A (2:59)

Hi, everyone.

B (3:00)

Amber, the head of Revops over here at Passetto. And I think the short and sweet of it is that I, I'm at Pesetto because we have such an amazing CEO in Carolyn, and I love this vision. But, no, yeah, it's, it's. It's been great to hear so much feedback from the community around, whether it's the podcast workshops around what is it exactly that you're struggling with and how we can help you to be able to implement some changes now, you know, and so it's been super inspiring for us and helped really spur a lot of conversations internally around how do we take, you know, what we help customers do and be able to just share it and share frameworks. So we really appreciate all the engagement from the community and everyone for showing up to the workshop and, yeah, just continuing to create this snowball effect of folks being able to leverage frameworks and methodologies out on your own in the wild. And it really is what fuels us knowing that these are takeaways that you can learn from, and we all learn from each other. So thank you so much.

A (4:09)

Cool. Thanks, Amber. All right, so before we get started, well, two things. One, we don't typically share the recording afterwards, so if you are here, please tune in. Give. Give this your attention, be present. I think you'll learn a lot from it. That's one thing. And two, if you have questions, drop them into the chat. Amber is going to be following the chat real time, whether she, you know, answers back or we bring it up at the end. We're going to have time for Q and A, but please feel free to. We love when people have conversation in the chat while we're teaching these things or while we're talking about them. So we encourage that. Other housekeeping items. Oh, well, what we heard from you, some of you will have gotten an email from me before which says, hey, you registered. Great. Can you tell us like what your pressing issues are right now, we want to hear from you so that we can tailor this session to speak to that. And so some of the things that we heard from you, new ARR growth is almost flat. It's been flat for multiple quarters in a row. You know, our GTM is way behind target. We need a better data to inform the strategy. You know, our company doesn't even know what actually generates ARR and what to do more of or what to do less of. We need new data to stop the obsession with MQL metrics. We need KPIs that generate better sales and marketing alignment. That's a very big one. And then how to make the case for a different set of metrics. And then finally the reason we are all here today, which is to see examples of what reports for high performing teams or what forward thinking teams are using now to make decisions. And so we're going to be sharing some of that shortly. But if you have something that you didn't see on here that you're really hoping to, to get from this session, go ahead, drop it in the chat right now. Like what is keeping you up at night? As whether you're a marketing leader, sales leader, CRO, whatever you are, please drop it in the chat. What is your biggest issue right now? What is giving you like the most heartburn? Come on folks, let, let us, let us hear from you.

B (6:03)

Annual planning has is definitely going to show up.

A (6:06)

