Data Sync or Sink: How Does Your Tech Stack, Stack Up?

Drew: Hello, Renegade Marketers! If this is your first time listening, welcome, and if you're a regular listener, welcome back. Before I present today's episode, I am beyond thrilled to announce that our second in-person CMO Super Huddle is happening November 6th and 7th, 2025. In Palo Alto last year, we brought together 101 marketing leaders for a day of sharing, caring, and daring each other to greatness, and we're doing it again! Same venue, same energy, same ambition to challenge convention, with an added half-day strategy lab exclusively for marketing leaders. We're also excited to have TrustRadius and Boomerang as founding sponsors for this event. Early Bird tickets are now available at cmohuddles.com. You can even see a video there of what we did last year. Grab yours before they're gone. I promise you we will sell out, and it's going to be flocking awesomer!

Welcome to CMO Huddles Quick Takes, our Tuesday spotlight series where we share key insights that you can use right away. In this episode, Ryan Koonce of GrowthBench, reveals why your martech stack may be doing you more harm than good. Tune in. Want to hear the rest? In the full episode, Ryan and I dive into the convergence of product-led and sales-led growth, tech stack audit best practices, and what the stack of the future should look like.

Narrator: Welcome to Renegade Marketers Unite, possibly the best weekly podcast for CMOs and everyone else looking for innovative ways to transform their brand, drive demand, and just plain cut through, proving that B2B does not mean boring to business. Here's your host and Chief Marketing Renegade, Drew Neisser.

Drew: Hello, huddlers. Welcome to today's bonus huddle. I'm thrilled to introduce you to Ryan Koonce, who is the founder of GrowthBench and a real expert on marketing technology. Ryan has recently completed an extensive analysis of 100 websites from our CMO Huddles community, examining the tech stacks that power our members' digital presence. And it's really an interesting time to be talking about this because we're at this inflection point where, with marketing technology, with generative AI reshaping everybody's capabilities, you're looking at it differently. And we, of course, have this issue of budgets being under scrutiny. So, you know, is it time to be swapping things out? Is it time to invest more in a single platform to try to get economies? There are a lot of questions out there, so Ryan's going to share some insights on where marketers may be overspending, where crucial investments are being missed, and how to prepare for the Gen AI future, particularly looking forward to that part that's rapidly becoming our present. With that, Ryan, welcome.

Ryan: Thanks for having me.

Drew: And where are you and how are you this fine day?

Ryan: I am in San Francisco, California, and it is still pretty early here, and this fine day is going exceptionally well.

Drew: Awesome. All right. Well, so we do this on all these conversations, which is before we dive in, in case the audience needs to leave early or needs—we need to convince them to stay for the full session. Could you share three big observations from your tech stack study, and just give me the highlights, because we'll go through those? And these are observations that you think CMOs really need to care about or pay attention to?

Ryan: Yeah, sure. I guess the first one is probably that as we analyzed the stacks of 100 companies from the Huddler group, Salesforce still dominates. HubSpot is gaining traction fast. But I think one of the things that we can talk about today is how AI is changing that calculus pretty quickly. Number two: looks like everybody has Google Analytics. And so one of the things we're going to talk about is, if you are reporting to the board from Google Analytics data, why you're kind of flying blind, and you might not know it. So that's number two. Oh, cool. And then number three is, it seems like the attribution tool stack is all over the place, and people are doing it a bunch of different ways. And so whether you're PLG or SLG, we can talk a little bit about how you can connect the dots on that front.

Drew: Okay, and just in case we have somebody who's actually listening who's new to the PLG is product-led growth. SLG is sales-led growth. Okay, let's go through those one at a time, because it's really interesting. So yeah, Salesforce—at 5, we did a survey. My guess is 70% of our community is on Salesforce and maybe 30% is on HubSpot. But you see that this dynamic could change as a result of AI. So let's talk about that.

Ryan: Yeah, I think, well, first of all, we're just seeing the economics around HubSpot and Salesforce affect the market, where Salesforce is expensive and expensive not only to buy, but also to operate. And we're also seeing this sort of massive—and then, you know, this doesn't affect the marketing team probably directly. But, you know, with the advent of AI-based BDRs, you know, AI-based outreach and some of these other things, the, you know, Salesforce ecosystem may or may not support that in the way that we expect. And so companies are starting to migrate to HubSpot, and then I think in that process, they're rethinking what is available to them in their stack, and kind of it's opening up options to everything, because Salesforce is so entrenched, you just expect it to be the foundation. And if that changes, then it could be anything. It doesn't have to be HubSpot. It could be some of these new tools.

