Why Most Marketing Leaders Can't Prove Their Revenue Impact [Workshop Recording]

Summary

GTM Live – Workshop Summary

Episode Title: Why Most Marketing Leaders Can't Prove Their Revenue Impact
Date: March 23, 2026
Hosts: Carolyn Dilks & Amber (Co-Founders of Passetto)
Format: Interactive workshop with live chat and Q&A

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Episode Overview

This workshop confronts a central, persistent challenge for B2B SaaS marketing leaders: the inability to demonstrably prove their true revenue impact using traditional GTM measurement models. Carolyn and Amber dissect why legacy attribution and outdated metrics fall short, describe how shifts in buyer behavior and AI are compounding the problem, and provide a more modern, data-driven framework for measuring and maximizing marketing's real influence on revenue.

The tone is candid, practical, and supportive, targeting leaders frustrated with old playbooks and looking for actionable, credible solutions to improve unit economics, efficiency, and sustained long-term growth.

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Key Discussion Points & Insights

1. Why the Status Quo Fails (and Why It Matters Now)

• Traditional Attribution is Broken (03:44–05:05)

  • Marketing leaders often dislike being on the hook for revenue outcomes because current measurement methodologies (like last-touch attribution) underreport their true value.
  • Attribution models are narrow, omit “messy middle” activities, and end up reducing marketing’s leverage and credibility in the boardroom.

• “Only the elite 3–5% of GTM leaders have started to evolve.”
  — Carolyn (04:29)

  • Despite years of warning (Chris Walker, “Refine Labs,” 2020), most orgs are stuck on obsolete metrics—out of fear or lack of a workable alternative.

• AI & Buyer Behavior Accelerate the Crisis (07:20–08:27)

  • Zero-click search, LLMs, and growing data opacity are making attribution harder, not easier.
  • Focus must shift from attribution models to holistic pipeline analytics.

2. Legacy Metrics: What’s Wrong?

• The Legacy Demand Waterfall is Outdated (08:43–13:22)

  • Metrics like MQL > SQL > Opportunity reward volume, not revenue efficiency.
  • “You know your influence is more comprehensive than what actually gets credited.”
    — Carolyn (03:57)
  • These metrics show “fragments, not the system”—making it impossible to understand where conversion breaks down, why, or how to fix it.

• Black Box/Messy Middle Undermines Insight (18:04–20:17)

  • Traditional tracking covers lead creation and opportunity close, but ignores the vital—and lengthy—phase between: what happens to leads after they’re handed off? Why do so many die there?

• “This is a very reductive approach when we don’t track all of this stuff. This here is what we would call the genetic makeup of a deal.”
  — Carolyn (20:17)

3. GTM as a Revenue Factory: The New Mental Model

• Three Core Stages

  • Engagement: First tracked signal; awareness and lead generation.
  • Prospecting: Active attempts by SDRs/BDRs/AEs to initiate conversation.
  • Closing: Sales process from opportunity to win/loss.

• Each stage has its own critical metrics—volume, conversion, and time—and overlooked data.

4. A Modern Measurement Framework

• Three Key Metric Tiers Across the Factory (27:00–43:00)

  • Volume:

    • E.g. Number of target accounts penetrated; prospects passed to sales (by “trigger” like demo request/event/free trial); opportunities created; revenue won.

  • Conversion Rate:

    • E.g. % of prospects that become pipeline by trigger type; win rate by trigger type; cohort comparisons across markets (MM:36:00).

  • Time:

    • E.g. Days spent in engagement, prospecting, and closing stages.

  • Ancillary: Marketing Influence Signals

    • Avg. number of marketing signals per opportunity per stage.
    • Breakdown by channel and activity type.
    • “We’re not trying to ascribe credit, but to see what’s actually influencing buyers.”
      — Carolyn (42:22)

  • Efficiency Metrics (“Ninja” Metrics)

    • $Cost for acquiring new logos, pipeline, or closing.
    • Segment by sales vs. marketing cost.

5. Real-World Challenges: Cultural & Systemic Barriers

• Sacred Cow Mentality

  • Many orgs can’t (or won’t) move off legacy metrics due to political risk and habit.
  • Change is hard: Requires layering new KPIs, not ripping out old ones overnight.

• “Don’t rip and replace. Begin by showing what you can do in marketing, adding new metrics, then cascade.”
  — Carolyn (15:07)

6. Audience Q&A: Practical Problems, Lived Experience

• Aligning GTM Teams

  • “Would love to hear your thoughts on how to achieve this outcome as a marketing leader if not all GTM teams are aligned.” (13:58)
  • Incremental improvement and internal advocacy, not all-or-nothing transformation, is recommended.

• Messy Middle Data Fixes

  • Solutions like custom CRM objects (Hubspot prospecting cycle container) discussed (52:03–54:24).

• Cultural Roadblocks

  • Several attendees (Lindsay, Carrie) described real-life frustrations fighting for revenue alignment—and losing out to outdated mindsets.
  • Carolyn: “Anecdotes and perception don’t win in a boardroom… The moment you have a data point, you can build momentum.” (49:47–50:43)

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Notable Quotes & Memorable Moments

• On Attribution Models:
  • “Ambiguous attribution is the enemy here. You don’t need it. There’s a better way.” — Carolyn (07:30)
• On Obsolete Lead Handoffs:
  • “There is a huge fricking gray area, the pipeline black box, as we would call it, between lead created and opportunity created, where your leads go to die—and we don’t know why.” — Carolyn (18:23)
• On the Need for Data:
  • “To achieve these things… reporting your performance needs to be the no. 1 priority.” — Carolyn (06:49)
• The “Factory” Model:
  • “We look at GTM in three stages: engagement, prospecting, and closing… marketing has an impact across each of these mini timelines—we just don’t know what they are because typically we measure marketing over here, lead creation, and then we look at pipeline and opportunity closures over here, and then sh*t is all over the place, performance is down. What the hell happened? We don’t know because we’re not tracking it.” — Carolyn (22:50)
• On New Metrics:
  • “Stop encouraging me to drive MQL volume because we’re qualifying those at 2%… Not smart for marketing to do that. You need granularity in the data.” — Carolyn (38:19)

