B (3:38)

Yeah, I think it makes me think about how slow moving B2B is and so we'll get into it in terms of like some of these. It feels like kind of on a merry ground that just like keeps going around to the same problem and it's like not really a whole lot of innovative ways of looking at this out there. Sometimes, especially when it comes to what's driving revenue, I feel like we just are still stuck in like a 10 year old like something that we were using 10 years ago and it's like, oh, it's hard. Change is hard. I know, but we love it. We love hearing from the community and having the opportunity to have these conversations and also reflect and see how can we help and how can we help clarify some of these things.

A (4:20)

So.

B (4:20)

But yeah, B2B is slow. My first career was in startups, like really early stage startups, some B2B and some B2C. But it's just like a totally different mentality when you're like in startups and you want to move so fast, right? And you're doing things internally so fast and you're trying to iterate and then to zoom out and think about what are we really not good at innovating at. And I think this view of like, how do we adapt? Like the world is changing so fast and we're like not really adapting that well in B2B.

A (4:48)

So.

B (4:49)

Yeah, so let's talk about it.

A (4:51)

Yeah, totally. I agree with that. And the reality is though, is that like we are stuck in a measurement model and even an operating model from at least a decade. Some companies are catching up and some are not. And yeah, so I'm thinking back to. We had. This is referring back to three conversations that we had last week. One is from a CMO of a 30 million ARR company. One is from, I think it was a VP of field and demand or something like that from a $2 billion company. And then another from I think around like 30 to 50 million. And it was a CMO on the call. Okay. So we get a lot of inquiries, I think from CMOs or people in marketing who feel this problem. But the one thing that all of these people had a problem was their pipeline conversion efficiency. So like inefficiency basically. The 30 million company had a MQL to pipeline conversion rate of 6%. Okay. So like quite low. And then a win rate of around 12%. The 2 billion company had an MQL to pipeline conversion rate of 2% and a win rate of 9%, which is like wowza low. And then the other one said they were meeting revenue goals, but the level of repeatability was really hard because they didn't really know how to like engineer repeatability over and over again. It's so, it just so happens that like things are going well, but I think that they're sort of preparing for like, oh, well, what do we do if it breaks and how do we just get better? So I think the core theme out of all of that is that everyone, specifically marketing, is struggling to convert demand into pipeline and revenue at healthy rates. Usually it's that volume is declining and then overall underperformance. So yeah, let's, let's get into that. What's your take, Amber? Just in general on like pipeline conversion and efficiency. Like why, why is that happening?

B (6:42)

Yeah, it's crazy because I know you talk about this a lot, Carolyn, but it's like you have people who are so good at what they do and so smart and forward thinking and so capable with this model that's like, what's our MQL to SQL conversion rate? And like it's like, oh, this is what they're being held up against, right? And like this is what, this is like what they have in their arsenal to go like, talk about like, are we doing a good job or not? It's just like, wow, we really need a better system. But my take on that is, first of all, what is really happening inside? We're not talking about attribution here at Pesetto. We're really talking about all of the different factors and influences and all of the different pieces that happen that lead to an opportunity that we're either not tracking or if we're tracking it, we're not capturing it in any meaningful way to show, like, what's driving results to pipeline into revenue and what's not. So when I hear a term like, or I hear a question like, oh, yeah, our MQL to SQL conversion rate is like 2%. I'm like, for what?

A (7:48)

Where?

B (7:48)

Like, what do you mean? Like, that's just like, what are you talking about? Thousands of leads that converted to a handful of opportunities. There's a lot going on in there. Regardless of, oh, this was the last touch or the first touch or all these things. Like, there are so many other factors. And when we constrain ourselves to this outdated demand waterfall conversion framework, it really just forces us to way oversimplify what's happening. And I think that is so heartbreaking knowing that it's the model's fault. And like you said, it's like the way our systems operate as well. So I just feel like you have a million questions. When someone tells you, like, oh, you know, our MQL SQL conversion rate is 3%, it's like, okay, well, where do we start?

A (8:37)

What about you? Yeah, I would agree with you and what you're saying around oversimplification. What I see is sort of like the visual in my head. Like, I'm like, hardwired always to look at GTM as like the bow tie, basically, or like the left side of the bow tie where you have like, awareness and then you sort of like at the one end and then closed one, right? And you see how people sort of like move through that. And so when I hear MQL to like SQL or MQL to qualified OP conversion rate, I just feel like you're seeing one total side of the bow tie and then like, or the funnel and then a metric on the other side. And I just sort of think of like, everything in between. And so for an MQL or an MQA to start, it's like, okay, well, like, why did they become that? And then you have opportunity. Well, like, what happened in the middle? Right? So when you think about conversion rate, the way that I think about it is like, okay, so like, it's either low or it's high. Or like somewhere in the middle. But do we have a view into why that is? Yeah, and I just, I think it's a really limited metric and I would love to talk about how we would view that. You know what I mean? Like, if we're wanting to help customers get away from just measuring MQL to opportunity conversion rate, it doesn't solve what we call the pipeline black box problem. And so if we were to take that example and hook in the pipeline black box or show them the data that's hidden in the pipeline black box, what might we actually see that would give them answers that they're like MQL to SQL or MQL to op conversion rate would not.

