When Dashboards Divorce the P&L

Machine-generated transcript from the episode video. Speaker labels are not included and some names and product terms may be transcribed phonetically.

[0:00] The dashboard was green. Pipeline was up. Forecast confidence was high. And the quarter still missed. Not because demand slowed. Not because sales execution failed, but because GTM metrics stop mapping to financial reality. Welcome to GTM WA. Today's episode is about why GTM metrics break as companies scale. Not because teams stop executing, not because demand disappears, but because dashboards drift away from financial reality. My guest is Rowan Funkin, CMO at Planful, a company where finance planning and go to market are forced to reconcile inside the same system. This is not a conversation about better dashboards. It is a conversation about coherence between GTM and the P&L.

[0:46] Let's get into it. Rowan, where does the disconnect between GTM dashboards and financial outcomes usually begin? I'd say the uh you know the the guilty party is the CRM. It's a it's a designed tool for activity tracking for salespeople helping their managed business and ultimately we've let that become the foundation of our insights for the business. And and so as you think about kind of how that model works, it's designed for sales behavior and sales outcomes, not financial outcomes. And so ultimately that that becomes the big disconnect where you've got different metrics being tracked, you know, at a very operational level and then the finance metrics on on the other side don't don't connect.

[1:30] What early signals does finance see before GTM teams notice anything is wrong? I'm not sure finance gets that many early signals. is typically a bit of a lagging part of the business. But what they would tend to see when things are starting to go to go wrong is slowing collections. You know, sales have sold the wrong type of customers, those customers are unhappy. Deals that close but never expand. So, you know, customer churn. If you're in an MR based business, you'll start to see that decline of usage because sales are selling the wrong thing. Um, CAC payback will slow down. Um so it just really becomes kind of um you know finance are the warning system for the macro. I think in the in the micro sales and marketing tend to see um the early signals of kind of behavior but often uh they're optimists right um us in marketing and uh our sales team we always think we can sell through something or market ahead of something uh and finance meaning that the the pragmatists really can can cut through and and find that gap. At what stage do

[2:32] dashboards start telling a story finance no longer believes? I'm not necessarily sure it's about the dashboards themselves. I would say it's when finance uh finance starts creating those shadow tracking tools, right? So they start haircuting a sales forecast for example because the sales forecast isn't accurate. It's not giving them the insights. So they go and build a shadow model. So that's typically what I see is where where that breaks down is is the trust in the existing system the existing dashboard goes away finance therefore go and build a second version or a second model and that's also true sometimes I think for operators when they're looking at what finance has produced it at a plan level finance doesn't have time to plan at maybe all the dimensionality that uh the business operates in territories different markets different segmentation and so then the operators go and build a more detailed plan of how they want to execute against the plan. And so then you get this big disconnect between the two.

[3:32] What is the earliest warning sign founders almost always rationalize? It's probably the gap between pipeline and bookings, right? Um yeah, we've got a full top of funnel. Pipeline's good or it's bad. And the the delta between the two could be segment, it could be conversion rates at each stage of the funnel, it could be ARR value. And you know, you got a lot of different things in the mix there. And and they'll often try and rationalize away, oh, that seller can sell and they they'll figure it out or this new product is going to help us change our win rates. And so they rationalize away, you know, the real kind of core unit economics of the business based on kind of feelings of what's going on and what's about to happen. Which GTM metrics are most trusted and least reliable?

[4:20] I'd say the forecast. Um I think you know sales leaders truly believe in their forecast. They believe in the system that they've built to underpin it. But you know I would say you know if you ask finance what the forecast accuracy of a sales forecast is their confidence level in that is typically pretty low and by the time you get to a board meeting or a kind of uh you know an executive level presentation that sales forecast been haircut by finance probably two or three times. Why does pipeline coverage create false confidence? pipeline coverage creates false confidence because it's just a raw number. So, you know, that could be your ARR is maybe you're selling into multiple markets and your ARR number for a certain type of deal is larger. So, people don't look at it by count and by ARR. The other part of that is it doesn't often get to conversion rates and getting to the true analysis of that pipeline coverage. sales velocity, sales quality doesn't exist inside inside the coverage metric and people don't get um granular enough in terms of the unit

[5:23] economics they're seeking to then analyze the coverage against that. Do we have enough deals in the right stage in the right segment with the right sellers? It's really hard to get that level of granularity and and folks miss that. How do conversion rates lose meaning with our margin and quality content? So, let me pause on that one. How do conversion rates um reply that one again for me? Yeah. Yeah, sure. How do conversion rates lose meaning without margin and quality context? Pretty simply, you could be looking at conversion rates being quite successful in one part of the business, but it's a losing business in terms of unit economics. Uh you're setting yourself up for churn. Maybe the customer is too big, maybe the customer is too small. Implementation quality is mismatched with what you historically do. And you might find an opportunity within a market segment, but it's not a market segment that you should financially go after because over time you're going to be losing money. You might be pouring too much money into it from a from a marketing perspective. You might be spending too much time on it from a sales perspective. So just the raw

