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- [00:03]
A
You're listening to gtm live, a podcast by passetto.
- [00:12]
Hey, welcome back. Today we're excited to walk you through a case study for a company that recently completed a 14 day sprint with us here at Passetto. We wanted to share this because every company is different and the results and the growth output that we see from these sprints are unique to every company. However, we know that many of you are in a situation of struggling to understand how to generate pipeline consistently and efficiently heading into 2026. This is a great option to kickstart that going into the new year. And so we wanted to sort of just peel back the curtain a little bit to let you see what the process is like and what this company, for example, was able to see on. On the other side of that. So let's get into it.
- [01:03]
B
Love it. And I just want to add a little bit of a note here. So this was once something that took us three months to deliver. And as a co founder of Passetto, I'm so happy to see how much we've been able to evolve this in the last, you know, year or so, where something that was just highly complex at one point took us three months. We're able to do that in 14 days now.
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A
We're.
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B
Which is just such a value add to the people that work with us. Because a lot of companies can't afford to wait to get answers. It's just like they are in, I don't even want to say, like, crisis mode, but it's just like we want answers now, we don't want to wait for it. And so the fact that we can turn that around in 14 days is just such a remarkable feat. I'm really proud of that. I'm really proud of all of us at Pesetto who have been able to make that possible. And so, yeah, really excited to be able to turn out more of these, like, case study stories, stories for people. Because I think if you're anything like me, I love to just learn from other people's experiences, hear their own stories, hear what they went through, hear what they learned and blah, blah, blah. And so that's what I think is going to be most exciting about doing this, like, series of podcast episodes.
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A
Cool. So where do we start, Carolyn?
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B
Why don't we talk about. Obviously we're going to keep these confidential, but let's talk about, like, what type of company this is and just the stakeholders that were involved. Amber, you want to take that one on?
- [02:22]
A
Sure. So this is a cybersecurity company and the stakeholders that came to us is really the marketing leadership, I would say VP level in that team. So recent changes in the company are they had a restructuring that had happened, it sounds like, earlier this year. And then a new CMO came to the table right around the time that this company engaged with us. And so it was not the cmo, it was the head of marketing. Is that right, Carolyn?
- [02:52]
B
Yeah, I think so. I'm more specifically thinking about the one stakeholder that we worked really closely with, which was the head of the performance marketing function. But yeah, there were some other folks like sort of that he reported into that were part of the engagement too.
- [03:06]
A
Yep. So setting the stage for that. The performance marketing member came to us and there was just a lot of. I'm sure folks can relate to. You're in a period of change and potentially even uncertainty in the business when these sort of shifts are happening. And so it seems like they really wanted to be proactive about going into the next quarter and really being able to surface more insights around where can we improve on the marketing side to really help support the revenue team as a whole, to just be able to like, sort of open up the purview. I mean, we can talk more about the legacy metrics and the way that marketing at this company had been run. I think that would be good to give some extra context there. But they were really saying, like, hey, like, we're basically putting everything on the table because we know what we have been doing is not going to get us to where we want to go next and we want to try do something different. Like we've been trying the same old way and it's not working and stuff is shifting in the business because of it.
- [04:08]
B
Right? Yeah. I think the biggest thing that stood out in like why this company approached us in the first place is because opportunity creation and revenue is measured on last lead source. So for them, like what was the last thing that happened before the opportunity was created? And as a result, the person that came to us, the head of performance marketing, was like, we are not diversifying at all. Like, we want to basically modernize our demand gen strategy, but we're really not even doing demand gen. We're doing demand capture because we funnel everything to like basically convert on a product trial page, which means most of their investment is in paid search. Really not doing a lot of like awareness or engagement type of marketing to begin with. And I think just like, as a responsible leader, this person was like, that doesn't seem like smart marketing for us. And so like, I want a third Party objective opinion, using data to tell us like, is this even working? What else could we be doing better? They had also acknowledged like, I don't even think we're doing marketing. Like we're basically running like paid search to drive product trials. And I feel like they all sort of felt in that team like it wasn't smart or responsible or proactive or modern or anything like that. But the business has been built around that. And this is a $500 million company so like not easy to instrument change at that size. Right. It's like very much a legacy approach. And so I think what they really wanted is their hands on some better data that could help them understand the story better because they just didn't have the ability to do that on their own. So I think that is a really important piece of the story. Yep.
