Customer Data Perspectives, Ep. 4: Scott Brinker, Chiefmartec

The MarTech landscape has seen continued growth, consolidation, and shifts that reflect the changing dynamics of marketing needs. In this episode of Customer Data Perspectives, host Isaac Sacolick, StarCIO, sits down with Scott Brinker, editor of, VP of platform ecosystems, Hubspot, shares his insights on how evolution in marketing technology is unleashing creativity and productivity, the challenge defining the CDP category, and the value gains across the entire company when customer data is integrated into a common data foundation. 

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Read the Transcript

Sacolick: Hello, everyone and welcome to this episode of Customer Data Perspectives. And I’ve got a super treat for you today. Joining me is Scott Brinker. He’s the VP of platform ecosystem at HubSpot and editor at And, everything in anything around marketing technology – he’s the go-to guy. Hi, how’s it going, Scott?

Brinker: Hi, Isaac, thank you for that wonderful intro. Great to be here with you.

Sacolick: Great, Scott. So we’re gonna jump right into this. I look forward to this time of year every year. You just released the 2022 marketing technology landscape. And if you’ve never seen this, it is a work of art, and I’m sure a labor of science, to put it all together. I think it’s the ninth edition. The first one had 150 logos on it. If I read that right, you’re up to 9,932. It’s up from about 8,000 from a couple years ago. Tell me – what were some of the learnings and surprises in putting this particular edition together?

Brinker: Oh, well, I mean, I’d say the single biggest surprise in the entire journey here has just been, the incredible expansion of the number of products in this space. I mean, don’t get me wrong – I’m an advocate for Martech. I’m an advocate for software, and I would have never been able to predict  the scale at which this has grown. And so that continues to be something I learned again and again. 

The other thing that is maybe not as surprising anymore – but still, you know, like every year I do this, I always get asked like: “Oh, well, where are the categories where there’s new innovation happening?”

It’s like pretty much every category keeps going through like this renewal. Like one example that would be, you know, arguably the oldest category on that landscape is content management systems, right? I mean, all right, back in 1995, that’s where we started. And so if you were gonna say any category at this point, should be done, mature, consolidated – we’re all wrapped up there – it’ll be that category. Yet that category has seen all this disruption, all these new companies. I mean, things around like headless CMS, you know – I mean, it’s just, it’s amazing. It’s that same dynamic and so many of these categories is that they just continue to evolve, which is both exciting, and also for the marketers, frustrating, because it is a constantly moving target.

Sacolick: Yeah, I can totally agree and subscribe to that. I mean, you think about – why are we constantly reinventing and re transforming what we’re doing? Well, the competitive landscape is changing, you know, what customers are looking at is changing. But the technology is just evolving. And what does that mean? Well, there’s certain things I can’t do on a platform they invested in just two or three years ago, that my competitors are now getting a leg up on me and can do. And so that constant churn of what do I need to do better than I didn’t even have the option to do three years ago, is I think what’s fueling this. 

Brinker: It’s a really important point, by the way. Because like most people, when they’re complaining about all the changes in the MarTech landscape, it’s like only looking at that piece. But yeah, exactly as you describe it, there’s basically these three pieces that are all intertwined. It’s: What are the competitors doing? What are customers or consumers expecting because those expectations continue to change? And then, there’s the technology that continues to change. And these are all intertwined in really interesting ways. Unfortunately, yeah, it just keeps things very difficult to say like: okay, yes, we made our choice, here’s our five-year 10-year plan, we’re done.

Sacolick: I think the only dimension we left that was data, I mean, our requirements of what data we want to collect and how we want to use it. You know, now we have machine learning as a capability that we never had before, five, 10 years ago, that can be part of our ecosystems. And then you throw regulation in on top of that, and so that’s the fourth dimension. Do I have that right too?

Brinker: I would buy that, yeah.

Sacolick: So  there was one thing that surprised me on the landscape – I wonder if you can comment on this. {There was} a 12 percent churn from the previous version that you did, although you did add 2,900 New technologies. Is there any messages, do’s and don’ts around this? When I see churn as a technologist, I’m like, how do I protect myself against churn? When I see new incumbents coming in, I’m like, well, do I want to sit on this requirement another year and wait, because there’s new technology coming out every day? So I’m wondering how to best interpret this. What do you see out there?