[6:26] conversion and win rates don't tell the true story. Why does pipeline often look strongest right before performance misses? Depends on the sales leader mood. Sometimes I think uh you know if someone's under a lot of pressure they're going to stuff the pipeline. The inverse is true though, right? Often sales people will sandbag their pipeline to make it look like they're doing it tougher than they actually are. They'll push deals further out so that they don't get the operational scrutiny of the sales team on them. And uh that really creates a a bunch of distrust with finance because they're looking at a pipeline gap that is is much larger to plan than they they want. Yet the coverage is actually there. it's just sales sandbagging and so you end up with this big disconnect between the operational metrics and the financial metrics that uh finance are looking for.

[7:12] What do most CMOs never see because they are shielded from the P&L uh CAC and LTV? So a lot of CMOs live in that operational world of driving more meetings, driving more MQLs and uh they look at things like rorowaz and CPL and they're not actually looking at what the total cost to the business is. I think that's evolving right now, but that's a trap that some some CMOs absolutely fall into, especially if they come from that paid media background. They don't kind of see the overall impact to the business that that type of pipeline and that type of customer can create. How do budget decisions change once cash timing and payback enter the picture? Um, well, marketers, uh, we never went to school to learn acrruel based accounting. And so we tend to think of just as we spend money, that's how it goes, right? And when you actually look at when the cash goes out the door versus when the the spend hits the books, they're two different things. And so marketers really need to understand kind of acrruel based accounting so they

[8:14] can help help try and manage cash flow, especially in an early stage business. You might be signing up for a lot of events. They might have positive ROI. They might be really good, but all of those events put cash out the door of the business really, really quickly. Most of them require 50% deposits up front at time signing cash before the event. And so the payback from when that cash goes out door to when the customer actually pays can be 9 to 12 months. And that that can have a huge impact on a on a fast growing business in terms of cash flow. And where does demand genen look efficient on paper but destructive in reality? those met magical metrics I was talking about earlier. Those operational metrics MQLs, meeting bookings, ROAZ. Um it's it's the same story. You've got the the operational metrics on one side which we care about, we look at and we absolutely need to care about them. Um but uh it's it's what then comes out of that in terms of the outcomes. I think marketing obviously we've we've had attribution capabilities for a long time now. We try and count everything along

[9:17] the way and then sometimes we forget to step out and look back at the bigger picture and say okay well um what's the macro uh investment in this system and what's that producing? How does this blind spot compound as teams scale? It really becomes the problem that you're funding an inefficient model right if you don't have a really strong view of the unit economics that you're seeking for the business. You can keep investing in growth, but growth doesn't make it efficient. And so, as you keep investing in in a really bad model, which looks good on paper cuz you're growing, you're building bookings, you're doing all the things that you you know, you're being asked to do, but until you step back and look at the underlying unit economics of of where are you efficient, that's where the masking happens. So growth can mask a lot of things. And so it's about is this efficient growth? Is this growth that we can keep reinvesting in? Is this growth that is durable? They're big questions that only really getting down to that unit economics level can can answer for you.

[10:16] Why do forecasts feel precise but collapse under scrutiny? Accuracy is a great signal of confidence, right? I mean, if you can uh you can create a a forecast that says, hey, uh you know, I've got this down to the penny, then people have this misleading guidance that it's actually accurate, but it's definitely not right. So often granularity becomes the goal when actually accuracy should become the goal and uh and people try and put too much weight into the granularity and that creates this uh false need for precision and uh you end up being more inaccurate because of that false uh false goal of precision. How does weighted pipeline create false certainty? I think it doesn't get into enough detail. So number one, I've never met two RVPs that follow the same process in a sales organization. uh one will treat one one seller differently and a different seller differently. And so unless you're waiting the pipeline against all of those variables, which becomes, you know, operationally inefficient to do, weighted pipeline

[11:19] becomes just a proxy and again you then get back into the accuracy problem, right? It becomes really inaccurate really quickly. It's a helpful metric. It's a helpful directional metric, but it's not something that you can really run the business on. which inputs matter far less than teams think. Pipeline coverage, it's just a I have seen teams uh do really well with weak pipeline coverage because the quality was strong. The ICP of the the pipeline was much stronger than previous quarters. And so if you're just seeking this perfection of pipeline coverage, you've really got to look under the covers to understand what is the shape, health, velocity, conversion rates, um you know, quality of that pipeline. And there's so many factors that go into that that uh it becomes challenging for people to to operate. Um and we always just think of the 3x pipeline number and that's the magical answer and it's very rarely true.