Drew: It's so interesting because there's such—I mean, it's kind of like there's this moment where, well, you're a real company when you have Salesforce. Yet everybody seems to hate it.

Ryan: Well, let me tell you something that's even crazier. If you take the modern martech stack, think about things that are not Salesforce or Adobe, okay, and if you add up all the revenue of any company you can think of—so think about all the new CDPs, all the analytics solutions, all the LCM solutions, all the attribution solutions, all the conversion rate optimization solutions, A/B testing solutions. If you have all their revenue in aggregate, it's still less than Salesforce Marketing Cloud. So there's something broken there, but that's probably beyond the scope of this conversation.

Drew: Right? So, if you're moving to a new organization as a CMO, and you're thinking about, well, we're on Salesforce, but we're not that big, it might still be time to change. This is kind of a moment where that could be a big opportunity. And it's funny, you know, the challenge with any of these software, particularly Salesforce, is it's only as good as the people who are using it. And you know, I don't think this one problem that everybody talks about in our community, which is salespeople never put in the right data or keep it clean, so you have bad data to start with. I don't think that has anything to do with Salesforce.

Ryan: It doesn't have anything to do with Salesforce, but has to do with the way that data is stored. And so when we think about data accessibility in the organization, and well, it's available, it's accurate, reliable, and consistent data, right? And so is Salesforce the best place to be the source of truth for the organization? The answer is probably no. And so we're seeing this sort of massive shift to BigQuery and Databricks and Snowflake and medallion modeling and providing people with an opportunity to sort of normalize that data in a way that, you know, is happening now faster than ever.

Drew: Amazing. Okay, so that's one big thing, and we'll probably come back to that in a second, but let's go through the second point of Google Analytics. Yes, I don't think I know of a single company that doesn't use Google Analytics, and you use the term "flying blind." Why is that, and what should they be thinking about?

Ryan: Yeah, so if you take a business that has auditable data trail, where you're tracking user journeys, and you're able to bind visits and activities to specific users and to specific companies, and you try and match it to Google Analytics, you'll be very frustrated. And maybe some of you have tried this before, it's like, well, it doesn't match Google. Which one is right? And the problem is, it's always Google that's wrong. And so that's a big leap for people to take, because you can take something that's auditable and you can verify that it's right, and you can take something like Google Analytics, which is often wrong, by the way, usually not in the same direction every day, and you can't tell why it's wrong, right? And so I think there's this leap of faith that you have to take at some point, which is, hey, maybe this thing that, you know, is tracking all of our data that Google has access to, might not be the thing that I want to run my business on.

Drew: Well, I just have to stop you there for a second, because I think that's a moment of truth here for everybody listening to this that we've been thinking we could look at our Google Analytics data and we think of it as truth, then what you're saying is it's not, and that's empirically wrong every day, empirically wrong every day. And yet, pretty much every single stack that you looked at had Google Analytics, and because of, you know, it's free for most, but it's not accurate. Okay, so we're going to have to come back to that, but I do want to—so obviously you have a solution in mind for that, so let's just go there. What is the—I mean? Because you can't do attribution tracking, you can't do journey mapping, if you start with bad data.

Ryan: That's right. And so I think, you know, most modern companies are doing some version of a CDP on the front end for their website, for the Google-type analytics. And then, you know, the variety of tools you can use once you have that data collection layer - there's tons of them, right? So how do I use first-party cookies to identify users and track events over time, where I combine those user sessions across devices and then provide the data in a warehouse that can be audited against other known truths? So when you think about conversion metrics, or you think about cost metrics, there's a way to add everything up. And I think one of the big components to that is just having that audited ability provides a level of clarity and sort of a foundational insight that you're not going to get from Google Analytics.

Drew: Got it. Just remind us what CDPs are.

