Making Sense of Martech

As 2025 closes out, Juan and Jacqueline team up with Keanu Taylor, The Martech Weekly's Head of Research, to read the tea leaves on what 2026 has in store for marketing technology.

Their forecast is a "make or break" year in which the winners won't be the loudest early adopters, but the teams whose data and operating habits are clean enough for AI actually to stick. Keanu predicts a sharper split between the AI "haves" and "have-nots," with organizational readiness acting like gravity on every agent, model, and workflow.

The bet: 2026 will expose which stacks are built for decisions, not demos — and which ones were never ready for either.

Timestamps

01:08 ExactTarget's 25th Birthday/Anniversary

03:54 Prediction 1: AI Agent Adoption and Readiness

10:35 Prediction 2: The Rise of AI Decisioning Silos

21:48 Prediction 3: CDP and CEP/ESP Stack Consolidation

Brought to you by Hightouch - the leading composable CDP and decisioning platform trusted by brands like Domino's, Chime, and Aritzia. 90% of customers have a real use case live within their first week, delivering world-class personalization at scale. Learn more at www.hightouch.com/msom.

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Creators and Guests

Host
Jacqueline Freedman
Host
Juan Mendoza
Host
Keanu Taylor

What is Making Sense of Martech?

Unfiltered takes on the biggest shifts in marketing technology. We spotlight what matters, who's leading (or lagging), and what's next. In Martech, clarity is power — and we're here to deliver it.

00;00;04;29 - 00;00;07;28
Speaker 1
Welcome to the Making Sense of MarTech podcast. I'm Jacqueline Friedman.

00;00;08;00 - 00;00;21;20
Speaker 2
And I'm Mendoza, and this is Office Hours, where we cut through the noise to give you the real insights on the latest and greatest in the marketing tech industry. Today we are joined by a special guest, our global Head of research, Keanu Taylor. Welcome aboard.

00;00;21;23 - 00;00;25;23
Speaker 3
Thanks, guys. Thanks for having me. Excited to, to get involved in office hours.

00;00;25;25 - 00;00;26;11
Speaker 2
Oh, yeah.

00;00;26;12 - 00;00;27;21
Speaker 1
Welcome back.

00;00;27;24 - 00;00;37;00
Speaker 2
It's been a long time coming. Keanu and I launched a podcast many years ago now, and it was very different to today, I think, in terms of our audience growth. But it's great to have you back in the saddle, Keanu.

00;00;37;03 - 00;00;41;16
Speaker 1
You stole my line.

00;00;41;18 - 00;00;46;23
Speaker 1
This is what happens when I say good things before the recording starts. My own fault.

00;00;46;26 - 00;00;49;17
Speaker 2
This is this major.

00;00;49;19 - 00;01;00;05
Speaker 1
It's true. It's true. With that, happy holidays, everybody. There'll be two fun episodes coming out over the break, and we'll be back. Prime time in January.

00;01;00;08 - 00;01;09;13
Speaker 2
Cube. So, speaking of holidays and celebration, we have to say a quick congratulations and happy 25th anniversary to Exacttarget. If you remember.

00;01;09;15 - 00;01;10;11
Speaker 1
Happy birthday.

00;01;10;15 - 00;01;28;19
Speaker 2
Oh. Happy birthday. Yeah. That's right. Exact target. One of the legacy first players in the customer engagement email marketing space. You know, 25 years ago. When was that? That was literally in 2000. Yep. A long time ago in our world. Tells me about the company, how it started.

00;01;28;21 - 00;01;48;18
Speaker 1
Yeah, it actually started fresh off the dotcom bubble. And that's when venture capital was basically non-existent. Sounds a little familiar 25 years later, but they started the company with 200 K and seed funding. It was three of them with their first employee. They had zero technical background comes to show, and they were definitely, and against all odds, underdog story.

00;01;48;18 - 00;02;03;12
Speaker 1
That said, they really defined the entire industry at this point. And granted, we've outgrown it. But then again, it's been a quarter century midlife crisis. You know, it makes total sense that you.

00;02;03;28 - 00;02;28;19
Speaker 2
Like to say that exact target, even though it was acquired by Salesforce, nothing's changed, you know? Yeah, exactly. I got 25 years ago to what? Literally what the biggest brands of the world using today. Not a lot of change. Obviously, you know, Salesforce has rebranded it and things like that. But if you go into the platform, yeah, you would still have plenty of, experience with the platform if you used it 25 years ago versus today.

00;02;28;22 - 00;02;28;29
Speaker 2
Oh, look.

00;02;28;29 - 00;02;53;21
Speaker 1
Give it some credit. It probably hasn't changed since 2007. So like it's it's 18 years of not changing. But nonetheless, it is still a huge testament to how early we are in this industry and where martech really starts and begins. It's always interesting to just kind of see the inflection points. But speaking of inflection points, there's a reason why we have Keanu here.