B (10:13)

Yeah, totally. And we just want to stay on this thread for a few more minutes with the concrete examples. And what does that measurement system look like? So some of it I feel like companies track today to a certain extent, every company is different. But what you want to look at is you want to look at like, well, what are the next steps? Like, what are the next things that could possibly happen once you get an mql?

A (10:33)

Right.

B (10:34)

So again, can vary slightly from company to company. Let's assume that you have an SDR motion, right? So mql, okay, great. Now you've got this lead shows up for your SDR or your bdr, whatever you call it, and then they're going to go do some qualification step, right? So this is like really being able to understand where these leads falling off. And I think that that's something that sounds so obvious, but in practice, when we analyze companies and we talk to them about their systems, they're probably tracking that like, what happened? Like, were they not a good fit or was it spam or did they just their email bounced or they never responded? Like, those are some of the next things that can happen. But actually tracking that in a meaningful way that allows you to like pull the thread and see, like, where did this come from? And not just, oh, it was direct traffic. Direct traffic doesn't respond well. There are other things that you can look at and see what was the next thing that happened. Right? Because that is a treasure trove of information. So you can see where things are dropping off in your quote unquote qualified bucket of leads that are being handed over to sales. And then you can see, is this happening consistently? Is it something that we're doing or is this the nature of that's not a good fit and is there sort of dropping off or is there some other way that we could approach this but just to be able to see like that drop off point, like so many companies don't even track that. And that's what really starts to show you like what's a huge waste of time and money. Not only spending resources, but also your team's internal resources. Yeah. So I don't know, we could go on in terms of like how to track what else to track. But are you thinking about.

A (12:20)

No, that makes perfect sense. The one thing I'm also thinking, so like I had said, there's sort of like this messy middle, right? An MQL becomes an MQL and somewhere before opportunity creation it either accelerated or fell off. And so that's what I would want to know. Are there any patterns that we can recognize? Where did they fall off? How? And then if there is a small percentage of those MQLs that maybe made it to opportunity creation, like what did that sequence of events actually look like? Can we extract any patterns there that are different than everything else? Right. That's not to say that like your MQL conversion rate is always going to suck, but there's a lot of really I think important breadcrumbs in that journey to like either becoming qualified pipeline or falling off that we miss. And that jumps us to the next thing, which I think is just like a lack of revenue visibility and just an over reliance. We talk about this all the fricking time about first touch or last touch or whatever, like flawed attribution model that you're using to understand what led to opportunity creation. Here's the thing is that when you get to that stage, okay, now you're looking at the 2%, okay, so if your MQA to opportunity conversion rate is 2%, so you're now seeing, well, what is the thing that led to that opportunity? Maybe they were like an inbound demo request hand raiser or your SDR team went out and like cold outbound at them, whatever. Okay, so you've got that. But what about the 98% of the other MQAs that you worked? You spent money to work, probably spent a lot of time. You have such a limited data set now that you're only looking at the 2% that you're really not looking at the inefficient stuff that you did before that you should probably stop doing or that's just leaking your resources to do you know what I mean? Again, pipeline, black box, it's all that shit that gets stuck in the middle on the line that we don't know like where did it fall off? Why did it fall off, et cetera? Yeah.

B (14:19)

And that's why we're developing Passetto. And what we have developed is basically a different fundamental model that really helps come in and, you know, sort of insert adjacent to this demand waterfall thing that's going on, because that's what's driving a lot of this and that's what drives GTM misalignment is when you have the marketing team is optimizing for X, the sales team is optimizing for Y, and each teams have their own tools and each teams have their own way of leveraging machine learning to understand how can we do better. But there's no, like, connecting the dots that says Joey on the sales team's the most prolific account executive that we have. And according to Gong, this is how he wins and this is how he gets his deals across the line. And, like, this is a sequence that he uses even to book meetings. Like, okay, you've got that and you go share it with your sales team. But there's no tie in about all the other things that are happening that influence this prospect to become an opportunity that wasn't in sales purview. Right. So it's just like we're not tying those things together and it just leads to massive inefficiencies because you can't see what's happening on the other side of the factory wall.