Ryan: Okay, sorry. CDP is a customer data platform, so you can think about things like Segment or Tealium or RudderStack or Hightouch. Salesforce has a CDP. Everybody seems to be a CDP today. We could do a multi-hour conversation about what that really means. But fundamentally, it's "I can identify users and track events, and I can bind user sessions across devices and over time."

Drew: And even without being a techie, it's easy to understand based on what you're saying that this allows you to connect the dots because you have accurate data from the get-go. That's so interesting. Okay, the third one was attribution, right? And we talked about that. Let's just make sure we covered that in terms of the issue and the opportunity from an attribution standpoint.

Ryan: Same issue as Google Analytics in some ways, in that, you know, if you don't have a foundational data collection strategy that you can use to bind things - say, for a visit to a website that ultimately results sometime downstream as a closed-won in Salesforce or HubSpot or whatever - you know, if you don't have that entire customer journey, not only as an individual, but as an account, then it's going to be very, very, very hard to reconcile these things. And I think a lot of marketers that we talk to have a lot of tools and a lot of information that they're trying to bind together that doesn't tell the full story. And so an example for the product-led growth people might be: a developer signs up on the site, starts a free trial, likes the free trial, invites the finance guy. Finance guy signs up for an account, ends up putting in a credit card and starts paying monthly. And so on an individual basis, the person that clicked the links and did the activity to become a customer - the developer - looks like they had no revenue, whereas you have a guy, the finance person, who came in from an invite, which, by the way, maybe wasn't even tracked as an account, and now has revenue. And so it looks like there's all this direct traffic that's driving revenue, when, in fact, it wasn't that person at all. And so that's the case with the sort of product-led growth problem. And then sales-led growth seems more complicated, because you have, you know, webinars and events and other things. And how do we track these different components? We have multiple salespeople, maybe, and multiple stakeholders that are being tracked through a pipeline in Salesforce. And so we need to be able to look at these cohorts over time, because the closed-won might be six months from now, right? And so how do we think about predicted revenue against our marketing spend? And how do we think about, you know, binding some closed-won opportunity further down the line back to the actual activity that drove that closed-won? And so those are things I think today that if we have that sort of data foundation, and we talk about data sync between systems, that we can do that - you know, it just becomes less nebulous.

Drew: Okay, so if we're going to summarize this portion of it, it's like: don't just assume Salesforce is the answer - part one. Two, Google Analytics is a problem, not a solution. And three, you need better tech to really think through and connect the dots here. And what we're really talking about - the whole purpose, or I shouldn't say one of the best purposes, and I probably should ask this question - of a tech stack is that it helps you build a predictable, reliable, measurable growth engine, right? That's why we have these things. And yet, when you look at it foundationally, you say Salesforce is a mess, the way that data is going in is a mess, Google Analytics is a mess. There's like no hope for success already.

Ryan: Well, I think it's a matter of taking - Google, let's set Google Analytics aside, because it's useless. But if you take Salesforce, which everybody has, and you take some of these other tools that we looked at in the stack, which cover a huge, wide array of tools, most of these tools are pretty good. And so for example, when we go into a company, everybody always wants to rip out the thing that they already have. And that's usually not the answer. The answer is usually, let's just fix the thing that you have. And the reason is, it's not a tool problem, it's a sync problem. So we have data, and the data doesn't line up across these different systems. That's something that's a solvable problem. And so I think the key is not necessarily throwing the baby out with the bathwater - that's a pretty big lift - but going into the, you know, the root of the problem, and providing sort of a foundational infrastructure for data sharing and data sync, which, by the way, often ends up happening in the warehouse today.

Drew: Interesting. Okay, so I think that's really important. So we're not gonna throw Salesforce away if we have it, we're just probably not using it optimally, and the dots are not connected. So this is a lot of plumbing, and now, but aren't the rev ops teams and the marketing ops teams - isn't that their job? Isn't that what they're supposed to be taking care of? So the CMO doesn't have to look at it, and they get a nice, pretty dashboard that shows them what they're doing.