On Integrations and Tech Stack Architecture

Brinker: Yeah, no, it’s a great question. Um, so this is one of the things where I feel like to get nerdy right away, I guess is, you know, this is where architecting your tech stack matters. Years ago, Gartner and published in the IT world, their whole concept around piece layering. And this idea of oh, there’s systems record and systems of differentiation and systems of innovation, and the sort of recognition that different layers of our tech stack do change at different paces, and as a result, you kind of want to manage those layers slightly differently. And I think, yeah, MarTech is an environment where that really holds true. I mean, there are certain fundamental systems, you know, like, what is the source of truth for like, your customer data, what is the foundation for website customer experience – any sort of system you’re using to architect like, say, your marketing automation at scale – these are things that generally you do not want to change very frequently. 

But a lot of the innovation that happens with these more specialist apps, okay, well, that’s kind of at a different level. And it’s one of the reasons why I’m a big advocate for integration between these systems, because ideally, what you want to be able to do is, as new technologies emerge, you want the ability to experiment with them, but to be able to experiment them with them with relatively low risk that, hey, we can try this, {and} whatever interaction we have with it, we will bring that data fundamentally down to our core platforms. If after time, we realize oh, well, that one didn’t work. Or maybe there’s another vendor who we think is better at it. 

I mean, a great example would be for instance, online webinar or virtual event platform, of which there’s hundreds, you know, right? I mean, yes, I’m not saying it is a zero-cost difference to switch from one virtual events platform to another. But it’s nowhere near the effort that’s required to say, like, oh, I want to fundamentally switch my CRM platform, you know, at this point, and as long as you know, if you start with one virtual events platform, and it’s pumping all of its data down to your core CRM, if you decide to then switch to a different virtual events platform, as long as that is also then pulling that shared data down, then yeah, the cost of switching the cost of experimenting with new innovations becomes, I think, a little bit more manageable.

Sacolick: Well, you’re talking like a CIO to me now, because we think about this changing architectural landscape all the time. You know, when we started the web, it was a two-tiered model. And then it became a three-tiered application model. And then it became cloud. And then it became mobile and web, and then it became mobile first. And now, there’s IoT in the middle of this, now I got to separate out API’s from the customer experience out in support. So I mean, it’s a changing landscape. And what we think about is not trying to get the architecture right – it’s planning for what happens when the architecture gets wrong. How do we make it easy and defendable to move from one system to another? How do we think about, you know – we buy a platform, we’re going to incrementally experiment with new capabilities and say – what’s working well in this platform that we want to continue to invest in? And maybe something else that the platform has, but we want to go get something super specialized, because we know, we can get a differentiating value if we do that. 

And that really leads to my next question, because we’ve been talking about customer data for a long time. Centralizing customer data, customer 360s; we’ve been talking about activations and attribution models. And then I see another article that you wrote on, not too long ago – 120 marketing cloud technologies being used in the average large enterprise in that article. So you know, how do you deal with this, more companies succeeding with customer data, despite the 120 platforms? Or are many companies just still catching up and trying to figure out — how do I make 120 technologies work? Centralizing data, enabling machine learning, but most importantly, personalized experiences for my end users? 

On Customer Data and Tools in your Tech Stack

Brinker: Well, there’s a lot to unpack there. I would say, first of all, like these stats about how many SaaS apps companies have are always surprising because if you ask people how many apps they have, or platforms they have, they always underestimate it. They’re like, “oh, yeah, no, we only use two or three products,” or, “oh, okay, maybe there’s like five or six.” And then you connect them to these SaaS management platforms that actually go – and whether they’re looking through like your accounting records or what you’re subscribing to, or, single sign-on for what you have connected – all of a sudden they pop out and say: “no, I actually have hundreds of these different SaaS subscriptions.” 

And the truth is that they’re not all created equal. I mean, like, you know, in marketing again, it was kind of like this pace layering thing. People automatically think of like, yes, I have my CRM, I have my marketing automation platform, I have my CMS, these are the big, big rocks – big ones that come to mind. But meanwhile, when I’m like: “oh, well, I’m running a podcast. Let’s see.” So I have maybe Descript, which is a tool, now I’m using to edit these videos, maybe I’m using this social media scheduler for how things are being pulled out. And what’s fascinating about a lot of these things is, not all of them are like directly generating or using customer data, they’re often, you know, like just sort of pieces in a tool chain of how marketers create collectively their digital experience. 