A (15:30)

Yeah, well, like, think about it. So if I think about, say, like a $100 million AR company, okay, so you think that they might have 30 BDRs or something like that, they've got their AES that are out there doing stuff, they've got their marketing engine that's firing, they've got a bunch of tools, automations, they've got their sales enablement tools. They've, you know, maybe layer it in some, like, AI. So you've got a mix of tools, processes and people. And all of these people combined, all these things combined are trying to generate pipeline. And so you have millions of things that happen in a month of emails being sent, or, you know, people basically trying to connect and get a meeting right across a bajillion different channels. And we don't have a stitched together view of all of that. So, like, you might measure your MQLs over here in marketing and then some of that gets passed over to sales and sales is doing a bunch of stuff and then that's out the other side. You have pipeline. Right. But, like, everything in the middle is happening in, like, all of these different silos. And then how can you Optimize something that you can't really measure. Right. Like you might be able to infer in your gong data, your reply rate, but that's all based on individual activities. The key difference is at the person level. Like you don't have like a simplistic person level view of all of that. And to add to that, when we're working somebody, how many attempts does it actually take to eventually get a meeting? An SDR might work. John Smith, back in May, didn't go anywhere. They might try again in June. Didn't go anywhere. Might try again in August. Didn't go anywhere. Okay. Now in September we were effective. Right. But those are several attempts over several months. How do we really understand what that journey looks like? Probably overcomplicating that a little bit.

B (17:20)

Yeah, well, we track the sales pipeline religiously. I mean, this is of course another huge variation that we see company to company in terms of sales opportunity, pipeline discipline and rigor and cadences. On the sales side, there's a lot that you can do to optimize that, but for the most part that's kind of common knowledge. Right, table stakes. Right, it's table stakes, exactly. But we are not measuring the pre pipeline black box largely at all. I mean, yeah, these aggregated numbers and volume metrics, they're not cutting it and they're not giving revenue leaders answers. For example, who's in charge of pipeline creation? Typically you have a head of revenue, a CRO. Some CMOs that we talked to have recently taken on like a pipeline target or a revenue target. Right. So if you're in a position like that every day you're waking up and you're constantly, you know, you're looking at all these dashboards, you're looking at these reports, you're talking to your teams, you're getting rolled up feedback around about pipeline reviews and stuff like that. And so you're constantly making sure that you're aware of what's broken, what's working, and where should I invest my time to help the team, where should I invest my team's time and resources and where should we invest to help grow pipeline? Right. So you're not getting those answers with the systems that you're currently using to generate pipeline, you're just not. So then you're a CRO waking up every day trying to go into your data. So in the weeds or, or you're asking Your RevOps or GTM operations person go pull reports. You're like out of nowhere because you're grasping at straws, trying to Figure out where the patterns are, what is consistently happening or not happening to generate pipeline. And then you're left like trying to basically piece together a story which we all know is not scientific and that's not the way that we want to run our organizations.

A (19:20)

But yeah, and then the over reliance too, I think on like volume metrics or activity metrics. I've heard, I've seen this a lot where, oh well, let's just like layer in an automated SDR or just hire more BDRs. Like I can literally think back to this 50 million company that we worked with where they're like really like BDR driven, but still no real visibility into what was actually happening, like on the production line, if I want to use that analogy. And if meetings were slowing down or a pipeline was slowing down, the first thing that would, they would turn to is like, well, we've got to like just get more bdrs dialing. Right. But my perspective is that a volume number or an activity number isn't really insight. Like just doing more is going to burn your cash, it's going to burn your resources. So like why would you want to work 10,000 leads in a month or something like that to convert 6% of them or you try and scrutinize what's actually making it down the line over to pipeline and start engineering that repeatedly. Because again, like not every lead is created equal. That's why I find like volume metrics are so frickin misleading. Because just dialing to dial is super inefficient. When we, when we don't understand the patterns of who we should dial when, why, how long is it gonna take them to like get a meeting, et cetera.

B (20:50)

Yeah, welcome to 2012 where we're really. All we have to go off of is we know that we make this many calls to this certain type of ICP and we're gonna get this many meetings and that's about all we know. So let's go do it. That is. First of all, it's not working out like that anymore. Right. So you need to lean into more resources that you have available. And we all know that we have so much data that we don't leverage.

A (21:19)

Yeah, yeah, for sure. Can we talk about.

B (21:22)

Also leads to the attribution mirage.

