The Evolution of Marketing Ops in the Age of Generative Digital Experiences with Paul Wilson

Michael Hartmann: 0:26

Hello, everyone. Welcome to another episode of OpsCast brought to you by MarketingOps.com powered by MO Pros. I am your host, Michael Hartmann, joined today by both of my co hosts, Naomi Liu and Mike Rizzo. Naomi in

Mike Rizzo: 0:39

the house!

Naomi Liu: 0:40

Woohoo!

Michael Hartmann: 0:42

Hi guys! It's been a while. Glad to have you back. This is exciting times. So we'll, uh, We need to do we need to do one of our like three amigos episodes so we can share what's going on. We've all had stuff going on in life. So we'll get to that another time. But this will be a fun one because I think I want to get to this when I'm actually really like this is an area that I've been really curious about. So. And what that is for our topic today is we're going to talk about, it's going to really be our first time in this, at least in this format, talking about the intersection of generative AI and marketing operations. So joining us to really help us with this conversation and for us to learn is Paul Wilson, who's chief strategist of GTM systems, a consulting company focused on reinventing the intersection of marketing sales and customer success operations. So prior to starting GTM Systems, Paul had several leadership, senior leadership roles in marketing operations and marketing technology, including at OneTrust, Salesforce, Slack. I realized in my notes, I forgot he was at Marketo for a while. So, um, he's got deep roots in all of this. He's also involved in some cases with sales operations. And if I, if I understood it right, Paul, you can, you can clarify this. I think you were actually in sales for a while. Um, so, uh, and he's also an advisor, some startups and, um, just sort of an all around, uh, person who helps the community. So Paul, welcome. And thank you for joining us today. Awesome. Great to be

Paul Wilson: 2:08

here. I'm very excited to chat on this topic and yeah, the, there, there have been moments where I've been in sales. I've touched on multiple disciplines. I think that the core of my journey really started as. You know, at the dawn of what was then called inside sales, uh, in the early Yes, early phases. Uh, I remember doing my first, uh, implementation of this new software as a service tool called Salesforce in 2001. Right. And I started tinkering with CRM and CRM con things, did a lot of CRM work, and then got into marketing technology. So it's a. I, I don't want to talk about the decades, but it's decades that I,

Michael Hartmann: 2:46

so I, I'll age myself a little bit. I, I remember doing work with Siebel Systems, right? Oh,

Paul Wilson: 2:51

yeah. Scope. So we, we had a Scope implementation. Yeah. Scopes was

Michael Hartmann: 2:55

bought by Siebel. Oh, yeah. I was, I we had worked at a consulting company. That's what we did. Um, so yeah, we all, we know, all knew about the, the Tom Siebel, you know. Yeah. Uh, departure from, was it Oracle, right. And, yep. So, And it was a pretty interesting time, but yeah, like I tell people, like I was in marketing operations before it was called marketing operations and it titles like e marketing and internet marketing and whatever. Yeah. When the internet wasn't

Paul Wilson: 3:23

black and white. Yeah. That's

Michael Hartmann: 3:28

a good call. Yeah, it's fun

Mike Rizzo: 3:31

for better or worse. I know like maybe, you know, 40% of what you're

Paul Wilson: 3:35

talking about, right? Well, this was back, back in the really interesting times where data enrichment was going out of Hoover's and looking up companies and

Mike Rizzo: 3:46

people's names. I have to say there was definitely like a task thrown my way at one time. How do we leverage Hoover's to be able to do so? I was like, wow, this is crazy.

Paul Wilson: 3:57

Michael and I have seen things arrive, like. LinkedIn

Michael Hartmann: 4:03

Yeah. At one. At one, at one point. Um, gosh, this was probably 10, 15 years ago. But I remember someone pointed out that if you looked at your LinkedIn profile url, you could actually tell what, like if you were the like whatever number, know the OGs, you know? Yeah. And I actually had a number and I was like, I think I was in the first million or something like that. It wasn't that like, like hundreds, but it was at the time. I was like, wow.

