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- [00:01]
Amber
You're listening to GTM Live, a podcast by Passetto.
- [00:07]
Mo
Hey everybody, welcome back to the show and I hope life has been great for all of you. Amber and I are both pretty energized because we have some much needed vacations each of us coming up in the next few weeks. And we've also just been recording a lot more content recently as well, hoping to get more of our conversations out here on the podcast for you guys. But before we jump into today's episode, I want to ask you if your data is isn't perfect right now, do you feel confident making decisions and reporting your team's performance? Or are you like most of our listeners, where you feel yourself stuck in this like, cycle of analysis paralysis because you just don't trust what you're looking at and you can't get at what you really need. This is what we see across almost every Go to Market team right now. I've been there myself too as I talk about a lot. And I'll tell you this, teams aren't lacking data. What they're stuck stuck doing is trying to make sense of it and have it in a format that is digestible. And what that leads to is a lot of waiting instead of actually moving and making, you know, making gains. And that's what we get into in this episode, why that's happening, how to actually figure out what's working when you don't really have the data. Also, what to do when your data is just like incomplete or messy or hard to trust. So we break down why perfect data is actually slowing most teams down. Ways that you can find real marketing leverage for where to focus right now and where AI can actually make this problem worse, not better. And as you listen, you'll probably start to recognize some of these patterns in your own team. And that's because most companies don't have perfect data, but the ones who are accelerating aren't waiting for it either. So we'll talk about that. But if you didn't know, we have a platform that makes this kind of visibility possible without rebuilding your systems or, you know, fixing everything underneath all of it first, just using the data you have and pulling it together in a streamlined way. I'll include a link below if you want to explore that. But for now, hope you enjoy today's episode. Thanks for listening. You had sent me like a slack huddle last week on this topic. Certainly relatable. So why don't you tell me like where, where this is coming up for you right now?
- [02:11]
Amber
Yeah, I think it's Something that we, we sign up for. Just like everyone in Go to Market signs up for this problem. And the problem is how do we increase performance and increase efficiency and have fun while we're doing it? And in that is this inherent just reality of like, there's so much to look at. Like, we're literally drowning in data most of the time. And so what it is is that you end up being constantly just pulled into these really for us, it's like customer conversations. And just the reality of data is like so many different ways to look at things or so many different potential gaps to go fill. And I think we had said this in a, you know, a recent case study that we had published on the podcast as well, but it's like at a certain point you have to be that executive in the room or that decision maker in the room that says we're not. Like, you're basically bubbling up and managing up the. The problem to pick something. Like, you gotta pick something to optimize for and know that you're directionally going to get better visibility over time. You're going to get better answers over time. But like, if you wait until everything's perfect, like, that's just an excuse to not do anything, which as we know, not make. Not taking action is a decision. Right. And so we, I think just, it's just something that I know as a revenue operations person, like the bane of our existence to an extent, because you always want the data to be a certain way, but then there's just a fine line. You end up sacrificing prioritization. You end up sacrificing strategic insight because you're trying to do everything versus just focusing on. Let's clear one hurdle at a time, right? Let's reduce our conversion to pipeline by 30%. Like go, let's do this versus oh, but yeah, data health or oh, but yeah, opportunities. Like the qualification criteria to even create an opportunity is wrong. And like all these other things, it's like there's just too many moving parts. You have to pick something and go own it and go make an impact. And that's how you prove roi and that's how you get that seat at the table. So just something I see a lot of people struggling with.