A (21:24)

Yeah, I was just going to say that. Can we talk about, can we talk about attribution again? Cause I just love talking about it. Attribution frustrates me to like no end. Because I think, okay, here's the thing that we see. I think That a lot of marketing leaders or executives think that, like, an attribution tool is gonna be the thing that solves their problem. Let's talk about why attribution is actually like a secondary thing. It's not the thing. Okay. And so I can tell you about my own experience. One working at a Series B tech company where we had really terrible CRM data. I plugged in an attribution solution. I was like, oh, this is going to be sick. It's going to give me, like, the answers I need to justify my investments, tell me what to do next, and blah, blah, blah, only to, like, I ended up ripping that out because I realized I had a bigger issue, which is, like, the data I really needed to see first is attribution doesn't fix a pipeline problem or doesn't fix, like, a flat pipeline trend at all. It's just sort of ancillary data that might help you make some decisions. But, man, I don't know.

B (22:28)

And then let's talk about that because I think a lot of people can relate. Carolyn, so can you walk us through? I know it sounds like you're not going to name. Name the attributes of solution. No, like name and shaming. But can you talk? Because this is a big player, right? So this is something that VPs and CMOs are looking at every day. Is this going to answer my problem? So you can you, like, kind of walk us through what was happening at your organization when you decided that you needed attribution? And like, what did you. What did. How did you make that decision? How did you get buy in? Like, what did that look like? And then what did the process of actually rolling it out look like? And what happened?

A (23:07)

Oh, gosh. Okay, let me just go back into my. My recollection. I have the worst memory of probably anybody on the planet. Okay. And also just like, no shade on attribution. Okay. Because, like, attributions are great and I think that they have a certain place in the gtm. I just think that if you're pipeline, I don't know, I think there's a bigger, more valuable solution that should come first before attribution. Okay, so let me think back. Okay, so scale up Series B tech company. We need to, like, ramp up our pipeline. We need to just, like, get more pipeline. Okay. How do we do that? Marketing needs to go out and get more leads. Okay. And this is before I really, I think, even realized I was on the MQL hamster wheel at that time. So this would have been what, like, 2020 or something like that. Okay, so like go get more leads. How do we get more leads? Well, I want to know what people are doing, right? What are people doing when they become an opportunity? Like what type of my programs are they engaging with? We ran a pretty lean marketing engine, so I didn't have a lot of paid digital out there, but of the events we were going to, of the con, like we had a huge content engine. Like what type of content were people consuming? Right. And so I think there was a larger issue that I didn't quite recognize at the time, which is, well, our opportunity data was really poor in general opportunities very poorly tracked, if at all. Like nothing being like account data on the account object was really shitty. We have like had a lot of like parent child sort of like opportunity account associations, arbitrary like pipeline numbers entered into CRM. Really poor process around opportunity creation and stage progression and all of that. Like deals sitting open for fucking ever, never getting closed out. And so did you know any of.

B (24:52)

That going into this?

A (24:53)

No.

B (24:53)

Okay.

A (24:54)

No, I didn't. So yeah, like I just sort of assumed a certain level of like data hygiene in CRM only to realize like the data hygiene was just really poor. And so how. Okay, so I've got like all my. And like even too on the marketing side, like our campaign, our, our use of like campaigns was a little bit messy, inconsistent. Like use of UTMs was a little bit inconsistent. And so like I'm not just throwing shade on sales for their OP hygiene. Like the whole sort of like data architecture piece needed to come first versus like slapping another tool. So I feel like that's just like the theme of the day, like let's just slap another tool. It's just going to get us the answers. Right? And so that was my first thing is like how can I extract meaningful insights from attribution first when just like my underlying data is not great for one, and then two, I think to anybody listening, they might say, well my opportunity data or my data hygiene is good. So I'm ready for attribution. Yeah, I think the attribution piece, where it is good is when you actually can understand your system for generating pipeline and then want to look back and understand, okay, what signals or interactions are people having before that. But, but here's what it does not tell you is if you have a conversion rate from lead to pipeline that is terrible. Attribution is only going to show you the stuff that made it out. The other side to opportunity creation is not highlighting all of your inefficient spend on all of these other things that are not working right, that is the biggest differentiator is that it's not going to show you your inefficiencies. So if I was to do this all again, that would be the first thing I would want to illuminate is like, where are we leaking resources and budget and pipeline, I would want to know that first. I would want to understand that as a core thing before layering on attribution. So I think that's what a lot of similar leaders do, is like, pipeline's flat. Okay, what can I do more of? Let's look at what's working, what signals are people showing or like what brand interactions are people having before they become an opinion. Well, if your conversion rate is not good, like you're looking at a pretty small sample size, what is that really going to tell you? You've obviously got bigger issues to look at.

