A (0:00)

Foreign. You're listening to gtm live, a podcast by passetto. Hey, everybody. Welcome back to the show. This is the first episode of 2026. It's January 9th, so first one that I'm recording this year, and before I even get into this episode, I just wanted to take a few minutes to say I'm just so excited for this year ahead. I'm really excited for all of the transformation I know we're going to see just in the GTM space this year. I'm excited to see more CMOs and revenue leaders succeed this year. And I'm really excited as well for Pesetto's growth. That goes without saying. I know it's going to be a dope year. And part of that is that I'm just so thankful for you, our listeners, for continuing, continuing to be on this journey with us. I appreciate you. I appreciate that you turn to us for thought leadership. And what's also most exciting for us this year is one, if you didn't know, we recently sort of stealthily acquired Amber Williams RevOps agency and we have now built it on a RevOps practice at Passetto. And secondly, we're actually moving into full fledged SaaS territory this year. So that's really exciting for us. A bunch of our current customers in our portfolio, they leverage our technology, which is a unified go to market analytics platform. But we're really working to bring this to market at scale in 2026, and I know it's going to be huge and I am just so excited for what's ahead. All right, so today we're covering stage four of the five stages of revenue transformation in this series. And this is a stage where we focus on actually initiating the process of transformation like in practice. And we call this stage architecting transformation because this is where you shift from recognizing what's broken to designing what comes next. If you've been following along, you'll know that we have covered stage one, the panic response where leaders scramble to do more when pipeline slips. Stage two, the QBR fire drill. It's that moment when you realize you can't answer questions that leadership is asking. And then stage three is the model collapse. And that's where you hit that sort of like breaking point and realize that the entire foundation that you've been taught is just not. It's suboptimal. It's what I would say is broken. And so we're at stage four now. And this is where you stop defending, you stop that feeling of being exhausted, and you really start architecting you start architecting a future that looks different. And this is where you move from, I know something needs to change to actually actively designing a new system. This is where you become the architect of your own transformation. Here is what is critical here. Stage four is not about tactics, right? We are taught as leaders, it's about execution, it's about doing more, and it's not about campaigns or tools or quick fixes. Stage four is about a fundamental shift in your thinking, in systems thinking, and it's about recognizing that the way you've been measuring or tracking or, you know, even optimizing pipeline creation and revenue creation is structurally flawed. And no amount of tweaking the old system at this point will fix it. You need to rebuild from a place of first principles. And I'm going to walk you through exactly how to do that. It's going to be a very practical episode today. So here's what we're going to cover. What triggers the shift to systems level thinking, the core elements that your new data model must have. So I want to provide a little bit more clarity on that. How to get buy in from leadership and stakeholders, the mistakes that kill transformation before it even starts, and then what it actually feels like to step into this stage. The truth is here at stage four, this is where transformation moves from concept to practice. This is where you stop talking about change and start architecting it. All right, let's go. So what actually triggers the move from stage three to stage four? What makes somebody go from, I'm exhausted, this is hard to I'm ready to architect something new. It's not about, you know, suddenly having all of the answers. And it's not about clarity just appearing out of nowhere. It's about recognize that fixing this is first principles to being able to do your job well. This shift happens when you acknowledge that you cannot succeed in your role, you cannot drive the outcomes the organization needs without fundamentally changing how you measure and how you track pipeline and revenue creation. It's not a nice to have okay, this, at this stage doesn't just become a maybe someday project. This is foundational. This is the infrastructure that, that everything else sits on top of. And so once you internalize that, once you realize that transformation isn't optional anymore, it's essential, that's when you start thinking like a systems architect instead of a tactical operator. That is the biggest difference. And that is the shift that I am seeing now going into 2026 is that more CMOs are starting to think like this. And here's what I've learned Both from, not only from my own journey, but also just from working with, you know, a bunch of other revenue leaders. Episeto is that that shift doesn't happen overnight. So for me, it actually took about a year. It took about a year of me listening to content, educating myself, consuming everything I could about why the traditional model was broken and what the new model needed to be. So this was a few years ago now and at the time we would have heard a lot of talk around the MQL hamster wheel and splitting the funnel and things like that. And so I became just, I started to understand those concepts like inside and out. And I've seen the same things now happen with our customers. There's a threshold, there's like a moment of readiness where suddenly you're not frustrated or confused any longer. It's like you move from frustration to being ready to take action. And that readiness typically comes from two things. One, understanding