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- [00:01]
Carolyn
Hey, everyone. Welcome back to GTM Live. If you're a CEO or a revenue leader watching CAC climb and you can't explain why this show is for you, we're talking signals today. So those are the breadcrumbs that show all of the ways that your prospects are engaging with marketing before they ever get past to sales and why your CRM is probably missing most of them. We see it all the time. That means you're likely under crediting the work that marketing is doing and why so many companies are really doing this wrong. Let's get into it.
- [00:34]
Trevor
You're listening to GTM Live, a podcast by Passetto.
- [00:43]
Carolyn
Hello, Hello. What's up, everybody? I see you all got the memo. We had to change the time this week, so we are recording different day, different time than usual. So a little bit of a smaller audience today, but thanks y'all for showing up. Donna, what's up? Andre, Charity, it's good to see you all. So by the time you all hear this, we will have actually recorded a few different episodes with the new name go to Market Live with Trevor and myself. But today actually marks the official day that we drop our first show on Apple, itunes and Spotify podcast. So that's very exciting. We're actually had made the decision too, to keep the show within the Revenue Vitals feed. Originally, we had talked about starting a new feed, new channel altogether, but since we have such a good repeat audience from Revenue Vitals, we're actually going to keep it in the feed and so that's really exciting. Secondly, we took your feedback last week. A lot of people who are attending live, you know, we were talking about some of these technical concepts said, can we get some visual diagrams? So we took that. We heard you. If you're part of the live show, tuning in with us for the live recording, you will be able to see those visuals. We'll share them with you here. Or, you know, if you want to look back to the video once we publish it on YouTube, you have a chance to look at some of those diagrams there too. But if you're listening as just a podcast listener, we'll do our best to explain what we're showing in those diagrams so you all can learn from that. But I mean, we want to encourage more live participation. So of course, please join us live if you can. We'd love to have you. So before we kick it off for this week, I have a few introductory thoughts. I have a lot of conversations with listeners throughout the week who hit me up on LinkedIn and you know, have questions for us or that are actively trying to work with Pesetto. And there's one theme that has really stood out over the last few weeks, and I just wanted to mention that here, which is the cost and potential consequences of delaying data architecture work. Right. So a lot of people that come inbound to Passetto or that listen are a lot of revenue leaders. I mean, we work directly with CEOs and CFOs too. But the entry point for a lot of organizations really is through sales or marketing or rev ops or sales ops or something like that. And these people feel the pain of not having a data architecture in their organization. And they go on to pitch this concept of, hey, we want to do data architecture work. You know, we want to make improvements to CRM and how we're tracking everything. And we want to potentially even work with Passetto who helps us optimize our go to market. And so one thing that they struggle with is how do I actually communicate this to my leadership in a way that they're going to understand and see the value in doing that. And so I want to talk about that just quickly before we get into the actual topic of today's podcast. And I think the common thing that we see or the, I guess the common pushback that we get from a lot of CEOs or CFOs is we want to basically chase MRR before we actually address the data infrastructure, which I get. Right. Like companies want to grow their revenue and see data work and see anything related to data or technical data architecture as something that's not a top priority. Not really understanding how the two things actually go hand in hand together. Right. And I think until you fix that data architecture, everything remains a guessing game. And over time, that guessing game costs you revenue. And I don't think that's the thing that CEOs or CFOs really see. They're not in CRM. They're not seeing how everything kind of comes together in the day to day tactical stuff that your team is doing. I think what we need to do is speak the language and talk about the problem in a way that the CEO and CFO is going to understand. And so when I think about that, while you're living and breathing the pain or the frustration that comes with a poor data architecture every day and your inability to track things, we need to stop framing this as a data problem, frame it as a revenue problem or a financial problem if there's a problem at all. Right. Some people sort of do this preemptively to set themselves up for success for the future. But we need to talk about the metrics that actually matter to your CEO board investors. Growth rate, cost of growth, CAC payback period and potential revenue and pipeline loss. Those are the metrics that they are going to care about. So if you're looking for buy in to do this type of work, whether it's with Passetto or you know, internally or what have you model the risk, don't just talk about it. Say your cost of growth is five times the norm. What does that look like in six to 12 months from now? Sure, you can say that's what it is right now. What is it going to mean long term for the business if you don't do something about it? So let's just use an example. Let's say your CAC payback is seven years. Like that is a real life example with somebody I spoke to this week. How sustainable is that? What happens if pipeline velocity keeps slowing or efficiency stays flat? These are the kind of scenarios that I think change the minds of your C suite, right? So again, not talking about the problem being the data, talking about the consequence and the impact on the business. And so if you ever want help, sort of like a real life scenario, how to go about doing this? Happy to work with you all. Shoot me a message on LinkedIn. I'm doing this for a few prospects and members of the Pesetto community right now. Always happy to lend a helping hand wherever I can. I come from that world. I came up through marketing. I felt the pain. I know what it's like to go through the process of trying to bring on other champions within the company to see the impact and the value of this type of work. And so if I can do anything to help and anybody at Pesetto, we're always happy to do that. So with that said, let's talk about today's topic, which is Signals. We started to get into this topic last week and we sort of moved away from it. And I'll kick it over to Trevor to do a refresher on at a very high level. What are signals? Why are we talking about them? But if you didn't listen to last week's episode, I would say go back and listen to it. I kind of see today's topic as like a part two to that series. And so if you really want to understand how all the nuts and bolts and the pieces of the story come together, I would say do that, go back and listen to that one before you listen to this. So with that said, Trevor, you want to take it away?