- [04:37]
Mo
Yeah, I definitely see that too. And sometimes even from our vantage point, I feel like it's kind of unavoidable, you know, being that we're in the seat of being what I would consider like an analytics partner to a lot of companies, we're not immune to it either. You know what I mean? And I see myself in that seat a lot. And one of the things I really. Not that I like, it comes with the territory, but, like, one of the things that used to really keep me up at night when I was in house as a VP of marketing is, like, just needing the data not only to, like, make sense, but needing to feel like it was really, really trustworthy. And when something would come up that emerged in the data that I didn't quite understand, just this obsessive need to know what changed. Like, why does the number look the way it does this quarter? When I looked at this number last quarter, it was different. Why? Right. And so it's like spending so much time trying to isolate or diagnose what changed. And I just saw that, like, I know you. You. We had talked about this last week, and, like, similarly, I was doing a quarterly analysis for a client, and when I looked at the data one day, it said one thing. When I looked at it 30 days later, I'm like, oh, something changed. You know what I mean? And I did go down the path of trying to understand what changed. But at the same time, there's always a reason, right? Like, if you track your data fairly well, I think data is supposed to be there to help with decision making. It's supposed to be there to be directional. It's supposed to be there to help us make decisions and give us visibility. But it's going to be a very difficult process for a lot of people if there is this incessant or obsessive need for it, like, to make sure it's perfect. Like, data inherently is dynamic, meaning it's always changing. There's always something happening that may change it. Perfect example would be, you know, if you, like, classify an opportunity belonging to one industry and then you go change it later if you delete it, you know, delete a deal or something like that. Like, it's going to throw everything off. And that is just the reality of, you know, having multiple people in a system, having sales in a system. Like, these things happen, right?
- [07:00]
Amber
I think it can be overwhelming, too. And hey, everybody who is joining, it's great to have you here and please put stuff in the chat. So we'll be circling back with the chat and just whatever this brings up for you throughout, you know, the session. And then we'll have some time for Q and A at the end. Yeah, totally. And it's like, what data should change and, like, what data will definitely be changing. That's because we work with a live data versus what should not be changing. But it's just like, yeah, when you start doing the QBR fire drills around. Oh, well, you know, last quarter we had reported on this much pipeline creation, you know, but now, but now that's showing us something different. It's like, well, opportunities are alive and they change and they will be updated. Especially if something's getting, you know, deleted or moved to a different pipeline or having a value overwritten, you should expect that that's going to be a live data set and so don't expect for that to never change. Yeah, I think it's just more of a solidarity thing than anything is like, hey, if you're going down all these data rabbit holes, zoom out and think about how, first of all, you're human, you're not a machine. And so you can use my friend Claude to help with a lot of this stuff. But also that can lead you down a lot of RABB as well, in context that it will never have about your business and about why data might have changed, you know, or when it changes. And so it's like, yeah, just being able to prioritize, focusing on moving the needle but not trying to like turn the entire ship, if that makes sense. It's not how.
- [08:48]
Mo
Yeah, yeah, I had. Yeah, so I had sort of like two related thoughts on this, what we're talking about right now, which is the dynamic nature of data and data always changing. And I was laying in bed last night and I had sort of like similar related thought, which is just trust issues around data. And I feel like there's sort of like two extremes of what we see with marketing and just GTM measurement. One, if it's not perfect, we can't rely on it. Right. On the other side of the spectrum, like, it's, it's like we know it's like moving a boulder up a hill to try and go get people to change anything in their system. We talk a lot about data architecture and how people should properly measure things and how most companies don't have the right data architecture. So think of these like two totally, like different dichotomies. On the one hand, you're saying we can't track anything. Help us. We, we can't track anything. We can't get revops to help us. We don't even know what to change. And then on the other, you have, well, if the data is not perfect, as our system tracks it well, we can't trust it. Right. And so it's like, what is the middle ground? Where do you land when you don't have a system to track everything properly and you cannot trust the data. There is this sort of like place that you land in the middle of all of that, which is having directional data and having decision grade data is the best place you can be. Doesn't have to be perfect in your own system. It also doesn't have to be, you know, like a fully rebuilt system either. It just needs to be something that gives you better visibility into what you have now. And I feel like a lot of GTM leaders are on like one end of the spectrum or on the other end of the spectrum. And it's just like there is a middle ground to be that is a lot better than both of those things.