- [05:49]
A
And that reminds me to another key focus. What they had already tried before they came to us. It seemed like they spent the last 12 months or even the last 24 months really buttoning up their opportunity pipeline, the process and the forecasting inside of the opportunity pipeline. So we see that a lot too. It's like, well, what's sort of the low hanging fruit is to go create a cadence and structure and forecasting and tighten up your opportunity pipeline. A lot of revenue leakage happens there. But then the next logical place that you look is, okay, well how do we build pipeline? And actually you realize once you go that far that you don't really know what's converting at the highest velocity and why. So, so that's what they wanted to know. That's what we helped them see for sure.
- [06:36]
B
Two other things jump out at me too, because I'm just looking back at some of our notes from our earlier calls with them. So one thing that they had said is we, and this is just like so relatable I think, to what we're seeing in the market overall, which is we can't hit numbers with the knowledge or the data that we have at hand. People are going to leave or they are going to get fired. Okay. So like that is a very real scenario for a lot of people. But they had also said, listen, we're measured on last lead source, so it's like really hard for us to prove any sort of return on demand creation. Right. Because they are very much demand capture at this point. And so they're trying to like cobble together stories and spreadsheets and things like that to try and like piece together stories in the data. But that has really been difficult for them. And the other thing too that's worth noting is I think within the last like six months or so they moved about 2 million in marketing spend to SDRs, which is interesting. We see that a lot too. Right. Just like, let's dial up the SDR volume, call more people. So I think that is very relatable for a lot of folks right now.
- [07:38]
A
Yep. And they had said they were in a super hard environment for marketing to be able to thrive based on these constraints that we have mentioned. For sure.
- [07:47]
B
Okay, let's just quickly go through what their tech stack was for some folks too. So Salesforce for opportunity tracking, Marketo for marketing automation. I know that they are also in the process of standing up an attribution tool. I'm not going to name which one that was, but it's not yet configured. So we didn't even really look at that data at all. And then we had also looked at outreach data for their sales automation and we also looked at the chat tool where some activity was happening on their website from Channel Chat. I think it was qualified in drift, if I'm not mistaken. All right, so basically, just at a very high level as part of the Sprint, what we do is we basically take a company's data and basically through a connector and we pull that into the Passetto app and stitch everything together into a unified data layer which looks at the GTM factory stages. Right. That's how we think of pipeline and revenue creation, which is engaged stage, prospecting stage closing stage, which is essentially like an active sales cycle. We know that most people do not track with like any level of sophistication, the prospecting stage. So our objective out of all of this is you don't track this, but we have a way to make sense of who you're prospecting to and how long it's taking and why you started prospecting to them and what that led to. And so basically that is what we were stitching together. We are wanting to understand at the very top of the funnel what basically first party signals are your prospects having before they're ever passed to an SDR or like an AE or something like that. The prospecting process, which is, okay, we're calling people to try and get a meeting to create an opportunity, we're going to look at that. And then of course we look at closed stage dynamics like win rate, sales cycle length, acv, that sort of thing as well. So just a little bit of like a 30,000 foot view on what exactly we actually look at.