- [06:40]
Trevor
All right, great intro there. Yeah, this is definitely part two. I'm actually going to start by backing up a little bit. For those of you that did see the previous one, it will be a little bit of a refresher. But in. In case you're coming to this fresh, want to give a quick overview with a visual, which is what people asked for last time, of how we think about the moving parts overall of the architecture. And so I'm going to share a diagram or two. So I'm just going to share a slide and I should be sharing right now. All right. Yeah. So we've talked about the architecture overall, and I'd say some of this should be pretty much what you expect it to be. This is how we think of the quote unquote funnel, but we're adding some finer points to it here rather than just thinking of sort of. Things start as opportunities and they go down through the stages and they become won or lost opportunities. We are taking that back a few steps. We're talking about engagement as a piece of the journey, which is where signals really live. In a lot of cases, though, we'll see that they are relevant across the entire journey. We talked a lot last week about prospecting. We meant to talk about signals, and actually we got so into talking about prospecting as a stage that that's where we really spent most of our time. And then pipeline, that's your sales opportunities. Pretty much everyone knows and lives this part of the journey all the time because that's what people are generally paying attention to. What is our pipeline, how are we closing, what are we winning, et cetera. But we are making this argument that you have to pay attention to all three of these pieces in order to really refine what you're doing. So this diagram's fairly simple, really, but it's a visual to explain. These are three different parts of the puzzle. You are working with engaged targets at the start. They become prospects, those prospects, some of them become opportunities. And underneath all of that, you have signals. And signals are those those things that people are engaging with, the things that your marketing team is investing in to produce and execute those signals? And those are all the little interactions. And I'm going to talk more in the session about what might, you know, within a signal, an individual signal, what's that going to tell you and what are you really trying to capture there? So I'm going to talk about that here in just a moment. But the last piece of this diagram is trying to also explain it's not just about the results of those journey stages, it's the what did you invest? So at the bottom that this bottom row of expense is really where it all comes down to efficiency. And so we want to break the entire architecture into these three big phases of generating a new logo. We want to know what we spent to get there in each part of the journey. We want to know what came out of it. That is, we got people to interact with us and generate signals, we got people to overall become engaged targets, we got them to become prospects, those prospects became opportunities and some of those became new logos. And that is what's tying this whole thing together in a way that lets you go down to this granular level and say, here is how efficient we are at generating those interactions, at driving those to become prospects, at qualifying those prospects. Here's what it's costing us, here's what we're getting out the other end. Is that working? Is, is that efficient enough? What things are more efficient, what things are less efficient? How could we make something more efficient? And you know, we're going to talk quite a bit more about the whole idea of efficiency because that's where we're trying to get to. We want to know all these pieces, but at the end of the day it's are we growing and are we growing efficiently? And so that's the top level story that we want to be able to tell across the board. But you can't just tell the top level version. You need to be able to tell it down in these different stages of the journey and down into the channels you're using and all those different components that matter in how are we going to choose to execute and drive these results? We. So talking about signals specifically, I'm going to leave these diagrams up because I think I'm going to wander through a few of them as I go to set the stage here. When we talk about a signal, it is the individual interaction that a person has with something that you've executed out there in the market, generally speaking. So that is just your website in general, that is SEO efforts that you are putting money into get better organic search results and drive people to the website. That is any work you're doing in social, whether that's paid social or organic. It's other types of ads, of course, paid search, video ads, all those kinds of things. Those are different channels in which you are driving people to have interactions, to engage, to generate signals. And so when we think of an individual signal, we're capturing a Bunch of different information that we think we want to know this for every signal, whenever we possibly can, we want to know, when did the signal happen? Who did the signal happen with? And then we're getting into some standardized categorization of how we think about the interaction itself. So what is the type of signal? This is how we think about it, the type of interaction. So this is not paid social or anything. This is, well, they engage with some web content, or they just visited the site, or they attended a webinar, or they filled out a contact form. All those kinds of things that are saying, okay, what did the person do? And then we're also saying, what can we know about that thing they interacted with? So getting into the detail level. So in my example on the screen, we're saying, all right, well, this, this is a signal with John Doe that was interacting with some web content. We want to know what that piece of web content was. Because we're going to ask ourselves later, hey, web content is, is really, really showing some value in the journey and driving, driving things towards success. What are those types of content? Same with any other type of signal events. What was the event? And so on and so on. So we're saying, how are people interacting with us? If you were to visualize this on a timeline of interaction, you could visualize like, okay, here, here's the journey. This person started off by visiting the website. They engaged with some web content. They registered for an event. They then attended that event. They engaged with an email that we sent after the event that drove them to have more of an interaction with the website. You want to know all those things as they lead up to becoming a prospect and further on through the journey. So that's the, what, what do they do? And then we have the what drove the interaction side of it. So this is where you start to think about channels, the things that you're investing in that got those people to have those interactions. So in my example here, we know about the channel, we know about the source within the channel. So that's a, you know, that sort of detail level below your channel. In this case, in the example, paid social. Okay, you did some ads. Where did you do those ads? You did them on LinkedIn. And what campaign did that whole effort support? This is a coordinated set of things that may be in multiple channels and might generate different types of engagement throughout the life of that campaign. To drive a lot of that information, we rely on UTMs. You know, good old trusty Utm. Something's been around forever and ever And I think most teams have a sense for what they are and why they matter. But we do often see that this gets sort of left to the side and really not made a priority. And we definitely argue no, just because they've been around for a long time doesn't mean they're not as important as ever. And so we, and maybe we'll talk more about this in this episode or later. But we often see really poor structures to UTMs, really spotty coverage. You know, some things that you execute out there in the world might have UTMs, other things don't, for whatever reason, just lack of valuing that as a piece of the puzzle. But yeah, we say, yeah, you're going to lean on this a lot. How else are you really going to know what channels are driving those interactions? And so we're really relying on those for your signal categorization. This tells us the campaign, this tells us the channel and the source. And you want to know this for any digital interaction whenever possible.