the risks of staying in the current model and truly understanding, like, if I stay in this role in this system that I'm in right now, right, the status quo, I will fail or the business will fail. And there's no third option. And it's not a matter of if I will, it's a matter of when. Right? And so you have to internalize at this point the cost of inaction, the cost of continuing to operate without visibility, or, you know, the cost of making decisions based on surface level data, data that hides the truth. Second, and this is a big one, is seeing lived examples of what's possible. You have to believe here that better outcomes exist on the other side of the transformation. And that's when you know, when you're like self educating and you're seeing what other people are doing in the market, that's when you can see and start to visualize, like I could be there with, you know, that camp of people who have already made this change. And it doesn't, this doesn't come from theory. It actually comes from seeing other leaders who have rebuilt their data foundation or you know, who are measuring performance differently and they can now answer questions that you can't. Right? And so they can see patterns that you can't see, they can optimize ways that you can't yet. And so it comes from understanding what's possible when you have the right data model in place. Right? We like to learn from other people and then believe that we can do that for ourselves. And then when those two things, click, the things that I just mentioned, when you start to understand both the cost of Staying stuck. And then the possibility of what's on the other side, that's, that's when you shift into starting to architect change for your role and for your organization. And then that's when you stop being reactive and you start being the architect or the person, like instrumenting that change. And so here's the key differentiator here. You're shifting now to systems thinking. You're not thinking. This is the biggest difference. The questions that you ask at this point are not the same as they would have been. You're not asking, what should I try next? Or even how do I get better data out of my current system. You're asking what does a system need to be in order to give me the answer, answers that I need and how do I architect that from a first principles place? This is a fundamentally different way of thinking. Not everybody will be here. But it is what separates transformational leaders from everybody else. Because if you can't think in systems, you'll never get anybody else on board. And stage four is all about getting people on board. It's being that change agent in your own organization. So let's talk about what the new model requires and what it actually needs to look like in practice, right? Cause we talk about, okay, old model, status quo, legacy model, new model, like, what does that actually mean? Right? And so when we're talking about this, it's not necessarily just about incremental improvements. It's not about better dashboards or cleaner reports in the same system. Right? That's the biggest difference. It's about building something new, a new sort of system or way of measuring gtm. And there's a few core elements that must be in place to actually make this happen. And I'm going to walk you through the highlights here. But I want to be clear, there's a lot of nuance underneath all of these things. Like this is the foundation, and each element has a lot of layers of depth that will unpack over time. Right? But I'm trying to condense that into like, you know, a 30 minute episode to make this sort of digestible for all of you. So core element number one, remove the department silos. So most GTM measurement models that companies use right now measure pipeline based on departments or functions. Right? You have marketing pipeline and then you have sales pipeline, and they measure that separately. Marketing is measured on what they generated. Sales is measured on what they generated or what they closed. And this creates a fundamental problem because it pits teams against each other. And I feel like everybody here knows that it's like we've belabored that point to death at this point. And marketing is of course because of this model being perpetuated, is focused on getting credit, sales is focused on getting credit, and everybody as a result is operating in their own silo, defending their own function. But the reality, and like the reality check that everybody sort of has at this point is measuring what creates pipeline and revenue by marketing or sales just doesn't work. Because pipeline creation as we know, is not a single or like one single touch or like one dimensional thing that happens. It's a not a marketing did this or sales did that. It's a journey. It's an interplay between both teams. And when you try and isolate a single department's contribution, you immediately lose the full picture. Now, does that mean we strip away all source tracking? No. Right? Because I've heard people say, well, you know, we don't do that anymore. We just track one pipeline. That's not the solution either. Okay? So absolutely not. You still need to know where things come from. You still need some level of understanding. I hate to say this word attribution, but you do need to remove the department focus and replace it with a journey focus. It's more about journey analytics. So instead of asking did marketing generate this pipeline or did sales generate this pipeline? You ask what did the journey look like that led to this opportunity being created or this cohort of pipeline being created? What were the marketing signals that maybe happened before? What were the sales activities that happened? How did they work together? Then this leads us to the second element, which is multidimensional data tracking. The legacy model gives you one dimensional data. Do you see? Did this lead because become an opportunity? Yes or no? That's