I'm just waiting for the yes. While most of the yeah, attribution. That's meaningful. It's a big one. I hate the A word but we're going to talk about it anyways. Yeah, annual planning is a big one because folks are thinking about how they're going to deploy their budgets and they're getting, you know, their targets for next year and they're like, what the heck am I going to do? How do you start the conversation about Pesetta with leadership. Definitely something we can help you with. And I'm actually just going to tease. Our next workshop that we are doing is going to be hands on building a business case. It's actually going to be a two day thing. I'm going to walk you through it because I've lived that before so just plug in that now but we can talk about that later. Proving activity equals revenue alignment. That's a big one. Are we efficient? Are we inefficient? How to articulate what you need? Yeah, definitely. Cool. All right, thank you for that. Keep on dropping them there because we are going to look through this transcript after and make sure that what you are telling us we can, you know, try and help you with. So to kick things off, I want to talk about the status quo. Our last workshop, some of you folks were there, some of you were not. But what we had talked about in that last workshop is like, what is the status quo framework? Why do people use it? You know, if it's so antiquated by now, right, we're talking like decades old, a model that we're still using, why does that exist? Why is it still perpetual today? So if you didn't see that workshop, definitely send me an email. You maybe drop it in the chat. We can definitely send you the link. It's not publicly available. But since you're here and since we know you care about this so much, we will happily share that with you so that you can go back and just understand like the background on why doesn't this old model even work. But to quickly recap what I wanted to share with you is the four funnel model, like summarized, because every company is still using this today. Maybe you have one pipeline. There's a few that you know, don't even look at like the pipeline sources, but most have a method to the madness on how they, you know, credit teams, right? And how they, you know, measure their GTM functions. And this here in the left. And I'm using Miro just because it is more interactive than a PowerPoint. So you'll see me started scrolling in, scrolling out, maybe making some notes here. But here is an example. This is a real example of a 2025 pipeline report pulled from Salesforce. You can see the total ARR and pipeline. You can see the total number of opportunities there. This particular company doesn't have a four funnel model because they don't have like a, you know, a channel or partner motion or anything like that. So it literally is, did an AE source this, did an SDR source this? Or did marketing sources? Okay, and you can see now, quarter recorder, what that looks like, what the performance is like. The problem with this is, okay, if performance is down, if pipeline is down, or even if it's up, whatever. If you're wanting to make strategic adjustments to scale your pipeline, you know, to fix underperformance or whatever, this is not telling you a damn thing. It's going to say marketing's contribution was down, they need to go fix it. Oh, our SDR contribution was down, they need to go fix it. Right? We know that pipeline creation, revenue creation is not that simple that you can Just isolate it down to a single team that went and sourced that. It is a process. Much like a factory or a manufacturing facility creates a final product, it goes through a process. We need to know exactly what happens at every stage. And that is why I always use the factory analogy, is because a factory is extremely operates with an extreme level of precision and extreme level of rigor and therefore they measure everything end to end so that they can operate with the highest level of efficiency and know exactly how much supply they need to put into the facility to generate the final product. Okay, and that is exactly how we think about pipeline creation and revenue creation. And so just quickly, every company probably has a version of this and every, you know, company's definition might be slightly different. But I'm going to just share with you what we see most commonly. Okay. If it is marketing sourced, it typically is that marketing sourced the lead or that the lead reaches a score threshold. Maybe they did five things that, you know, caused them to reach a 100, you know, point score. Okay, this is now a lead that has been been marketing qualified. We're going to pass it to sales. Or maybe they raised their hand and filled it out. A demo request 30 days before op creation. Great. We're going to give marketing the credit. Okay. And then similarly you have, okay, is it AE sourced? It might be a marketing lead. Maybe it never became an mql or maybe they had a demo request that was, you know, 35 days ago. So it falls out, you know, falls out of the 30 day look back window, but it's worked by an AE. So we're going to give an AE the credit. And then similarly, sdr, Right, similar rules, but like an SDR worked it, so SDR gets the credit. In my opinion, that's extremely backwards because this traditional three funnel or four funnel model attributes opportunity creation to a single source or a single thing that happened. Even though we know in reality in all of our functions here that it's almost always the result of a combined effort between sales and marketing. This tries to narrow it down to one thing and we're saying, look, it's usually a systematic sort of like factory process that involves more than one thing happening. In order to scale pipeline or fix pipeline or just be better and know what works, we need to measure it better with more precision so that we can actually see more. And so the biggest drawback with this that we see over here is that it's overlooking key stages that we're going to talk about in a second called engagement and prospecting. Those are Two very distinct stages that happen before an opportunity gets created. And so when we're only looking at it in this one dimensional model, it is very difficult for teams to diagnose performance issues or identify what needs to be fixed when we just don't have visibility into everything that actually occurs before pipeline begins. Anything I'm seeing the chat popping off in the background. Anything that's coming up, Amber, as I'm talking through this, it's worth calling out.

B (12:22)

Yeah, I thought it was interesting that Tatiana mentioned that the, the spreadsheet example that you have over here on the top left is, could be applicable for D to D2C or right consumer, you know, direct to consumer, but definitely fails when it comes to B2B. That being said though, you know, for looking at the 2010s days, there was an era where this model was effective and it was good enough, right? Not that it's always been so broken, but it's definitely not helping us today in an era when building pipeline is more expensive than ever. And so that's what we definitely want folks to move away from that.