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Timestamps for Key Segments

• Status Quo Problems: 03:44 – 13:22
• GTM as Factory Model: 20:17 – 26:40
• Detailed Metrics Breakdown: 27:00 – 43:00
• Audience Lived Experience / Q&A: 13:58, 47:34 – 54:28
• Notable Quotes and Recaps: 44:35 – 45:51, 49:47 – 50:43

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Structure: The Seven (Eight) Key Metrics Summary

(The hosts reference a detailed slide deck with the metrics; highlights included below, [accessible in the upcoming deck for attendees])

1. Volume of Prospects (by trigger/campaign) passed to sales
2. Pipeline opportunities created (by campaign/trigger)
3. Win rates by prospect trigger type
4. Average time between each stage
5. Conversion rates by stage and trigger
6. Number & composition of marketing signals per opportunity and stage
7. Proportion of opportunities with any marketing influence
8. Cost (overall, pipeline, and closing subsegments) per new logo/revenue dollar

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Actionable Takeaways

• Start Small: Layer new metrics atop legacy ones; let data drive the conversation with leadership.
• Build Groundswell: Show early wins in marketing before pushing organization-wide change.
• Get Granular: Report by prospect/campaign “trigger,” segment cohorts (market, region, owner), and scrutinize the prospecting “messy middle.”
• Emphasize Unit Economics, Not Attribution Credit
• Utilize Peer Resources: Self-assessment forms, prospecting cycle objects, and workshops are available.

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Final Notes

This workshop encourages marketers to move past “fluffy” metrics and dubious attribution, focusing on real, actionable data that can bridge the trust (and budget) gap between marketing and the boardroom. Attendees appreciated the pragmatic guidance, validation, and encouragement for change—paired with tactical, modern KPIs they can start implementing right away.

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Resources / Follow-up:
Passetto will circulate the recording and detailed metrics deck, plus the free pipeline scoring/assessment tool discussed in Q&A. Connect if you want to go deeper or need implementation support.

Transcript

[A]

Foreign.

[B]

To our webinar. We're so excited. We had so many people register and so many new folks that are new to our Pesetto community. This is going to be so much fun. Drop in the chat where you're calling from today. Amber and I are in Florida together this week for the above the Fold conference.

[A]

Yeah, it's so sunny down here. Carolyn lives in Toronto so we felt like we need to, we needed to defrost. I feel it's been a really cold 30 days. Hope everybody's staying warm. And it's been really fun at the conference where we're going to be doing a lot more in person events this year.

[B]

Awesome. Okay guys, let's kick it off. We're in Miro today. Please. We love the live chat so keep dropping your stuff in the chat there. Amber's pumping away at the keyboard and the responses. So anyways, why we are here today we we want to help you shift from lead gen to real revenue impact and we're going to go through it might now now actually be eight metrics because we built on this but we are going to cover today examples of metrics that actually explain pipeline performance in B2B SaaS. We have just keep hitting home this topic. We got a bunch of people who are here over and over again. That to us reinforces it. This topic is super pertinent right now that LinkedIn posted it inspired. All of this came from the place that a lot of revenue leaders don't like to be measured against revenue. And I think that is because they don't have a really good way to actually do that. Which means if you're using something like a last touch attribution model or something that is really narrow and doesn't give you the full picture and it becomes really, really hard to really communicate what your revenue impact is. And I think a lot of leaders don't want to do that because of the fact that it likely underreports their true value. So when you're saying squarely like oh, based on this last, last such attribution model, you know, revenue only contributed 20%, right? That might make you look bad. It might, you know, cause you to lose credibility. It might end. The other thing too is that it really under reports your true leverage that you have and the impact across the full funnel, that hits very, very close to home for me having lived that. And so that is why we do what we do here is to help really build a story and a narrative using data around how marketing really influences pipeline and how you can Actually measure that with data, not anecdotes and not beef in the boardroom. So why are we here today? This probably resonates for many of you, but you're probably measured on pipeline creation AKA dollars of new opportunities created based on some sort of very narrowed attribution model. Probably last touch. But you probably know that your influence is far more comprehensive than what actually gets credited. Okay. And sometimes that field in on the opportunity record might be overwritten, which is bullshit. Win rates are slipping. Right? Yet the answer always from leadership what they insist on is add more top of funnel. You as a leader probably recognize that that's not smart but don't have anything beyond anecdotes to explain why. Once a deal enters sales, marketing's impact disappears from reporting even though the work continues. I think that's the biggest one is especially for companies that operate in with a mid market or enterprise motion marketing's impact is probably in most cases more important after an OP gets created than versus before. So that's another one. And you want to rebalance investment across real revenue drivers but you don't have good metrics to give you the proof to do it. Brand obviously pushing for, you know, brand investment is a really really hard thing to do. So the purpose for all of this is that with AI mostly channel saturation and shifting behaviors with buyers, but as well as just the ecosystem in general, that's changed everything. So the old playbook for measurement and execution is entirely obsolete.

[A]

Okay.

[B]

Only the elite 3 to 5% of GTM leaders have started to evolve. Chris Walker talked about this literally back in 2020, okay, in the Refine labs days and he was banging the same drum then. And what he said at the time was this is going to be generational change. Right? Six years later we're still having this conversation and we're only really now starting to see that shift. Not everybody has caught on yet, but what we are seeing is more leaders recognizing that the model is outdated but they don't have a framework for for what to do differently. So they just keep, you know, keep operating this way out of fear that they don't want to be disruptive in their organizations. They don't want to potentially lose their job or ruffle feathers. There's a lot of factors that are preventing people from making the change and being change agents. But I think a lot of it really comes down to a lack of understanding of like I don't know any other way. So every cmo, maybe you're a CRO, maybe you're a vp VP doesn't really matter. But if you're responsible for pipeline or you have a role to play in that these same four things are priorities for folks in these roles in 2026. Grow pipeline consistently, quarter over quarter and consistently, but also systematically. Like actually having a proven motion that you know is going to scale and not be volatile. Number two, being able to tie your marketing activity directly to outcomes with like without a shadow of a doubt, you have the data to go to the boardroom and be able to communicate that, have a seat at the table where the function is truly understood. I think this is a big one for a lot of people where just the CEO, the board, the elt, like they just don't get marketing right and they don't get what you're saying to them because it's filled with a bunch of bullshit fluff that doesn't tie back to true business impact and then be the catalyst for pipeline and revenue growth. I think that's what every CMO and growth leader strives towards. Like they want to earn respect through actual performance and be like, yeah, my function did that. Right. But you will not be able to get there unless you solve the underlying data problem. All decisions start with data, right? Not data invisibility. So full stop, if you want to achieve these things and you have volatile performance, these need the data foundation and you know, reporting your performance needs to be the number one priority. So. So any questions? Guys? Feel free to raise a hand at this point, but I'm going to shift now before we actually get into the actual metrics. I'm going to cover the status quo data limitations. We always go over this. I feel like everybody always needs a reminder, especially too when you're trying to reinforce this with your leadership. Anything that we should pause on? Amber, any thoughts come up for you or for anybody in the chat?