- [15:39]
Carolyn
I wanted to just interject there for a second. Yeah, I had a thought. And that is. As you're explaining this, the question that I have that I think the listeners will have is understanding how this differs from the standard traditional way that a lot of companies measure channel performance overall. And as I'm thinking about this, I think we take a pretty firm stance that lead or top of funnel sort of like generating programs aren't necessarily the ones that we can tie back to like a return on the investment. And I think a lot of companies really are flying blind. Right. Because they're focused on top of funnel metrics like leads generated, things like that. Right. We have them sort of at the very one side of the funnel. And the reality is that because they're not actually utilizing UTMs or tying channels to campaign performance ultimately to be able to see signals across the whole journey, they're sort of getting this like very narrow view of that to say, oh well, you know, our lead gen programs on LinkedIn that we're spending a shitload of money on are producing leads. But this approach takes us away from that and lets us see like the full journey and then being able to tie that back to the unit economics. Right. Making sure that what you are spending money on is an efficient way to spend your go to market investments essentially.
- [17:02]
Trevor
Yeah, yeah. So I think, I think there are a few things that we see as recurring themes that are attempting to tell part of the story, but really just are falling short a little bit as they try to bring it all together or actually really, they're not trying to bring it all together, which is really the problem. For example, using ad vendor reporting directly in their platform useful. You should use it, absolutely. But it's going to be very specific to that specific channel, that vendor even. And you're not going to get that holistic view of all the things that are involved in the journey. You're going to get their perspective, which just by the nature of the information they have available is biased toward. Like, we're going to tell this piece of the story. We're going to tell you how this ad drove certain outcomes that are trackable by that platform. And yeah, that stuff's useful, but if you just make your decisions based on that, you're going to see, well, that's what's the rest of the story. Where are all the other things that people are going to interact with that are driving those, you know, that development of their interest and understanding of the product over time? The single ad's not doing that. It is providing a very specific piece of the journey. But you want to know more. So that's one thing. I think what we also see is organizations probably are tracking some kind of quote unquote campaigns in CRM. You know, Salesforce campaigns are pretty heavily used. We generally see that they're most, most companies we work with do have something, but it's usually been put together without an overall framework in mind. It's like, yeah, we, we did this thing, we want to know what people responded to, to it. That is sort of where the logic ends in a, in a lot of cases it's just, yeah, we did this thing and this is the 16 people that responded and great, it was a success or not. But that's not really measuring success, that's measuring a specific outcome. And it's again a very important operational thing to know, but you're not bringing it into the bigger picture of all the different interactions. And then I think the other thing is the idea of channel often gets left behind when you look at an organization that has sort of tried to pull this together but just didn't quite connect all the dots. You do see channel analysis being done either in that specific channel's platform or in Google Analytics where you're looking at some high level results in driving web traffic, which again, super useful, you do need to know those things. But it's, it's anonymous. It doesn't get tied back to generating a prospect and qualifying that prospect and closing that deal. It's really hard to tie those things together with anonymous data. In GA or any similar platform. And so yeah, trying to pull this all together where you know what they're doing, you know how they're interacting along with this very clear, structured approach to what drove that interaction. You start to put, put the pieces together, you're connecting those dots for the individual interaction and then you zoom out and if I go back to our diagram where we started, you are then saying, well, what are all the things that are happening leading up to becoming, to driving prospects? You want to know that. What are people interacting with? What are the channels that's driving those interactions on the journey leading up to identifying a prospect? So these might be things that are specifically driving a hand raise, you know, they're filling out contact forms or whatever. Or it might be your scoring model or it might be the sales team just picking some people out of the database for, for various reasons. Those all probably have some signals that led up to them. It would be pretty, pretty unusual to see a large chunk of those prospects that just didn't have any interaction before. And you want to know what those things are. This is helping you really identify those sort of top of funnel, middle of funnel, bottom of funnel kind of groupings as well, which I think we know people are familiar with. But a lot of assigning things into those categories is done just sort of on gut feel. This can help you say, hey, here are the things that people are interacting with during this part of the journey and we know this for a fact and this can tell us some things about how we are going to operate.
- [21:44]
Carolyn
Totally. So Paul has a really good point in the chat. I just want to call it out right now. So oftentimes marketing might point to sales and say sales isn't selling, we're generating all of these leads and then vice versa. Right. Sales is saying we're calling all of these people, but marketing's not doing anything. I think that is like the age old story. We see it all the, all of the time. I think at a very philosophical level that is the thing that we're trying to solve for at Passetto is creating more team alignment. Because once you have team alignment and you really understand the journey that the prospect is taking, you start to work together. Right. Because you're no longer like pitting teams against each other for the KPIs that you're measuring. And so how can we use signals to remedy the rift between the two departments? I think that's a really good question. I definitely have some of my own perspectives on that. But Trevor, what do you think about.