That being said, there still are a fair number that do have some kind of customer touchpoint, in which case, it’s really important for there to be a two-way integration. You want those tools to genuinely be aware of existing data, existing relationships that we have with customers. And then also, if they’re going to participate in some kind of touchpoint or dialogue with the customer, How do we make sure that data in that interaction then makes it back to our core platforms, too?

Now, for years, this was the single biggest challenge for marketers – is that a lot of these tools just did not talk with each other. I would say at this point in time, we’re, we’re getting better. You now see – for so many of these MarTech products, when they come to market now – they are actually touting their integrations with these major platforms as one of their key features. 

You know, a while ago, integration was kind of like oh, well, that’s not the fun stuff, I guess we’ll get to that someday. But you know, that isn’t where the real thing is. And marketers have just reached a point where they’re like, listen, I’m just not gonna buy a tool if it doesn’t integrate with my core systems. The feedback loop finally started to take the fact that now companies are like: “Okay, well, if I want to sell my product, I have to make sure it is a first-class feature that integrates with the major platform my customers are going to be using.” So we’re getting better there. I don’t want to say it’s Nirvana yet, it’s a journey. But at least it feels like it’s headed in the right direction.

Sacolick: No, I think you’re right. Because that was the question, the head of IT would always ask anytime somebody came in with a platform is – how do I integrate with these things? And very often, it was left to us to figure out okay, you know, does it have a built-in integration? Do I have to put an ETL in there? If I have to put an ETL in there, do the tables line up? So it’s an easy mapping to the fields line up? Because it’s an easy Mapping? Does it need a two-way conversation between these two systems or is it  just really pulling data? Does it need to be real-time or non-real-time – these are all factors that went into this. And you know, now, we at least have a common language of, you know, we’re expected to be looking at solutions that have APIs, we’re expecting to have solutions that have integrations with common integration platforms. So if I buy an integration platform of some kind, chances are it has a built-in configuration to go talk to certain CRMs. And certain CMS and certain marketing automation platforms. 

I still have some work to do, right to get like, the data aligned, right. And we’ll be talking more about data. But you know, some of the tooling is there, some of the checkboxes is to at least know that if we put the effort into it, we can get there. And I loved seeing this in one of your articles earlier this year – three big MarTech innovation themes into 2022. You were seeing one of the songs that I’ve been seeing for 20 years, which is about low-code, and no-code technologies. And I looked at it back then, when I was CIO, as a cheat. I had to build applications out, I had to build integrations out. I was like every other CIO out there – too much demand for what IT could do. And so I was looking for – how can I build apps faster and cheaper? Or even better, how can I enable the marketing department or enable the finance department, a member of operations, to build some of these things themselves? Because chances are there was a millennial in there working in that department who had enough IT talent to actually go build some of this stuff, enough subject matter expertise to go build this stuff. And I have to provide some governance and some practices for them. But I can actually hand the keys to the castle over to them. 

And so, give me your perspective on this. Is this something we’re going to continue to grow? Do marketers like it that IT solutions have no-code capabilities built into it? And maybe even some examples of you know of any that you could share about how marketers are using these no code capabilities?

On Benefits and Applications of Low-Code/No-Code Capabilities

Brinker: Yeah, this is a favorite subject of mine. So one thing I guess I’d start out by saying is, I think of no-code in a very broad sense. For me, it’s not just about like, oh, I’m building an app that I now do visually, instead of having to type in, you know, Java. It’s actually to me, I think of it is, anytime we’re now taking these tools to empower general business users – you know, marketers – to be able to do more and more things that previously, they just couldn’t do on their own. They needed some sort of expert to go and do it for them, which either meant it takes a lot of time, or it’s very expensive, or a lot of skill. To be honest, for a whole bunch of things they were like, actually, it’s probably not worth that time, or money or skill for that. So yeah, I had this idea, but screw it, I’m not gonna even bother with it. 

And what we slowly see with all these no-code tools is as they start to move to more of the domain experts and eventually, more and more of these power users – whether it’s, oh, I need this little workflow for what happens when someone signs up for my webinar, and I want to kick off a couple of different things, can I just configure that workflow on my own? Or if I’m launching a campaign, and I want to have a web page that’s going to do a special offer, and I want to be able to execute that transaction – can I sort of create that, a landing page through a template without having to have a web developer custom build that for me? Oh, I have a question about data, do I have to, like get a ticket in the queue for a specialized data analyst to go and track it down in the data warehouse? Or, is there some sort of simplified interface, where actually, for a bunch of questions, I can just now go and directly query myself? 