- [09:40]
A
Another layer of it is that we do have our rubric that we use, you know, when we kick off an engagement that helps us directionally understand where are we focusing. Because we do in the sprints, do like data interpretation in those first couple workshops. So we have to map your environment to, as Carolyn mentioned, our model. Should I go over the score that they had in each one of these categories? Okay, cool. So we score you upfront on the three different stages, as Carolyn mentioned, engagement, prospecting and closing. And it doesn't matter whether you come to us with a prospecting motion that's being tracked or not. That's really the whole point, is that most people, we have yet to seen a company that really does track prospecting comprehensively, as we encourage everyone to do so. We infer it, that's what we do. So we give you a score though, up front and then our goal is to help give you as much visibility as possible, even though you're probably going to have a low score. So they did, let's see, engagement stage, they got 67% out of a hundred in terms of what they were tracking already. And that's because it's actually kind of good based on what we see. But they did track digital events as campaigns in Salesforce, which is super important, as well as offline events. They do have a UTM framework, which is also super important for visibility, but they weren't using it consistently. So they got a few different dings there. In the prospecting cycle, they got a zero percent, which we also see very often. So they are sometimes tracking what's happening before an opportunity gets created, but oftentimes they're really not, or that lives in outreach or somewhere else. And so we can't stitch all together. And we definitely did not know. They don't know what all their prospecting triggers were. And so that's something. Again, we were able to infer this for them as an outcome, but yeah, they had a 0% score going into it for prospecting and in closing they had 70% score going into this, which again is pretty good. So closing is the opportunity stage. It's. I wouldn't say good. It's more like typical. It's like they weren't necessarily below average. Right. Coming into this with a 70% score there, but where they got dinged for that was they're creating opportunities immediately. Some issues there with like legacy provisioning that's happening with their platform. And then, yeah, so they're just basically not actually qualifying an opportunity. And then there's also really a bunch of gray areas about when to create an opportunity, depending on which department is creating the opportunity. So that's definitely not what we would need to see in order to be able to measure your closing cycle efficiently. That's where they were at. Which left them with overall a score of 55%. So we knew they were going to have some significant gaps. The great thing is that we were able to give them visibility even with that, which is amazing. So let's get into that. Cool.
- [12:46]
B
That's the Passetto magic.
- [12:48]
A
It is kind of magical.
- [12:50]
B
And just to also mention the score, why we do that up front is like, we do want to understand where the data gaps are because out the other side of the sprint, we basically interpret what we're seeing in terms of what's working, what's not, what's generating pipeline, what's draining resources, but also giving recommendations around, like, how to go fix these gaps. Because we want companies to be able to like, have this visibility without, you know, needing like a third party partner to do it. That's the thing that every company wants to do, is be able to have that data visibility internally. So that also helps frame up what comes after this. Okay, so let's go through some of the findings. First and foremost, let's start with the fact that as you know, like, we infer what triggers the prospecting process to begin, because we want to be able to work back and say, like, what's creating pipeline? But for them, like, out of every opportunity that existed in the period that we looked at, so we looked at the last 12 months. So last year we knew that 40% of all of those opportunities were. Couldn't even be linked back to like a sales trigger. Like, we had no idea why a rep decided it was a good idea to start calling somebody. And because we couldn't track the trigger, we also couldn't track the process. So not only could we not understand why reps were calling people to try and get a meeting, we also didn't know how long it took or anything like any of those dynamics that we would want to know. Which means, like we're 40% of opportunity creation is like happening in a vacuum. Cannot literally cannot optimize what cannot be measured. But 60% of that could. Right. So we were able to see on all of opportunities created in the last year, okay, 60% of that can actually be attributed to something. So that's what we're going to look at for basically what we're talking about here.
- [14:33]
A
Yep. And then just to like double click on that for this company specifically, because everyone has A black box in prospecting who comes to us. This one specifically was what was happening was the BDRs are calling lists in Salesforce, but we couldn't even infer that in our model, like, because the phone calls aren't even being logged. So, like, there's literally a treasure trove of information. Your BDRs are out there calling thousands of people every month, and we have no way to see the outcome of really any of it unless an opportunity gets created out of it. So we see in the data, oh, look, an opportunity, but it's like, nothing. No activity, nothing that we could see before that. So it makes it really hard to understand what's influencing that to happen and when is it happening and why.
- [15:26]
B
But not atypical. Like, yeah, that, unfortunately, is pretty common. Okay, so when we look at the 60% of opportunities that were being created or that had been created in the last 12 months, the first thing, like, by no surprise, was that product trials were dominating as basically what was triggering the prospecting lifecycle to begin. So, like, out of everything we could measure, basically 55% of what SDRs were working essentially came from product trials. But then we were like, that's a, you know, a lot of volume. Not surprising, especially too, that that's where they were putting their, like, marketing dollars into a paid search on those landers. But those were converting to revenue at the lowest rate, sort of everything the SDRs were working in a period, majority of that was for product trials signups, basically. But when you look at the downstream conversion to revenue, those converted at the lowest rate. So the win rate on product trials opportunities was 5%. Okay, so like, for basically, like a PLG sort of focused company, we would expect that to be, like, at least 10%. Closer to 20. Like, 20% would be excellent. And so that, for us was a real big head scratcher. Right away. We were sort of seeing, okay, something here is not quite adding up in terms of, like, how you're measured, because, yeah, you're basically measured on your performance to generate these product trials, but those are converting at 5%. So you're having to work a shitload of those for very little yield on that.