A (13:06)

Right? Here's another example of like a typical demand waterfall, right, that we would measure, right? Mql. So like we also measure pipeline, we measure revenue, but we also typically in marketing measure MQLs SQLs opportunity customer, right? And so you can see now the conversion between each stage. And you can see even in this example, the conversion in this actual HubSpot customer example is like a 2% conversion to pipeline. Very, very low. The problem is when you measure things this way, we know we need to improve the conversion to pipeline. We don't know why we don't have any of the little breadcrumbs that tell us why we are only converting at 2%. We need that insight to be better. Otherwise we are literally just guessing, right? And educated guesses are not a smart way to make decisions now or nor are they responsible. And so I promised you guys, I had said at some point I'm going to show you a couple of examples of what some reporting I used to create and what I see is very common. Okay. So all of our customers, when they onboard with us, we, we take a look at, you know, recent QBRs or reports that they use and to be honest, I am always, always dumbfounded about like, you know, I see some of the smartest CMOs, the smartest CROs and when I see the quality of the data that they put in front of their board, I'm astounded that that is considered acceptable because it doesn't tell us anything. And I was one of those people, right? So I speak from experience where at one point I thought, hey, this is great. My board's going to love it. But in retrospect, in retrospect and, you know, hindsight is. Hindsight is always 2020. I look at this and I'm like, it's just a bunch of fluff. And that's what we see all the time. And so this here is what I always would lead, you know, my board meeting section with a summary, right? We've got the funnel. I'm showing the, you know, visitors, prospects, MQL SQL one deal. Great. Look at all of this performance on the side over here. We're up 65%. Our one deals are up 25%. But what this does not show here is how inefficient or how suboptimal the conversion between each of these things is. We had an extraordinary, extraordinarily high volume of prospects that converted basically to a one deal at an abysmal rate, right? And so there's a lot of, like, sort of underlying things here that are hidden beneath the surface because we're saying, wow, look at how great our performance is. And typically, as marketers, we are tend to. We tend to want to sort of like, justify all of the things that we're doing and just prove our performance, right? That's, you know, problem number one. And then you can see here, you know, just a bunch of anecdotal stuff around what we were doing. Not. Nothing here is really showing what is working and what is not. And it's also hiding the real story. Then we have marketing contribution, full funnel analysis. You know, what did marketing contribute? What percentage of pipeline is in that stage? You know, let's look at, you know, maybe the source, right? And I actually don't think this is too terrible, but I will tell you one thing. I was very proud at the time that we were able to sort of correlate those opportunities back to, you know, something that we did in marketing and be able to show some directional statements around, like, what was really working for us, right? In this particular company. Industry events were very, very, very powerful for us. And so this sort of validated that a little bit, but it required a lot of gymnastics to even produce this. It was not standardized. I was the only person that knew how to do this. It lived in Excel. It was something that I sort of concocted and had to, like, backdoor and, you know, look at, like, these hidden fields that I was tracking in Salesforce. So not sustainable. And that is what I call the QBR fire drill is like jumping through hoops to try and produce some sort of report that only you understand. Like not great and not, you know, what I would encourage today. But it was what I had to do to tie my con, my, my efforts back to an outcome. Now we have lead activity. Great. What does this really mean? Do these leads actually go anywhere? No, it doesn't say a whole lot. Oh, great. Some stuff around brand reach. Look at how much our brand, you know, is improving over time through LinkedIn engagement, web engagement, total engagement, in my opinion. Cool. These are great vanity metrics. They mean absolutely nothing if the business is not performing. Okay, so again, a lot of smoke and mirrors. Pipeline stage by ARR. Okay, this does not tell us anything about where the pipeline came from, how long it took us, what is our win rate by, you know, the different sour, et cetera, et cetera. And so what I am showing here is very symptomatic of what I see all of the time, which is grasping at straws to try and tell a story around impact, which really doesn't tell you anything at all. And so that is where we now move to the GTM factory model. So we think of GTM much like a factory. It is not special to Passetto by any stretch of the imagination because you have winning by design who has their bow tie framework. This is not unlike that really at all. This is just our unique way of looking at it. But the objective here of the factory is to use that analogy of a manufacturing facility where we literally track everything as a very systematic, rigorous process where we can see everything end to end. And the idea here is when you see everything end to end, you can know with precision what works and what doesn't in an effort to eliminate the things that are resulting in waste, resulting in burning your resources, resulting in inefficienc, resulting in burn all of these things and focus on systematizing. You know, the three to five things that we know work really well for your business. So end to end visibility of your GTM factory. And also the biggest thing that we love about this is that it measures marketing and sales activities as separate but parallel. Right. We're tracking both their individual but also comp. Combined impact on pipeline generation and revenue outcome. So revealing how activities across different teams compound to drive results because it's usually sort of a mixture of both things when they come together and they work really well. Great. We can now see how much faster pipeline is generated at this percentage of an improvement from this other source. And it takes us this many touches to do it. And this is exactly what marketing needs to do to prime that audience, et cetera, et cetera. So that is the GTM factory model. I'm going to show you a visual of what that actually looks like more conceptually. I'm going to pause there quickly. Any other comments coming through, Amber, that are worth calling out? Yeah, for sure.