And again, all these things aren’t to say that it takes away the opportunities for the experts. It’s just, it’s almost like – I often use Clay Christensen’s disruptive innovation model. It’s like, there’s low-end, mid-end and high-end use cases. And certainly, for the high-end use cases, and even a lot of mid-use cases still, you really do need the experts – because it’s not just about the mechanics of it. It’s about understanding, the domain expertise, and what’s being done. But for all of those low-end use cases, quite frankly, it’s a waste of time for the experts to spend their day. I mean, what web developer wants to spend their days building landing pages for the marketing department?

Sacolick: It’s not just landing pages, it’s many, many landing pages and permutations. It’s, we’re not just doing A/B, we’re doing A-Z. And we want to enable that right. That’s, that’s the experimentation. So there’s a volume effect there that we want to empower marketers to do.

On Empowering Marketing Teams with Self-Service Tools

Brinker: Yeah, and you probably actually, where you want the expert is very often to do the templates for those pages to put the guardrails of like, okay, this is the data you can collect. This is where there is definitely need for, you know, governance and oversight, and in how we’re empowering people with these tools. But if you put that governance and oversight and guardrails in place, to then allow those frontline domain workers to be able to sort of just run with things, it really is a win for everyone. 

And so, again, kind of like the integration story, I feel like this is we’re still pretty early, you know, both in the capabilities that these tools provide to people but even more, so, I think we’re still trying to wrestle with the way to governance to manage this. Like, what’s this balance between empowering people to do more and more self service, while also making sure that yeah, we’re being responsible in what’s being executed? What’s the data? You know, I mean, are there security issues here? Are there legal issues here? I mean, it’s both a really exciting field, because you see all this power of creativity and productivity being unleashed. But also, yeah, a lot of really serious questions of like, okay, but how do we keep this from being a runaway train? I think it’s gonna be maybe the single greatest challenge for us, you know, for this next decade is like, how do we harness this at scale? 

Sacolick: I mean, the governance and IT planned. I mean, we’re still figuring that out. And that’s been a mature area for 20, 30 years now. And just the basics of, putting your code into version control and testing code and automating deployments. It’s only really been the last five years. where, you know, a lot of companies have been able to use technologies to do that end-to-end governance model. With cloud native technologies. Now we’re going to a marketer, and saying, you know, copying and pasting that landing page 100 times over to do 100 experiments, is probably a bad idea. And maybe we need to find a more efficient way to generate 100 versions from a template. Or, you know, you’re gonna go create a data visualization in a tool, creating, you know, 25 dashboards to answer five questions, that your peers’ doing the same thing with a different version of probably a bad idea sounds like Excel 10 years ago, just more complicated. 

And, of course what you can enable and the governance model’s so tool dependent – not all tools are equal in terms of what they enable and what structures they provide out of the box. And what we’re really trying to do is that what you started with – which is we’re trying to do more of the high value things a lot easier, right? In that quadrant of, let me do the high value easy stuff first. And no code is pushing that bar up, we can do many more things easier. And it’s giving us more choice to go after high value areas. That’s where I see no code’s been playing out for the last 20 years. But it is a lot of figuring out for organizations. So let’s try to do a figure-out scenario.

I’m going to start at the top of the food chain. I walk in, you’re the CMO, and I’m the CIO. We walk into this organization, they have 120 marketing technologies, they have a growth charter, they have no integration. You know, I know what I’m going to look for. I’m wondering, what are you looking for there to unravel that and say, here’s the things that we should do in the first few months around something like this?

On Defining MarTech Use Cases

Brinker: Yeah. I think one of the smartest ways I’ve heard somebody approach this is to not start with the tech stack. Instead, actually start with, okay, what are the use cases? What are the things that we’re trying to do? How does our business run? How does a company acquire leads? How does a company make customers? And I want you to take me through these processes. And then for each one of those processes, explain to me what’s happening with the different technologies that are connected to that particular process or activity. Because actually, a lot of times that’s really uncovering where the problem isn’t so much that underlying technologies itself – it’s that we haven’t yet…like we’ve had a bunch of these things sort of organically pop up in different ways, and there hasn’t been enough reflection on, okay, well, let’s not look at this through the lens of the tools, let’s look at this through the lens of the actual customer journey. And then at least find out, okay, as we’re going along that journey, are there things where, oh, wait, actually,  this tool isn’t communicating where it should be. Or, this tool is redundant, we don’t actually need this one we can be using the other one. Or, oh my God, this tool is generating a ton of data and it’s not going anywhere. 