Paul Wilson: 4:29

And here we all are now talking about what our number is on thread. I mean, it's just, it's all, it's all

Michael Hartmann: 4:34

still happening. Right. Um, okay. Well, so we can, we could go reminisce for a while, Paul, maybe you and I need to just do like a different podcast, virtual, virtual happy hour or something and just talk about, you know, Life, uh, before the internet, but let's, let's get started with like, so as much as I'm interested in, in AI topic in learning, like there's a part of me is like, is it just hype? Is it reality? You know, what was it? Is there something that's down the road? It's probably a little bit of both. I'm guessing or. Somewhere in between, but I'm, I'm like more interested in learning a little bit about, cause I think your, um, GTM systems, you know, kind of, uh, thing right now is a relatively new adventure for you. I'm just curious about what led you to, to start that. What was the, you know, timing was just right or something you've been thinking about, what's the background. That's

Paul Wilson: 5:26

great. It's, it's something that I've been thinking about for a while. And, um, you know, the, the time was right in terms of. The bigger topic we'll get to in terms of general, but to me, the timing is right for companies to be more mindful about the intent, digital experience and regardless of whether or not there's a significant technology shift around the corner, which I, I believe there is. I see an opportunity in the market for there to be an end to end consulting practice around aligning data, technology and processes across the disciplines. We are currently seeing as marketing ops, sales or rev ops and customer success. I really think that the change that's coming around the corner is going to really accelerate and drive requirements. Across all of those silos today and what GTM systems is focusing on is how do companies start to address the, the siloed nature of those operations functions and what do they need to do and what can they do to really establish a cohesive brand experience for all of that digital work. And it's that transformative work that in the first phase of generative. Technologies coming into the market. It's that foundational work that I think is key. And so GTM systems is going to be a consultant practice focused around what are the foundations that are needed in order to enable that kind of transformation across those core disciplines of sales, marketing, and customer success.

Michael Hartmann: 7:12

Got it. Okay. So good. It's like a good overlap here for what we're going to talk about. So in our quick preparations for, for the conversation about generative technologies, I'm going to use that terminology instead of AI, which is interesting to me. I picked up on that. So I'm curious. Maybe along the way you'll describe what that difference is, but, um, the, you mentioned that marketing technology, marketing ops folks were kind of pioneers. So kind of connecting the dots from our early initial part of the conversation here on digital experience, you know, websites, email, marketing, automation, SEO. You know, search and all that kind of stuff. Data. Um, I guess in the latest lately, like abm things like, but when you say that, what do you, like, what do you mean? Can you elaborate on what you mean by like, they were, I mean, I appreciate it. Like, I feel like, yeah, pioneers, but what do you mean by that?

Paul Wilson: 8:06

Yeah. So again, perhaps it's the perspective of age. Uh, but you know, I've, I have seen the transformations that have occurred from, you know, if we put the line in the sand of 2002 today. You know, I really kind of see there have been a significant number of innovations that marketing has driven that really are those digital experience foundations. And I kind of look at those foundations of, you know, the emergence of that digital B2B marketing notions, the notions of, um, microsites on web pages, the ideas of email marketing, right? Microsites in a while. So that idea of tying email marketing to landing pages, then what happens when you can gather information that way? And how does that data then get transferred into the CRM? So rather than the CRM just being, pardon me, an address book. And a deal book, there's the idea that there was live data that was going to be accessible and available. So that dawn of digital B2B marketing moved then pretty quickly into. Social media and advancements inside the CRM related to those more catered experiences that marketing develops and then passes to sales. And it's that curation of digital experience from the site through social media, through the sales organization. And then we, you know, really started transforming infrastructures. We needed, we got the ability to have deeper analytics, deeper insights. The idea that mobile was coming to bear in the, you know, 2010 to 2014 range, all the transformations related to mo, to mobile, and then the notions of content marketing and dynamic content personalization at scale in the, you know, 2012, 13 range to 2000. Well, till today, but you know, all of these changes. Are aligning and changing the digital experience and marketing technology and marketing operations have curated and delivered those transformative platforms that are actually well beyond marketing. The website doesn't stop when you get transferred to sales, the, the benefits of analytics and data have become critical for customer success, just as critical and customer success as they were in marketing. You know, I remember in probably about 2013, 2014, establishing a scoring algorithm. On behaviors on the knowledge center of a client's website so that their support organization could have a engagement, what we would call an engagement score or a knowledge score for someone opening a support ticket so that if you open a support ticket and you've consumed almost no content off the knowledge base, the support person you're dealing with will start with the did you unplug it and plug it back in questions. But if you have a very high knowledge score, and you've been digging deep into white papers and case studies about custom integrations and custom implementations, and you open a support case, you're going to, in this example, you would already be on the second tier of support. And so that's a marketing operations function that left marketing operations and moved into the rest of the business. And so, from my perspective, this is where those ideas of. Marketing Ops, Rev Ops, Customer Success Ops become a platform of go to market operations. Got

Michael Hartmann: 12:00

it. I mean, I think for machine learning,

Paul Wilson: 12:03

machine learning is going to level that very quickly because it will curate the entire end to end experience, not necessarily in 2023. It might be in 2025, 26, 27, that that is the reality that we are living in, but the foundations today need to be built that lay the groundwork for those systems to really take advantage of the data that's available.