- [10:48]
Amber
Yeah, that makes sense. I'm also thinking about marketing measurement specifically. And like marketing is often talking a whole different language than sales, right? And so marketing can be, you know, sometimes criticized as like bringing their own data to the party. Whereas sales data is more, you know, foundational to the business or more, you know, regimented. And so one way to overcome that is to just align yourself with sales data and like align your data to the sales data and attach yourself to it. And so when you do that, you will have certain things that you just don't know that you want to know. Right. Like coming to the table with your own data set is like perfectly curated. And here's the marketing activity and here's all these things that we know. Really great. And we can quantify all this stuff. It's like, okay, well it's not speaking the same language as the revenue organization. And so when you shift to be able to speak that same common language, you're going to be able, if you're going to make that shift, it's not going to be perfect, it's not going to be 100% comprehensive. You're not going to have every single question answered around, you know, those pipeline or revenue numbers. Like there will be some gray area. But again, being able to. What we see as being the most successful time and time again is when specifically the marketing team and the marketing leader can say, hey, we're going to address fast closed pipeline and helping sales generate more fast close pipeline this quarter. We're focusing on this right now. What are all the inputs that go into it, all the outputs that come out of it? How do we help? And then you just go do it and then you measure it and you look back at it and you say, did it work? Did it not work? But you've got to pick something that was just an example or we're going to help win rate in enterprise. Like this is what we've identified as a common business objective. We're going to go focus on this. What are all the data points that we know go into that that are business critical and how are we tracking it coming out? And you just focus on that. Actually talks about this a lot. Right. With the experiments and in her business that she's launched is all around, you know, making these bets and then going back to them. But yeah, just I think the bigger the company becomes as well. It's like you just get into decision analysis paralysis with the data.
- [13:10]
Mo
It can be totally overwhelming. Yeah, I think what she, I mean, she's gone fractional now, but one thing she's selling is like these strategic focus cycles. Right. It's like sometimes you try and boil the ocean and do all sorts of things and we know just like when you're in it, it's a lot easier said than done to like drum up these ideas but then actually go execute. And so that's one thing I like about her, from what I know about it, like her fractional model, which is like helping companies execute a focus cycle on like one thing. You know, really well. You mentioned the fast close thing. Do you want to talk about that? Since I know that that was one thing that we were really focused on this week.
- [13:48]
Amber
Yeah. You're closer to it though.
- [13:50]
Mo
Yeah, I can share my screen if we want to go through it because we actually, we actually have a little bit of a case study. Actually. You know what, I'm not going to do that because there are elements on here that are not totally confidential and I don't want to jeopardize any of that. Okay, sorry, I'm just admitting somebody in the room here. So we have a amazing CMO client who we have been working with and as part of that we have been helping her gain more visibility into their data. So I know, Amber, you're helping them with their Revops team and just making incremental improvements to their data architecture. And then on my side have been helping them with their analytics and how they can basically be more strategic in marketing and really understand what's working and what's not. So for them, they were short of their revenue target last quarter and it sounds like there's a gap this quarter again too. So one of the questions their CMO came to us with was, hey, is there any insights around like, what deals are closing fastest? Because we have this gap and we want to, you know, not try and boil the ocean and just do more. We want to really focus on where is our fastest closing pipeline actually coming from and how can we replicate that. So I thought that was great because she brought that idea to us and that was awesome. And so we said of course, right. So they have two, they're a European company. Well they are global but we focus on their European markets. But anyways there's two sort of like core geos that marketing is focused on. So we took both of them. Geo one, they're more mature market and go two which is their growth focused market right now. And so we had said okay, in each market what is happening? Where is the fastest, highest quality pipeline coming from? And the biggest eye opener for me when I did this was just knowing how different the dynamics in both markets really are. Very different markets. And sometimes in marketing we have a tendency when pipeline is down or when we got to hit target to just do more without really understanding the nuances of how buyers behave in each market. So in their first geo, the primary geo where they have more like a brand or more penetration, more brand maturity, most of their revenue actually 60% of their revenue was actually coming from like these fast closed deals. And so when we looked at that we had realized like end to end deals typically close from like first signal in marketing to close one in like 40 days. Amazing. That means we have real leverage there. Where are those coming from? Most of those are coming from warm outbound, not their like digital marketing motion. So that was really, really interesting to know. Hey, the best thing you can do in this market isn't necessarily to go run programs to basically convert demand. It's supporting your sales team and knowing how they are targeting that market. Right. And what they are doing and what, how, you know, how marketing can be really effective in warming them up. And when we looked at the channels in that specific market we had said, you know what, paid search doesn't really play a role here. Like you're not running paid social. And so we got into the nuances of like what things are actually warming up the market before sales goes to pick them up. So that was just super interesting to see that you know where marketing plays the best role in their more mature market and where the fast close deals were coming from.