where we say, you know, what was our conversion rate from MQL to SQL? And if yes, you then you try and isolate the one thing that caused it. Whether you know, you use first touch or last touch or whatever, it doesn't matter. But that's just not how B2B buyers buy. And it undervalues as we know. It undervalues marketing. That is why I'm here, because I have felt and I have known with data that the legacy model undervalues and under reports how marketing contributes to the buying journey. So the new model tracks both marketing and sales. We measure them in parallel, separately, but also together. And so for marketing, you know, a few examples of what you're measuring. How does marketing influence buyers across the full life cycle, not just at the top, not just at the bottom, but at every stage including an active sales cycle, including, like when a deal is being worked by an ae, what types of marketing engagement improve deal velocity or win rates or you know, speed to sale, which channels or, you know, which content actually correlate with closed revenue and not just MQLs. And on the other side for sales, you could start measuring things like how do opportunities get created? What was the trigger that initiated a conversation to happen before an opportunity was created? We might call this the tipping point. Like what was the tipping point? How many attempts did it actually take to book a meeting before an opportunity got created? How long did it take to move from, you know, attempted contact to actually qualified opportunity? Was it two months? Was it, you know, two weeks? And then when people disqualify, right, they never became an opportunity. Why was it the wrong buyer? Bad timing? No budget, Wrong fit? Like these are a lot of important, you know, breadcrumbs of information. And here's the key is that you're measuring all of this in context. You're not just counting activities in GONG or whatever, you're understanding the dynamics of pipeline creation. You're seeing what happens in that big black box between, okay, we've got a lead identified and opportunity gets created. And you're tracking how different triggers or different journeys, different engagement patterns lead to different outcomes downstream. The fact is that many organizations have probably hundreds of thousands of different patterns or different journeys that a prospect takes. And it's about identifying what are the high leverage ones and what are the ones that just cost us a lot of time and money. And this is multi dimensional data and it's the only way to truly understand what's working and what's not. Core element number three is different funnel stages. So the legacy model as we know uses what's called the demand waterfall, right? It's fairly familiar to everybody. Probably listening to this probably needs very little explanation. But if you're not Familiar, the traditional B2B funnel looks like this target lead, MQL SQL opportunity, close one. And the problem with this model is that it overlooks massive amounts of nuance. Full stop. Like that's it. There's a lot. It's, it's an overly simplistic model where doesn't give us the answers to answer the questions of what we need to be asking today. It treats every lead the same, it assumes a linear path and it also ignores the reality that buyers don't move in straight lines anymore. I hate saying that because I feel like everybody says it, but it's the truth. And the new model, as we See, it has three stages. One, engagement. This is where you're building awareness and capturing demand. Target accounts are showing interest. They're interacting with your content, they're attending events, they're visiting your website, they're clicking on your ads, what have you. Stage two is prospecting. Okay, this is the most overlooked stage. Nobody tracks it. This is where sales or your SDR team, whoever's responsible for prospecting, is actively working an account or contacts an account. Outreach is happening. Conversations are starting. You're moving forward from demand created and demand captured to is this a qualified opportunity or is it not? It's that messy middle where millions of activities happen before an opportunity was created. And then finally, you have the closing stage, right? This is an active sales cycle. This is where a qualified opportunity has been created and you're actively working the deal to close. I want to be very clear about something here. This new model, for one, it sounds linear. It might even sound more simplistic than the demand waterfall that we all know inside and out. It's not. There is a lot of nuance within each of these stages. Accounts, you know, don't necessarily move in straight lines. They loop back, they stall, they re engage. The three stages are the framework, but within each stage, you're measuring dozens of, you know, signals or activities or different patterns and different dynamics. Think of it like this. So the demand waterfall tried to create a stage for every single, like, micro step in the journey. And in doing so, it became really rigid and almost like, disconnected from reality. The new model simplifies the stages so that you can actually add more complexity to the data and the analytics underneath it. So you're actually measuring more, not less, and you're organizing it in a way that actually reflects how GTM works now. Now in, you know, 2026. And here's why this matters. Because each stage has its own KPIs, its own conversion rates, its own velocity metrics. And when you measure things this way, you can actually, and I've said this so many times, you can actually operate GTM like a factory. You have your supply, you know, your prospects or your leads. You have demand that's created and worked by your sales team. And then you have throughput, how efficiently accounts are. People move through each stage. And then you can see where things get stuck. You can see where, you know, velocity slows down. You can see where conversion