- [22:30]
Trevor
This Yeah, I think the us versus Them, yeah, it's been around forever. Every go to Market team member on either side of the aisle knows that pain. You're absolutely right. We've all seen it, we've all lived it. We've been in the heated arguments in meetings and finger pointing and all that. And you know, I touched a little bit on it last week. I think where the point we're trying to make here is this is trying to get away from the us versus them. The this was sourced by marketing and this was sourced by sales. And sales isn't doing anything with these things we pass along. Like, I think we're trying to say, like that's not even the story you care about. We're trying to say here's what's driving prospects. In other words, here's what is causing sales to know that they need to follow up on something that is a piece of the story. So that might be people are filling out contact forms. It might be your scoring model, it might be intent data that you're getting that just goes directly to the sales team to act upon all those different things. Like, you want to know that. And that's not about, oh, this is, this is from marketing or this is from sales. This is about just sort of the mode medium, whatever word you want to use that describes like, how did we get here? How do we get to saying, hey, here's this person. Sales should probably follow up on this. And I think there's a missing layer often, and I'm trying not to go too far back into the prospecting layer that we talked a lot about last week. But it's relevant to this question. We're calling out that as a specific separate part of the equation so that you can measure, hey, here's the stuff we're really passing over. Here's why we thought it was worth passing over. Here's why they're, why they're being disqualified. So sort of, I, I, I'm sure I talked about this last week thinking of analyzing prospect data similarly to win loss data on your opportunity so that you can say like, hey, things are coming in from marketing. And we created a prospect because the scoring model said we should. A lot of these are getting disqualified because sales says they're not, they're not worthwhile being able to say that and then do something about it by looking at sort of the signal journey that led up to it. So that's one piece. So bringing this back to the signal question, I think that's an important piece. And then showing that you are able to analyze that level of information, make tweaks and then adjust how things are being passed over. If you attack that sooner than later, your sales team doesn't get so frustrated and they continue to actually pick stuff up and work it. I think what we've all seen in this piece of the world is sales gets frustrated because the quality is low. There's no real sense for understanding that it was low or why. It's just anecdotal from the sales team. So marketing doesn't really know what to do about it really. And then marketing or and then sales gets frustrated and they just start lingering on, not picking those things up anymore as quickly. So to tie it all back together, giving yourself the visibility to know what you handed over and why, what happened to it, how long is it taking? All that stuff is great, you need to know that and then pulling that signal story into it to say, okay, what is leading up to prospects that are actually turning out to be quality? And that's what I always want to know when I'm looking in the data. It's this sort of success path concept where you're saying, well let's, let's look at the stuff that's working and let's understand the journey those are taking. Let's look at the stuff that's not working. These prospects are being disqualified. Let's try to understand what the difference is in the journey between those two groups. Things that are ending up where you want and things that are not. And can you pull out some insights to say, okay, well we can see this pattern of how people are engaging when they're actually really engaged. And here's what we see when they're not really actually useful. So that'll help you refine your scoring model. It'll help the sales team better understand what people are interacting with and hopefully everyone's a little bit happier because of it.
- [26:46]
Carolyn
I think too that you said this in your explanation is just like having that data changes everything. I think teams are almost like inadvertently forced to pit each other against themselves because they just don't have any other way. They don't have data visibility to tell them a different story. I'm thinking of a real life example of how we might see this in the real world. For companies that essentially don't look at their data across these three very distinct stages in the pipeline creation process and everything that happens leading up to pipeline. Most marketing teams, let's face it, measure success on MQL volume, right? And so if I'm a marketing Leader a marketing team, and that is my target that I have to reach. I spend 150, 200 grand on an event to have this big flashy trade show booth. Because I know that's the thing that's going to get people over to my booth. I'm going to scan their badge, I'm going to get a list of 2, 3, 400 names. I'm going to call those leads and I'm going to kick them over to sales. And sales is going to say, these people are cold. They have no interest in our brand or our product. They don't care to talk to us. Why are you giving us this name? But I don't have any other data to tell me otherwise. I don't know how those people are progressing through the engage stage, moving on to potentially or not becoming qualified prospects. Right. So when you're looking at only one component of the entire process, you don't really have any other data to know differently. And of course this is what teams are measured on and so that is what they are doing. But if you learn very fast that those leads from an event aren't qualified and sales isn't picking them up and they're not going anywhere, you might totally adjust how you actually spend your marketing dollars at an event. Maybe you don't do the a hundred K booth. Maybe you go and you just send a few sales reps there. You do a few like VIP dinners at a really nice venue or something like that where you focus in on 10 or 15 more qualified prospects and that actually might go a lot further. And then at the end of the day, when we talk about efficiency and unit economics and all of this stuff, the ROI on something like that becomes so much greater than spending 2,300k on this big thing that doesn't really do anything.