Michael Hartmann: 12:30

Yeah, you know, um. As you were talking, I was thinking like the probably the tipping point is the right term, but the tipping point for me in terms of seeing the potential of technology and applying it to marketing was when really Google search came around first, right? And the ability to really target, um, an experiment and try things and quickly get it. Data to inform decisions like that was working. Don't do that. We're going to switch this right. We don't know which term is going to work. We're going to, we're going to experiment or which phrasing on an ad. Um, and then it quickly became data. I think data has been a huge one. I, you know, people who are longtime listeners will know that I still think we have a problem with we generate lots of data. Don't get a lot of value out of it for most people. So I think that's a challenge. Um, curious, like Mike, Mike and Naomi, if you like, was there anything like that for you where you're like, Oh yeah, this is like, this is a game changer in terms of what can be done. Yeah. I

Mike Rizzo: 14:37

mean, Paul and I have talked about this a little bit, or just on the side, a couple of sidebar conversations. Um, and certainly with, with a few other folks in the community recently, like, um, I think. Um, Peter or yeah, it was, it was another community member I was speaking with recently, but we were, um, talking about just, you know, how, how much impact will this sort of new era of technology really have on, on this particular role, right? Like we've, we've seen it immediately take shape against creative, uh, specifically like design and content copywriting. Uh, we've seen some of it start to impact even development level stuff, uh, right? So GPT and, and whatever, uh, tools to help you write code. Um, and, and when I was asked, do I think that this is coming after our jobs in sort of the marketing ops function? Uh, my immediate response was like, no. Um, and I still hold on to that today. I really don't think it's going to be able to completely replace, uh, people. Because when you think about people, process, and technology, and the unique nature that is every single business genetic makeup, so to speak, The nuance of I use this field to make a key decision in my business or these five data points to make key decisions in my business is unique to every organization. And there isn't going to just be this blank, just like every business is different from the next, there isn't a blanket statement that a Uh, a system could necessarily say, well, now you're building a go to market services business. And so Paul, this is your model for how to go get sales, right? Like, like it just, it's just not going to quite do that. Right. And it's like, oh, well you're using HubSpot and you're using Salesforce and you're using, uh, whatever else like, well, this is how you could do that to help, you know, hire these staff to then integrate these tools to then go to market and sell the products. Like there's. conversation that has to happen between people and how you want to sell and the emotions and the stages you want to go through. And there might be some, the fallacy of best practices that we all go up against, but I just don't see a system, uh, an intelligence of any kind necessarily being able to completely replace the need for that discovery. You might actually be able to talk to it and it could do discovery with you if it's trained to ask the right questions. Um, but I don't, I don't know that that part's ever going to go away. What I, and to just to try to end this, cause I want to hear what other people have to say too, um, to end this, what I did realize though, is if you, you know, just like if you know how to use GPT today and you know how to sort of ask it the right questions and, and, and, and tailor it a little bit, you get some really good output from it. If you know the right questions to ask of a wholly integrated system, and I happen to use the HubSpot tech stack. So when it's built on an infrastructure that is one code base, and it's a shared and unified sort of structure with HubSpot releasing some of their own AI and ML stuff, there's going to be a point in time where I can go to their tool and say, I would, I'm obviously, I don't work there. I have no idea, but I assume there's going to be a point in time where I can ask the AI, I want to launch a five touch nurture sequence to this persona, Can you help me build that workflow? And it would be like, and it would spit it all out, establish the enrollment criteria, put in the placeholders for the emails. And then it's going to ask me, would you like me to write the first draft of each of the five emails? And I'll say, yes, please do that.

Paul Wilson: 18:27

And it'll go, and then you'll swipe right or swipe left on which option of the email you like, and then establish what the five final pieces are. So you're a curator, not an author.

Mike Rizzo: 18:37

Right, and so that part is going to happen, right? But you have to know, it's not going to come to you and be like, Hey, I think you should launch a five touch nurture sequence for this audience. Like, it's not just going to start prompting you that right out of the gate. You should, I mean, it might eventually start making suggestions, but The point is, is it has to know what your end goals are and you have to know what to ask it and it can start doing a lot of that legwork for you that is manual effort, otherwise is manual effort, right? Oh, now I gotta go build a whole workflow and build these five emails and connect them all together and set the enrollment triggers, like, that's all gonna change. And that It's coming after our jobs,