rates drop, and then that's where you can optimize accordingly. And of course, too, you have leading indicators, not lagging. Core element number four, unified metrics with separate accountability. So each of these three stages, engagement, prospecting and closing, have both marketing KPIs and sales KPIs, but they're all looked at in a unified way. I know that sounds complicated, but hear me out. So across the factory, you can start to isolate different patterns of things that, you know, one could either kill deal velocity or two, improve it. Each stage has its own average length, its own number of signals or channels that drive those signals. And when I say signals, you know, things like, you know, first party signals that your marketing team tracks, right? Web visits, form submissions, content downloads, event registrations, event attendance, that sort of thing, a certain number of sales activities, two unique conversion rates, and then very specific velocity patterns. And all of the above directly correlate to pipeline created revenue, closed win rate, average deal size, sale, cycle length. This is the foundation. These are our must haves. And again, there is so much nuance underneath all of this. We could spend honestly an entire episode probably on each one of these core elements. But these are the pillars. This is what you're architecting. And when you have this in place, you're not guessing anymore, you're not defending anymore, you're diagnosing, you're optimizing, and you're running GTM like a science, not an art. We will have heard that before, because I know Chris talked about that a lot. So now let's talk about what it actually looks like to operate like a relay race. Because what I typically see happen is once you move to sort of this new approach, you start to see instead of marketing and sales, sort of like either operating separately or with some alignment, what you really start to see is teams operating GTM like a relay race. And I use this metaphor a lot. I think one of my customers for that, for raising that to me. But I want to make sure you understand what it looks like in practice. Because the old model, typically, not always, but typically has each team, like running their own race, right? Marketing is over here measuring their own metrics, sales is over there measuring theirs. And they're both trying to win their own individual race. But the new model is all about pipeline and revenue creation, almost like a relay. Because when you have that level of visibility, what you can actually see is where marketing has leverage in the journey, where sales has leverage in the journey, where one team needs to pass the baton to the next, and then where velocity gets stalled and, you know, maybe where the other team can come in and support. This is different, obviously, for every organization, depending on, you know, like the GTM motion, but the Principle is the same. You're measuring the same journey, just different legs of the race. And so marketing isn't just measured anymore on how many MQLs did you generate. Sales isn't just measured on how many meetings did you book. You're both measured on how efficiently are we moving accounts from engaged to prospected to pipeline and then closed. And each team has their own KPIs set of KPIs within that journey. So why don't we take a minute to talk about what the different KPIs might actually look like in that relay race that we're talking about in practice. And let's start with marketing. So first, for example, one of the best sort of metrics that I love about this framework is the percentage of pipeline that has marketing influence across each stage. So how many opportunities that were created or in a period actually interacted with marketing before they were ever passed to sales? Right? What percentage of pipeline created interacted with marketing programs while they were being worked by an sdr? And then of course, what percentage of opportunities interacted with a marketing initiative during an active sales cycle. What were those interactions? What content, what channels performed best at each leg of the, you know, the relay race? This shows for us, where is marketing strong and where does marketing need to up their game? Now what we typically see is because we tend to jam marketing's focus on top of funnel, what we often see is that the level of engagement is typically concentrated more in like that first stage, the engagement stage, versus like prospecting stage or closing stage because we focus marketing on top of funnel. But what also happens here is that we tend to see also like a low percentage of pipeline that ever had influence from marketing in that early stage. Because oftentimes what happens is, is that the MQLs that we generate in marketing rarely make it to opportunity created. And so therefore the opportunities that are created don't necessarily always have a marketing influence early on. Right? This is where we see the misalignment between sales and marketing, because marketing is out here getting leads that don't go anywhere and then sales is out here closing stuff that maybe marketing didn't give them. And not to say either team is wrong or either team is underperforming, it's just that they're not operating synergistically. And so this is an example of, you know, some new metrics that sort of tell you a different story. Now let's look at, you know, some sales KPIs in the relay race story for sales, a key metric that teams don't typically measure, but should is actual conversion from all of their attempts to actual meeting book. This might be more of like an SDR BDR type of KPI. So how many people did you know your SDR team have to call to actually get a meeting? Most companies just measure meeting booked onwards, right? Are those attempts converting to pipeline at 0.03% or 5%? This is a metric that truly sets apart not just like an SDR's ability to book meetings, but how efficiently and how