[A]

Yeah, I think Donna just sort of highlighted what you had mentioned around AI being an inflection point for this. We were talking about this at dinner last night too, Carolyn.

[B]

Yeah, we had a really like cool conversation with actually an AI SEO leader yesterday at this conference and he was just, he was telling us like how we're seeing less get tracked. Right. With the rise of AI and you know, search now happening in LLMs, there's this rise of zero click search which means everything that we're doing in brand that potentially is feeding into the knowledge of these LLMs or anything else, we're losing more clicks, which means we're losing the ability to track, which means there is more of a reason to Shift away from ambiguous attribution, which doesn't even capture everything. A lot of folks think that they need attribution to actually measure their impact. We're saying no, that's actually the enemy here. You don't need that, There's a better way. But I think with AI, the proliferation of AI, the fact that so much more is getting going on, tracked just like creates a really like systemic issue in marketing.

[A]

Yeah, put it in the chat. If you have questions from leadership or questions that you're asking within your team having to do with AI and how that's impacting your go to market performance or visibility into what's working, put it in the chat. If you have ideas or questions.

[B]

Awesome. Okay, so let's talk about the status quo, right? We're all really familiar if you're a marketer or even rev ops with how teams right now mostly measure performance, right? We either use deal source, right? What was the last thing that happened before the OP was created, or we track MQL and SQL, or we track, you know, some form of attribution. But the problem with all of these things is one, they're fucking outdated, okay? Like these metrics are decades old, right? They have not caught up to where the ecosystem is at. Secondly, all of these things show you fragments of the story and not the system. So when we hear marketers constantly say, I have a really hard time correlating my activities to actual outcomes. This is why these metrics show you fragments. They don't show you one stitched together view of the very non linear buying process and all of the little patterns of things that happen to actually create revenue and helping you surface those, because those are going to inform your decisions. So first things first, this is the demand waterfall, okay? This was conceptualized in the early 2000s, okay, more than 20 years old, if not before that, evangelized heavily by HubSpot. But here's what's happening. You have the, you know, traditional funnel MQL as they move through, these are. This is very much a system that rewards volume, right? This is what encourages us and what insists that we do more, right? The current model rewards volume because you go do more, you get more MQL, presumably you get more SQLs. If you're good at it, then you get more pipeline and you get more customers. But here's the thing, you can see the conversion rates between each stage, right? But if the conversion rate is low, this doesn't show you systematically why or what happens in between or help you understand where the drop off is. And most commonly, as we know, businesses Reward Marketing for MQLs. This is obviously a widely understood sacred cow of marketing. But what happens is the conversion rate sucks. Opportunity creation sucks. Our jobs as CMOs are on the line, our reputations are on the line, our credibility is on the line because we can't understand, okay, if our conversion rate is 2%, we know it's low, but we really don't know what levers to pull to go fix it. And this is exactly why the legacy demand waterfall doesn't work in our market anymore. So when we're trying to answer the questions that the board cares about, the model can answer for them for that. Answer them for us. For example, if our MQL to opportunity conversion rate is down 15%. Why? Why? What should we fix? I don't know. Our conversion rate's down. We can't really understand what we should fix. We might try and use attribution. It's not going to give us the answer that we want. How long will it take for MQL to actually become pipeline? The ones that are created today? Right. We know like that typical meme, which is like, okay, we're going to launch a campaign and then sales is knocking at our door, asking us where the leads are from that campaign that we just launched this week. Most companies have no idea how long that stage really is. For a lot of companies, especially enterprise and mid market, once you create a lead, it might take 2, 300 days, sometimes a thousand days to actually amount to something. Having that data to show your board gives you leverage to basically defend and also just make smart decisions about what you're doing and predict when pipeline is going to come, which channels and campaigns are actually influencing deals that close and how, where throughout the process are you seeing more higher composition of, of those activities versus a lower composition. And then what's the quality versus quantity trade off? I think a lot of leaders try and do a little bit of everything their leadership insists them to do. A bunch of shit that they probably know doesn't work. So what's the trade off? What are we going to scale back on? What are we going to double down on? We can't really answer that question confidently. We get that question a lot from folks that we talk to, which is like, okay, if I had, you know, a million dollars worth of pipe or a million dollars to spend on pipeline creation tomorrow, where would you put it and would you be 100% confident that that investment would pay off? I think that's the dream of any CMO to have that level of clarity to be able to make those kinds of decisions. I put in this meme too, because I was a little inspired by Jess Cook. She's a VP of marketing at Vector. She did a content session yesterday all about memes and how they use memes. So I thought this was pretty funny and relatable because we as marketers tend to keep investing in this stuff that we either know doesn't work, but we, the leadership insists that we keep doing it or that we're oblivious because we don't have the right measurement model. So here building a brand which is increasingly hard to track, increasingly more important now more than ever. And nobody's spending money on it the way we should because, well, we can't justify why and we can't justify our true impact on pipeline and revenue. All right, I'm going to pause there. Amber, you want to chime in with the questions that are coming up?

[A]

Yes. Lindsay had a great point here. She said, would love to hear your thoughts on how to achieve this outcome as a marketing leader. If not all GTM functions, marketing, sales, product, customer success, are marching towards the same goal and what you do to get everyone moving in the same direction. So, Lindsay. Oh, great. So I asked Lindsey, how is marketing gold at your company right now? And she said actually left because of this issue. Good for you, Lindsay. Yeah, love, that can be so hard. She said, I fought to have marketing aligned with sales on revenue. However, in my opinion, we needed to shift our product roadmap strategy, our narrative without pacing product reality, and increase our CS effectiveness retention with suffering. Wow, Lindsey, it sounds like you really identified some, some high level points there.