- [17:30]
Amber
Thinking about how you could do that yourself, right? So like think about what your segments are. We used, I think it was a geo segment in that example. But what segments matter to the business. And sometimes you might be you know, tracking a legacy segment but there's something else in there that's actually A gold mine potentially. So you could look at it in a new way. You could use the way you currently segment. But thinking about it all like culminates that pipeline and it all culminates at revenue. So we always use that as like it's like kind of the keystone in any analysis we do. So you could do the same thing on your own. Which is to say let's look at what's winning. You know, take a cohort of what you know, definitely opportunities created and closed in quarter if you have those in your business. But even what Carolyn's saying is like actually the entire customer journey, like you probably have entire customer journeys that are happening in the span of a quarter. And if you're not looking at that, it's like so wild because we look at these, it's just the status quo is to look at like this demand waterfall or this model and you're like. And then marketing's like again coming to the table with activity metrics and like all these attribution, you know, maps that you try to put together. But the nuance of where's the highest leverage, you know, opportunity at this moment for the business is like you're usually that those reports are not made to help identify that story. They're made to like tell some high level board narrative to like, you know, get them off your back basically about marketing is like doing enough activity. And so it's just like if you think about it in terms of like strategically what would you do if this was your business?
- [19:17]
Mo
Yeah, for sure.
- [19:19]
Amber
Yeah, if this was. Yeah, exactly a tangent about that. But you would start with the segmentation with pipeline or revenue and then you would look back and see well are there any that were created and you know, and closed in a certain period? And then you can absolutely do your own thing in bi. In Claude if you have a HubSpot to say like what are the journeys? Like what's going on here? Like, like and thinking about it, definitely recommend outside the box, like outside of your four funnel model. Like don't like just take your kind of blinders off, like put on different glasses and see what's happening here and what motion really is happening. Even if it means that it's not demand conversion on a web form. Even if it means that it is actually your sales team is doing a great job with intent based outbound. And look, there's opportunity for marketing to continue to or do more to help make sure those accounts are aware by the time they reach out.
- [20:19]
Mo
Yeah, definitely. I have a thought on that which I Want to come back to but just quickly on their second geo, very different dynamics in that particular market. Totally different whole sales cycle. Like, and so I thought that was really interesting to see that in their less mature but like growth oriented market that the sales cycles or sorry, I shouldn't say sales cycle, that the entire journey from like first signal to close one was much longer but much shorter up front. Like the path to becoming an opportunity was about half the size as the other market. But where the longest part was is in the sales cycle. So like in Geo1, deals close way faster. But in Geo2 most of those deals are actually coming from web form submissions for this market, from paid search specifically. So like obviously that's a good opportunity for this company and where to focus in that. In this geo deals are larger but they're harder to close. So like you can just totally understand or you know, see where, where does marketing have the greatest leverage? And what I was going to come back to is the point that you had said, which is every business has, you know, a different lever to pull for marketing and it's not always where we think it is.
- [21:41]
Amber
Right.
