrained, it, like, becomes muscle memory. And I think that, like, first you got to get the organization there and then you got to get like each individual team there, then you got to get the rep there and then you hire the new rep and you have a brief onboarding and then you're there. And now it's just like, well, where's this person struggling? Maybe I know the process perfectly, but I'm just not good at running discovery and as a result, I'm losing a bunch of deals. Maybe I can't get access to the decision maker. I know that I need to, but I just don't know how to do it. Okay, cool. We've got visibility here and now we can coach that rep on how to get access to the decision maker. And that's more of an art than a science, um, or at least more of an art than a science versus like running a report in Salesforce, obviously. And we can focus in on like how to do that better, but it's really hard to do that when you don't even know that that's the problem.

Speaker B: And when the metrics are all finally accurate, how do you decide where to focus on next?

Speaker A: What's the next step as a manager or like as an executive, like deciding like, you know, what the team structure should be in the strategy going forward. Like, what level are we talking about here in this question?

Speaker B: Um, like as an executive, like, for ensuring that your reporting is actually useful?

Speaker A: Well, I think what I described is like the basis of setting up reporting that's actually useful. I think that, you know, the basic reporting in go to market is pretty basic. It's like how many leads or outbound activities did we generate and how many meetings did we get? How much pipeline did we generate and what, uh, did we close when. Right. That's all pretty simple. Where it gets really complicated is across different products, across different geographies, different segments, et cetera, et cetera. How much pipeline are we generating from each channel? What of that will close? Where are we expecting to land like three quarters from now? How much pipeline will we generate between now and then, and then close at that period of time? Where are we going to land at the end of the year? And you just have like a lot of funky different math, right? And I think the math itself is relatively simple on its own. But when you think about an entire organization in like a $200 million company where maybe we have S and B, mid market enterprise, we've got the U.S. we've got Europe, we've got three different product lines. You know, we've got outbound, we've got inbound, we've got partners, we've got all these different sources, and for each and every one of those things, we are trying to figure out what's working, what's not working, where we're going to land. And so I'm thinking of one individual customer that we worked with and the Sierra over there, super, super sharp, understood all this stuff. And at the end of the day, he hired us because he's saying, uh, like, I get it, but I have all of those things that you just described and I. Not all of them are running on eight cylinders. So now we got to go in and like, clean up all that data and fix these problems we just talked about so that we can simply forecast accurately and decide headcount, etc. Once we get there. And I do think, like, this is, you know, piece by piece, right? I don't think any organization, even Salesforce was perfect in every way, shape or form and every single thing in this regard. Because even if they were, Salesforce is going to go acquire some new company tomorrow and then that company is a shit show and they bring them into the fold and you're like, what do we do with this data? Right? So I think that that's always going to happen. And we've seen this a lot with M and A. A lot of our customers have done mergers and acquisitions. And you're like, oh, my God, this one company is, you know, running on eight cylinders and the other is just like a dumpster fire. Okay, we got to fix this, right? But when and where we have accurate data, and it's pretty simple, like double down on things that are working and either fix or cut back on the things that are not working. And I think when you like, zoom in on one specific thing and you're like, how much pipeline are we generating in SMB for this product through this, through ads? Okay. If we have accurate data, it's relatively simple to try to decide what to do there. The problem is when you're trying to put all the pieces together and then say, we've got this much, you know, this many resources to work with, and we need to figure out, like, where we put the players on the chessboard.

Speaker B: So we know it's important to, like, segment out our data, like you said, by product or whatever other, uh, vertical we want to segment out into. But how do we choose, um, from that much more massive data set, the top 15 to 20 most important metrics to put on an executive dashboard that are going to be the most useful in a meeting with the board or with the rest of the executive team.

Speaker A: I mean, I think that like some of those metrics are just really obvious. It's, you know, closed one. It's, you know, pipeline, it's leads, it's outbound meetings, et cetera. I, um, think you can have a pretty simple dashboard. I think the problem with dashboards is like, it's a sort of like high level, surface level view. And that's cool for like, you know, step one, but where you really get like meaningful improvement in go to market is when you start to build out those segments. And what I mean by that is like, let's say, for example, you have a full time analyst, uh, and that person can go in and say, I have a hypothesis that maybe we win more deals with tech companies than we do with manufacturing companies. Well, do we have the data or the ability to get the data to see all the deals that we've worked, all the leads that we've gotten, all the outbound activities that we've done against tech companies versus manufacturing companies? And this is the problem you always run into. It's like, okay, maybe we don't have that data. Now we can go in and we can enrich that data with some tool, we can get some idea. And then now we go, oh, wow. You know, every time we make a cold call to a tech company, we generate twice as much revenue as we do if we make a cold call to a manufacturing company. And it's like, I always think of Moneyball in this example and I remember this scene where Jonah Hill is coaching this baseball player and he's like, hey man, every time you hit the left field, you're on base percentage is X and every time you hit the right field, it's twice that much. So why don't you just start hitting more to right field? And it's like, uh, okay, like these are the kind of things we're trying to uncover, right? You have to have accurate data to do that, but you also have to have somebody who has dedicated time to go in and analyze this data, come up with hypotheses and then say, I wonder what happens if we look at this? Or we'll look at that, or maybe our podcast does really well, really well, like with enterprise customers, but not with SMB or vice versa. Um, where are we going to place our bets based on that, that information and that, that insight? And then I think like the next step is we have to have a forum where we bring those insights. Let's say we have this full time analyst doing all this analytics work. Do they Bring it to the CRO and is this ongoing dialogue. Do we have like a go to market council meeting where the CRO is there and the, you know, maybe VP of Sales and CMO and head of customer success product finance. And we discuss these insights and decide what to do with them. Because this thing like anybody can build a dashboard, but if you just like let the dashboard sit there, it does nothing.