I think if you audit it through the actual use cases, and you prioritize the use cases by the ones that are actually running the business performance, that’s a great place to start. You know, I think if you then get to a place where you’re like, okay, we now understand everything that we have today, and how it fits together, and we agree, this is probably the right configuration for it – then the next question becomes like, okay, well, what are the things we want to do that are able to?

Sacolick: That’s always my first question, right? I uncover where the issues are by looking at, I assume, I want to get to tomorrow less about how it was supported today. And so that’s where I start from, and then I can start unraveling – where’s that tie into existing processes? Where’s that tie into existing systems, through the lens of we can’t do these things today that we think customers want, or we think that our competitors are doing? 

Let’s bring this down one level, because a lot of times when I do that analysis, it comes down to, we really don’t know, because we’ve never brought all our customer data together to analyze it. We’ve never done a true, you know, customer study, we’ve never done a true market research study. So we have a lot of people say what we want to do, but we don’t have a lot of data to back that up. Right. And so I’m wondering, you walk into this organization of data all over the place. How do you start unraveling the customer data issue around that?

Defining Use cases: What’s the Best Approach?

Brinker: Yeah, I mean, I think I kind of go back to the use case lens, because in many cases, it’s like the use cases are either, they’re either a source, or a sink. Now in some cases, they’re both. But you know, it’s like, okay, for this use case, what data would we need to be able to execute this? Well, and then, alternatively on the other side of that – in this use case, what data is being generated, and where is it going? How do we use that data downstream? And I think just getting that map to make sure that there aren’t – you know, if you think of it, like in graph theory, or something like this – it’s like, okay, well, is this just a completely disconnected graph? Because ultimately, all this stuff comes together. But I think for most companies today, it is still largely a disconnected graph. Like if your graph is totally connected today, you are in the top 10 percent, 5 percent of creation. 

Sacolick: Or you’re a ground-floor startup that just hasn’t gotten to that point yet. 

Brinker: That’s true. Although, man, boy, it doesn’t take long for startups to very quickly. If someone isn’t paying attention to this, you know, yeah, these things proliferate without being connected together. And so I don’t know, I kind of feel like yeah, if you can get that map, that’s the first half of that journey?

Sacolick: Yeah, I’m going to share with you one of my secrets around this, because the mapping is actually pretty hard, right? And time consuming, especially if you have 120 systems and you don’t know – what’s the source system for a piece of data? Well, the chances are, it’s four systems that are the source of it, they’re just not talking to each other, and you have a merge that you’ve never actually done before. So I take a step back and say, let’s go create the reference data model of what this should look like, right? If we’re gonna go after this type of journey, with this type of workflow to support it with these types of experiments, it’s much easier for me to get a group of people to conceive what the ideal model looks like, right? And then when I have the ideal model, which by the way, there’s going to be huge debates over if it’s graph, is it SQL…And whatever it is, I don’t care how you represent it, I just need a conceptual model, then we could start working backwards.

Brinker: It’s almost like, yeah, thinking back to my days long ago of being a software engineer, it’s almost like this difference between like a top-down design versus a bottom-up design. And there’s kind of pros and cons to each one of those approaches. But yeah, I would agree what you’re describing is the top-down, like, let’s start with the idealized model, and then fixing as we go down, versus yeah, well, I was kind of advocating is like, let’s start bottom up of like, what the hell is actually happening today, because I have no idea.