B (19:29)

We will definitely circle back to these. So keep putting the comments and the questions in, whether you want to use the chat or the Q and A function, these are relevant. And so we will be hopefully answering some of these questions over the course of, you know, our time today. And then we'll circle back at the end for anything that we want to dig deeper into. But yeah, these are measurement questions. For example, Paulina, what if you already look at pipeline as combined and you're not necessarily stamping pipeline by the four funnel model? You know, how do you improve? Improve measuring that way? And many questions similar. So, yeah, that's what we hope to break down for you here now.

A (20:09)

Awesome. Yeah, and I sort of said this at the beginning, like the de facto measurement for a lot of companies is like the forefather model or using like the demand waterfall in marketing. Right. But there are a lot of companies that like strip away any sort of attribution at all and just look at like one pipeline. But it really doesn't matter what you are doing right now if you're not tracking each stage. And this is where rubber meets the road.

B (20:34)

Right.

A (20:34)

We think of pipeline as stages. And most companies, even if you are looking at pipeline combined without looking at a source, there are still components of the factory that are commonly not measured, which still makes it hard to figure out what's working and what's not in terms of not necessarily which department is doing what, but just in general, how do we systematically produce pipeline on a repeatable basis? And that's what we're going to cover now. So first things first. When we think of creating pipeline, obviously we think about the importance of segmenting by opportunity type. Right. And so sometimes we see this where all pipeline is just one giant pipeline and we're not actually being able to separate that by new business renewals or expansion. Okay. And so what we were talking about today is very specific to new business. So that is why I highlighted this, because we want to really hone in on that because for a lot of companies, they are really focused right now on generating new logo acquisitions specifically.

B (21:36)

All right.

A (21:37)

So when we think about pipeline creation, we Think about three very distinct stages. We think about the engage stage. That is from the time in which we have the first captured signal from a person where they have, you know, visited your website, they have done something that we can actually track. Maybe they converted through a form, maybe you uploaded them into your database through a list. It doesn't matter. It's the very first thing that we can see in their activity. And then that goes until when that person is picked up by sales. Whether it's an SDR, BDR or your AES, it is that time between first signal. Okay, Sales went and pick them, pick them up, right? That could be 200 days, it could be 400 days, it could be 30 days. Depends on your business. Every business is different. Then we have the prospecting stage. And I put an X here because this is the thing that no company ever tracks well at all, almost 100% of the time, and is absolutely critical. And I will explain why in a sec. This is from the moment where this person, this individual contact or lead is picked up by sales. And that when they are worked through either an opportunity being created or disqualified, they've maybe said, you know, leave me alone, I have another contract. I'm not looking to buy, I don't have the budget, I'm not the decision maker. You know, I was just poking around, I'm not interested. Whatever the reason, maybe they just ghosted us. Whatever the reason is, it's from the moment we started calling them in an attempt to get a meeting to either that person became an opportunity or became associated with an opportunity, or they died out, nothing happened, closed, okay? And then finally we have the closing stage. That is the moment when an opportunity gets created through close, close, win close, one, close, lost, close, canceled, whatever. These are the three stages. This is the one that literally every company tracks very well. Most companies have flaws in terms of when they create an opportunity, how an opportunity gets created. But most commonly, of course, historically, we have always measured this stage very, very well. What I put across the top here is meant to reflect the thread, the thread that ties the whole thing together. Because the common denominator across everything is the individual person. Yes, we sell to accounts. Yes, we have account based marketing. Yes, the opportunity and the revenue comes from an account. But the person, the common denominator is the contact. Because the contact is interacting with your marketing, they're interacting with your salespeople and they are associated with the deal. Okay? And so the moment, we can't measure this entire thread of things that happen at the contact level. To the final outcome of the opportunity. This breaks and you lose full final visibility right away. And that's when we were trying to be left connecting the dots between what did they do, what do we do in marketing that influence them and what was the outcome. We need to see the causal chain of events or things that happen, linear or not, that happen from that very first moment to the end of the journey in one stitch together view that is possible. Everybody can do it. There's a framework to doing it. There's no secret sauce. And we're going to show you what that is. Presumably there's a timeline that occurs over all of this, but also there's many timelines. Whoops. There is a timeline typically for most companies that they don't know about, for when a person sits in this stage, for when they sit in this stage. And then when they sit in this stage. We want to be able to measure all of those things independently, but also as one full journey. On average, how long does it take us to close a brand new lead knowing that they sit in this stage for 200 days? Okay, they're going to sit here for 40 days and then they're going to sit here for another 75. That is the level of precision that we want to see. And then of course, marketing is also not just isolated to the things that happen before a journey begins. They have influence across the entire cycle. Right. And so we call that engagement, we call those signals, and we call that engagement. And so we want to be able to also see what level of engagement they're having between each stage before they're ever picked up by sales, while they're being worked by sales, and then in an active sales cycle. And that's one of the biggest flaws with GTM today, or you know, legacy gtm, is that we sort of like narrow in marketing to only really focus on top of the funnel and say, we're going to cut you off at the knees, just go find us the leads, go find us pipeline, we'll do the rest. Well, we have data now that actually suggests otherwise, which is when marketing is doing a good job supporting sales in this factory process, those people close faster, they're likely to close at a higher conversion rate, so on and so forth. And so as a former marketer, I really care about this because I want to help marketers justify, not even justify, but understand the influence that they have beyond just top of the funnel. Because marketing is a powerhouse. We provide a lot of enablement, a lot of support to our sellers. We run events, we do all kinds of things that are not just influencing this engaged stage, they're influencing prospects to book a meeting and then they're influencing pipeline to close faster. We are building trust, we are building credibility. And that is what this whole framework is about, is to measure what is happening with sales and marketing and then combined as a factory.