- [21:41]
Mo
We always sort of assume while marketing is out there to get leads or at least like leadership insists that. Right. And as you can see in this example for their, you know, their core geo, that marketing's best leverage isn't to go get the leads at all. And so like when you have that data, it's really informative. And if you think about first instinct, which is, you know, we gotta, you know, launch more, we gotta push more campaigns, we gotta get more leads into the database and things like that, would that be productive to go and actually close pipeline faster? No, it would probably like totally inflate your customer acquisition costs and not generate the results you want. And so it's really important I think for marketing especially to know what levers to pull to do that. Because it might not just be, oh, you know, marketing go gets the leads and it's just knowing that for your business. And then this CMO had said, you know, when she brought this data to leadership, like the receptivity around it from, you know, from sales especially was amazing. She was like, I've never seen us have this kind of strategic alignment which I know is really hard in marketing to like have Mark have sales understand the marketing data that marketing is bringing to the table. So to see like this type of analysis foster that kind of discussion with sales and marketing, like that was a huge win. And I'm like so happy for the CMO to have that level of visibility to spark that kind of discussion. I know. It was really, you know, eye opening for them.
- [23:08]
Amber
Yeah, everyone's looking at, like, what are the new ways of potentially doing things? What are the new ways of looking at everything? Because we're going through this paradigm shift now, you know, with AI, like, disrupting essentially everyone's business. Like, you've got to just think, if you're not zooming out to say, like, what is a different paradigm that we might operate in? Like, if you're not thinking what those possibilities are, then I. I don't know what to say. Like, you've got to get to that point where you can say, you know what? Let's throw out everything that we Learned the first 10 or 20 years of our career around tactics and what to look at and what a dashboard should be, and just start back from the basics of, well, how are we doing? What is sales focused on? How can we help bridge that narrative with them? And it does feel a little bit like kind of sticking your neck into a territory that maybe isn't yours sometimes I think for marketing, which is fair, but again, you do it in a strategic, you know, pointed way, and then you just test the waters and then you see, like, how much credibility that adds to have that partnership.
- [24:23]
Mo
So, yeah, what else? Anything else on that topic specifically? Oh, I just have a bone to pick. You had mentioned Claude, and I think, as. I think an instinct that a lot of people have right now is that, you know, they can just dump whatever into Claude and get data and. Listen, I have been playing around with Claude a lot. As you know, I have spent hours upon hours in, you know, engineering prompts to run analysis. And there's a lot of amazing stuff that we can leverage CLAUDE for in terms of KPIs and analysis. And I. We've talked about doing like a live workshop on that. So just planting the seed might come up in May if you want to know how to use AI to do some of your analytics. But the als, the other thing that I am so frustrated at, I just, like, I yell at Claude now because I'm like, what is happening is the hallucination with data. Like, my prompts are extremely clear. Like, I, like I'm telling you, I spend a lot of times, a lot of time refining them and being really specific about what to do and what not to do. But anytime I run any sort of analysis in Claude, I say, double check the data. Great, I found 10 errors. How the fuck did you do that? It says, oh, you know, I Miscomputed something. And so I just want to put out a huge, There goes my mic. A huge, you know, warning sign that be careful with what you put into AI because it's hallucinates and miscalculates data all the time.
- [26:04]
Amber
Yeah, it does do it. And it's like we're just kind of out here, you know, La de da. Like the models are getting better and they definitely have come so far, even from a year ago, like can you remember like how much just random stuff would be made up? But yeah, it's like you have to really bake it into the model that there's a series of checks and balances and if you're not baked, baking it in to say every single time we run this analysis, here's what you're going to do. You're going to go get this, you're going to cross check it against this. You're going to come back and ask me, does this look right here's where I am here. Then you're going to go cross check reference this and run through this whole list. That's how I do it anyway. The whole list of checks and balances for it to validate itself and then flag itself. But even then there's stuff that happens, which is why again, even specifically with AI, very specific use cases like you've got to map out in your organization where is it most impactful versus just maybe a glorified time saver that's actually maybe not as much of a. It might be saving time but it's not necessarily leading to better outcomes. So that's a fallacy that a lot of companies are just jumping to and you really gotta think about where is it best suited? Like what, like work streams, is AI best suited to come in and actually do a better job with that human in the loop and where. And maybe sometimes the human isn't in the loop, but it's definitely not just like ad hoc reporting requests. Like if you're going to use it, you need to know that you're going to have to go into it with setting the stage and actually treating it as a project to go make this thing, not just like a one off analysis of your CRM data. Good luck with that.