Using a Customer Data Platform for Use Case Mapping

Sacolick: And that’s the issue, people don’t have any idea. I mean, some of it is baked into common platforms that are reasonably well understood from a concept. But then we go and customize the heck out of them. And we have to go unwind all of the logic that went into that. And then we have a whole slew of proprietary applications and integrations and they’re buried in code, in terms of how they’re processing. And if you start from that bottom up, I know, I’ll be in the weeds for weeks, months for people to unravel all these things. But you know, I might not have to go into that part of the forest, if that data is not important. You know, if I know, this is what we’re after, we’re really after this type of journey. This is, you know, it’s it, we don’t need addresses for this type of journey. So I’m not even gonna care whether or not I collect customer addresses for this, I only care about this type of data. That’s what intrigues me about, you know, customer data platforms in that I can use that reference model to go back and say okay, I have a place to conceptualize that. And now I can bring my data in from all these different places to start mapping into that I have some tooling around to put it all together. But there’s still a conversation, right? Do I need a customer data platform to do this? And what are some of the signs that I might need a data platform like this to do this? I’m interested in your perspective on that.

Brinker: So the short version is it’s hard for me to imagine a business today that doesn’t need a primary centralized customer data authority, right? I mean, like, if you don’t have like one place where ultimately that’s becoming the system of record by the customer. 

Sacolick: That’s gonna be the quote from this from this recording, it’s exactly that I’m telling you.

Brinker: Yeah, mean like, how can you do it without that? So I think in that regard, the answer is almost always yes. I think where things are challenging is when we talk about customer data platforms, CDPs as a category. There’s a challenge right now, where the definitions in the category are very broad. Like, we could look at 10 different CDPs and they do 10 very different things. You know, some of them are almost marketing automation platforms that call themselves CDPs. There’s others on the other end of the spectrum that are really almost like sort of data infrastructure. You know, and then there’s others in between – some have identity resolution, as like a key capability, some of them no. And so that makes it even more confusing. There are people who are doing things that are like a CDP, but they’re not using a quote unquote, CDP product. Either their CRM has expanded to become their CDP, or they’ve kind of built their own thing, you know, on top of Snowflake. And so all of this tends to make it actually very challenging to answer the question of, oh, do I need a CDP? And if you do, of course, you do. Okay, well, what vendors, what CDP do I need? That may be the harder question to answer. 

On Categorizing CDPs and Other MarTech Tools

Sacolick: I’m laughing so loud, because you’re the one putting the product into a box on a landscape. And so somewhere in here, you have to decide, is this a CDP? Or is this a CRM? Or is this a digital experience platform? Or is this an MDM, or data management platform? And it’s not so easy to figure that out, is it? 

Brinker: I mean, I’ve joked with people for years about this, but categorization basically sucks. Because yeah, these things that are really hard to categorize, Like, legitimately, because here’s the thing: categories are very often a way for an analyst to think about an industry like we’re trying to put these things in boxes. But the truth is, ultimately, companies don’t care about those boxes. What they care about is, I have a job to be done, right? Think Christiansen, again. What’s my job to be done? And what software do I need to execute those jobs? And very often, some of the most innovative software out there that’s really good at getting jobs done for companies sort of span or blend the boundaries that, you know, a more classic analyst would say, like, was it this? Or is it that other thing? 

Sacolick: And they come up with a better category name for it. 

Brinker: And everybody wants to be their own category creator and have a category of one, it’s a mess. And so I always say, like, yeah, categories suck, the only thing that would suck worse than categories is having no categories. And so one year, I took the MarTech landscape, I basically removed all the categories, and just threw all the software logos in one big mess. I’m like, well, okay, I mean, this might be technically more accurate. But is it helpful? 

Sacolick: I couldn’t agree more. That’s the challenging and the fun part of, of recommending solutions, or at least analyzing solutions is this sort of wavy…this platform has this, this platform has that, what are your use cases? Again, what are you trying to solve for? And what technologies do you already have? So you can figure out which box fits your trap much easier than others. And is it a cost effective solution and so forth. 

But what really intrigues me around this, Scott is, when I think about however you get there, and you bring customer data together – going back to something we were talking about, right before we recorded – this notion of bringing the organization together, right, bringing marketing and technologists together, breaking down silos. We know we have a lot of systems, some of them 120, some less than – some companies have 25 content management systems, because they had 50 magazines that they were building at one point – and they each pick their own platform. So I’m kind of interested in how you see, bringing customer data in as a way of breaking silos down organizationally, and aligning the organization to this is what we’re really trying to accomplish here.