B (26:44)

Awesome. Thanks, Carolyn. So we could actually pause on that last slide real quick. A couple of rapid fire questions just to answer before we go in more detail on the data model. So one question was how would you bucket signals from the dark funnel, for example, intent, anonymous web activity. So really the great thing about signals is if you can track it, you can find a way to pull it in, then it can be counted as a signal. Right. So if there's a way that you can get that data, whether it's a LinkedIn ad view or a comment or something like that, some of the dark funnels still dark.

A (27:19)

Right.

B (27:20)

But if you can, if you could track it, you could pull it in as a signal. And that really just varies from company to company, what you want to make happen. Another question, how does prospecting work for a quote, high intent mql, if you book a meeting and create an opportunity immediately. So yeah, that's a fundamental part of this model is that you should not be creating an opportunity immediately. An opportunity should be qualified based on a conversation and either any other layer of qualification that applies to your business. But we do not advocate for creating opportunities from someone, even someone who may be an icp just booking a meeting. Right. That should be in the prospecting cycle. So you need to break that out.

A (28:03)

I also think I just want to interject there too. I think a lot, I think, I think it was Cali that asked that question, but I see that a lot. Right. We do that. That is sort of like the de facto right now is like create a stage zero or you know, stage one. That really is, it's like pre qualified pipeline. Definitely not something that we recommend, but we see it as sort of like a stopgap because like I had said, most companies don't track this. Right. So creating like, you know, automated opportunities for those sorts of things is a way to understand that journey sort of in like a haphazard way that we wouldn't necessarily recommend. Like an opportunity object is not meant to capture something that maybe isn't qualified yet. Right. But it's not atypical because that is the workaround that people have come up with.

B (28:46)

Yeah, it's actually a huge problem. And so breaking out your prospecting Cycle separately opens up a lot more granularity and visibility and strategies for you here. So we'll go into that more in detail. A question about target accounts. If you're, you know, targeting an account and maybe you're contacting multiple people at that account, yes, you can absolutely accommodate that with this sort of model. And so there's a couple different ways to do that. Feel free to send us a message separately if you want to talk about that. But basically you can have a prospecting motion, a prospecting cycle for the sales team prospecting into that account. There could be multiple contacts associated with that. That's one way you can do it. Or you can say, hey, each contact has its own separate prospecting cycle. But the main thing is that we want to track it.