Michael Hartmann: 19:17

but

Naomi Liu: 19:17

that's also assuming and just piggybacking off of what you're saying, like, is that, you know, what you just described is very contained in the hub, the hub spot silo, for example, right? That's assuming that you're not also integrating things like survey monkey and bed yard and zoom info, all the other stuff where. You know, you're, the artificial intelligence doesn't know that, okay, you also want to do lead scoring based on an email click within a video that they watched and add it to their scoring HubSpot or something like that, right? You're still inputting that stuff in and you have to do QA and whatnot. So I don't know. It's, it'll be interesting to see. I mean, there's always been in the history of just advancement in technology, things that have been invented that people are like, Oh, what does this mean? Like when the calculator was invented. Right. Computers, streaming services, you know, all of that stuff. It's just the way that people interact and work just shifts.

Paul Wilson: 20:13

Yeah. And I think that the tech, the impacts of technology on these roles and the work that we do is it's, it's next to impossible to protect. And this is where, you know, when I grew up, I remember I hated math class. Like it was just, I did not, I was, I sucked at it. It wasn't what I liked. And I remember, you know, my mom telling me, well, it's not like you're gonna carry a calculator around with you all the time. Well, mom we do now like So, you know, it's not

Michael Hartmann: 20:49

only it has the calculator, not only is it a, you know, calculator, it's also a phone. And I, I like, I very distinctly remember the very first time I saw a mobile phone. It was, I was, uh, as I was back in Phoenix, I was working in a summer between college, my first and second year in college. And I was a runner for a law firm, which meant I was like, had to go. I was the one that they send out in the heat to go deliver documents to the courts or other law firms. And that was at one law firm in the lobby. And I hear this loud voice coming from behind the receptionist desk. And this guy comes walking out and he's carrying, it's like that he's got the case in one hand. The, you know, the connected phone, you know, headset and the other, and he's basically yelling whoever was calling him for, because it was so expensive. Right. Right. I also had long distance cars back then.

Paul Wilson: 21:36

So I have a question. So one of the, one of the areas, and I think Mike, your, your discussion around, you know, what's going to change and what's threatening our jobs prompted this from me. I look back at the accidental changes. But the, the, the knock on changes that happened because of major technology shift, and one of the ones that comes to me as an example related to, you know, Benioff and the notion of software as a service, I can recall because prior to the, prior to this gig, I was a print broker, like physical printing broker, and I produced. Documents, you know, books, packaging and CDs or morale for other software companies. And when they had releases, they would produce just kids and skids of boxes of manuals and discs and packaging and ship stuff out all over the world. Software software as a service ended all of that and I, my hypothesis, and I'd love to hear other people's thoughts on this is today. We see data providers, enrichment providers. We see all of these people. Trying to help with data. And it's my hypothesis that sector of our economy is going to be massively transformed when you see technologies like the open AI platform that are contextually aware of the outside world starts selling silos. Where a company can pump their internal data sets in a protected funnel into the open AI framework and have curated live to the Internet, plus all of my internal knowledge base data, CRM data. Marketing history data. So when, you know, they only fills out a form on my website, the engine is contextually aware of not only her, but every support ticket that's ever been opened by her company, every opportunity that's open and closed from her company and the news that. It's just announced that her company is acquiring their biggest competitor. Like all of that contextual framework will be there and immediately available. And I think that where we see all of the land grabs right now from six and zoom info, like all of the players in the data space, I think that's going to be where we see a massive change fairly quickly. Because of machine learning, but I'd love to get other people's riffs on that.

Mike Rizzo: 24:38

I totally agree. Um, I was talking to the CEO of, uh, I remember the name of this organization at the moment, but, uh, which is terrible, but, uh, he showed me some of the capabilities of, of this platform. And, um, in the context of my looking at, you know, your profile, Paul on LinkedIn, uh, I can. Initiate a conversation with you, and in about 30 seconds, it generates a post that is contextually relevant to not just your content that you've been sharing, liking, and interacting with on LinkedIn, but also if you're tagged to a company and the company's news or your blog and your content that you've published externally, and it's, it's creating this highly personalized outreach That is totally based on you and your interactions and how I may want to present myself It's happening today. Like yeah, it's already happening. You start filling it with your own proprietary You know crm data and a protected silo and have it do that holy cat like I don't know what it that At some point, I'm just going to assume that every person who's ever reached out to me just has that tool now, right? Like