effectively they can actually do it. So, for example, do they need to call 5,000 people to get five meetings or did they need to call 100 people to get five meetings? Right. It's a huge difference in terms of like resourcing and efficiency. And that can be massive for an organization, especially those that want to get more out of their SDRs or out of their prospecting team. These are just a few examples of the types of KPIs you can introduce in that new model. But the key is that these metrics complement each other. They don't compete, okay? And marketing and sales are both looking at the same funnel, the same journey, the same goals. And when one team sees where they're strong or, you know, sees where they're weak, they can collaborate with the other team to fix it. That's the relay race. And it's only possible when you actually architect the system to measure it this way. So at this point you might understand what the new model needs to look like. I know a lot of folks that are, you know, in this community or that we talk with every day are sort of in this place. They're like, yes, I'm ready. I get it. It like, I get it. Help me now make this a reality. You've got the blueprint in your head now. The question is, how do you actually get buy in to build it? Because this, in many ways, it's not a small project, right? One, it requires investment. Two, it requires, you know, some level of resources. But most importantly beyond that, I would say that those are sort of small considerations. The biggest consideration that is that it requires leadership or your ELT to say, yes, let's look at these new KPIs. Let's all sort of like rally around them. And here's what I've learned both from my own experience, but also just from working with, you know, hundreds of customers in the last year at Passetto and what we've learned. The key to getting buy in is explaining why your current data model limits your ability to get certain hours when I pitched this personally to my leadership back in, you know, 2022, I think it was. Here's what I said. I'm not looking to stop tracking what we're currently tracking. I'm not saying like, let's stop tracking MQLs. I'm not saying that our data is useless. But I want our organization to generate more results. I want my team to drive more impact for this business. And to do that, I need to expand the aperture of the data. I can see some of that data doesn't exist and I want to go get help to build it. And then I walked through two things. What are the blind spots in our current data model? I explained, okay, here's what we can see and here's the other stuff that we cannot see. I was really specific and really detailed. So for example, we can see how many MQLs we generated. Okay, easy. But we cannot see what happened to those MQLs in the sales process or the pre sales process. I should say, did they get worked? Did they convert at what rate did they stall? We have no idea how long did it take sales on average to pick it up? Is it a quality problem or you know, in terms of our leads or is it an execution problem? We can see which opportunities closed after they were in the sales cycle, but we cannot see what marketing engagement happened specifically during an active sales cycle that might have influenced velocity or win rate or, you know, other dynamics. We can see sales activity, right? Measure all of that using Einstein activity capture, for example, on an opportunity record. But we cannot see how efficient that activity was. How many attempts does it take to book a meeting? We don't know. And so I laid out all of the blind spots, all of the gaps, every question I couldn't answer. And I had a checklist, hey, of this checklist, what was true, what was not true? And then I covered the new model and what it unlocks. I showed them, hey, this is where we're at. Let's look at what a new model might look like. I explained all of the nuance that could be tracked, all of the questions we would be able to answer, and I framed it around outcomes. Here is the biggest thing. Outcome number one, an increase in performance. This would allow us to be more focused, smarter and importantly aligned across gtm. We'd stop guessing, we'd stop having to defend, and we'd start focus on optimization. Number two, this is a big one, is controlling CAC and eliminating wasted spend. This would help us not only drive growth but also be responsible, responsible about how we're spending, meaning I don't want to go spend on stuff that doesn't generate results. We want to see what's working, what's not. And we could cut what's not working and double down potentially, or reallocate on what is. And for some leaders, that could be enough that framing, you know, better performance, lower CAC is enough to get buy in. But for others, you need to quantify this in dollars and cents. Right. If you're bringing this to your CEO or CFO or whatever, you need to speak their language. And let me walk you through how to do that. Okay, so how do we quantify the business case? And to be honest, this is where Passetto can be very, very helpful. Of course you can try and do this on your own, but it could be complex, could be time consuming, and there is a fast track to doing this. So I just want to put that out there, not as a sell, but just as, hey, we can help. And so the framework is simple. Let's talk about how you do this in practice. Step one, split your pipeline into two buckets. Okay, we're talking a little bit now about this concept of splitting the funnel. Bucket one, we're going to call that high intent revenue opportunities. You will have heard Chris talk about that. Or refine labs, we call them hero opportunities. For most companies, the best way to measure this is through demo requests on your website or like contact form fills or hand raisers. Those are people who have basically raised their hand in some way and said, I want to learn about your product, I want to know about your pricing, I want to