[B]

Props to you.

[A]

So yeah, Carolyn, how. How should we be thinking about that in the context of this workshop around if you're naturally not gold on revenue

[B]

or even pipeline creation? It sounds like. I think this is not a unique situation. I think a lot of folks like you, Lindsay, are in that situation. And unfortunately that's think that's a real bottleneck for like shifting the status quo and what's acceptable in GTM right now because it requires almost like this huge sea of change and overcoming what we would call like the sacred cow metrics of gtm. Right. So it's not a rip and replace. That is the thing we always say there's probably a bunch of strategic priorities that compete with one another. But honestly, to go from like A to Z, it's a process and it's a hard one. I'm not gonna lie. I'm not gonna sugarcoat it. And so I really respect the leaders that know that their, their innovative thinking like you have is going to be valued somewhere. Right. But there's a lot of legacy thinking that makes this type of work really, really hard. Um, so I just want to call that out. But it's not a rip and replace. It's identifying strategic priorities and incrementally making improvements. And so we always say don't rip out the, you know, analog or like the archaic metrics. It's like layering in new ones and eventually moving off and sunsetting the old ones. Because you, once you have new metrics that illuminate problems for the business and build alignment and show where performance is breaking down or accelerating, you can get leadership behind that. Right. And so we always say, like, start in marketing. Start in marketing and show what you can do and show the metrics that you can generate when you just go track a few other things or track things differently or start to make strategic shifts. And then eventually you can cascade that into other departments. But, yeah, thank you. Yeah, totally. Thank you for sharing that story. 100% relatable. I personally would love to pick your brain another time and just hear your story because super, super relatable. I think it applies to a lot of the challenges that folks are going through right now. Hopefully that that helps. So here's another thing. We have the demand waterfall. So that's one legacy metric. The other thing is the who sourced the deal. Like, we know that that's the sacred cow gtm, like the holy grail of how people are measured. Right. We know it's one dimensional. And I'm gonna explain why. This is a great visual. If your leadership, who doesn't really understand marketing, wants to know why you can't answer questions that they want answers to. This is a great visualization. It's a little far away from me right now, so I can't quite read it very closely. But over here on the left is all of the stuff that we typically track in marketing. Right. We generate leads. We usually map where they came from. Their, you know, original source, the original conversion point. Was it a webinar? Was it at the website? Was it an event?

[A]

Right.

[B]

First touch. Okay. And then over here, this is the thing that we track religiously and with a high degree of rigor, opportunity gets created. It moves from, you know, stage one to close. Right. Where did it come from? You've got a deal. Source field. Right. Tracks. Presumably the last thing that you know, the lead, if there even is one on that opportunity.

[A]

Did.

[B]

Right. Was the last thing that they did. And you have what we would call the messy middle. Ashley Lewin, the former head of marketing at Aligned, named this term, or at least she shared it with me. But it's this huge fricking gray area, the pipeline black box, as we would call it, between lead created and opportunity created, where your leads go to die. And we don't know why. We don't know what happens with them. Did sales pick them up? Which leads are more likely to result in Pipeline? Which types of leads are more likely to disqualify? How long did it take to disqualify them? How much effort and resources did we burn to disqualify them by just trying to get them on a call when they disqualified? Why are they not the right Persona? Are they not in market? Are they not ready? They don't have budget. There could be a slew of factors, but unless we know why, for example, our MQLs that reach a score threshold never make it to Pipeline, we need to know, did we like just burn a shitload of our SDR hours trying to get them to a meeting when they never went anywhere? And then the devil is in the details with the DQ reasons why. Maybe we're marketing to the wrong people, for one. Maybe we're scoring our leads way too early. That's just one classic example that we see almost near 100% of the time. But what we are saying here is there is a lot of architectural data, things that marketing should be looking at in this stage here, which sometimes can be, you know, 10 days, it can be 20 days, it can be two years, we don't know. But this is where marketing goes from guesswork to actually strategically understanding what's working and what's not. And being able to call that out. Right, that's not easy to say. Look, what we've been doing in marketing sucks. We gotta stop doing it. But here's why. Look at these conversion rates. Look at this high level of effort for stuff that goes nowhere. That's just converting, contributing to our cac.

[A]

Right?

[B]

There's a lot of stuff in here. And so this is a very reductive approach when we don't track all of this stuff. This here is what we would call the genetic makeup of a deal. That really specific DNA that gives you insight into understanding how Pipeline gets created and how marketing contributes to that. All right, so this is what we would call GTM as a factory. Three core stages. I'm going to send you guys all this afterwards, but this is how we look at gtm. We look at GTM in three stages called the engagement stage, the prospecting stage and the closing stage. This is that big like pipeline black box. We know that nobody tracks this and this is why it's so hard to get answers. But there is a factory process for all, like basically revenue types, new logo, expansion, renewal. But for the sake of this session, we're focusing really on new logo. But as we can see here, engagement stage really is that awareness period where we have basically the first tracked signal. Right. Remember, we can't track a lot of stuff too, but this is where we have the very first touch point that is tracked in our system. Even like record contact, record creation, lead record creation and all of the little touch points or even the time period where somebody sits in this stage before they are ever picked up by an SDR or BDR to be worked. Okay, so for a lot of companies, this day range can be very, very different depending on your motion, you know, what market you're selling into, industry, all of this other stuff. But it's usually an eye opener for a lot of companies to know how long this actually is and what things are happening in this stage. Next you have prospecting. Every B2B SaaS company does prospecting. Whether your AES are running that, whether it's AI, you got AI SDRs, whether you got a BDR team, doesn't matter. Whatever you get a lead or whenever you identify a target prospect, you are actively making attempts to book a meeting with them, probably 99% of which go nowhere. But we don't know that because we don't track it. We might have the tools that capture the data like Outreach and Salesloft, but what we're not doing is pulling that data into the journey that we're trying to understand what happens to different people, where they fall off, how much effort does it take to actually get a meeting with them? So this is a big X because this is a thread that stitches the whole story together. And this is where rubber meets the road. For a lot of companies that can't track this. We have a very special solution that can infer a lot of this data. So if you ever want that insight, you can hit me up, we can help you. But that's not the point of this webinar. But anyways, so we've got that stage here, can answer a lot of those, those questions that we can't answer right now. And then of course we have the closing stage, which is basically stage one through close pipeline, which we know, um, we track really well. But this Whole process has a timeline overall that a lot of companies aren't aware of. But then each of these processes also have their own like mini timelines. And marketing also has an impact across each of these mini timelines or long timelines. We just don't know what they are because typically we measure marketing over here, lead creation, and then we look at pipeline and opportunity closures over here, and then spoom shits all over the place, performance is down. What the hell happened? We don't know because we're not tracking it.