Paul Wilson: 26:00

they're not changing the

Michael Hartmann: 26:01

paradigm. So I got to tell my kids, just assume you're being filmed wherever you are. Um, so, uh, maybe, I don't know about skeptic is the right word, but I'll try to play a little bit of like, okay, what if here, and that is, so I, what I. What I have heard, and it's very minimal because I haven't had time to really dig into what's going on, is that one of the things I've heard is it is a challenge for generative AI is how much access, how much data and input it has access to actually is beneficial, right? But it takes time for it to learn it, that if you have, like, if you just want to use your own company's data, the likelihood that it will be as effective is a little bit lower, right? You know? Um, so it sounds like you're describing is a little bit like some access to public data. You know, and then some access to your, um, right, specific data, but in a protected way, and it can feed into that. Um, my head immediately goes to like, well, how do we, how can we trust that? That's those are actually siloed bits of data, right? Because there's now, especially with all the concerns with privacy. Um. I just, I sense that, like, that could become, whatever, I think there's, the challenges I'm hearing, like, that kind of problem with privacy and access to data and could it be, um, compromised is one, the other that I've heard about, and I think this is more from a content standpoint, both content and creative is, is, uh, like rights or IP rights, um, copyright, that kind of stuff. How does that fit into all this? Thanks.

Paul Wilson: 27:44

There's a huge, and this is, this is part of kind of the, the landscape that I'm, that I'm working in today as companies are looking at the topic of leveraging generative AI or machine learning, however, and we'll get to that topic too. But as companies are leveraging technology in new ways, not only is it what is the technology capable of, not only is it what is the data that we're providing to this technology to educate and curate the stories this technology can tell, where are the rights management factors, what are, what are the foundations that companies need to begin building today around Capabilities, not only so that they're respectful, one of the legislative requirements that are being drafted and developed today in, in Europe, there are a number of countries who have, who are drafting legislation for consideration at the EU that will mandate the same sort of cookie consent or any and any use. Of P. I. I. And behavior in machine learning. And so companies are going to need to have the infrastructure and capabilities to opt users data in or out, offer them the experience like a choose your own adventure website where do you want this site curated with machine learning data? Yes or no. Like all of the infrastructure, all of the notions of privacy and consent reference. The right to be forgotten the implications across a brand. If a brand says our data cannot be used in your curation of any of our customers. Employees experiences like there is going to be a lot on the journey, not only about isn't this technology cool. And do we have the data to feed the technology? It's what's the right way for us to deploy this? How do we need to think about it? Like, there's positive experience, you know, there's positive ideas that are rampant right now about, you know, personalization at scale, being able to really provide you with. Your tailored experience of our brand. optimizing recommendations and things from, you know, other B2B customers who have this product are also very pleased that they bought that product, like that notion and the ideas of personalization interaction at scale. But then closely following that are the potential negative implications of over personalizations where people feel like it's A scene from minority report and there, you know, you're, you're, you're anticipating my next need before I even know it. And you've got it wrong. And then the idea is, I think, as you were sort of leading to Michael of the, the, the bias, if a company improperly implements a machine learning algorithm to curate the content that's being delivered, and it's Aggressively biased based on the customers the company wants you, you might end up pushing away more customers than you should because you're biased to a company that isn't like them or, or whatever the case may be. And then of course, there's the, you know, the, the, the potential, really negative bias. The, the potential of, um, you know, human bias, racial bias, and issues that, that. You might not even be aware are occurring in the back end of algorithms leading into the content, and you're not aware of it until it's too late. And most of your infrastructure has been degraded because the data that the system has been building for the last however many micro cycles it takes. Is now all up and how do you roll it back? How do you retrain the large language model when a large language model has wandered too far in one direction or another? So all of these kinds of foundational topics are the topics on the journey. And I come back to my original thinking that it's marketing operations and technology professionals today. Who have as the top of their, their point of view, that they're there to curate the brand experience and the digital experience. And as all of these technologies roll out across other parts of the business, it's in our purview to be the subject matter guides and people navigating through with the other parts of the operations business in a go to market model, where we're the, we're the curators of that digital experience. Not just marketing technology, it's digital experience.

Michael Hartmann: 32:53

Yeah, I mean, it seems like it, I mean, there's so many new things to kind of solve for here. Um, yeah, let's, like, what, you know, what do you think puts marketing ops, revenue ops type people in sort of a unique position to be able to, to be a, I guess, a guide for their organizations or their. Customers like in your case, your clients,