get a demo. These are the things that we can track actually really well because oftentimes they involve a track conversion on the website through a form. Right. So that means we have the data and then you're going to have bucket two. Right? That's everything else. All of those other sources of pipeline, outbound, partner sourced, event sourced, whatever you want to call it. And then step two, measure the performance of each bucket with a certain set of KPIs. For bucket one, we call them hand raisers or demo requests. We are going to measure how long quarter over quarter did it take to take those demo requests to and make them, you know, turn them into pipeline. Right. Was it two weeks? Was it 30 days? Was it two days? What was the conversion rate from that demo request to opportunity creation for those opportunities that came from that source? What was the average sales cycle length? What was the average win rate and what was the revenue outcome from that? How much revenue did you Generate quarter over quarter from. From that source. Look at the trend over time. Is it going up, is it going down? Is it highly volatile? Right. Those are three very distinct trends. Right. And they're important to look at for various reasons, which I'll get into in just sec. For bucket two. The everything else, measure the same metrics. Okay? Exact same things. Then compare the two side by side. Typically, not always, but typically. What you might find is this. Your high intent pipeline performs way better. You might have less of it, but you might see that you convert pipeline and revenue faster and obviously more efficiently. And the inverse is you might have a whole bunch of other stuff where you can't really understand what happens to generate that opportunity other than the last thing that happened. Sales reached out, maybe they attended a webinar or the department that created it. And then you're limited to say, what were all of the other dynamics? Right. When did sales even start working that opportunity? And maybe the downstream, you know, revenue outcomes on that cohort of data is that, you know, win rates are super volatile or sales cycle lengths are like 3x's long. There could be a number of different scenarios. But let's talk about step four, which is quantifying the gap, right? You've got two cohorts of data now. And so if you could fix the performance of the bucket of data or the cohort of data that is volatile or declining in performance or has really low win rates, right? Like spot the issue for one. So if you could approve something about that cohort, maybe the win rate in that cohort is like volatile and ranges between, you know, 10 to 15%. Okay, let's just use that as an example. Or the sales cycle is really long. If you could stabilize that and model out, you know, the next four quarters of what that pipeline looks like with a different, different set of performance, how much potential revenue would that deliver to the organization? What if instead of a declining trend in bucket two, you have a steady increasing trend? Maybe it's a, you know, a 3% increase quarter over quarter. What would that be worth to the company? This is different for every company. Okay. And this is again where we can help you. But once you can quantify that in dollars and cents, the story becomes just so much cleaner because at that point, you not really asking for a favor, right? You're not asking for something that's nice to have. You're saying we're leaving, you know, $30 million on the table because we don't have visibility into knowing what's actually driving pipeline for this Cohort of data. And the only thing that can help us improve performance is knowing and using data to discern what is happening. Otherwise it is guessing or having very limited data or very little aperture as we call it. Data in this situation can illuminate so much for an organization. But the first step is explaining why it's needed in the very first place in a way that people can understand. It's not complex, it's not complicated, doesn't require a bunch of like gymnastics to explain it. At that point, you can make your hypothesis scientific in nature. You can explain that GTM can actually be scientific with data. And here's your two allies that are typically in that process. One, having RevOps on board or aligned with you is super critical. And then two, your management, okay, depending where you sit in the organization, it might be a CRO, it might be a CEO. You need them to be aware of what you want to do. And here's the interesting thing, is that not everybody will understand at first. And that's okay. Okay. The key here is making this something that they can understand. Can this improve performance? Yes. Can this improve our CAC and our margins? Yes. Typically here finance is a really good advocate if you can get them in your corner. Because finance cares about margins, they care about efficiency, and they care about predictability. Right. And this gives them all of that. All right, so you got the buy in and you know what needs to be built in theory, maybe not in practice. Now the question is how? How do you actually go build something that you've never built before that you understand conceptually, but you don't really understand the mechanics of it? Right. And here's where leaders typically face a critical decision. Do we do this ourselves or do we bring in a partner? And let me be really clear about this, there are various solutions to do this work. Custom in house builds are absolutely a path many companies go down them. But my perspective, obviously I'm biased because of Passetto and what we do. But. But there's a cost to that approach. And it's not just money. Okay. It's what I call the time tax. The time tax is the cost of lost time while your team is figuring this out on their own. Okay, what's included in that? Months of missing data required for smarter, more strategic decisions. The longer this takes you, the