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A
Yep. And because they do have the cadence on the sales side to close opportunities out, this is a real win rate here. So the way we calculate win rate is in a period of all the opportunities closed, the percent that were won versus loss. It's not the same as a conversion rate necessarily, but this is a legitimate. The legitimate way to track win rate. And so, yeah, we can See that, that's just not it for trials. Yeah.
- [17:27]
B
And also something that came up was we also reject a lot of those trials that like our failed trials, basically once they're created for this company specifically, we told them we think this is flawed. But basically anytime a product trial is initiated through their website, it like triggers the auto creation of an opportunity. So like, doesn't matter who I am or who anybody is, you go sign up for a free trial, you have an opportunity related to that. And they were saying, well like, you know, 5% might be lower than the actual win rate because we get a bunch of opportunities that are like rejected very quickly. But that then leads us to say, well, those should have never been opportunities in the first place. Like, don't.
- [18:08]
A
Like is the win rate. The win rate is the win rate is the win rate. So it's actually not higher, but exactly like the opportunity pipeline is for sales. So if you're using it for provisioning or something else, there are ways to still accommodate that while having your sales pipeline be for sales. It's a revenue, it's a place to track revenue.
- [18:32]
B
Yeah, but anyways, I digress. Two recommendations that we had for them out of that was, listen, don't auto create an opportunity. Create the opportunity when it's a qualified opportunity, have the product trial actually trigger the prospecting process instead and then track that to opportunity creation and then obviously opportunity to close one for one. But then right away one of the biggest takeaways is like, okay, those are resulting in a less than ideal amount of revenue. And also the other thing is, out of all of the different triggers that are leading to opportunities in close 1 revenue for this company, the product trials were the lowest acv. So the range we're looking at for them is their higher ACV is around 10k. And then product trials were sitting at the lower end of like 2 to 3k. So again, having to work a lot of those product trials for like a suboptimal return basically in terms of revenue and then also like having to work more volume because the ACV on those is much lower than some other sources of revenue. So that was one of the biggest takeaways that we had for them. But then when we double clicked into that, we looked at, okay, well where else is like marketing driving, I guess their contribution of pipeline. And then we looked at hand raisers specifically. So hand raisers in this context would be basically anybody who requests essentially like a meeting, you know, through the website, like demo requests, quote request, anything that's basically not a Product trial, but like a high intent form submit. And when we looked at those, the win rate was literally so like lower volume, okay, so like overall lower volume volume of opportunities from that Source. But literally 2x the win rate on hand raisers deal sizes were in the upper range. So like two times the deal size. And basically the revenue yield on hand raisers compared to product trials is like x greater. Right away we were like, aha. This is the hidden nugget of information because in order to drive hand raisers you've got to diversify your demand strategy. Like this is a company that is doing very little sort of like top of funnel advertising in the way of awareness or brand awareness and things like that. Like all of their ads are conversion oriented landers on the product trials. And so the opportunity to diversify their demand strategy, like they could be doing so much more to generate more hand raisers. So we definitely went deep with them there to explore different ways, you know, and things that they could be doing differently. And just a perfect example too of, you know, is the path to becoming a hand raiser. So when we looked at their website we had realized, well, you're, you know, your contact form and your quote request form are really hidden. And so that creates sort of like a more friction in that conversion process. Whereas like demo requests are just slapped everywhere. So it's no wonder people are converting on the product trial.
- [21:31]
A
The trial, yeah, I know that was an immediate takeaway for them too when we did deliver their recommendations was that they came back and said, yeah, we are absolutely going to take this. And turned right around to start testing new CTAs beyond just the trial signups. So interesting too that they shared that the sales team had been beating the drum about trials don't convert. And they, you know, are like, stop sending us these, stop sending us these. But it's such a typical situation that we get ourselves in this rut of either you don't have the data to look at it in the right way or it's not comprehensive enough or what have you. But once they saw this data, they said that this was finally what they had finally needed, that they were just didn't have all along was the data to really show it, to be able to justify. Yeah, Changing it up. Right. Doesn't mean you turn that all the way off immediately, but just to switch it up.