Ryan: It would be awesome if they talked together. If the rev ops team talked to the marketing ops team, right? So I think, yeah, I think you end up with data silos, right? It's not marketing data or product data or sales data or finance data or ops data, it's customer data and company data. And so we have to start treating it like that, and we have to start treating these tools like they aren't just sort of random appendages to the company. And so we want to look at a customer, and we want to know that I have the data for that customer in all of these places. And by the way, by doing that, I can start to consolidate how I think about leveraging the tools in these places, right? And so yes, lots of rev ops teams, lots of marketing ops teams, lots of different systems and tools. And even within those teams, it's the Marketo person, and it's the, you know, Salesforce person, and then you have a Salesforce consultancy, and you have all the things, right? And so how are you doing data governance? How are you coordinating between all these systems? I think that's where a lot of companies break down.

Drew: Right. I mean, this thing that you mentioned earlier, and I just want to make sure we put a nice little bow on that, is you've got the person who is interested in using this product or service in the PLG model. They come in, they sign up for the free trial, and they like it, but they're not going to put the credit card in. They're going to let the finance person. A lot of folks talk about account-based marketing and bringing this account together, but it feels like there's this fundamental - this problem is that you're saying those two entities just don't get connected.

Ryan: Yeah. And so it's sort of what is the canonical - and again, going into sort of this vocabulary in the company - if we ask an executive team to write down the definition of revenue or lifetime value or customer or conversion, which is a trick question, fundamentally, you'll get a bunch of different answers. And so even in the vocabulary in the company, there aren't canonical definitions of the things that matter. Where I might be saying revenue and you might be saying revenue, we're actually saying two different things. And so, you know, that's like, there's a foundational sort of people-process-technology problem where, you know, it's not just about the technology. Again, it's sort of funny. We talked about when we were, you know, contemplating this podcast. I said, "Look, you know, we've done over 1,000 engagements, and almost always, if I talk to an executive and they tell me their stack, I can tell them what their problem is." And it turns out, when we were going through this exercise, there's like this massive full factorial of companies and their technology. But the common theme is that the data in the technologies that they have doesn't sync together, and then the vocabulary that the executives have maybe, maybe isn't the same. And those two things are things that are pretty easy to solve.

Drew: And you put together a great deck, and for anybody listening or here with us, if you want a copy of the deck, just, you know, put it in chat or send us a note at support@cmohuddles.com. And so literally, in the - you talk about this data - all of these problems are data sync problems. So when it looks good, what's happening? And when it looks - and we've talked, I think, about a failure - but what's success look like when you're talking about data syncing?

Ryan: Yeah, definitely. I think data sync doesn't remove the fact that there are some tools that do things easier than other tools, but you also have to incorporate the human equation. For example, if there's a new tool and no one in the company knows how to use it, it's not going to get used, right? So I think that's an important component to this. What great looks like is that one, data entry happens in an accurate way, and so, you know, we can throw the sales teams under the bus for this. They're notoriously bad. I see a world where AI seems to start fixing some of those things, and we're not there yet, but we're on the verge of auto-correct. And, you know, sort of the proctor screaming at the salesperson as they're entering data because it doesn't look like it's supposed to look. I think that's going to be a solved problem pretty soon. I think the idea that companies have a data warehouse, and the data warehouse isn't blocked by a data team because of things like AI - we have this concept of DDT medallion modeling that we can use to, like, quite frankly, like, go around the data team, if we need to, as a marketing team, right? You put in your requests, it comes back God knows when. And so, you know, great looks like getting data, the right data, the data that you need, quickly. And when I say quickly, you know, I mean basically less than a week. Okay? Bad companies take more than a week. Great companies, which there aren't that many - like 1% - it's sort of real time, you know, full access to data availability, all the data you need at all times, right? And that's really empowered by some of this AI stuff, and then it's really just a matter of benchmark, test, and optimize on top of the data that you have. And so if you're not benchmarking, you're just, you know, sort of blasting and praying on emails, or you're just putting up landing pages without understanding what the benchmark is. You know, having the information at your fingertips allows you to leverage these tools in the right way. And so if we're benchmarking and testing and optimizing, we can always make it better.

Drew: All right, like with everything in marketing, in any aspect of it, better data always leads to better outcomes. I love this notion that you're right. It's easy to imagine an AI, if you will, supervisor that says, "You know, that data doesn't sound right. And by the way, I just checked the company name against their database and you spelled it wrong." And so it feels like we are going to get to a place where the agents can help the humans get this data right.

Ryan: Yeah, the agents are here. It's just a matter of which one do we pick.