more months you are left with missing data. Two months of unseen pipeline opportunities because you can't spot the patterns. Months of delayed potential revenue because you're not. You're optimizing blind until you figure it out. And then months of wasted resources because you might be running experiments that are costly and inefficient without the data to know what's working. Have you ever wondered, so if you're a CMO, here's one thing you might wonder. Why can't my RevOps team just fix our funnel reporting and get me the pipeline data I need? It's a fair question. And to be honest, if you gave them maybe like a year or two, they might go ahead and figure it out. And that's what a lot of companies do. But the exception. The exception is the elite few who recognize one simple truth. A strong team moves faster and more successfully when it has the right frameworks to execute. Okay. And so let me tell you a little bit about an experience that I had. Okay? So in the last two weeks, I ran five revenue data assessments using our scorecard with companies that already have RevOps capabilities in house. And I will add really smart RevOps people, too. Okay. Not junior skilled. Skilled RevOps people. And our rubric scores organizations across 33 critical elements required for, like, full funnel or full lifecycle visibility. None of them scored above 50%. Each of them had pretty solid fundamentals. Right? They're not, like, totally immature, but different major blind spots across the funnel. And every single one of those companies or, you know, those leaders that we spoke to want to fix it. And many of them do believe that they have a plan. The reality, though, is that their current approach, trying to figure it out on their own, is costing them millions in. In pipeline and revenue. And so the hard truth here that I want to call out is one, a proven framework gets you there four to five times faster. And frameworks work near 100% of the time. No exceptions. So when internal teams try and figure it out, the cost here isn't just lost efficiency, it's the time tax. And in my experience, I made this mistake. And so that's why I have a really strong stance on this. At the time, when I did this work, I hired a RevOps CRM, like admin agency to help me build this, but they didn't have the framework I did in my mind. Okay? And I guided them. I. I used my mental blueprint, never having done this before, to guide them. And it was a lot of trial and error because my ideas were new. Right? It required a lot of training and educating and learning. And yes, we eventually got there, but it was a bumpy road and it took us really long. Like, I'm talking probably a year or so and months until we actually started to make gains until we started to see results. So the real choice isn't necessarily fix this on our own or, you know, hire a partner. Right. The choice is whether or not you want to have the ability to fully measure your funnel fast and successfully, or whether you want to keep paying that price for lost time and lost data. And in my opinion, again, my bias, bringing in the partner is the right play because the risk isn't just the time. It's also the skills and the confidence to know what to do and to how to do it and to join all of the data and to understand all of the mechanics underneath all of that. And it can be pretty complex if an organization is trying to figure it out of their own. But of course, the benefit for somebody who's done it a bunch of times is they know the pitfalls, they know the shortcuts, and they know what's going to work and they can get you there faster. All right, so let's talk about implementing this in practice. Okay, we're moving on now to, let's say you've got the buy in. Let's say you've decided how you're going to build it. Let's talk about how to actually implement this without overwhelming your team. Here's the mistake I see a lot. Okay, so leaders throw a net new data model at their team and expect them to get behind it immediately. And it doesn't work because transformation or doing things just differently. I keep saying transformation because it is a transformation, but it also feels like heavy, right? And it doesn't have to be that way if it's done right, but it does involve education because you're getting people rallied around a new concept. And so it involves getting people to understand what different metrics can tell us. And so it can be an evolution, it can be transformation, transformational, but it does need to be done incrementally. Here's the approach that works. Start by layering in new metrics in some sort of sequence. Don't try and change everything at once. Introduce one new metric, educate the team on what it means, show them how it improves performance over time. And then once you start achieving performance improvements, it then becomes easier to get more members of the team and get engaged and bought in. But you need to balance two things. One, we're going to start doing things a little differently. And then two, let's look at how, you know, revenue is increasing or pipeline is increasing or win rate is improving with the changes. Okay, now we're going to go layer in a little, you know, A little more new data and eventually the transformation does get buy in from more teams and it will eventually help you sunset the old metrics. But it can't just be a rip and replace. This will be transformational. And if you try and do this all in one like fell swoop, you risk introducing too much complexity and overwhelming the team, which can be very distracting and frustrating. The best teams start small, they prove the concept, they show early wins, and then they scale from there. Before we wrap up, we're almost there. I want to talk about the mistake that I see leaders making in stage four that can kill transformation before it even gets off the ground. Okay, so mistake number one, don't be this person. Don't try and go it alone. I'm not sure where this