[12:14] What financial signals actually improve forecast reliability? What finance signals improve forecast reliability? I would say it is the unit economics on the back side. So, hey, we know that this customer, this type of customer in this segment of our market is a highly repeatable, great lifetime value, low CAC. Um and if you can work together with finance and and go to market teams to go and uncover a lot of look alikes in that then uh the forecast does become more predictive and more accurate because you've got it's a it's a kind of reinforcing story where you've got really good customers that can tell that story that got the benefits and you're more confident as a goto market organization. So I'd say it's the unit economics underneath that help really drive the go to market efficiency in that part of a business. If a company wants GTM metrics to reconcile with reality, where should they start creating a relationship of the of the metrics they're looking at? I always like to say that there's three types of metrics in a business. You've got

[13:15] financial metrics uh which Binance care about. You've got operational metrics which sales and marketing teams care about and agreeing on the focus in the middle which are the commercial metrics. If you can agree upon those, what do we want them to be? What do they look like? What's important about them? Then that translation between the operators and the finance function becomes a much better engine and everyone can really agree on how the operational metrics drive the commercial metrics which drive the financial metrics and the financial metrics are what get put out to the street, what gets put out to the board, what gets put out to investors. And so uh if you can be really confident in the underlying operational metrics that drive there, that becomes a much better story for everyone.

[13:56] What shared definitions matter most between GTM and finance? Yeah, that's the that's the commercial metrics that I was talking about in in the middle there. So, that's a lot of the unit economics. I know I've kind of um uh been a bit uh harsh on pipeline coverage, but um if you can really agree on the detail of pipeline coverage in in that middle area, then it does become really important. um when you can start to uncover uh things like cost per opportunity for marketing within specific segments then again that does drive the financial metrics. So uh it is pipeline it is the sales forecast but you've got to really agree on how we get there and and what they're built upon in order for them to become um you know metrics that have confidence within the business. Now, how should planning cadence evolve past 10 million in AR?

[14:46] Quarterly is too slow and then sometimes monthly is too slow, especially right now. You you've got to have a continuous rolling forecast that allows you to make plenty of course corrections in the business. If you're course correcting your business once a quarter, it's too slow. If you're corre course correcting the business 12 times a year, it's too slow. You need to be thinking about how you course correct the business, you know, 52 weeks. That doesn't mean you need a a finance uh closing the books or anything like that at that level of frequency, but you need do need insight into into your forecast into uh where spend is at at a far more accurate basis and and so you can make much faster frequent faster decisions and that allows you to course correct more quickly.

[15:29] What is the single highest leverage question leadership can ask early? That's a good one. The single highest question I would say, you know, being able to just say, you know, if this forecast is wrong by 20%. What's the future impact to our business? What's the cash impact? And when do we feel it? And that gets both sales and finance agreeing on the on the real metrics underneath that uh that forecast. Why does AI amplify bad GTM systems faster than it fixes them? Uh it's an inputs outputs, right? Uh, most GTM systems have pretty low quality inputs. Last time I checked, sales reps aren't really great at entering in all the information into a salesforce.

[16:13] They're incentivized to shape that narrative with uh either individually or collectively through a through, you know, maybe a sales leader that's like, hey, let's uh let's kind of move those deals over here and, you know, focus on these deals right now. Uh, incentives drive so much of the behavior of a sales organization. And so when you're pulling all of your AI precision against a uh against a system that's already heavily influenced and and and not influenced in a good way, it's it's influenced by incentives, then you're you're really barking up the wrong tree. From my perspective, how does confidence inflation show up in AI assisted forecasting? It creates that false precision uh that we were talking about earlier. So um the you know you get bad inputs stressed up with precision and uh people then think precision equals accuracy and uh that's not always true. So it's ultimately coming back to what are the actual predictive inputs into this system and how are they going to help us get to accuracy not to precision. What has to

[17:16] be true before AI actually adds signal instead of noise? Clean historical unbiased data. I wish we all had more of that, but you've got to have that as your foundation. And then consistent definitions, you know, so so often finance teams, go to market teams, you know, even sales versus marketing have definition, different definitions of what pipeline is, different definitions of what, you know, stages might mean, different definitions of what the average customer pays per segment. And so if none of those definitions are agreed then it becomes it becomes problematic as those three kind of systems converge and again then you get uh then you get kind of those challenges also that culture of forecast precision versus forecast accuracy. Everyone wants everyone but they believe precision is the way that they're going to get there.

[18:06] So it's it's about kind of stepping back and and focusing on accuracy. I'd like to move on to the rapid fire section of the pod in one sentence. First instinct, Rowan, what GTM metric is most misleading? I think I said that one earlier. Pipeline coverage. What finance metric should GTM leaders understand better? CAC payback period. What is one sign dashboards have drifted from reality? Finance building a shadow model. What mistake do founders at 10 million to 30 million AR kit keep repeating? scaling inefficient growth because they don't understand the unit economics. What belief about forecasting is simply wrong. Precision equals accuracy. For years, GTM rewarded volume, more pipeline, more activity, more tools. The next era rewards coherence. Revenue does not fail because teams lack data. It fails when systems stop telling the truth. This is GTM Vault. Thanks for listening. The GTM operating system for teams building repeatable revenue.