[A]

Oh my God. So much good stuff in here. So while we're looking at these three stages, let's talk about this. Carolyn. Okay, so Carrie says it blows my mind how many really high level marketing leaders throw this away. I appreciate the validation. So when she says throw this away, Carrie means the messy middle marketing leaders rely or really focus on those front end metrics in handing off to the SDRs. That is so true.

[B]

Yeah, but what, let me just interject there for a second. What if, what if those metrics were to show you that that was like totally inefficient for the business? Like what if you learned all of the stuff that you spend money on that you kind of think is working because you're doing your job, you're giving SDRs the leads. What if the story in data told you like that was super bad for the business? It's like, was like, you know, totally inflating. Your CAC was not contributing to revenue. It was super inefficient from, you know, a unit economic standpoint. Like what if you knew that? I think a lot of companies don't have that data to be able to communicate that.

[A]

Yes. And Carrie's saying actually it never worked. We say it used to work, it doesn't work anymore. It's just like it never worked, but

[B]

it used to be. Except acceptable I think. But that's when like the, we had this big VC era for a long time where money was like, they say money was like virtually free. Which meant people, companies could spend in excess and not have the level of scrutiny that we have today. Whereas why it's becoming so much more prevalent now. Prevalent is because there's so much more scrutiny on the, on the economics of the business. Right. That's why we're getting these hard questions from leadership that we can't answer. Right. The model hasn't caught up yet.

[A]

And hard answers, you know, so Carrie, you're so on point. So let me just bring this back again. You wanna, people wanna hide it. How do we reveal this truth without putting people on blast. And that's absolutely. It's a sensitive situation that we deal with every day. So yes, for sure. And it takes a lot of. Just a lot of. It's very bold. And then Reece said, stop doing sucks because if you're in a CRM or really, it's just so ingrained. The demand waterfall is so ingrained. Right, guys? And so he mentions that if you're onboarding, you know, into HubSpot, even if you're new in your career, this is the out of the box functionality. And I applaud HubSpot a lot for implementing the prospecting object, but it's kind of like half baked the way that they roll it out. And it's not out of the box something that you can just like take with and run with it. But yeah, the, the life cycle stage thing is just like, wow. People have tried to hack that in so many ways to answer these questions and we end up doing just the ass backwards stuff with lifecycle stages that it was never intended to do and it still doesn't work. So. Yeah, I hear you, I hear you.

[B]