Speaker B: Absolutely. And you know, the topic is like why most go to market reporting is useless. And I think that is a huge pillar of that. Why? Um, because they create people create dashboards and reports stuff and then do nothing about it. And nothing like big issues aren't actually discussed with the entire team that's responsible or has the jurisdiction or power to fix those issues. And so they never get fixed and then it just keeps getting kicked down the road.

Speaker A: Yeah. And I mean I want to make sure that like I don't paint like this really dark picture. I think it's a spectrum. Right. I don't think there's any CRO out there, you know, that has more than one or two sales reps that's just like operating completely blind. I think a lot of CROs are focused on hitting their number. If their team is small enough, they're in every single deal. They know what's going on, they have an idea of what's going on with Outbound, they have an idea what's going on in marketing. And sometimes it's a little bit difficult to justify the investment of time, money, resources into what we're preaching here. But I think like at every point, I mean I found huge success with this. Even as small as our company is, we don't have a full time person doing this. But just dedicating a couple hours a week to it has opened up really valuable insights that have informed investment decisions, have informed what type of content we're producing, what channels we're going, uh, uh, we're producing that content on how we do sales, who we target. And to me, especially as a small business, which is even more true than like in a hundred to $500 million tech company, I have very few resources to play with and I need to make sure those resources are invested where they will have the biggest bang for the buck. And so yeah, it doesn't make sense for me to hire like an entire rev ops team and have one person dedicated only doing these analytics. But for us to like spend a few hours a week on this, make sure that we really nail things. To me that is a massive difference in the business.

Speaker B: So we're coming up on the end of the questions that I have for this topic, Eddie, but was there anything else that you wanted, uh, to talk about?

Speaker A: I think the only big thing would be to focus on one thing at a time. Right. So this is a question that I always ask CROs when I'm on the phone with them or on Zoom. If you can improve one thing in your business, what would it be? New business or net revenue retention or forecasting accuracy? Right. Within that, is it renewals or expansion, is it pipe gen or pipeline closing? Within that, is it inbound, is it outbound, etc. It keeps just going down, uh, peeling, uh, the onion, whatever you land on, just go build a report like today and then look at the report and ask yourself if you trust the data and then start to look at that report on a recurring basis. Measure what matters. If you want to generate more pipeline via outbound, then make sure that you have reports showing you what you're doing in outbound and how much pipeline you're generating and maybe take it a step further and look at what accounts you're covering and how many times you're covering them and all the things that you feel is important to generate pipeline via outbound. Build that report as soon as humanly possible, automate that data and just keep working on it until you get it right. You're going to have to train your team differently. You're going to have to hold them accountable. You're going to have to look at the data, go back in and fix it a few times. It's going to take some level of effort to get to where you want to be. And if you just focus all of your attention on that one thing, you're much more likely to achieve the result that you want. But if you try to fix all reporting across all of go to market all at once, you're probably not going to make much traction. While you're also trying to like close a bunch of deals and hire people and onboard them and do 8 million other things.

Speaker B: I think we can leave it on that note.

Speaker A: Cool. Thanks for putting this together, Rachel, as always.

Speaker B: Awesome. Um, thank you so much Eddie.

Speaker A: Thanks for joining us folks.

Speaker B: Thanks for joining us and yeah, I'll see you later.

Speaker A: Eddie, thanks for listening to the episode. If this resonated, please give us a five star rating and a follow. It helps us reach more people and you get our latest and greatest content without having to search for it. And if you're looking for hands on help in go to market strategy and or revops please reach out to us. We help our clients with everything from annual planning to improving processes in Go to Market implementing systems to support those processes and Go to Market AI uh. We're always happy to offer a free consultation to help you identify the best opportunities to improve your Go to Market engine with or without our help. You can find us@unionsquare consulting.com and the info will be in our show notes.