B (27:09)

Yeah, I love the way you framed that. Makes it makes me think about you can't see the forest for the trees. You can't see the trees for the forest because you have so much in here. So it's someone in that position you're just trying to figure out like, oh, how do we grow it, how do we grow it, how do we grow it? Not what should we weed out? Because also when you weed out the underperforming tactics, programs, channels, activities, everything, you weed those out, then it becomes much easier to see and really microscopically and surgically review what is working and how do we maximize that. But yeah, I think that that's what that brings up for me is like we have this problem of bloat and we're so used to it. And we're used to these bloated volume metrics and these bloated tech stacks that we are kind of just operating as if that's normal and that's okay. And that's where we see the rub. Happens a lot with go to market teams as well. Because these things that we're talking about are fundamental first principles. You know, process design, data structure, disciplined things. And as a marketer, I could see why that's not what you signed up for. Right. As a sales or revenue leader, that's not really what you signed up for either. And so then who's gonna fix it? RevOps. But then what is RevOps doing? Maintaining the system that you put in place five years ago that everyone is so attached to. So the load. Cleaning it up. Yeah, cleaning it. Maintaining it. Because yeah, it's like so attached to it. And so that's RevOps is doing. So, like, yeah, getting this. These things to. That shifts to happen. And like, how do we help companies have these transformations in modular approaches is something that we've been starting to grapple with more is, like, it is a big transformational shift that we're talking about, right? So, like, how do we do this in, like, modular pieces? And I think don't have a great answer to that because it's kind of like, well, they're all connected and so you can't really, like, knock down one leg of the building. Like, the other three legs are gonna need to also come down so you can build the right structure.

A (29:28)

But yeah, I don't think, though, it needs to be like, you're saying it needs to be this, like, big transformational thing. So I. I wanna challenge that a little bit because I don't think it needs to be. So, like, if I think back to, well, like, our customers at Pesetto, yeah, they work with us a long period of time. They're not just here for a short period. Like, they want to stay with us, right. Because we've illuminated things for them that they couldn't otherwise see. But this pipeline black box thing that we're talking about being able to see. I always like to use the factory analogy, right? Because I think it's just really easy for people to understand. When I think about prospecting or marketing passes stuff over to sales, it's what's going on the line. So, like, if I think about it like a factory production line in a facility, you have stuff on the line and you know with extreme certainty how it's moving down the line and coming out the other side, the finished product. Right. We should be able to have that same level of visibility or scrutiny in pipeline creation in gtm. And so that is a very specific process, right? Like, who are you working? Who are you connecting with? What leads to a meeting? What leads to opportunity creation? Did we disqualify them? Why did we disqualify them? To illuminate that pipeline black box. It's not that complicated. For one, we can do it in a defined period of time. Not going to name how long that takes because it's different for every company. But it's not that heavy of a lift. And then once it's done, you then can layer in these other complimentary pieces that can help you. But that I feel like the what's in the pipeline black box or what's on the line is the backbone, the nervous system of your gtm. Every company needs it and once you have it, it changes everything in terms of how you operate. And that is literally the thing. The connective tissue or the nervous system is a thing that immediately stops teams from competing for credit or fighting or getting defensive. It just vanishes because you now have visibility where the finger pointing just stops. Because now we all have visibility and we know what to do about it, you know.

B (31:29)

Yeah, I'm so glad you called that out because it's easy to get back in that mindset over engineering or just thinking of like all or nothing. And I've fallen prey to that in the past as well, specifically as an operator. But yeah, it's not all or nothing. And that's the thing. As I start to see this play out in the real world more and more and more and just really going really deep on this problem of the pipeline black box, that's where I've really started to see it as well. Like you're saying it's like, okay, there are so many different facets to this. Yes. Okay, we acknowledge go to market is complicated. Yes. And also we acknowledge that there's a black box right here and you can just shine some light on this and it's going to massively help you, point you in the right direction. And then you'll be able to say on your own CRO or CEO or cmo. Well, now we can tell that there's something that's sort of breaking down over here that we can't really see it that well. It's like something in the opportunity cycle or the pre. The engagement cycle. And we'd like to be able to see that better so that we can make even more informed decisions. And so then that's where you say, okay, we're now we're going to prioritize this as a strategic initiative and go track this and implement this system for being able to see that better. But you don't have to bite off the whole thing at once. Like, it's not how you have to do it. So I think it's easy to think that you have to because we do know that the system is kind of broken at large. Right. And like, we would like to do something different, but we. Everybody starts somewhere for sure.