A (29:37)

And again, that's not, that's not atypical, especially for sort of like a higher ACV like product that you might be selling or service. Right. Like generally in those types of environments, like we're, you know, we've identified maybe like five key stakeholders. Like we're not just going to call one and see if they answer and then move to the next one. Like typically what an SDR would do is like call all of them. And what we want to do is measure the lifecycle for each one. And typically an opportunity might actually have five or six prospecting cycles attached to it. Right. That's the thing is like prospecting to somebody is often complex. There's multiple people at an account. Not every prospecting attempt that we make is going to be successful. They might tell us to bug off the first five times before. Then they say, okay, cool, right. So there are a lot of variables that we want to see that nuance.

B (30:29)

Totally. And before we get into some really great examples too, the last thing there is, Donna called out how Salesforce, you know, the out of the box data model with Salesforce just like breaks the connection with a contact and opportunity. But there are ways to just go ahead and create this in your salesforce. A little more involved than using something that's like HubSpot, but absolutely a table sustainable nonetheless. So thanks for calling that out, Donna. All right, let's get into more detail.

A (31:01)

All right, so if you messed the, if you missed the last workshop, what we had basically said here or in the last workshop are like, here are all the data dimensions you need to go track. Here are the fields. If you don't have them, you need to go track these fields. So it was very much like these are the essential data points that you need to Track in this framework and then some questions out of that that inspired this workshop was like, what does that actually get us in terms of like our ability to report on different things? And so that's what we're going to start to move into today. But I actually want to, before we go to the next thing, which is actually to show you live examples of how all of this plays out, we want to walk you through what the core data sources and dimensions of data that need to be analyzed at each stage. And this is independent of how they're going to be measured. This is just like here are the things that need to be true in order to do any sort of measurement. Whether it's in your own system or in a third party tool or whatever, these are the things that need to be true. All right, so engage stage. Here are the common data sources for what we would track. We would look at marketing automation. That would need to be essential. Whether it's pardot, whether it's HubSpot, Marketo, you have a marketing automation tool that needs to be the single source of truth that we look to here, or at least a primary source of truth. We've got a chatbot, obviously that's something that you need to pull data from. Maybe you have a data warehouse that you're feeding data into. We'd want to get something from there. You have your CRM, HubSpot Dynamics, Salesforce, whatever it is that needs to be a source of truth here or a data source here. And then maybe you have like an event management platform like Cvent. There could be others. These are the most common ones we see from that. What we would want to do is understand the types of signals that are coming from these things. Right. Likely in your marketing automation, you're tracking web visits, you're tracking web form submissions. In your CVENT platform, you're tracking event attendance. In your chatbot, you're tracking web chats, you got a form. Maybe that's where you're tracking product trials. Maybe you've got Sendoso over here for GIFs, right? You're tracking gifts that are accepted. And so what we want to take is those data sources and be able to feed them into standardized signal types. Then of course every signal has a channel for the most part, right? So if people are, you know, heading over to your web chat and, you know, conversing with a bot or heading over to your website or, you know, filling out a product trial form, they got there from somewhere. Right. And this is a big data gap that we see in a lot of companies is we're not actually tracking channel types effectively with UTMs, but we want to be able to see all of the underlying channels that are leading to these signals that are basically living in these different data sources. Next, I'm buzzing over this, so my apologies, but I really want to get to the data examples today because that's, that's the magic of this workshop. Next, we have prospecting stage. Okay, this is the thing that I mentioned. No company tracks with any level of structure. And so what, you know, what we often do is say you gotta go have an object or a container for which you track. This sounds complicated. It's not. HubSpot has a built in functionality to do this. Most people don't use it or they don't configure it properly, but it exists. And then in Salesforce, this doesn't exist. And so we have basically a custom object that we would say go plug in this custom object and track it. But I'll get to that in a second. But same thing. There are data sources here that fuel your prospecting or where data is stored. The biggest one would be your sales engagement tool. If you're doing, you know, outreach, it's likely that you're using a tool like sales loft or outreach. You know, you're tracking calls potentially in gong, there are things like