- [28:00]
Mo
Yeah. And if you do that, like, yeah, if you run that and you're trying to get a quick answer out of AI, my recommendation would be to ask AI to validate the data and then do that probably like five, six more times. And even sometimes if Claude or whatever comes back and is like all clear, even still when that happens, I'll do like one more thorough check, validate everything and it'll be like, oh, I found, you know, three more errors. So I, I. While I am super optimistic about AI for analytics, I think that there's just so many, I think like there's a lot of guardrails and considerations to keep in mind for that.
- [28:46]
Amber
Yeah, you can really go wrong kind of like trying to diagnose your symptoms using like WebMD or something. Like you'll land on like the totally wrong thing
- [28:56]
Mo
for sure. But yeah, that just goes to like get me thinking about. I know something else that came up for me which is, I mean, GTM is evolving at lightning speed. Like it's freaking crazy to see what is happening and AI being like so responsible for that. I had a conversation with this guy who does paid media for like larger enterprise SaaS last week. We were just chatting. I was getting to know him a little bit more and I was like, what are you seeing right now in the market? Like what is happening from your vantage point doing this kind of work with companies? And he was like, you know what? I'm having to do a lot of convincing like to have companies rethink how they're running their strategies. Right. So as we know, Refine Labs and Chris and you know, all of these industry thought leaders for so long have been saying stop running demand capture, start running like brand awareness plays, that sort of thing. And so Mo, this guy was like, you know, I'm really having to speed people along to do more of that now because they're trying to play catch up. Because the early adopters of what we were talking about five, six years ago are ahead of the game now. Like, and now all these people who never adapted are trying to play catch up and trying to like totally re engineer their strategies to be more demand creation focused and not demand capture. And there's a lot that's changing that. I was like, what is changing that? And he was like, well, Google as a platform is totally less efficient than it used to be. Right. And that's where everybody ran their demand capture. Like just pouring money into paid search, which was already hard enough to optimize. And now we're saying, man, it's just like getting even harder. So I'm just reminded just how much AI has a role in that.
- [30:55]
Amber
Interesting. Yeah. Also I feel like there, well, first of all, yeah, there's a huge performance gap and the gap is widening with companies that are willing to kind of do testing and like trust marketing to like test things and Then see what works and pour more, you know, on that versus some kind of, I don't know, like, I don't even know what. Honestly, like the way that marketing teams are like gate kept still is a, is a huge problem. But there's a huge gap with performance where you see like a small number of companies just like dominating with these approaches. But in general, I'd say like most of the, most of B2B has not caught on to these things that, you know, have even been widely accepted. They haven't implemented them. So I also feel like it's easy to feel like you're behind or you have FOMO or whatever. Everybody's doing it better than you. But that's not what we see actually most often is that it doesn't take a lot to again, just like shift your perspective and approach like slightly to make a huge difference. Most companies are still not doing that. And so if you can even do it, you know, 5%, you are now putting yourself in this like elite category by even taking that step. Like those initiatives get blocked.
- [32:37]
Mo
Yeah, for sure. So with that said though, I think this question in the chat actually sort of relates to what we're saying, so maybe a good time to bring it up. So in the chat Stephanie says, I'm in a similar situation. From the customer story you shared. We are just building our demand generation function here and the challenge is focusing on just getting more and the challenge is focusing on just getting more leads isn't the right approach for us to get our pipeline. I want to learn more from you both on how I can look at data to better understand what our own levers are here to influence. Pipeline. Yeah, that's a great question. Probably a very common one. What if you want to come off mute, what is like what's the size of your company just for reference? Stephanie.