A (32:59)

I would just love to encourage. I don't know, I'm sort of thinking about like, well, who's the person to own this? Because for like, I think marketing executives want to be the person that owns it. But unless they are responsible for pipeline as a whole, which not always the case, I think it becomes really difficult for a marketing leader to solve it. And often because they're focused on marketing sourced pipeline, well, they're not really thinking about the system like the engineering pipeline, like a system. They're thinking just about like their programs, I think at that point. And so it's easy to sort of walk down that path of like, okay, I'm just going to go get attribution. That's going to solve my problem. And so I think who's going to champion this? It really needs to be the person responsible for pipeline overall. Right? Whether that's a CRO or like head of revenue or even sometimes a CEO. But it's really frustrating to have these conversations with marketing leaders who just feel that they can get their answers from attribution or looking at the signals when there's obviously like a bigger, the bigger thing that we need to measure. And also too, okay, so just reflecting on my frustration with attribution too, is because all of our customers, for the most part, like probably like 90% of them have attribution and they're still coming to us. So I'm like, dude, if attribution was the be all, end all solution to this problem, why are we here? Clearly the attribution data is just not really. It's not not really. It's just not giving you the data that you need to see. So like, let's stop pretending attribution is going to be the answer here. It's not the answer, it's the problem. In my opinion.

B (34:42)

Yeah, it's a mirage. Attribution is a mirage for what most people are trying to use it for, right? Carolyn, you called out. It's not useless. But the way that teams tend to use it as a justification is not gonna happen. And yeah, you're just gonna be left with questions as everyone is.

A (35:03)

So I kind of like, I think about like this thing that keeps coming into my head is like cooking a recipe. You know what I mean? Like if you're making some sort of like chicken recipe or something like that, the star of the dish is the chicken or the protein. You gotta make sure that you're able to cook that properly. It's gonna be cooked the full way through. You cook it at the right temperature, but you need the chicken to make the chicken recipe. Right. I sort of feel like attribution is almost like the seasoning on top, like when you wanna make it taste really good and make it better next time. Maybe that's a really shitty analogy, but you need the core thing in the recipe first. Whereas, like, I'll Give you my own experience. So when I used attribution, what I could see, okay, so like the data that I could see in it, right. We had a really solid content marketing strategy or thought leadership strategy. Attribution almost just validated that at a more nuanced level of like, well, where are people engaging with that? And everybody was engaging with the thought leadership. Okay, so. And everybody was engaging with events for the most part. And so like what did I really learn? New? Not much. It sort of directionally helped me with some decisions, strategic decisions around like the thought leadership strategy or content in general. But it didn't answer my fundamental question of like how do we engineer pipeline more predictably and grow it? So that's sort of my, my $0.02 on that just to like wrap up this conversation on, on attribution.

B (36:24)

And I think we, we should definitely talk about this more, maybe in a later episode, but absolutely. What you said about when we see teams that implement this alternative approach, which is tracking what's leading to opportunities or qualified opportunities, depending on how you know you're measuring it in your organization, what's leading to quality pipeline and the way that that brings your marketing and sales leaders together is incredible. And the way that it brings the teams together is amazing. And I was thinking about this for a customer that we have, Epicen, and it's been with us for over a year now. And it's wild being in those meetings with them as they're planning their QBR reports and stuff like that. Like, we've come so far from where they were reporting on these volume metrics and how are we converting from a meeting to an opportunity and now they are equipped with these metrics that just show so much more effectively what results their teams are driving for the business and what they're focused on and where the bottlenecks are and that we're focused on busting through. And it's like you don't even have to mention MQL count or SQL conversion anymore because it's just not relevant. And being able to be in these meetings where you have sales and marketing leaders collaborating together to help solve the same exact problem, which is creating quality pipeline is amazing. And it's totally transformed the culture at the go to market culture in the organization as well. So I'm super proud of that and the results that we drive. But it is so worth it. But yeah, I think maybe we don't talk about as much as we could.

A (38:07)

Yeah, I know we talk a lot about the problems, but that reminds me of another customer of Ours. And I just got a message from like our main like champion stakeholder there last week and it was like a screenshot of like an internal email that went around and he was like, success. Like we finally have escaped the four funnel model and like just so happy with that. And that's just because we've given them like a new lens to measure. And it just feels really good now sitting in those strategic sessions where there's absolutely no finger pointing, absolutely no mention of what department sourced what. It's strictly around performance. Okay. This quarter we created this many prospects, we work this many people. Here is the conversion rate to pipeline overall. Okay. What's driving that? Let's break it down. Okay, we're getting an inflation of MQLs hitting a score. The conversion rate is not quite as good as we want it to be. How do we fix that? Okay, let's make our MQL scoring model stricter so that we're not letting so many people through. Okay. We realize that's going to reduce our MQL count, but it's like MQL count is not even a thing anymore. We don't care about MQL count. We care about conversion to pipeline and everything else that happens in between. So it's love to see that. And then where does attribution come into that? Okay, well, that's the thing that we look at first. Then we look at, okay, of all of the pipeline that was created, what percentage of that pipeline had a signal on it or like, you know, a marketing interaction or something? It's 80% amazing. What's the average number of signals that person has? So like there's just different metrics to look at and it's like, it's just so much more productive. I fucking love it.