comes from, if it's pride or ego or whatever, but sometimes what I see here is that, you know, if a single VP or single CMO has this vision, right, Maybe they're a longtime listener of this podcast, right? So they've heard this for a year and they're like, okay, now's the time, I'm going to bring this into our own organization. But if they try and go through the early stages of the process alone, you know, discovery and some of that stuff, they risk having that fall flat. The best teams are the ones that find one to two key stakeholders and involve them from day one because there is a level of education involved, right? Maybe you as a champion know this inside and out, but if you get two to three people in your team to also see that vision, the work is more likely to succeed. Because if one person sees the vision and then tries to make it a priority, the risk of it falling flat is just so much greater. Number two, not involving the right stakeholders early. So what we feel and what we see in our experience of doing this is we find the best stakeholders to champion this are marketing. Okay, VP of marketing, the senior most person of marketing, CMO, whatever, with RevOps in their corner and then with having, you know, like the CRO or CEO aware of what you're doing. But we, what we also see is that marketing can do some of this on their own and then loop more senior members of the team in as progress is being made. Right? Because as I was saying, it doesn't have to be this big rip and release, rip and replace. You can do these layer in new KPIs incrementally. Some resistance will come though, from legacy thinkers, right? Sometimes rev ops might feel like you're threatening potentially the perception of their role, but the Key here is that we need to help them see that this is actually helping and accelerating them professionally, not necessarily threatening them. This is a benefit to everybody, and it's just about proper framing. And then, number three, trying to figure this out without a partner or even a facilitator. I mentioned this earlier, but I think it's worth repeating that. Trying to navigate this alone without somebody who can act as like a facilitator or sort of like hold your hand as you navigate some new metrics, what they mean dramatically increases the risk of failure. The elite teams bring in help. All right, so as we close out, let me ask you, are you in stage four right now? Here's what it feels like. You're excited because you understand that there's going to be new potential within your organization. You might be frustrated because not everybody sees the vision right away. Some people challenge it. The legacy thinkers definitely will. But that's human conditioning and it's to be expected. And then, number three, you're nervous because more data oftentimes means that more can be exposed about performance. And sometimes the story isn't always good. It can actually be scary because the realization is that maybe your current data model has been actually hiding suboptimal performance. It's sort of like doing, you know, like, if you ever go for one of those big, huge health workups, right, do all of the, the labs, you get that, you know, body scan. And what that might expose is some areas of your health that maybe have been hiding in plain sight all along. And so you have to be prepared to face those head on, not hide them from leadership. That can be scary, especially too for marketing teams where surface level metrics can make things look better than they actually are. So nobody likes to face the fact that maybe they haven't been doing a good job or that they're failing. For example, you might be measuring MQL volume that might be making you look good, but soon with new metrics that might quickly expose that your contribution to pipeline is actually negligible. That's okay because this is good for the organization. Okay, because you are one step closer to creating a plan, to actually fixing it. So stage four is where a lot of those, you know, emotions can coexist at the same time. Excitement, frustration, fear, hope. And if you can navigate all of that, if you can hold the vision, even when others might not see it yet, you are ready for transformation. You're ready to move into stage five. So here's where we are. You've shifted from tactical thinking to systems thinking. You've architected conceptually the blueprint for your new data model. You've built the business case, maybe you've gotten buy in by now and you've started to layer in. You're on the path to starting to layer in new metrics incrementally, and now it's time to go build. Stage five is where you, this is an oversimplification, but you turn on the new system where the data starts flowing and where you finally get the visibility that you've been architecting. So here's what I want to prepare you for. When you turn on that new data model, you're going to see things you didn't expect. Okay? Some of it will be validating, some of it will be uncomfortable. You're going to discover hard truths about what's actually working and what's not. And how you respond to those truths will determine whether transformation succeeds or fails. Stage 5 isn't just about implementation. It's about the reckoning that actually comes after that. It's about owning what you see with humility and then using that data to make your organization better. All right, so in stage four, you architected transformation. In stage five, you're going to face the truth of what transformation reveals. And next time, we're going to dive into stage five, the build and the reckoning, where you implement the new foundation and you discover what's hiding in plain sight all along. Thanks for listening, y'. All. If you have any questions, hit me up on LinkedIn. I appreciate you and I hope you have found this series helpful so far. Far. I would love to know what you think. I'm all ears. Let me know and I will see you the next time. Take care. Bye, Ra.