- [33:26]
Stephanie
Yeah. Hi there. So we're in a 60 million organization, but yes, but the marketing team I would say is fairly new. Majority of us have been here for under a year and we're just starting to build out the different infrastructures. So the company prior to that has been primarily sales led. And so there's a strong BDR function and a E function. But I think marketing, we're just trying to figure out how do we fit into that motion. You know, we've started LinkedIn campaigns and Google search. But again, I feel like there's a bigger gap that we need to look at. And so that's why I'm really interested to learn more about how would you approach this for sure.
- [34:23]
Mo
Well, congrats. That's amazing to be in your position with a company at 60 million where they're now bringing in marketing. Because I think a lot of times what we see is like really growth stage companies that are much smaller in revenue trying to like basically bring on marketing to help them scale. So that, that's awesome that you, you guys have achieved that revenue without marketing. Right. So I'm going to answer and then I'm going to let Amber answer. But the best thing I think that any company can do in your situation and many other situations is to actually look upstream on what is happening that actually is helping to create the pipeline to begin with. So that means walking back before opportunity creation and opportunity close to say how are those, like how are those opportunities even are, are even getting there?
- [35:18]
Amber
Right.
- [35:18]
Mo
If they're mostly driven by sales, what triggered the sales outreach to start? How long did they have to work those people before a meeting gets created? And just understanding like the effort that is required to create the pipeline in the first place and understanding how much time sales activity goes into that. And maybe if they're not necessarily driven by marketing, what they're doing, how they are engaging with marketing in that process, I think there's probably a lot of potentially patterns that you might see where pipeline is created faster when marketing is involved in that buyer life cycle. I mean, that's just a hypothesis. I don't know if that's true, but I think there's a lot of, other than just looking at leads generated, which I don't think is going to be productive for an established company that is primarily sales driven. I think that is going to be a battle I would not want to get involved in, which I'm sure you know. And so I think that there's just so much more granularity that comes with understanding where pipeline is even coming from and how marketing can accelerate that.
- [36:24]
Amber
Yeah, I would agree. I think it also depends on, you know, you're saying marketing's new at the organization. So do you have any historical like data to look at in your map or somewhere else around marketing engagement and like what would even have been eligible for that. And this is really related to what you would mostly think of as like a multi touch attribution journey. Right. So like that is what we're talking about is thinking about whether it's, you know, all pipeline or thinking about what specifically are the growth levers? What are the growth levers that sales is looking at right now. Right. Is it a certain industry? Is it a certain, you Know, buyer role, like unpacking the data related to that specifically. And then if you don't, even if you don't have a lot of marketing data to look at potentially historically, you could make a hypothesis and say, well, we're going to create, you know, these, whatever it is for you. Right. Like events to sort of just help engage, create more engagement. Right. Or we're going to do xyz. But some of these things happen depending on your sales cycle. They might, you know, take months for you to really see the results of them. So what sort of, what sort. How is marketing kind of being like graded right now?
- [37:45]
Stephanie
Yeah, I love these points. I think that's, that's what we're thinking about today. Right now our only, I would call high performing channel is events. We do get a lot of pipeline from events. And so one of our strategies is how do we start diversifying that? How do we start to generate across digital channels as well? Because that can help reduce the sales cycle length. Right. Which is one of the challenges that we have. So our company sales team is split between mid market and enterprise, where the biggest opportunities in the mid market space. And so I think that's where we probably want to take a further look at how is sales dividing the market? Where do they think there's bigger opportunities and maybe work back in terms of how marketing can help throughout that journey.
- [38:44]
Mo
It sounds like you've got at least an initial, really good pulse on emotion that is working. And to be honest, my recommendation and what I seem to work really well is really nailing like, you know, the two or three core channels that are going to be most effective. So if you already have, you know, indicators that events are impactful, I think it's wise to really focus on what we know is working before trying to just like, you know, what's the word I'm looking for? Like, I forget the term but just to do basically. What's that?