Paul Wilson: 33:17

my, my thinking is that it's the revenue ops and marketing ops professionals of today who truly understand the journey of the data sets that exist today. So when I've gone to do discovery meetings and had conversations with organizations from mid market through the low large enterprise to a T. The data science team understands the data from a logistical perspective. They know what data sets reside where and what are the pipelines. And the logic input pipeline pipelines from A to B it's the rev ops and marketing ops teams who, when they enter the room, say the data over there is crap, you know, that we had so many problems with the way that lead life cycle was managed back then, like that, don't, don't trust that. And it's that contextual knowledge that resides in our teams of the data sets and the technology stack. That means. As companies are looking to turn the corner and leverage new systems and new technologies. We're the ones who can really properly explain the data, explain what, what is good and what is not, and help curate the inputs to that large language model as it's learning what to do with what's in front of it. And I think that helping transform the infrastructure. So that our clients in sales and marketing continue to get the output they're looking for and improvements they're hoping to gain through technology. We're the ones who really understand those needs and can help drive that

Michael Hartmann: 35:09

chain. Oh, yeah. Okay. So that makes that I, it's like I, for our listeners, like I was sort of smiling when you described that scenario of, cause I'm actually like. That's a scenario that I have seen myself play out to where, um, yeah, like, and this is part of what, when I've struggled hiring date, like marketing analytics type people is I get a lot of candidates who are data scientists and their domain tips tends to be either finance or science or something like that, where it's usually highly structured, whereas marketing data is typically a mess. Right. Um, to some degree or another, or it's, and I don't necessarily mean that it gets crap, because I think that's, that's a cop out for a lot of people to say, we can't do any analysis because our data is bad. It's like, so what go figure it out anyway. But, but there is like, it's messiness. Like I had started, I started in marketing. Yeah. Like building out a household, building out a household model for consumer data was super, super complicated. And, but you just, you did your best to do it. Right. And B2B is more complicated. And, um, but that point about like, okay, knowing that nuances, like how that gets data to get there. The other, yeah, I think the other thing is like, the data is always changing. So I was just doing for my current job, a weekly reporting. Somebody had put something on a slide from a You know, a dashboard and I, the way I had a question was like, this doesn't look quite right. Just like, it doesn't make sense is, does it really mean this? And I, by the time I went back to the dashboard, pull the same data. It had already changed, right? Because that's just the nature of that data. I don't know. I, I, I, Mike Namby, do you see that too? Like, were you. You know, people look at reports and then they don't really understand, you know, the kind of the sausage making that leads to the data that they're looking at. Yeah, and

Naomi Liu: 37:14

I think that when people are asking for reports, um, I've always been of the mindset that they're not looking for the report, but they're looking for what they should do with the information in the report. So whenever I get. Uh, data and report requests like that. I'll always include the report or a link to the report if it exists as a appendix. But, um, to be honest, I usually just respond back with a, this is what you should do, and here's the information to back it up if you need it, because that's really what they're asking for. Right.

Michael Hartmann: 37:48

They're not. And this is why we need Naomi on this episode. She like nails it every time like that. Oh, trying.

Mike Rizzo: 37:56

I definitely. Yeah, that resonates for sure with me. I, I think, um, I'm in an engagement right now. You know, there's this desire to sort of like sync data to this, to the Salesforce campaign object, right? And, and so, you know, bear with me, we'll get a little nerdy for a second. But like, my, we have, uh, disconnected, we don't, we're using HubSpot. So it's not following the Marketo campaign hierarchy stuff that is so ingrained, uh, for all you Marketo users out there. Um, it's not going to mirror that at all. Um, and I, I posted this question to our Remote Pros Slack channel the other day. I was like, I'm not losing my mind, right? Like, the object has to exist in Salesforce in order to associate it to the campaign. And so if, if these records that you're campaigning against aren't ready for sales yet, Um, and you don't have a tight operation around, you know, who needs to be displayed to sales at what point, then associating them to the campaign is, is not going to work, right? Because we're just going to pump a bunch of records into the system and then sales reps are going to be like, wait, who should I be talking to? Who should I be following up with? Right? So there's this big problem there. Um, so like the one request to just like say, hey, I want to see, Uh, how many I want to see records associated, you know, I want to see campaign performance. I'm like, well, what about the campaign performance? Do you want to see? Because it's unlocking a bunch of other problems. If you want it that way, we're going to have to face a whole new slew of issues. Uh, and so really, what are you after? Right? If you're just looking for like. A high level, how many registrants are at the webinar? Like we can do that in a totally different environment. It doesn't have to be in Salesforce,

Paul Wilson: 39:43

right? But if what you're looking for is an attribution model that accurately reports the dawn and birth of a record compared to when the opportunity record gets created, then the association of the campaign record and Salesforce can become critical. And then marketers will be so upset. And so if you design it, just from the sales organization, and we don't want to have them see records until it's time. And then the marketer really said, he's just like. Well, what about attribution? Yeah, that's us. That's us. That's our