B (39:51)

Yeah, I love it too.

A (39:52)

It's amazing.

B (39:53)

And being okay, like having leadership get that level of buy in where it is a bit of a shock, right? When you're used to volume targets at this, like aggregated MQL, you know, opportunity volume, like 3x pipeline coverage. And we see examples where companies have 5x pipeline coverage and it's not converting. It's just not a very useful way to measure. But yeah. So I think someone we were talking to recently also mentioned, my CEO had pipeline shock. You know, like we don't want to have pipeline shock when all of a sudden the number of opportunities that we're creating goes down. But then it's like, okay, that's fair. But also got to give it scientific due diligence so that you can See what's happening on the outside, on the other side. And yeah, you have your hypothesis that's rooted in data. You can make those changes and say, you know, it's okay, number one, because we know that what we're changing is not going to massively disrupt the business because we have the data that shows that these things don't convert. We're not going to create opportunities out of this. And then you can see, wow, like, your win rate, like, shoots up. Okay, yes, great. These are the types of opportunities we want. Let's go get more of these. Great now because we cleared away all this other weeds and all this bullshit. We can go see where those are and how to influence those across the go to market. Right. Not just marketing. Go get us more.

A (41:17)

Right, yeah, love that. A lot of conversations, though, around channel tactics, you know what I mean? I think, like, marketing leaders coming in. Well, for one, I just want to clear the air. We're not a company that helps with, like, your demand strategy. There's other great companies out there. Like, we're not your partner in that. But I think a lot of people might think we are just because of Refine Labs and, you know, like, you know, Chris's legacy. There's. But still, we get a lot of leaders who are like, oh, I need to grow next year. Tell me where I should put my investments. What should I be investing in? I think we see a lot of that. I just feel like that's the wrong question to be asking, you know what I mean? And they're asking that without knowing, I think, what's creating pipeline or revenue in the first place. And so I just feel like there's just this huge camp or segment of leaders who think that way. And in my view, gosh, I just feel like it's really problematic to have that mentality sort of like as you're planning for 2026, without illuminating, like, the other things first, you know. Right.

B (42:20)

Putting the cart in front of the horse in terms of, oh, what channels everyone's, you know, using these and like trending and Reddit and all these things. And it's like, well, you don't even know what your successful customers are really doing, what they're finding value in across their journey, what they're interacting with. And you don't know all these things that you're investing in. Marketing. We talk to a lot of marketing teams that are so burnt out because they're just doing everything. And the perspective from, you know, marketing leader in that situation is like, we are bombarded with requests and ChatGPT has made it so much easier to make a blog post. So our team is sending us all this content. We have to cope with all these strategies and channel tactics. And it's, like, overwhelming. And they don't even know what's working, let alone all the stuff that's not working. And so that would be. Yeah, that's much more effective way to approach the situation.

A (43:18)

That reminds me of a call we had last week or something, Amber, where I think we had asked the person on the call a question around, do you have a good, solid way of knowing what's working and what's not first before you, like, you start thinking about new channels to invest in? And I think her response was no. And we use last touch attribution because it's just too complicated. That's the easier way. And, like, I don't think it's that black or white. And then I think she had said, pose us the question of, well, doesn't marketing just, like, need to do a little bit of everything? Like, that's how it works. Just marketing does a little bit of everything because, like, everything, you know, has an impact at some point or another. And I could not, I mean this, like, politely. Like, I could not disagree more. I've been there, I have felt that feeling. But it is black and white. And we can know that definitively with the right data set. And, like, no decision should be made from this blurry place of, like, yeah, well, I think you need to do a little bit of everything because that's exactly what leads to pipeline plateaus and decelerations. It's exactly what leads to CAC. An extra 50,000 here, an extra 50,000 there, an extra million here. Like, that adds up. And I think it's very irresponsible of a marketing leader to just say, like, well, everything matters because that's doing the company disservice. Like, that's potentially having a very negative impact on your company's ability to grow profitably, to have healthy EBITDA margins, to potentially have a solid exit strategy that they're going to meet? I don't know. I could go off on a tangent on, like, why I think that that's such a flawed way of seeing marketing, because marketing absolutely can be measurable, and it should be measurable. Yeah.