On the Benefits of Unified Customer Data Across the Organization

Brinker: Yeah, this is one of the most exciting topics to me right now. So my opinion is MarTech kind of spent the past 10 years growing up in its own silo, you know, that when MarTech was actually ready to start taking off 10 years ago, quite frankly, the rest of the organization wasn’t really ready for it. And through a combination of marketers just wanting to do what they wanted to do and more than, you know, MarTech vendors being more than happy to like, empower them to go do that. Yeah, this whole MarTech stack that really did get created in a silo disconnected from the rest of the company. 

That was probably okay at that stage of what needed to happen. But today, that’s a terrible thing, right? Like we’re now at a state you know, where the company is actually trying to connect the dots across, the digital customer experience, the digital operations of the firm as a whole. And so what we see happening here is the slow but steady reintegration of MarTech back into the core business. And I think the layer, which that’s happening first and foremost logically, is the data layer. And so you know, these common data platforms, these common like data ops processes, increasingly, the marketing stack has to integrate to that common foundation. And to be honest, this is a win for everyone. You know, for marketers, yes, they’ve got a ton of data, that they can now feed into that engine that then, you know, sales and customer success and product ops and finance, kind of people who can get a lot of value out of having that data now in the common environment. But also, marketing is a huge beneficiary of this, because now that they’re also able to do this in the other direction of saying, oh, well, all this data about how customers actually use our product. And when did they call our support department? And, you know, what were the sales interactions that work and which ones don’t. To be able to have access to that data is like a goldmine, you know, of what, you know, marketing can feed into. And so, I think we’re starting to see this happen at the data layer – again, all these things have a distribution curve, where you know, something, companies are way ahead of the curve, and a lot of companies are still trying to catch up, but at least it feels we’re headed in the right direction on that dimension, too.

If You Had an ‘Easy Button’ for Customer Data, What Would it Be?

Sacolick: Yeah, I think just as things start becoming easier, we start challenging ourselves to doing more and better things, whether it’s more data, or more real time, or with better privacy controls, you know, with more apps, doing more capabilities, experiences and physical and digital worlds, and mobile and web, and IoT, and wearable. It’s, every time we sort of simplify things, we start doing things more with it. So it’s always this evolving window of what we’re trying to accomplish. The good news is that some of the technologies are making that easier today. But I’m never going to use the word easy, right? Whenever we’re starting to put all of our customer data together. So just a question I’ve asked every guest on the Customer Data Perspectives podcast, – what’s your wish list for the easy button for gaining a competitive advantage with customer data? There’s lots of speed bumps that you can hit, just getting it together, and you say – if I had one easy button, where would it be?

Brinker: Wow, you know, I think it would be. And there’s a term for this, too – it’s the metadata management. So much of the pain I see even now with the reintegration of MarTech into the broader company, while I did ops layer, is just there’s a misalignment on labels and definitions. It’s easy when we’re like, well, email address, okay, there’s some fields, we’re like, yeah, check. Then you start getting into things like oh, well, segment – and how do we define segment and cohorts? And like, you know, when did these things get started? How do we calculate lifetime value? And is the way I’m calculating that for this campaign, the way that finance is calculating it, in the way that’s being reported? I mean, this has nothing to do with the technology, per se. This is mostly about, how do companies get alignment on a shared understanding of the data model? And it’s hard because it’s such a huge universe. And boy, if there was a way to press it easy button then, and just get everyone to at least agree on the same names for the same things with the same definitions. Holy crap. Yes. That would be awesome.

Sacolick: Yeah, the universal enterprise babelfish that we can all talk the same vocabulary. What’s the customer, right? Can we agree who the customer is? And what are we calling a name? And what are we calling a date? And what are we calling revenue? You know, these are…you know, we talk about technology, but the real challenge is getting alignment around all the data even though the tools are giving us more capabilities around us. 

Scott, it’s been a fascinating conversation, great to finally meet you, I want to thank everybody for joining us for this episode of Customer Data Perspectives. Tune in to watch more of our episodes coming up in the future, and have a great day. 

Brinker: Thanks for having me. 

Sacolick: Thank you.

Want more Customer Data Perspectives? Check out our latest episodes here.

Isaac Sacolick
Isaac Sacolick
Isaac Sacolick is the President of StarCIO, where he guides clients on succeeding with data and technology while executing smarter, faster, safer, and more innovative transformation programs. Isaac is the author of the Amazon bestseller, Driving Digital: The Leader’s Guide to Business Transformation Through Technology, and has written over seven hundred articles as a contributing editor at InfoWorld, Social, Agile, and Transformation, and other publications.
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