that that become essential to track here. Most people just do their prospecting and they're outbound in a separate tool. It goes on tracked until that person becomes an opportunity. And we say, oh, the SDR source this. We also know there are triggers. This is the biggest thing that we want to track. Right. This is not the last lead source or the last touch. This is totally independent of this. Okay, we want to say what was the thing, the tipping point that caused our sales team, our sdr, to call this person and to start working them? There's a whole bunch of different reasons. Like Callie said in the chat, they might fill out a form to, you know, book a free trial or book a, a demo or have a free trial. Great. That is a trigger. Somebody might raise their hand in an event and say, I want to talk to a salesperson. You know, maybe your intent data platform fed you this account and said, go call this. They, you know, have shown some level of intent. There's a whole bunch of reasons. We want to know exactly with precision what the trigger was independent of the thing that they had might have done in marketing. Right. Maybe they did attend a webinar Most recently in 30 days or whatever, but maybe that wasn't the trigger that actually went and encouraged your SDR to call them. Of course, at this stage we have activities. We want to know what those activities were and we also want to know what signals did those people show us in that timeframe. Surely when we are working them, trying to get a meeting, they're probably poking around on your website. Maybe they're going to fill out a form to watch a recent webinar replay. Maybe they're, you know, showing up at an event. Right, we want to know that, right? This is the thing that we often don't track, is that, okay, we pass this to sales. Marketing is done here, let's let them handle it. No, we want to know what impact marketing has in this journey. And when we're thinking about prospecting, as I said, most companies don't have any structure here. And so this is the structure that we would encourage, which is you have a new prospect, you're attempting them, you've connected with them, okay, you've either qualified them and they've become an opportunity, or at any point in time you can disqualify them for whatever reason. We want to know what that reason is, but we also just want to know when people are disqualified out of the stage. And then finally, this section here, closing stage. Your data sources, your CRM, wherever you're tracking opportunities, you have a bunch of different data dimensions you need to track here. Opportunity amount, the stages, the type of opportunity, you know who the opportunity owner is. But most importantly, and the reason I bolded this is opportunity contacts. Because this is like a classic flaw that most companies have, which is we create an opportunity, we worked a bunch of contacts. Maybe we have a primary contact, maybe we have five primary contacts that are involved in this decision making process. But we're going to get lazy. We're not even going to add them to the opportunity. Like I'm a sales rep, I know who they are, I don't need to add them. But that is a classic mistake because like I had said, the thread that stitches the whole journey together is the ability to know what is happening at the contact level. So the moment there is no contact on the opportunity, boom, you have no way to understand what that account or those people, the people at that account did before. So that is absolutely critical. Classic flaw, like I said. But there are definitely modern automated ways to make that happen. And of course we want to track activities and then of course, signals, same thing as before, right? Marketing has a role to play in moving a deal forward. We see in data, this is not My objective opinion, I have seen this now play out hundreds of times, that when marketing is focused on nurturing an account or people in an active sales cycle, those deals win faster and at a higher percentage than the ones that don't have a signal. So we want to know what those things are. A deal that closes in 30 days, what are their people more likely to do? Are they more likely to go look at resources on our website? Are they likely to show up at a webinar? And then if we know that the deals that close lost, they don't do anything. Well, great. We have an opportunity for marketing to get them to focus on those folks and encourage them to show up to events, because that is where we build credibility. Great. That is the data model explained.

B (38:35)

One more thing. On the prospecting side, we go back to the stages. So I just want to call it out. It's always. Are the prospecting stages in prospecting itself, Carolyn?

A (38:48)

Yeah.

B (38:48)

It is always worth calling out that this is not the same as lead status. This is not contact lead status. And if you're tracking it that way, you can't get these insights. You want to think about this as a box that's going down a production line. Every single time the prospecting cycle starts, you have a package that's on the prospecting production line. It's getting stamped with the all. All these details, all of this context at that moment in time. So that's why you have to treat it as its own separate container for each prospecting cycle. If you're using something like a lead status that's being overwritten, your data is being erased and overwritten. And so you cannot do any of this when you don't treat this like a cycle. So I just want to make sure that's clear.