I was gonna say, not only does it work, but it's just like, it's not smart to like operate a business where anytime you have to like pull together the data, it's just like a ginormous mess. And the fact that like only marketing then has access to the access to this data. So back to Lindsay's question, which is like, how do you, how do you overcome this challenge in an organization? Honestly, I really do think, and this is not, this isn't, this is sort of a subjective opinion, but I really do think that when you bring in somebody who has done it like a million times over and who has a framework backed by other success stories, I think that can build a lot of momentum internally within a company by having somebody, whether that maybe, maybe it's a, you know, an FTE in your organization who is hired to quarterback this, but I think really having somebody to own it, maybe not necessarily marketing, where there's a lot, they're dealing with a lot of like bureaucracy and internal politics and all kinds of nonsense, it gets really hard. And so one thing I would champion for having been in this situation before would be let me bring in a partner that I really trust to help me create, you know, groundswell around this. Right. Okay, I'm gonna keep going here. Okay, so this is the factory model. This is our visualization of it. I know it looks like a funnel. We don't Want to think about it as a funnel. In fact, we really try hard to not use the word funnel in any sort of like taxonomy that we use or, you know, however we define the process. But you can see here, this is a factory stages and it's, it's not unlike the revenue, the winning by design bow tie at all. We borrow a lot of our frameworks from that. We have a lot more tactical frameworks for execution, but it's not unlike the bowtie methodology at all. But I want to shift here now to helping you understand. Okay, I get it. I get you're showing me these stages. I get conceptually what they are trying to answer. How the hell now do I go track the right KPIs for this? And now this next visual that I'm about to share with you, which I'm going to send you all after, is very comprehensive and very detailed. So just bear with me. I'm going to walk through it and explain it all to you guys. And understanding, okay, knowing this is the GTM factory process, where does marketing and our marketing metrics fit into all of that? So on the top over here, we got marketing metrics. We're going to talk about that after. I'm going to zoom in over here. First we think of key GTM metrics. These are table stakes. Okay, I'm going to go through them. But I think the biggest problem for marketing is that typically we overlook the key revenue visibility metrics and jump to the marketing attribution metrics. So we're going to show you what the key GTM factory metrics are. We have three components. We have volume metrics, which are typically volume counts and dollar amounts. We have conversion rate metrics, and we also have time metrics. So starting with engagement, this typically is where marketing does historically and traditionally focus their effort. Right? This is us building awareness to generate leads to pass them to sales. Right. That happens in the engagement stage. But marketing impact is not exclusively here. So we're going to cover that in a second. But what do we measure here? We have volume metrics that happen typically at the one end of this process. We know our tam. We have target accounts. We want to know, okay, how much of our target accounts have we actually penetrated? But we can't know that unless we have a volume metric to show us what our target accounts are. And then at the end of this stage, before sales picks them up, we have another volume metric, which is number of prospects, basically passed to SDRs and worked. Now here's the kicker is that we want to Know this volumetric number of prospects that we're passing to sales by the trigger type in which we pass them to sales for. Why did we think it was a good idea that sales needs to go work these? Right. This is where we apply a lot more granularity here, which is not just okay, it was an mql we pass it. Some examples of this might be an event hand raiser, a free trial on your website, a contact form submission, a demo request, MQL reaches a score threshold, inbound email. Like there could be a number of them. But this is where we really want to know, okay, of all of the stuff that we're kicking over to our SDR or BDR team, how many of them are there and what's their type? What was the trigger for which, you know, sales is going to start working them. Now this is where we say start small in marketing because you can track this in marketing really pretty easily. But ideally we want to track this across the entire go to market so we can see beyond marketing. You know, we want to understand cold outbound and all these other motions and you know, what your AES are prospecting. But for argument's sake, and why we're here today, we just want to know what is marketing passing to sales and who are they? What is the composition of why we're passing them to sales? So that's another volume metric. Now out of that we have the closing stage after prospecting, which is another volume metric, which is number of opportunities created and also amount of opportunities created. But that's not enough. We also want to be able to slice and dice that by this volume metric, which is okay, pipeline and opportunities created by the various different trigger types that resulted in that opportunity. Right. So this is where you might start to be able to see the composition of pipeline from hand raisers versus pipeline from events. Right. Again though, this is not last touch. It might sound like last touch, but I'm going to show you why it's not in a second. We're trying to isolate not the last marketing activity that happened, but literally the catalyst for initiating our attempt to have a conversation with these people. So that's volume metrics. And of course over here you got revenue, right, that's the last volume metric. Okay. And then with each of these you have conversion rate metrics. Now I'm going to try and skim through these quickly, but what we're saying here is of the prospects that marketing pass to sales and that become pipeline, how many of them are we qualifying? Right. Overall in marketing, we Might only be qualifying, you know, call it 15% but the devil is in the details. Again, holistic metrics don't give you the real story. So we want to know basically was it do hand raisers drive up that increase, do free trials drive them up or drive them down? What is driving the conversion rate? And this is where we isolate basically where we're failing based on the different types of people that we are prospecting to. And we can do the same thing with win rate. We can say, oh well, our marketing's contribution to pipeline has a greater win rate when the prospect trigger is X versus when prospect trigger is why? So when you can go to your board and say stop encouraging me to do, you know, to drive MQL volume because we're qualifying those at 2% and those win, you know, at 3%. Not efficient, not smart for marketing to do that. But you need to have more granularity into the data. So I have a few more resources on that, but I'm going to skip up to over actually I'm going to cover this first. So these key metrics, this is where it gets fun because you can actually then start to basically run a bunch of different analysis based on different data dimensions here. So what you can do, and I'll explain in a second, is you can look at that factory process, you can compare, okay, let's look at all opportunities. For example, win rates by the deals that had marketing influence before and those that didn't. Oh great, our win rate is 2 times higher where marketing has this thing that happens in that journey. Right. We can also look at all of these components by the different trigger types which I had talked about. Right. MQL versus event, handraiser versus Demo, request versus free trial. You can also run an analysis and this is a big one, which is why it's bolded, which is to understand how you impact driving pipeline and revenue across different market segments. Mid market versus SMB. I know it sounds so simple and so easy to do, but I'm surprised to see how many companies don't look at marketing and different dynamics based on the market segment, of course, region, that's a no brainer. And then you can start comparing and contrasting. Okay, what happens with close one deals versus close lost. And then finally this is another big one. But it's comparing the dynamics of new deals versus boomerang deals. This is a term sometimes we hear with our clients which is it was a deal, it closed, lost, it became a deal again later. We call that a boomerang deal. And Then of course you can also filter or segment all of this data based on the opportunity owner, you know, the BDR owner or what have you. So that's all very, very interesting. And then let's move up to here, which is marketing influence, otherwise known as some level of attribution, although this is a little bit different than that. But once we understand, okay, in marketing, our performance really sucks when we try and generate pipeline from MQLs. Our performance really sucks when we try and generate pipeline from free trials. And then whereas, oh, our performance is great when we get hand raisers through the website, we gotta figure out how to do more of those. Right. That is when attribution or signal analytics becomes an ancillary sort of like additive to the story. Right now we're understanding, and we're working backwards and trying to understand behavior behaviorally, what people are doing through the various stages of their buying journey. And not just that top of funnel either. Okay, so the typical metrics that we look at here when we're talking about marketing influence, I know some organizations have done a good job of layering in these metrics overall. But we want to know, firstly we want to know on average per opportunity that gets created, roughly how many signals or touch points actually happen in each stage. Right. We might see really big differences in each stage. You might see, you know, a greater proportion of signals and engagement. And then commonly what we see is, okay, well, there's Nothing happening when SDRs are working that person or there's nothing happening when they're in a sales cycle because we're not syncing that, those contacts or those, those people back to our marketing automation platform. So a lot of dynamics there. But what I'm saying is just understanding on average how many signals per opportunities are even happening or how many signals per contact per opportunity are happening. So there's some interesting stuff there. But then also understanding the, the channel composition of those signals, which means what percentage of those signals are coming from paid search, what percentage of those signals are coming from paid social. And now we're just talking literal percentages and not necessarily trying to weight them with some form of attribution. We just want to know what people are doing. We're not trying to ascribe credit to things. So for example, you might look at your engagement stage where there's opportunities and say, okay, when there was a signal, 90% of those was coming from direct traffic, or 90% of those was coming from paid search, or maybe even 2% of those was coming from paid search. Right. Very useful. If you're spending a lot of money on paid search and you say, oh, that's interesting. The signals that actually show up in this journey that we're tracking aren't coming from paid search. And then you would want to understand the signal composition too, which is, okay, what are people actually doing? Are they looking at page views? What kind of page views are they? Are they attending our webinars? Are they attending our events? And then finally marketing influence overall, which is what percent of opportunities in this engagement stage? And you'll do this at every stage. But what percentage of opportunities in this engagement stage even had a signal? And oftentimes what we see is that we drive a lot of MQL volume. This is where you get that disconnect between sales and marketing, which is you drive a lot of, you know, engagement in the market. But when you're closing deals and you look back, you often see, oh, you know what, 60% of our opportunities that we created and closed never even had a signal. This shows that whatever marketing is doing in the market is not correlating with what sales is working and closing. And so that's often a huge eye opener. I will say I see this happening a lot, especially with companies that are trying to sort of move down into an enterprise motion or SMB motion. A little bit different with high volume, low ACV businesses, where this is a little bit more likely to. Marketing is more likely to have influence here, but very, very hard for marketers driving strategy in larger enterprise driven organizations. So anyways, these are the three key metrics that you would want to track at every stage. And I think what is super eye opening for a lot of companies is the propensity for deals to win when there are signals and when you can see what people are doing in the closing stage. Right. And so oftentimes a correlation that I see a lot is that marketing influence is super low in an active sales cycle. Meaning, okay, maybe 20% of our opportunities actually even have a signal. Now that doesn't mean that they're not doing anything. There's limitations with signals, meaning we can't see everything that's even happening. But from what we can see and from the contacts on those opportunities, they're not really interacting with any of your marketing assets at all. And oh, look, your win rate is going down. But what if we look at this cohort of opportunities, maybe the 20% that did have active marketing interactions, maybe they come into events, maybe they're consuming your content, what have you, oh, look, those win rates on those deals are 20% greater. That is a spectacular narrative to go to your board with to justify reallocating budget to more pipeline acceleration programs. So there's a lot of really cool nuance here. You can better understand what channels are more effective at the various stages and what are not. Oftentimes you see channels are underutilized like email and things like that. So a lot of really cool details here. And then finally, for the organizations that have achieved a certain level of maturity and knowing what works and actually tracking it, these are the NINJA metrics. But we like to think about GTM efficiency. Many of you will have heard Chris talk about that so much, but this is understanding what our cost to grow is. And now you can do this in marketing, just with marketing costs. But ideally this is looking at all cost for acquiring new logos. That would be sales. That would be your SDRs or BDRs. That would be your marketing headcount. That'll be your program spend. It would be every cost in your organization to generate new logos. But it also doesn't have to be because I know that's not always realistic for marketing to get their hands on that data. But anyways, what we want to do is understand, okay, from a dollars in, dollars out standpoint, how much are we spending per $1 of new logo growth? Is it $10? Is it $5? Is it $2? Is it, you know, $200? I don't know. But what we want to see here is just our general efficiency, right? Are we in a decent range from a unit economic standpoint, from a CAC standpoint, in terms of what we are actually spending to generate new logo business? So that's a really big one because all of this is really important context. So if you can get your hands on this, this is key. But you can also break that out. You can look at the cost of closing. Now this is the typo on here, so I got to fix it. But this is the cost of closing, which is basically your sales costs, any cost that you're allocating towards pipeline acceleration to close. And then over here you can look at cost of pipeline. Okay, so this is meant to say cost of pipeline. This is meant to say cost of closing. But together these two costs make up cost of new logo growth. So if your cost of new logo growth is really, really high, you want to be able to know why, where is that breaking down? And so is it because you're spending way too much in pipeline creation for what you're getting back, or is it that you're really inefficient in terms of you got the pipeline, but you're terrible at closing it. And there could be a number of factors that are driving that, like low quality pipeline to begin with, underperforming reps. But this lets you see that. Right? And so it's one thing to understand where marketing has impact and where things are breaking down. It's another added really insightful strategic layer of data to know how. What your financial efficiency of all of this is. I just went on a fricking tangent, so I'm going to stop and let Amber jump in with the questions.