Drew: Okay, all right, so we're going to live in a world soon where AI will help with the quality of data. You know, the benchmarking of all of this is so important. And I just want to remind folks that for all your tech initiatives, having a benchmark, and particularly this is true for AI initiatives, it's like, what is your workflow now? How many hours does it take you to do this? Okay? Now, can we create an AI-based workflow that shortens that by 20%? But you can only do the 20% if you had the benchmark to begin with, so that all makes sense. And again, I think the key here is our goal isn't to get the CMO to do this, but our goal in this call is to make sure that the CMO knows what great looks like and sounds like, so that they can ask the questions of their marketing ops and their rev ops teams to do this. So unfettered access. You talked about that. What's that going to look like?

Ryan: I think to your point, the first thing is just feeling empowered to put your foot down when you don't have what you need. I think we run into companies so often where the marketing team is so frustrated because the tech team or the engineering team is blocking the things that they need. It could be adding tags to a site. It could be building reports in a dashboard. It could be implementing a new tool. And I think one of the things that we always try to do is empower the marketing team to really take hold of the—you know, outside of the political ramifications of that—like take hold of the responsibility to make all these things available to them. And so the truth is, we're able to do that now more than ever. And the reason that we can do that now is because the warehouse has become a more central place for syncing all of these things together. And by doing that, you kind of don't need a bunch of people in between, right? You need somebody that has access to the warehouse, and you need the ability to sync these things into the warehouse. But the tools are becoming sophisticated enough where you can do that without a big engineering team. And so there are ways to sidestep sort of the confusion that happens in companies and these sort of power dynamics that happen around data, because remember, data is political. At the end of the day, not everyone wants to be measured. And so those are things that you need to keep in mind as you bring the truth to the surface.

Drew: Not everyone wants to be measured. Okay, so we've got our data warehouse. It's got good data in it. The CMOs have access to it. It's funny, because a number of folks in our community have become—like they're now responsible for pipeline. Well, they clearly could not do that job if they didn't have access to the data. They could be asked to do it, but they couldn't do it, right, because you can't look at it. And you talked earlier about the timing of all this. I mean, we all know that if there's an engineer in there playing with your product and using it and so forth, that's a hot moment. I mean, you know that. And so timing does matter. And if you can't access the data, it's a problem.

Ryan: Here's the future. We'll jump forward a little and then come back. So one of the things we're seeing is, in the old world, when you think about data and you think about engineering, and you think about pulling these things together, you had to put a request in, maybe to the analytics team or the data team, and somebody would have to go into the warehouse and create a table and write some SQL, and then at some point in the future, some report would show up, okay? And that process can take weeks. Like actually, one of the new things that we're seeing, and in particular Mammoth Growth is doing this, we can basically leverage AI agents to do all the work of the data engineering team. So literally, you put in a request into a Zoom chat. It transcribes it into a business requirements document, which creates all the JIRA tickets, which creates all the DBT models, which writes all the code. And so now you say to the engineering team, "Say, oh, that's going to take you three weeks. You know what? We're just going to get it done tomorrow. So don't worry about it. Keep doing what you're doing." And the marketing team now can kind of do whatever they want, because they're not blocked by the engineering sort of legacy process that exists in these companies. And so in a sense, and again, this is a change management issue. It's not a technology issue as much as it is to say, "Hey, marketing team, you can be your own data team now. You don't actually need the data team. You just need access to the data." And that's a massive leap from where we were even at the end of last year.

Drew: Yeah, and "be your own data team." I love that notion. And certainly, I suspect CMOs would leap for joy if they could do it. How many—is this real? Are any CMOs actually doing this?

Ryan: Yeah, it's only been possible since December when the new reasoning engines were released. It's only gotten better since then, and I get a demo every Friday, and every week, I'm blown away, across the board. And so we're reaching this point where, like many IC jobs, data engineering or analytics engineering jobs are going to sort of not exist at the end of the year. That's great for CMOs, because now you can sort of just get what you want without all the waiting.

Drew: Sounds very promising. I'm sure some of the folks listening on this call are wondering—you know, probably could recommend having some thoughts on the barriers that they're running into and the change management issues. But let's keep moving down this. We've got our data warehouse. What are the sort of common mistakes you're seeing when it comes to data warehousing, modeling, and maybe using things like Snowflake and others?