- [28:55]
Trevor
I'm glad you brought it back there to the efficiency unit economics piece because again, that's what we're all trying to get back to where that event scenario just described. We would look at that. So you've, you have this event, people do go to the booth, they have a signal that is, hey, we, we attended this event, we interacted with the, the brand at the booth. It's a single signal. And in Carolyn's example there a lot of those just aren't leading to anything. So they're, they're not generating pipeline, they are not certainly not becoming new logos. And so when you start to walk backwards, looking at the journey part, you are able to say, well, yeah, we, we did generate a lot of prospects. From those events. But we don't see that those signals are being attributed to real, real pipeline, real closed one business. And you should be able to then then know, well, that that isn't so efficient. We're spending a bunch of money on this thing. We're not seeing money come out the other end. And that's where we get into saying, okay, well what's the overall journey? What are all the signals that led up to that winning that opportunity? And let's assign some portion of that opportunity to each signal and then compare that using this repeatable structured framework to bounce that against what are we spending on those same, same channels? Really it comes down to sort of measuring the channel. We do events, we, we do paid ads on, on LinkedIn, we do all these other things. We invest in SEO. When we pull that back and look at success, ultimate success, which is new logos, new revenue, are we seeing that those signal types, those channels are having an efficient impact on generating those new dollars or not? And that's the kind of thing we definitely always want to know.
- [30:54]
Carolyn
So we've got Evan in the chat with another finance based question which I love to see. Thanks Evan for showing up and asking these. So to what extent can unit economics and signal based reporting change how marketing is seen across the organization? How might this look for a manager or director level marketing marketer? Which I think is a really good question. I have felt this, I have been there myself. I, like I said, I've come up in marketing and I've felt this pain. I'm doing all kinds of stuff, but I don't really know how to communicate the impact overall in like a clean and precise way and a really common, like I think I said this in a previous episode. Like I would say 90% of the time or greater. The companies that we work with have really poor go to market efficiency because they're spending all of this, their go to market budget on things that don't necessarily have roi. And so when you can track signals in a very clean, organized way like we're showing you here, you have the ability to very clearly and definitively say here is essentially my cost or my efficiency to do each of those things by each of these channels. And here from a ratio standpoint is what I'm getting back in either pipeline or new logo acquisition. It gives you a very clear and simplified way to basically help you make decisions. So it takes the guesswork out of that, right? And it makes your impact that much better to say, well, we're spending all of this money and it's not actually producing anything meaningful for the business. And so to come with that recommendation to say, I want to stop spending 40, 50% of my budget on events, it's generating leads, but they're not going anywhere. Right. And so I think you are now seen potentially as a more proactive, data driven thinker because you are using that data to say, I only want to do the things that are going to be meaningful for the business financially and I want to do less or potentially fix the way we are doing the things that have very little roi. So I think it gives you an anchor or more leverage to be seen as a stakeholder in the business versus just this department potentially that spends all of this money. And we don't really know what they're doing. We don't really know how to track it. Like, what is marketing really even doing? Right?
- [33:00]
Trevor
Yeah, I think, absolutely. And you said it at the end there. I 100% agree. We're not trying to chase an exact ROI in literal terms, right? This, this idea that you can say, well, we, we spent this money, generated these signals from this channel, and that directly is worth X other dollars. I mean, you're, you're doing something like that. But I don't think you want to walk into a room, you know, the board, boardroom, and say that in its literal, literal way, because that will be questioned because everyone acknowledges it's a little more complicated than that. Let's, let's not try to claim that this thing is fully responsible for these dollars. But instead, like Carolyn said, it's about making it clear that you are making responsible, informed decisions about doing the efficient things. And so, yes, you can tell this, this story about the dollars involved, you know, coming out the other end of these signals you're generating. But it's not really to say, okay, well, we had this many signals from this channel and they generated a million dollars. Because then the response is going to be, well, what about all this other investment in things that supported executing that channel? How about the salespeople, blah, blah, blah, blah, blah. Right? Like you end up getting in that argument again, like, well, no, our salespeople are just that good. It doesn't matter what you, what you did. We've all heard that one. So it's not about that. It's not about saying like, we drove X dollars. It's about saying, we can see when we invest in this, we get this, Carolyn used the word ratio, sort of get this ratio of efficiency back. So when we spend dollars on this type of thing, the ratio of those dollars spent to impact on closing new logo are that ratio is higher, we spend less, we get more influence on that new logo revenue. So I think it's. This is a less of a technical comment and more of like a storytelling one. How do you actually understand what this, what this is what you're really measuring here and how you tell that story to the people that need to hear it and want to be confident that you are making good decisions with the money they allocate for your department.
- [35:18]
Carolyn
Right, thanks for chiming in there. Okay, we've got a question from Donna but I'm going to pause that for now, let you continue Trevor, with what you're covering here.