[A]

Yeah, resonating. Seems like this is resonating and a couple things. So Lindsay said, when we think about the signals that you layered in at the end there, Carolyn, and you're always so careful to preface that with a. Almost like a warning.

[B]

Warning.

[A]

Okay. So Lindsey said those marketing influence metrics are really where your quote, fluffy brand and content marketing can get the credit they deserve. Mm.

[B]

Well, again, I hate to use the word credit because it, you know, responsible GTM is not about credit. It's about doing smart, strategic things for business impact. But yeah, I mean, if you can track something and you have a data point for it and you want to understand what things are influencing the full journey and where they're influencing them. Yeah, I think that's an important metric. It's something I would advocate for, but I would only advocate for it and show it to my board once I really understood, like systematically, like what's working and what's not. Right. It's additive. Your board doesn't care about attribution or influence or any of this. If you're not hitting the numbers and if you're not hitting them, they're going to want to know why. And so this story over here is just not going to give you that. This is just really good data. And I've seen a lot of marketers use this really well, like Sam at Loxo, for example. Like, there's a lot of companies that do this really well, but again, it's additive. It comes after the fact. Once you know, oh, is my marketing function strong or weak? And why?

[A]

Another one from Carrie. She said, you're so right. I've seen this, especially with events where the growth, the growth teams want to support marketing investments. And she said, I've been successful in the past, but by pointing out that event engagement with folks in pipeline is typically a great way to move pipeline stages. Yeah, we see that a lot. And what we really love to also enable is prospecting as well. So It's a little bit easier to track when you have somebody in the event. You can just create, like, a custom field in Salesforce. Did they attend the event or whatever. But pipeline acceleration. Yes, absolutely. That's amazing. So we have a few minutes. Carolyn, what do we want to do?

[B]

Yeah, well, I do. I just want to show everybody. But I do have seven key metrics, so I kind of talked about everything at a high level. I can go through the seven key metrics if we want. They're all here. You're all going to get this deck afterwards. So I can go through what those are because they're very specific. Okay, go track these things. So I can cover that off, or we can kick it over to Q and A.

[C]

What do you.

[B]

What do you guys want to do?

[A]

Yeah, I feel like we should do Q and A. We often don't have time for it, and we. When we send over these. Donna says yes to the metrics. We'll send over the metrics, but we'd love to see if anybody wants to raise your hand,

[B]

come off mute. Yeah, I love that. Yeah. And if we don't get any questions, we can totally jump to the metrics, too. All right, folks who are still with us, what do you think? What questions do you have?

[A]

I want to hear from Carrie, specifically. Carrie, can you come off mute? Because I feel like you're.

[B]

Ah, there she is.

[C]

Your child.

[A]

Okay, we want to know what's happening.

[C]

Thanks. It's just super validating, right? It's hard to come up with questions when it's like, yeah, yeah, yeah. But I'm the person still sitting there going, why are we still talking about this? Because, like I said, I've been in complex sales. I think you guys do a great job of breaking this down into systems that are convincing. I think if I was to say one question. Do you have, like, a fun success story where you had a breakthrough, right, where you were not getting heard, or they, you know, somebody brought you in as a consultant? Because, I mean, obviously, I'm banging my head against the wall.