- [39:26]
Amber
Trying to diversify.
- [39:27]
Mo
Well, diversify. But there's a term that Brendan Hufford uses and I can't remember what it was, but it's a good one.
- [39:32]
Amber
Marketing.
- [39:33]
Mo
What's that?
- [39:34]
Amber
Is it checkbox marketing?
- [39:36]
Mo
Yes. Yeah. Like just checking all the boxes. Okay. You know, like just because, you know, like channel diversification, because, you know, paid search and paid social and you know, YouTube and display and all this other stuff might work. It's like. No, I feel like the best companies that we analyze are the ones that have just nailed two, you know, sometimes even one channel really, really well and that's their growth Lever.
- [40:00]
Amber
Yeah, I agree.
- [40:01]
Stephanie
I do think I, I think right now we probably have been looking at it more like a checkbox. Oh, we need to have LinkedIn paid, we need to have paid search. But I think we, we need to start opening it up a little bit more in terms of what are we actually trying to do and how do around events one channel at a time.
- [40:23]
Mo
Yeah. What is your, do you know what your average sales cycle length is right now?
- [40:29]
Stephanie
Yes, in the mid market it's about six months.
- [40:32]
Mo
Okay. So similar to what this other company did, I know they have shorter sales cycle. Their, their average deal sizes is, is probably smaller. But again like doing a spot analysis on where what are the deals that closed the fastest in like the last two three quarters and trying to like reverse engineer what happened with those versus the other others and seeing if there's anything you can glean from that that may have helped with the, you know, pipeline velocity versus like pipeline drag in these other buckets.
- [41:07]
Amber
It's really helpful to be so specific. It's like incredibly empowering to be able to look at it that way and just ignore like so much other noise and, and place your bets on something. So shout out to you. Thanks for sharing that, Stephanie. So relatable, I'm sure to very many folks in a similar situation. So let us know how it goes and maybe come back in a few weeks and let us know what you decided to, to do moving forward. Yeah. Thank you.
- [41:40]
Mo
Cool. Yeah, thank you. That was a good question for sure. Got us talking about some good stuff. Anything else, Amber? This top of mind for you. We've covered my topics for today.
- [41:50]
Amber
Yeah, I think that's it. I'm super excited for our live virtual event next week. I don't want to call it a webinar, that feels so lame, but our live virtual event on the rise and fall of the mql. So that'll be super fun.
- [42:06]
Mo
By the time this goes up, that will have happened already. Otherwise I would say go register, but We've got over 150 people signed up for that. It's going to be amazing. We're probably going to, you know, put the recording on the podcast anyways. I know I normally don't do that, but I just think that John Miller is so amazing that I don't want to like gatekeep that from anybody. But I'm already thinking about our May topic and I know I've talked about this a lot with you, Amber, but I really want to potentially do some sort of like live or some sort of like case study where we use AI to do some like KPI slash marketing reporting. I think that would be cool. We don't talk about AI a lot, but we use it all the time. So I think it would be helpful to show people how we're using it because it's done some cool stuff for us. So.
- [43:03]
Amber
Yeah. How to use it. What's a good use case for you?
- [43:08]
Mo
Yeah, for sure. So if you have anything, anything else you want to learn about. Yeah, send us a. Send either of us a message on LinkedIn and let us know what's top of mind. Definitely love your topic suggestions when you guys come to us with them.
- [43:22]
Amber
Yeah, and I'm gonna be at Chili Palooza in May, so if you're there, make. If you're going, make sure to hit me up.
- [43:29]
Mo
Yeah.
- [43:30]
Amber
So cool.
- [43:30]
Mo
Yeah, that's coming up soon. Where is it? New Mexico?
- [43:32]
Amber
Yeah, it's like in the desert somewhere. And I'm excited to learn a lot more about the. The best use cases for AI and go to market.
- [43:42]
Mo
Awesome. Okay, well, good chat and thanks for coming out. Thanks for listening and see you guys next.