Ryan: Yeah. So it doesn't matter what your warehouse is, as long as it's a modern warehouse. I think that's one of the things that we're seeing, is that if you have Snowflake or BigQuery or Databricks, you're sort of set up for success, and you just have to leverage the tool in the right way. And it really needs to become a central part of the stack. So if you have Salesforce, Salesforce data needs to land in the warehouse. If you have Marketo data or CDP data or site data or whatever, it needs to land in the warehouse. We use this terminology called medallion modeling. And so that data that lands in the warehouse is going to be kind of messy. You might even have revenue from multiple places. You have Stripe revenue and PayPal revenue and closed won invoice revenue. Well, that revenue needs to turn into something that somebody like you as a marketer can use. And we call that sort of medallion modeling. So you turn what we call the bronze model—this garbage of data—into a silver model, which is sort of filtered and cleaned and aggregated, and then that gets put into a gold model. That model is what you're going to use to take advantage of that and all the other tools, all systems. So revenue is now revenue. It's not PayPal revenue and Stripe revenue and invoice revenue. It's just revenue, and that revenue has a canonical definition that everybody can use in the company. So that process of getting the data into that place is becoming easier and easier and easier, and that's the thing now that you can leapfrog from where we were yesterday. And then it's just a matter of taking advantage of it. So where am I going to leverage the data and what tools am I going to use it in, in order to do better testing, send better emails, complete more events, et cetera, et cetera, et cetera. That's the foundational element that you can now take advantage of.

Drew: Okay, so I want to put this in a series of questions that CMOs could be asking, whether it's rev ops or marketing ops or, you know, a combined group. We've got our CDP in the middle, and we're, let's say we're about to bring on Clay. For whatever reason, we've decided that we need just better data enrichment or whatever. So we're considering this new tool. Is it? Yes? Does it integrate with our CDP? I mean, what are the questions that they need to do to make sure that you're going to get to medallion modeling?

Ryan: Yeah, definitely. Well, I think that's a great point, which is the strategy matters. So you have to have an opinion about how you're going to use Clay. Clay isn't just going to magically work for you, right? It does all these incredible things. And I think the first thing is, as a business, what is your outreach strategy going to be? And then, how can we use Clay to leverage to take advantage of that strategy? To be honest, the first part of Clay doesn't have to sync with the warehouse. Clay does it all for you. But what you're going to want to know is, what did I do with Clay, and how does it impact these other systems that I'm tying these users into? So does Clay go into the CRM? Does it then go into a, you know, sort of web onboarding process? Does it go, you know, into some other funnel that you've designed? And so the point of the warehouse in the data sync isn't that it's going to drive the strategy that you're using in Clay. It's going to allow you to know that the thing you did in Clay actually worked or didn't work.

Drew: Interesting, yeah, because, you know, I feel like every single software that you bring in has its own sort of dashboard and metrics of the situation, and that's the problem, right? That's a part of this problem.

Ryan: I think that they, you know, we talk about this idea that you spend $5 for a click on Google and $5 for a click on Facebook, and you make $8, right? And you know, Google says you made $3, and Facebook says you made $3, but you lost $2. It's not that Google and Facebook are, like, overtly trying to screw you, it's just that they don't know what the other systems did. Okay? And so I think you could say the same thing about Clay, and you can say the same thing about most of these technologies. If the sync isn't overt, they're just operating within the vacuum. They don't know what your other system is. And that's actually part of the issue. There's so many systems. I mean, even if you look at the 100 Huddles that we looked at, you know, it wasn't like, sure, everybody had Salesforce or HubSpot or something like that. But then you get into this, like, really deep, you know, expansive set of CDP, CS, or analytics tools or whatever. And you know, it's hard to keep those things for any one company to build all the, you know, direct syncs together, because they don't know what all their customers are going to have. And so I think that's where having a CDP or taking advantage of the warehouse as the sync, you know, does become an opportunity. And then also remember, use data where it's needed. So you want to sync to the data, but like for certain things in Clay, you don't need another system. You don't have to take the effort. You know, the cost of getting data matters too. And I think those are things that are certainly worth keeping in mind.