Michael Hartmann: 40:08

world in a nutshell. Okay. So, so Mike, I don't know, maybe you had more to go, but like, this is begging to like, to me, two big questions that where I would love to see this new technology help help us is on the analytics reporting insights side of things, right? So, because I've always thought of that, um, as a, the analogy I use is like, it's playing defensive basketball, right? You, like, you can be an average, Defender. But if you work your ass off, right, you put in effort, you'll, you can become pretty good. And it feels like getting reporting and getting the kind of insights that Naomi describes, right, takes a little bit of effort. And it's not just go pull a report, right? Um, so if there's a way to try to make that faster, because the other thing I've seen is like, you ask, somebody asked for a report, they see something and it begs another question, it begs another question and so on, right? So it's a continuous cycle. That's one. Other. You were just describing like the scenario of HubSpot objects or Marketo objects, Salesforce objects. And like one of my assertions right now, it was where I think the technology could have the most impact. Is it none of these like Salesforce has, I know it's evolved and I'm not the expert on Salesforce at the same time, right? How many times have we seen in the last week? Month, two months, six months conversations about, do we use continue to use the lead object in Salesforce or not? Right. Like it's, it's a, it's like, it's a holdover from those early Benioff days. Right. And so like, I always like, and then all the big marketing automation platforms other than HubSpot, right. Have been acquired HubSpot's continuing to innovate, but I think there's a perception about their ability to scale, like all this stuff. Right. Right or wrong. So like, my question is like. Is this going to force them to change, or is there going to be new players that come into space that take, take over and do things in a very different way? Like, I'm, that's what I'm really expecting to come out of this, or maybe on the data and analytics parts, hoping for some, something that can make that part faster and more effective.

Paul Wilson: 42:24

Yeah. Are we doing another podcast after this? You just opened a whole new door.

Michael Hartmann: 42:31

I've got time. There's

Mike Rizzo: 42:32

like, uh, we're going, we're going in folks. Here we go. Uh, I don't, so I'm funny enough, like this era that we're entering Will either be the saving grace for, for Salesforce, like that. And, and people are like, Salesforce isn't going anywhere. What the heck are you talking about, Mike? Well, there, you know, there's some challenges and like, clearly HubSpot is doing something. They're moving up market. There's challenges that are happening right now. And it is a disconnected ecosystem. Like. They're bolting on frankenstacks left and right and none of it really talks to each other And so either this new era is going to be their saving grace because they can finally figure out how to interact with all of it because it doesn't need to now be rebuilt into a its own tech stack um, or the other players are going to move so much faster that it's just going to be like Sorry, I, like, that's too complicated for me now, so I want to go over here to these other environments that are easier to interact with because the technology is prepared for the ML, AI era. These other tools, potentially, are more prepared to take it on faster. I don't know. It's either going to be a saving grace or a downfall or maybe neither. I, but, yeah. So it's my

Paul Wilson: 44:03

hypothesis that there, there are so many things that we aren't necessarily looking at that are happening behind the scenes. And the example that comes to mind broadly in generative technology is the company that has seen the greatest increase in their value and the largest longterm impact on their business. Right now from open AI's work and all the other players is NVIDIA, the chip maker, because they're the systems doing the thinking, it's their chips that enable the technology. I think in our space, where we might not be talking right now, we want to talk about Salesforce, Adobe, HubSpot, 6th Sense, ZoomInfo, that's where we want to talk, but we're not talking about. Like we aren't talking about other places where the game could change because all that we're talking about in terms of reporting and analytics and curating digital experience and managing the emails that are sent. That's all the, the operational layer, the data is the foundation. So if we can take the telemetry from HubSpot, Mercado, Salesforce. Push it all down into a repository like Snowflake or Synchry or one of these players who's just getting data from everywhere. That layer of data becomes the source of truth. And then other systems can report across everything.

Mike Rizzo: 45:48

For the listeners, I threw my finger into the air and I was like, yep, spot on. Like this is, we're all watching each other on the video. But yeah, I fundamentally, like there was a post on LinkedIn yesterday, I think. Dave Otti, he's got a great product. He's working on inflection hooks into Dave's office warehouse. Right? Like he talked about Dave and Aaron. Yeah, yeah. Dave and Aaron. Yep. Um, he talked about in his post, you know, uh, this notion of warehouse native applications, right? And mm-hmm. Um, and my, my response to that was I think that, that the problem today is that there aren't enough, uh, experts in market to try to even understand, understand the orchestration. Of a centralized repository and then doing warehouse native deployments from there. Yeah. There's just not enough of us that have the skillset yet, but like, and, and to Adam's point who commented on it, like Snowflake's cost to actually leverage accessing the data is, is exorbitantly high. Um, and so as the, for now, exactly. Yeah. And so as those costs to interact with the data come down and the skill level comes up to understand, understand how that orchestration can happen. I think that will be a very interesting Future for all of us,

Paul Wilson: 47:06

these Warehouse, what happens when Snowflake says, I'm gonna provide you with the next best opportunity set for you to go after?