- [22:24]
B
Yeah. The interesting thing too is that like at least the pipeline and revenue from product trials was consistent. Like there wasn't a lot of volatility, which is actually pretty good. Because we definitely see a lot of companies with high volatility in pipeline and revenue. But yeah, switching it up and just doing more could definitely this company's for sure gonna start to trial that out and see what comes of it. So that's pretty cool. Okay, so two sort of other things that came up is one, this company's win rate was sitting around 6 to 7% pretty low because ideally we would want that overall company win rate to, you know, for like a mid market SaaS company to be around 25%, maybe 30%. So definitely that was sort of like a cause for concern. And also like the win rate was decelerating over the last 12 months. So that was definitely a call out, not like a huge surprise to them. But the one thing that we could see correlating to that was we're going to talk about now signals, right? So signal being like the first party interactions that a lead has with a brand. So like it could be like an event attendance, a webinar attendance, a page view content download, things like that. So when we look at signals happening before an opportunity gets created, very few opportunities actually had a signal. Like only like 35% of all of the opportunities had like one signal. Okay. So like even a page visit, because we were tracking that very, very well. And so that was a big eye opener is to see like you're creating more cold opportunities in the last, you know, 12 months, but you're like marketing influence on those is declining at the same rate that your win rate is declin. So right away that had suggested, okay, you're putting all of your gasoline basically on paid search and ignoring all of these other channels that can drive engagements in other places. And like now look at, look at what like downstream impact that's actually having on like overall revenue performance.
- [24:24]
A
Yep, very exciting for them too because through the course of the engagement they realized that demand generation is something that they're going to start taking a lot more seriously. And I'm super excited for them because it sounds like they're going to hire new, they're going to bring in the role like head of demand generation. So that's obviously incredible and exciting, but can you imagine Carolyn coming into that role and already knowing in the engagement stage how many signals are there so that you have something to measure against and improve? Super, super amazing.
- [24:57]
B
Yeah, I think that that was one of the biggest takeaways for them and actually not surprising because when we have done this sprint for other companies that are very like have like a strong PLG motion, we often Find that, like, honestly, I think PLG is really hard to nail. There are so many characteristics that are, like, sort of below the iceberg that determine your success with, like, a PLG model. And so we have seen so many times that once you start to, like, diversify your strategy and focus more on just, like, high intent hand raisers, like, so much can change for the better. We definitely have a lot of really good case studies for companies that have made that transition. So that's been awesome. And I think that they're going to kill it. So excited for them. And then one of the biggest takeaways that we sort of like, wrapped up this process with them is the recognition that they need to go track prospecting better. Because what we had seen is basically their biggest revenue source is presumably like, SDR outbound. And so highest ACV opportunities come from that. It's where the largest volume of what they're working is coming from. For example, in the last 12 months, they worked like over 200,000 prospects, presumably from, like, cold outbound. And their conversion rate to opportunity on those was like 3%, which is really suboptimal. And so it's like, if you want to go fix that and be better at your SDR motion, like, you need better tracking on that. So they walked away being like, oh, for sure. Like, we're going to basically start to stand up, basically prospecting tracking immediately because we now see the value and, like, the insights that we get from doing that. So, like, I consider that a huge win.
- [26:37]
A
Yeah, super exciting for that success story and see how that continues to help that team. But if they can do it as a $500 million company, no excuses, you can do it too. And it really so much easier.
- [26:52]
B
I'm going to wrap this all up with one quote from their key stakeholder, which to me, really stood out. And so they had said Passetto Sprint brought a level of inspection beyond what we do internally, but exactly what we require to be strategic. They compressed what would normally take months into two weeks and gave us a clear roadmap forward. It was the forcing function that we needed to move from analysis to action.
- [27:17]
A
Woo.
- [27:18]
B
Chills. I love that. It was so much fun working with this particular company and so excited to keep sharing more of these stories with you guys.
- [27:26]
A
Yeah, thanks for listening.
- [27:28]
B
All right, see y' all later.
- [27:44]
A
Sam.