[B]

Like, that worked.

[C]

My making sense.

[B]

Oh, gosh. We have all kinds of them. What are you talking about? Carrie, what's your role at the company?

[C]

Yeah, well, I lost it, so.

[B]

Oh, man. I'm sorry to hear that. Please don't take that personally, man. We're at a really tough time right now. Like I said, it's like a really big sort of, like, catalyst in the industry where. Yeah, it's not fun. I mean, the market is tough right now. I think for employee retention and things like that.

[A]

Just because you lost the role doesn't mean the role lost you. So what's your role, Carrie?

[C]

That is a really good point. So complex sales in health plan technology and there was a continuous focus on driving the old fashioned lead. So, you know, I invested in some phenomenal research that really identified what Mark Shaver calls the seam and ended up in a conflict where an internal consultant was like, well, that doesn't match what I'm saying is happening in the market. And I'm like, this is an awesome opportunity, then we could break through on that. But, you know, I lost that argument. But there was a real focus on that front end lead and people saying, okay, you had a great event. Okay, you had a great webinar. What C suite person can I call from that? You know, and I'm just standing there with my mouth and going, are we all speaking English here? I'm very confused. No one? No one?

[B]

No ce.

[C]

Something came to the webinar, but thanks for asking. Now here's your engagement across LinkedIn, across digital advertising, across events, across the research. These particular accounts are bubbling up as engaging with us in the last 90 days. But, you know, again, I didn't win that argument.

[B]

Well, I think. Did you have data to. To like emphasize why you had that stance?

[C]

No, not strong data. No.

[B]

So that's always. I know it, I know it sounds so overly simplistic, but anecdotes and perception don't win in a boardroom, sadly. And I hate that. Right. But the moment you have a data point from usually like, you know, you can pull it with, you know, your rev Ops buddy in the organization.

[C]

Oh, yeah, no, actually, Carolyn, I did have that. It was just disparate. Right. The ops team wasn't strong, so. So no, I felt like I had slides. I'm like, this is what's happening across. Yeah. I wasn't just talking with nothing in front of me. It was literally losing the conversation because that leader was like, look, we're going to focus on leads. We're going to focus on hiring SDRs out of India. We're going to call companies. And I'm like, that's not going to work.

[B]

Yeah, sorry, go ahead.

[A]

See you later. I would not want to be on that sinking ship either.

[B]

But yeah.

[C]

And you're not wrong.

[A]

Thanks for sharing that.

[C]

Besides, healthcare health plan tech isn't like gonna sell right now. Like, look at the market. Obviously that's what's really happening. You know, look at the stock market for the last two months.

[B]

So appreciate it. Yeah, I mean, I don't think you're really set up for success in that organization. Like, did you just. I'm out. I'm curious. Did the organization, like, what was their brand awareness strategy? I think you mentioned events, but, like, what gets people to want to buy in the first place? Right?

[C]

Yeah. I think that's what surprised me, Carolyn, is that I had been, I've gone in and out, back and forth between large enterprise, complex sales stuff and new SaaS to Enterprise. And so it was sort of like old home week, getting back and then going, oh, my gosh, you guys have made no progress.

[A]

Oh, my God. We were just talking about that last night. Yeah, it's like six years in B2B is like 48 hours, so. Yeah.

[B]

Yeah, we have time.

[A]

Okay, well, great to hear from you, Karen.

[B]

Thanks so much for that.

[C]

Yeah, I don't want to be your Debbie Downer either.

[B]

No, no, it's great. Thanks for sharing that.

[A]

So good.

[C]

Thanks for sharing that.

[A]

Okay, Reese, let's. Come on, because I, I, I don't think that your question's too big, so let's see how we can chunk it down real quick. We've got two minutes.

[D]

I love all this stuff, and I have been working on implementing this kind of system in my Org the last,

[B]

like, three or four weeks.

[D]

And I guess my question is, like, how does, how does one really start in their CRM, breaking this, like, building this kind of infrastructure? Basically what I did was created an object called a prospecting cycle and began, like, doing properties that are specific to the prospecting cycle in terms of signal, trigger signals, triggers, all that kind of stuff. So that kind of compartmentalizes it for every single prospecting cycle. Like, I'm kind of on the right track there. Correct.

[A]

You really are. You're in HubSpot, Reese.

[D]

Correct.

[B]

Yeah.

[A]

Yeah. I would use the prospecting pipeline, and I would use the leads as a, as your container. But you might, it sounds like you're on the right track either way, but I would use the lead as the container to stamp the data. That's what we do. Sounds like you're on the right track. I'd say, I'd say narrowing in and zooming in on the black box of prospecting is gonna really open up a lot for you.

[D]

I was on your, your webinar workshop two, three weeks ago or something and filled out the form and, like, realized just how much of a gap there was on the prospecting side. And I was just like, oh, that's, that's no mas. So that's kind of what pushed me to start building kind of like the product.

[A]

And you did. You got the scoring and everything, which anybody, if anybody else wants that by the way. It's totally free. It's like a self assessment and it scores you. It's very like operationally tactical.

[B]

We'll send around the link for that for sure.

[A]

Did that help you understand kind of where to start focusing? Sounds like Reece?

[D]

Yeah, absolutely. I mean that's. That was the entire reason. And it. It also kind of framed like because I'm a very visual type person. And so once I had that form and I was able to be like, okay, that makes sense. Like I can now see what that's talking about in the gap that I'm missing. Like functionally in my CRM, what is missing and what's not being measured and how I need to build something to measure that stage. So, yeah, that's I guess a plug for yalls form that helped me see the gap there.

[A]

So sounds like you're so on the right track and props to you. Let us know how it goes.

[B]

Thanks, Reese. We appreciate you for coming out to all these and super cool to see that you that. That that assessment was helpful for you. All right, guys, we are at the top of the hour. Folks are dropping off too because they've got other calls. So thank you everybody. If you have questions, let us know. We're always here to help and we'll send around the recording. We're actually going to do that this time. Folks really want to get the recording when they can't come or if they want to look back at this. So thanks y' all and we will see you next month on the next web workshop.