- [35:26]
Trevor
Yeah, so I think we're getting to a point where I'll sort of cap off the core topic about signals and I just wanted to show one more example here that is trying to illustrate what signals would you expect from a typical wander through interacting with the company. I've put together just this one example where we're promoting the campaign overall is about this big event and that's a fairly common scenario. If events are part of your overall strategy, you are usually executing a number of different things in support of that event. This is like a little, a little focused in version of that. But you can imagine that this is going to have really lots of other things. The emails that you send post events and other things you might, might produce that are in support of the event. But this is just showing you like a little, little view of hey, we have this event, we're promoting it with an email to generate some registrations. We want people to register and then attend this event. And so in this semilinear example you've got an email promoting the event. That email is not a signal in itself. It's the channel that's driving the actual interaction. So in this case we send the email to a bunch of people. Some of those people click on the call to action which drives to the event overview on the website. That right there, that's a signal. Some people might only do that, but it's still worth knowing. They may not go further, they may not register, they may not attend. But we do want to know, hey, this email we sent did drive people to actually get to read about the event overview. Maybe they'll actually do some other things as they visit the site and those might generate additional signals. But at the very least we have a signal here that is a web visit signal. It came from the channel email supporting campaign X. In this example we've got some raw utms that we used when we put our, our CTA and the email together so that we could capture all that information. Some people will get to that event overview, go to the registration form on the page, submit it. That right there is a different signal. They went a little further, they actually registered for the event. We know that it was in our example here, Event abc, but it was driven by the email channel and it was for Campaign X. And so that is yet another signal from the email channel that is telling this bigger story about how people interacted then the event itself. People attend the event, that is a signal. It's a different interaction from those first two things. This is the event channel and it's still supporting Campaign X. This sort of overall bucket, to be clear, you know, Campaign X is the emails we're sending, the registration forms, the event itself, all those other components of making this thing real. And then we've also sort of added in a little side example here on the far left hand side, which is like what if people raise their hand, really need follow up from the event? It's a thing that certainly happens. It's sort of a secondary outcome of that event. You were just trying to get them to attend. But some of those people had really meaningful conversations or specifically came up and said, oh this is great, I would love to talk with someone about it. That's another signal that was driven by the event channel. And so from that little bit of an example of how people might wander through your world, you actually potentially have four different signals. And when you look at the journey, the bigger journey that leads to a prospect, that leads to an opportunity, you're going to know those different things occurred and they can all be factored into, okay, well emails, they're effectively driving some interactions and events are driving other interactions, including some hand raises. Like those are all things that you can imagine, imagine you could slice and dice when you're in the sort of analysis phase of looking at this data to understand, hey, here's all these channels, they're making a difference. Here's the kind of things they're driving, kind of interactions they're driving. Here's the campaigns overall with a bunch of different channels within them that are really making a difference in leading to prospects and pipeline and revenue. So this is just an example. You can imagine there's a million different ways to draw this out and imagine different scenarios that you might execute in marketing. But the point I'm really making here is it's, it's not just one signal. This was actually a number of different interactions that were all involved in this. This really thing you might otherwise think of as just sort of this one thing.
- [40:05]
Carolyn
Question for you, Trevor. Where does attribution, an attribution model or methodology come into all of us? Like we're talking about the actual trackable interactions that people are having with a brand or with a campaign or what have you channel. What about attribution? We talk a lot about this too, but I think that's the elephant in the room. I think we should call it out and have a stance on that.
- [40:27]
Trevor
Absolutely. And I think, yeah, I'm probably done with my diagram, so I'm going to stop my share and talk about that. So attribution, there are a million different models. We acknowledge that there are different use cases for different models. Let me talk about that for a moment first. There's so many different ways to think about allocating essentially credit. I don't like the word credit, but allocating the. A partial percentage of the deal to the different things. And so the first thing I'd say about that is attribution in this case, that portion of an opportunity. It's not meant to say, this is the credit we should get for doing these things. This is back to what I said a few minutes ago about. No, this is just a piece of the story. It's about efficiency of your investment versus the piece of influence that it's having on the journey. You're not taking that dollar that was allocated to this signal or channel or whatever and coming back to the greater team and saying we're responsible for 35% of all the pipeline. We're not saying that. We are instead saying here's the journey of things marketing can drive. This is all those signals that can be generated and here's how we break them up across the journey from first, first touch, first signal to closing an opportunity. You have to take some stance on a model for this to be able to allocate those, those different signals with different pieces of the. The opportunity. We have a few different models that we use for different purposes. So for the full path, I'll call it from first signal to closing the opportunity. We do in that case care about every signal that can be seen in that entire journey. We don't want to make a judgment about which one might be more valuable than the other because that's a losing game. You're never going to figure that out in that context in a way that's any more accurate than any other way. And so we take this simplified stance where what we're