B (45:05)

Just because marketing can influence in everything doesn't mean that that's what's right for your business. I mean, we work with businesses that are around 10 to 150, $200 million in revenue. It's really, like, Our sweet spot. And so if you're at a company that's of that size, please don't go. Try to be everything marketing can be to everyone. I mean, oh my gosh, that sounds like a terrible, terrible solution if you're like an enterprise level company that has this like omnipotent position in the market and like even then you know what your most effective plays are. But you might say, hey, we want to be kind of doing a little bit of everything. But no, if you're a growth stage company, you can't afford to do that. Right?

A (45:51)

Yeah, for sure. I could not agree more. I'm looking at our notes. I think we went through everything we wanted to, to cover and I think we covered a lot of really good stuff. So I feel good about that. Anything, any like last words, Amber, before we close out on this one?

B (46:06)

Okay, so this has been really fun conversation.

A (46:09)

We talked about a lot.

B (46:11)

Yeah. Curious if you have feedback about some of the things we talked about, like just shoot us a message. But Carolyn, is there anything that we didn't talk about that we should cover?

A (46:20)

Well, I'm thinking about. I keep referencing calls. Cause we've had a lot of like super illuminating calls with people in the SaaS space in the last couple of weeks. But I'm just thinking back to that one VP that we talked to who was really feeling like this problem that we're talking about here. And I think we were asking him, what does that mean for you and your role? And he was like, well, I gotta do something differently because like I'm just gonna call a spade a spade. I'm either gonna like lose my job over this or like I'm gonna want to exit either one of the two if something doesn't change, you know, so.

B (46:54)

So horrible to hear, but so relatable too. Someone who's feeling burnt out and like you're kind of at your wit's end with how frustrate is. So you've been in that. You've been in marketing leadership roles, defending MQLs and defending marketing even. So like, what would you tell your former self about how to navigate those dynamics?

A (47:22)

Oh man, that's a loaded question. Because I spent a lot of time. Well, I think it's a philosophical thing that we're talking about. Right. Like it's a measurement model, but it comes with a different mindset or like way of thinking about this too. And so when I was in that spot, I felt like I was spending a lot of time educating and re educating my leadership or my executive Team of. This is how I want to approach this, and here's why it matters. And at the time, it's like, yeah, yeah, okay, got it, got it, got it. And then we would revert back. So it was like a lot of like, like, okay, we're bought into this. This is how we're going to do things. Like, we're going to measure it this way. And then like, lo and behold, two weeks passes and it's like back to the MQL conversation, right? It feels like, oh, man, I can't even tell you, like, how many messages I would field after hours to explain this again when, like, oh, where are my leads? You know, it's just like, well, I thought we were doing things a little bit differently here, right? It's exhausting. And so I feel if I were to, like, go, what I would tell this person, sort of like tell my earlier self, is stay focused. Because I knew at the time that the old legacy way was not going to work for the business. And it was my responsibility to, like, own that and to be the change agent. It's really easy to, when not everybody's on board with that way of thinking, to, like, get sideswept by old thinking patterns, you know what I'm saying? But the other thing too is I think people in this spot should really, like, reflect inwardly. And is this the place that I want to be building my career at? If you can't create groundswell and you've made a lot of attempts to influence change when you know things should be different, how much of your career do you want to spend trying to do that versus finding a better suited organization to solve that problem when there might be more appetite or less inertia, things like that? So I don't know, it's a really tough balance because, yeah, there's a lot of things that could be working against you. I think in that, in that environment, it's not an easy hill to climb. For sure.

B (49:31)

Yeah, that makes sense. Especially if you've already gone down the path of okay, executive. I know you're asking for this and this is what's important to you, so I'm going to give that to you. And also, this is what I know it's going to take to actually accomplish XYZ and go and do that as well, and then be able to show the results, like, simultaneously. Like, like, okay, this is the upside of doing this. And then now we can show you, here's the potential downside of not doing this anymore. It's like, let's go do more of this. But if you've already, like, tried that and gone down that road and you're still feeling that resistance, then, yeah, don't wanna try to force something that's not working anymore. Sucks.

A (50:08)

Yeah, for sure. Yeah, I agree with that. Cool. Well, I think we covered a lot of really good stuff on today's show, so I'm happy we just kind of winged this one. I think it was a good conversation. Of course, if you liked the episode, we would love and appreciate you to leave a review for us if you feel so called to do so. But we'll catch you all again in another show.

B (50:31)

Thanks, everyone.

A (50:32)

See ya, Sam.