Drew: Okay, so on the fifth page of your deck, you talk about this notion of speed to insight framework. And it's funny because one, a lot of the folks we've been talking about from a messaging standpoint, particularly in a down economy, if you're selling software, frankly, you're selling anything in B2B right now, speed to value is a really important concept to understand and make clear to your customer. So here we're turning it around and just saying, if you're bringing on a new piece of software, or whatever your goal as the CMO is to get your insights as fast as you can. Talk about this framework and how you sort of analyze stack performance, bad, good, great. So talk a little bit about that.

Ryan: Yeah, and I think every company has, I wouldn't say I've ever seen a standardized reporting framework in companies. You know, every company has different ways of running BI teams or analytics teams or reporting within marketing or outside of marketing, or in product or outside of product, et cetera. We view the way to think about measuring your efficacy on the time it takes you to get the answer you need. That's the most important answer to move the needle on your business. So first of all, not all data is created equal. There are things that if you get the answer to, they're going to matter more than other things. And what we find is that, because executives often don't have clean, accurate, reliable, consistent, accessible data, they're constantly lobbing in requests to the analytics team. It's almost like they're just grabbing for anything that they can get, like, just give me data. I don't have anything. I'm frustrated. I can't run my business. And so, you know, that's bad. So there's two sides to that. So the first thing is, the analytics team is basically getting piled up with requests, and they don't know how to prioritize those requests, because every request from a VP is important, even though they're not. And so the first thing is, how do you prioritize the request so that you can get the response back to the thing that's most important, most quickly? And so we think about like, bad being anything over a week is too long. I mean, you can't run your business if you have a weekly turnaround on data. Usually okay. And so one way to solve that is technology is to say, look, you need to have more data engineers, or you need to have a better warehouse, or better pipelines, or better things, and the other way is just to ask better questions, and prioritize those questions for the team in a way that they can go after the thing that's going to make the biggest difference. So that's the first thing.

Drew: It's so interesting. I mean, it immediately made me think of all right, there are a lot of questions, there's a lot of data, and sometimes you don't know what you don't know. You don't know how the different slices might yield a story that you were looking for. Again, sometimes you're trying to get the data to confess something that you think is true. It feels like though, if you were to, for example, go into ChatGPT and completely describe your data set in your tools and everything. And then you said, give me the top 10 questions that I could ask in order to get the most value out of my queries. Every week, I feel like you'd get in some interesting start. But here you are. You are the expert. What are those questions? The better questions.

Ryan: I'll give you what's a bad question. I went in, I went into a business, and this was before COVID. We showed up at the office, and they had this, like, beautiful mind, you know, sticky note button color test, you know, up on the wall, and they were going to test all the button colors everywhere, okay? And, you know, we immediately were able to convince them that that probably wasn't the best use of their time, right? And so, you know, I think there are things that come up that are, again, grasping at straws because you don't have any foundational information. And so I think the first thing is, what are those foundational benchmarks that you have to know in order to run your business? And if those aren't good, that's the place to start. So, for example, who were my customers? What is my revenue? What is my margin? You know? What are those foundational metrics that you have to have to run your business? You'd be shocked how many companies, if you ask them, how many customers do you have of what type they kind of don't know, or what's your revenue yesterday they don't know, right? And so if you can't answer the most foundational things, those are the things that you should be focused on. From there, you can build on that, and you can, you know, the sky's the limit.

Drew: Wanna hear the rest? In the full episode, Ryan and I dive into the convergence of product-led and sales-led growth, tech stack audit best practices, and what the stack of the future should look like. You catch the rest of this deep dive on our YouTube channel, CMO Huddles Hub, and if you want Ryan to decode your stack and pinpoint the pain, reach out to growthbench.com.

Show Credits

Renegade Marketers Unite is written and directed by Drew Neisser. Hey, that's me! This show is produced by Melissa Caffrey, Laura Parkyn, and Ishar Cuevas. The music is by the amazing Burns Twins and the intro Voice Over is Linda Cornelius. To find the transcripts of all episodes, suggest future guests, or learn more about B2B branding, CMO Huddles, or my CMO coaching service, check out renegade.com. I'm your host, Drew Neisser. And until next time, keep those Renegade thinking caps on and strong!