Trying to achieve is just the knowledge of all the signals. That's a hard enough task in itself. And so we're not trying to add more complexity to the attribution story by giving it some kind of crazy weighting model or anything like that. We just sort of want to say, like, here's all the signals that occurred from start to finish. Let's spread the opportunity evenly across all those sort of the linear model. Because again, we're not really trying to tell any more story in this case other than here's everything that's happened. Let's just talk about the journey as a whole. We can then, in the case of the full journey model, we can then take that down a level and say, okay, well, sure, we broke out linearly, evenly across all the individual signals, but we can also tell the story about the signals that are occurring in the engagement phase, sort of leading up to a prospect, the signals that are occurring during the prospecting phase, and then signals that are occurring during the pipeline closing the deal phase. And we can understand some other layers of information there which does tell us overall these types of signals or these channels are generally weighted heavier in a certain one of those three phases or various other things like, or we could say More generally speaking, 65% of all signal interactions usually occur in the engagement phase or something like that. Those are useful things to know which will be unique to your data, your, your target market, et cetera. And then we can also go into that and understand, hey, here's the things that people are engaging with during the prospecting phase. Here's what they're engaging in the prospecting phase when they do end up qualified as pipeline. And that could tell you, hey, those are great assets that we're sharing. Our BDR team is doing a great job getting people to go engage with these types of things. So let's enable them more with more of those things. That's an example of the kind of decisions you might be able to make with that data. And that's sort of going back away from attribution. Again, like, we're not trying to say anything about really attribution in those cases. It's just, what can we know about the journey? So that's the full path view we're then taking to say, okay, let's aggregate that all up. Let's look at it from a channel perspective and let's take that linear attribution and bounce it against the expenses for the same channels. And then that is what's starting to tell this story about efficiency. Like Carolyn said, like I've said, it's about looking at ratios and measuring everything the same way. So you've just got this consistent way of comparing things over time, comparing channels against each other and so on. Quickly, just some other comments about other attribution models. Cause I know I got off track there a little bit. There are some useful other models that tell you other other things. So we are fond of this method that we have used that we'll call the U shaped model where we're really asking specific questions about the engagement phase and we want to better understand like laser focus in on the things that seem to be driving people to engage initially. So those sort of first, first touch. I'll use the word first touch but I don't really mean it because we're not isolating a single individual signal as the first touch. We're using this time decay U shaped model to say let's give credit to a few different things based on their recency to either the start of the engagement journey or the end right before it becomes a prospect. And so again, we wouldn't use that to try and do the channel efficiency analysis because it's too focused, it's too specific. But when we do want to tell this deeper story about hey, what are we seeing at either tail end of the engagement journey that will tell us other things. So we're going to use that model to look at how might we better utilize certain channels or types of interactions or whatever in those different pieces of that part of the journey. And I could go on and on, there's a bunch of different other ways, but those two things that I've just gone into are really the things that we think are the most useful for our typical use cases with our customers.
- [46:45]
Carolyn
Right. I think once you start capturing all this data too, you really see things from a new perspective. I'm thinking like very practically with a customer example at Passetto, where if we look at all of the different signals with that U shape model that you were just talking about, Trevor, leading up to the prospect, then being worked by, you know, like a BDR or rep or whatever, there are some instances where a significant amount of time passes. They might have, you know, 1, 2, 3 signals in that engage period. Whether they downloaded something from the website, you know, maybe they filled out a content form, maybe they attended a webinar. They may have done these things in that process and then a BDR goes and calls on them. But we see with this data set that in some examples there was like three, 400 days that passed between when they had that last signal and then when the prospecting process began. And I think it unpacks like, well, why. Why is that period so long? Did marketing not kick those leads over to sales, you know, sooner, potentially, or where they passed a sales, but they were too cold at the time, and so they just sat there dormant for an extended period of time. But I think at the end of the day, what that really is telling us is that it's not always, and we've said this before, a prospect is rarely completely cold. I mean, that exists. That happens all the time. But we can see in this example that all of these different things happened before that prospecting life cycle. Potentially some things are weighted less because they happened a lot earlier than we intended. But I think the objective is, like, how do we actually shorten that process, make it more efficient and move people through that journey faster? And unless you look at the data from this lens, you may not have the ability to optimize your strategy with that much nuance and detail.
- [48:25]
Trevor
Lots of layers. Yeah. The challenge, once you have this kind of data, the challenge is using it efficiently. There's a lot you can do with it. You gotta. You gotta focus in and answer the questions that matter the most. That could be easier said than done, of course.
- [48:39]
Carolyn
Okay, well, we only have a few minutes left, and Donna had a really good question in the chat.
- [48:43]
Trevor
Great.
- [48:44]
Carolyn
So, Donna, do you want to come on unmute and ask your question?
- [48:47]
Donna
Hello, how are you? You started off the discussion about. With architecture. Data architecture. Can you make a link between data architecture and what you're saying about how to capture signals better? And it may be a longer discussion than we have, but just was asking, just thinking about that, and I was going down the path in my brain of UTMs and anonymous visitors and all that kind of thing.