A (39:38)

And then two, that gives you knowledge on like, how many prospecting cycles do we typically have to create an opportunity? Right. Because rarely is it. One, if you're just using like the standard lead stages, as Amber was explaining, you might just have a cycle that looks like it was like 250 days. Right. But when you do it, I'm going to give you a customer example here. We have seen, on average, it typically takes us seven attempts across six months. And each of those attempts is 25 days. Right. And that allows us to get systematic, knowing that, like, okay, this person is not going to, you know, be interested in the meeting in our first attempt. We might work them whole bunch. They might tell us to leave us alone and then we're going to try again in 60 days. Right. And so there's a, there's a journey here, there's nuance, it's different for every, every company. But when you do this, like the package, individual package for every attempt, that is where you actually start to get color and understanding around how much effort it requires to create opportunities from, you know, the different sources, basically. Okay, so before we get to the actual example, one more thing that I'm going to talk about here is the measurement ecosystem. Okay. When we think about measurements, right, we say, okay, if all of these things are true and you're tracking all of, all of, you know, these stages and everything, all the data sources that we want to be tracking, there are things that you can do with that. There's an ecosystem of measurements. And I want to be clear, not all of these measurements can or will live natively inside of your CRM. They are all possible, they are possible without a Passetto. What I will say is some of them need to live in your CRM. They just will inherently, because the data is natively in CRM. Some of them require a little bit of data transformation or merging together of the data. You can do that in a looker, you can do that in bi. You know, if you've got somebody really talented on your team, they could do that in Excel. It doesn't matter. All of these measurements here are possible with the skill set to be able to do that. I just want to be very clear and we're going to tell you which, which analytics can live inside your systems and which would be the ones that might live elsewhere. Regardless, we think that they're all important. Okay, we're going to hear attribution a little bit. We think attribution is important here, right? So we're not saying don't use attribution. It is part of an ecosystem. It is important at a certain point and we will explain what that is. All right? Okay. And so the other key thing that I want to be clear is the biggest differentiator here and what we're going to show is granular stage visibility, right? Most tools or most reporting aggregate, if you're even tracking it all pre pipeline activity into like a single view, right? Our stance is that you want to isolate, for example, signals by distinct factory stages, not just one mumble jumble mess of like, here's, you know, the stuff that somebody did before they became an opportunity. Right? The biggest differentiator here in this model is the three stages. All right? So for one, prospecting analytics, these prospecting analytics that track everything here, they can live in your CRM, Right? It's table stakes data. We consider this absolutely essential. It allows you to measure the performance and the efficiency of your prospecting journey independently from marketing, independently from pipeline. All of that stuff, okay, that can live in CRM. It tracks what initiated a prospecting journey, how long each attempt lasted, which triggers accelerate progression to pipeline, which ones do not, which triggers have better qualification rates, which do not, and granular insight into how effectively your prospecting motion drives opportunity creation. Great table stakes. You do need a container or, you know, an object to do it, but once you have it, the reporting lives natively inside your system. All right, next we're going to talk about signal analytics. This is measuring basically the mix of signals or things, first party interactions that your prospects or leads are having with your brand. If you recall what we talked about over here, that could look like a web visit, it could look like a web form, it could look like event attendance, web chat. Could be a plethora of things dependent on what data sources you have to track this. And this is what allows us to see the marketing impact at each of those stages. To say, what are people doing before they become an opportunity? What are people doing when they're being worked by an sdr? What are people doing in an active sales cycle? We want to see signals by those stages and across the full journey because this quantifies the stuff that we are doing in marketing. What is likely to have the greatest impact on pipeline and revenue, what is not? We commonly see things that companies spend a lot of money on that really don't show up at all. We want to isolate that, stop doing these things or stop spending so much on them because they're not having an impact.

B (44:15)

Right?

A (44:16)

That's what we want to see. And then finally, engagement diagnostics. This is unique to Passetto. Okay. This is where we now analyze more of, like, the behavioral tendencies of what folks are doing in their interactions with marketing. We analyze the quality, the timing, the depth of engagement across the factory stages. Right? Like, how many signals on average does somebody have in this stage? You know, how long are they sitting in this stage on average for how many signals on average are somebody having in the opportunity process? And so that allows us to just not see what things people are doing, but to understand the behavioral tendencies of what they're doing. And so something like this, this would not live inside CRM. You would require somebody like a Passetto or even your own, you know, team to go build this. It's possible. What we are saying here is that you need the underlying data to track that we think it's very important to track. We think it's very applicable and helpful to scaling your gtm. The thing is, this type of thing would not be something that you would go track in your CRM.