Michael Hartmann: 47:15

Right. Well, I like to me like the idea that the data is actually where the, the value is, is not a new thing. Mm-hmm. But I don't, I, but I, where I think where I was is where I was going. Right. I don't think the point applications. Are there yet. So this idea that there's gonna be new players, like who's gonna be the red box that takes out Blockbuster, then Netflix takes them out, right? Like it's, it's mm-hmm. like there's gonna be some of that that's gonna happen, right? Unless, absolutely. And I don't know that the big players, I mean, I'm happy to be proven wrong, but I don't see them doing the kinds of things in public, right. They're leading me to, it's not in public. And it's some, some degree there's, there's, it's in their best interest not to have things change.

Paul Wilson: 48:04

Right. But I can promise you, um, and uh, uh, their labs are actively doing bigger thinking than we're talking about.

Michael Hartmann: 48:14

Yeah. Well, I mean, when you said that, that NVIDIA thing about how that's, it, it, it made me realize a couple of years ago, my, my oldest built his own gaming computer. And like the most difficult thing to get was the video, uh, video processing card because they're getting used for other applications. Like. Day trading and cryptocurrency and stuff like that. Right.

Paul Wilson: 48:37

David. Yeah.

Michael Hartmann: 48:39

Yeah. So it was like, that was a really interesting point. Wow. Okay. All right. Well. I'm sure if we really wanted to, we could go for, you know, another 40 minutes, an hour, at least another hour.

Mike Rizzo: 48:53

Right. Hey, Hey, you know what? Beautiful thing. Listeners. Hey, hint, hint, shameless plug. Paul is going to be talking about some of this stuff at Mops of Palooza. So we can come hang out and talk for lots of time. Absolutely. In November. So yeah, let's keep that conversation

Michael Hartmann: 49:12

going. Yeah. General debate. I can't replicate that yet. Can it? Right. Not that

Mike Rizzo: 49:19

experience.

Michael Hartmann: 49:21

All right. Wow. That's

Mike Rizzo: 49:22

fine. With the idea of doing it once every other year, by the way. So there's some exclusivity here. Maybe we won't follow the same rigor of like, Oh, we're going to do it. Like all the other SAS companies and do it annually. It's like, well, maybe we'll create some exclusivity and create some breathing room for us all to go learn some stuff and come back to For

Paul Wilson: 49:42

us to all go implement machine learning across all

Mike Rizzo: 49:45

We're going to do it this year and then two years later we're going to come back. This isn't a guarantee, but yeah, we're going to come back and be like, what'd you learn?

Michael Hartmann: 49:52

Sounds good. That'd be good. All right. Well, so before we go though, Paul, any last, like any things that we didn't cover that you were like, if, if people, you know, leave this, this episode listening and say like, Oh, they should have really like, this is one key point. What would that be? I don't

Paul Wilson: 50:11

know. And I would love to hear about it. I think this is. Absolutely has to be a community discussion. You know, it is definitely something that across revenue marketing and customer success operations. We, we need to all collaborate on like these changes are going to be foundational. Careers are going to change. Deal sets have to change. Our, our employers are going to change. Our internal customers are going to change. And we need to be there with them. And I think as a community, we've got to stick together through it

Michael Hartmann: 50:41

all. Interesting. Okay. Paul, this has been very, very interesting. I've learned a bunch. I have now, of course, more questions. So more informed questions, maybe that's the way to think about it. Well, thanks. So if folks want to keep up with you and your, your thinking, or want to talk to you about what you're doing, what's the best way for them to do that? I think

Paul Wilson: 51:02

LinkedIn is a great place to start. You can find me there. Um, and that's, that's a perfect place to start to start the thread.

Michael Hartmann: 51:09

All right. Sounds good. Naomi, Mike, so good to have all of us here on, on one of these. So thanks for, for joining. Yeah. Glad to

Paul Wilson: 51:17

be here.

Michael Hartmann: 51:18

Glad to be here. Definitely. All right. And to all of our listeners and, uh, you know, appreciate you sticking with us. We, uh, we're trying to get back on that train of getting these out on a more regular basis. So stay with us. We'll, we'll get there, uh, until the next time though. We'll see you later. Bye.