- [49:07]
Trevor
Sure, yeah. It is probably a longer, longer deep dive, but I can try and give you the highlights here. Right. So there are a few moving parts that at least must be true to really get the level of data you want. And I'll acknowledge that some of the gaps are even still hard to fill because there's a lot of moving parts. Right. So we fill the ones we can. So tracking utms, that one is table stakes. You gotta be able to do it well. And so what we do is we have a script that we implement with some of our customers that is capturing UTMs across the entire session. So, you know, one of the problems that you see with UTMs is if you send people to a specific page, say, with A form on it with UTMs, that's, that's great. Maybe the form will capture those. But if you send them to some other page, they end up just on the, on the homepage or whatever with some UTMs. But then they wander around for a while. You're going to lose those utms unless you have a way of persisting them through the journey. So that's, that's one piece. There are solutions to do it. We have a script, other people have scripts, so it's not super proprietary to us or anything, but we've had to solve that problem so we wrote a script for it. And so what you end up doing is you capture those in the session and then that script is looking for forms on each page and inserting the stored session utms into hidden fields that will bring that through to the CRM system, to your marketing automation system usually. Right. And so for, in that once you have that in place, your form submissions will definitely have your UTM data for the session. It's a great step forward. It does require that you that the person submits a form though. So for the really high intent stuff, you're going to get better data than you had before. So the more anonymous stuff, there are a few ways to go about it. Ultimately a more robust tracking script is really the answer. There are attribution solutions out there that are trying to do this better. And it's my opinion that ultimately to get to the ideal state, something, some kind of technical implementation is going to have to happen. Like there's just no way around it. But there are ways to sort of fill the gaps using the tools that you already have in your marketing automation system. Possibly if your marketing automation system is exposing the raw URLs and UTMs and whatever of page visits, you might be able to do some automation in your market automation system to look for those in the raw URL of the page. I don't love any of those solutions because they end up being sort of big and hard to manage and so on and they're just not elegant. But if you work with what you got, you end up having to cobble together some solutions there. And ultimately unless you build something like that, you end up acknowledging that we're just not going to always get everything we would like to get. So that web visit type of more generic signal that I had in my diagram, I'd love to get that every time, but I don't know that you really end up being able to without some kind of technical infrastructure to do it, that most People probably don't have unless they've bought an attribution solution. So we're not promoting a specific attribution solution or anything like that. Just they, they are trying to solve that piece of the problem. Now for, for everything else, not, you know, not the web visit stuff, but more distinct interactions that are more trackable with the tools you already have putting UTM tracking or UTM fields on your campaign members in Salesforce, like that's another one that if you have Salesforce as your CRM system, you probably do have the ability to build this out. It's a little bit of work. But at the end of the day we're sort of saying like your campaign structure matters, so you want your campaigns to represent what did they interact with. So here's a piece of content, here's an event, whatever, and then your member statuses should accurately represent the types of interactions that could be driven by that thing. And the member itself should have the ability to store the UTMs. And then there's some moving parts, some automation that must be put in place to say, copy those utms from the form submit or whatever. So that's where it becomes a longer discussion about the actual making it happen. But that's sort of how you end up flowing into the tools that most people are working with today. There's always a way to do it better, but for sort of the basics, that's sort of the general answer.
- [53:36]
Donna
One thing I'll talk to you guys about at some point is we actually in bigger organizations, we would fund a body to sit under the Salesforce operations team because they would never focus on the marketing ops than marketing needs in the field. It was like, yeah, that'd be a nice to have. So if we funded a dedicated human and the person reported into the Salesforce team, we at least got some of our stuff we needed.
- [53:59]
Trevor
Yeah, sort of the brute force approach, when you must. Absolutely. Obviously our goal is to help people get to a slightly more elegant solution whenever possible. But we accept the realities of this too. Like we see all sorts of stuff like that where you're putting band aids on some things. I would say my concluding thought to that is if that's how you got to do it, that's fine. What really matters is that guiding architecture. Right. Sort of back to our opening point. If you know where you're trying to get to and it's really cleanly defined as these are the moving parts that we need to have by the time we're done with this putting this machine together, you can Then direct your resources in various ways, elegant or not, to get to that endpoint. It's having the endpoint in mind first. That really is the key. And that's sort of an obvious statement. But at the end of the day, we just don't see the vision of the architecture really driving a lot of decisions today.
- [54:59]
Carolyn
All right, well, that was a great question, Donna. Thanks for coming on live. We appreciate that we're a few minutes past time, but I don't want to leave the audience hanging on just like this very technical, sort of in the weeds path that we just went down. And so I have one final question to bring it all home. Wrap up this concept of signals. Although we could probably keep talking about this all day. I mean, Trevor and I certainly talk about it a lot day to day. So ultimately, when we talk about this technical stuff, I think people's eyes are very quick to gloss over, especially those in the company at the C level who just want the answers. They just want the revenue and they want it done quickly. So. And we see this all the time with larger corporations. Right. Like, it's when you've got a lot of people in a lot of technical debt, this type of work becomes an uphill climb, essentially. So why do we think it's difficult for organizations to really evolve how they are doing things right now? And how can we overcome that?
- [55:57]
Trevor
Well, I think again, it comes back to the vision. You're thinking of architecture as this set of outcomes more so than like the in the weeds technical detail. Right. Like, I think if you were to embark on a journey to build this out, you can't ignore the technical reality and the work required to actually make it real. But that's not what you're selling to the leadership team. You're selling this promise of we're going to know how our world works. And so I think you need to be able to tell that story in a way that says like, and here's what we're going to be able to know by doing this. It's not like, oh, we're going to have this prospecting object that helps us, you know, helps our day to day BDR team have a queue to work. The leadership team doesn't really care about that. They do care about the metrics that will be coming out the other end. And there are some, some key ones which I don't think. I think that's probably another, another episode where we get into, here's the things you actually should be able to measure with the architecture, because that's a good, solid list. But I think that's what you're really selling is we're going to be able to invest efficiently, we're going to be able to understand the or monitor our entire go to market operation as the factory, as this thing that has inputs and outputs. It's clear it's feeding the people that work that are managing these pieces every day with what they need to do to make their decisions. I think that's what you're, that's what you're trying to get across is we are selling the promise of structure and clear outcomes.
- [57:37]
Carolyn
And I think as much as everybody thinks or wishes that marketing or go to market in general was like this coin operated machine. You put a dollar in for this thing, you pull the lever and then you get dollars back. It's not. I wish it was that straightforward. It's not. And so I think this is like you were saying, Trevor, the path of getting closer to a world where you have more clarity around the things that have an ROI attached to them. All right, okay. So thanks all for joining this week. Appreciate you being here. If you like the show, please give us some love. Drop us a review where you list your podcast and we would appreciate it. See you all next week and take care. Thank you.