00;02;53;22 - 00;03;11;28
Speaker 1
He is our head of research. He's the chief analyst, and we want to have some predictions because what is a new year without some resolutions, predictions, assumptions, I don't know. And so Keanu, I'd love to hear your thoughts. And what what are you seeing out in the market?

00;03;12;01 - 00;03;30;17
Speaker 3
Yeah, I think this is a few things that are coming to fruition or starting to turn, in the market. I think doing predictions is always a bit fraught. If I, if I guess something add to wild and it's and it's wrong, everyone's going to call me an idiot if I, if I guess things that are to say, if everyone's going to say I'm an idiot for them to save.

00;03;30;17 - 00;03;54;07
Speaker 3
So, you know, this is, not much to be gained here, but I think there's a few things that we're noticing, particularly in the enterprise space. So I think firstly, the AI agent adoption is going to bifurcate. I think a lot of the narrative around AI agent adoption has been really positive and really excited, but we're starting to see a difference.

00;03;54;07 - 00;04;20;13
Speaker 3
And it's usually along the lines of organizational readiness. So the folks or the brands that, actually getting some solid benefit out of AI agents, across different use cases. One thing that they all have in common is strong organizational readiness. So they do the investment in data upfront. They do the investment in governance upfront. They do the investment in people upfront as well.

00;04;20;14 - 00;04;46;05
Speaker 3
So those folks, you know, we're starting to see really positive results, out of how they're adopting AI agents. But there's a there's another group of people who are using AI agents as a bit of a sticking plaster over what are essentially bad practices. Right? They don't have good, good data hygiene, good data cleanliness. They don't have the the team and the structure and the governance around how they're adopting AI agents.

00;04;46;05 - 00;05;04;22
Speaker 3
And a lot of the time it's it's quite separate and split across the organization. So I think next year is going to be a real clear tale of who are the haves and the have nots in this space. Hopefully this is is something that triggers a bit of a reckoning in these companies to finally go and address organizational readiness.

00;05;04;24 - 00;05;26;26
Speaker 3
I mean, I could go back, to the days of, exacttarget starting out 25 years ago, and you would still be having the same conversation with brands about getting your organization ready for these new technologies that have come along since then. I think that's the first one. There's there's going to be this bifurcation. We're going to have clear haves and have nots based on how organizationally ready they are.

00;05;26;26 - 00;05;34;01
Speaker 3
But what do you guys think? You think there's going to be the haves and the have nots or is it going to. Is it all going to go negative?

00;05;34;06 - 00;05;52;26
Speaker 1
Yeah, I think a lot of it's who has their data in order and who doesn't and who has our people in order and who doesn't, because the tools and the tech, they're coming, it's interesting, but you can't really leverage it unless you don't, unless you have those two things in order. And so similar to what you were saying 25 years ago, transformation was important, but you have to be ready for it.

00;05;52;28 - 00;06;02;06
Speaker 1
I don't know that many orgs that actually truly are ready, even the ones who are trying, they're still not there yet. And it's really like this kind of like North Star ideal that we're all aiming for.

00;06;02;08 - 00;06;26;03
Speaker 2
I think there's an opportunity here for like in terms of a prediction, there's companies that see where AI agents solve a real customer problem and a commercial problem. And then there's brands that are trying it because of the herd mentality and the, you know, the hype that sits around this. And, you know, to your point counter about bifurcation, I think it's organizational readiness is a part of it.

00;06;26;04 - 00;06;46;16
Speaker 2
Data readiness. Jacqueline, to your point, is a part of it, but also what's the business case and expectation if you're a marketing technology executive and you're saying, yep, this is going to be a two year journey and it's going to be all cost and no return, but we anticipate that we're going to see return. At this point. You're going to win as opposed to the folks that are setting huge expectations upfront and not meeting them.

00;06;46;18 - 00;07;07;21
Speaker 2
That is what I think is going to shift the narrative, is the companies that actually see this as a patient, deliberate effort where it requires a certain amount of investment and it targets a certain customer problem. That's kind of where I'm coming. Coming from is like, this is all great, but what problems as it's solving for the customer, what how is it going to generate more revenue or increase profitability?

00;07;07;24 - 00;07;26;28
Speaker 2
Answering those question first, I think is going to be the DNA of the enterprises that win here. And the way I look at next year is it's all eyes on enterprise. If open AI, anthropic and the slew of marketing technology platforms using AI tooling, if they want to be successful in the markets, if they want to attract more investors, the enterprise has to win.

00;07;26;28 - 00;07;43;27
Speaker 2
If the enterprise is not going to win, if they start scaling back their investments, then it's going to be pain and blood in the Straits. I can tell you right now, because I just see a level of investment that's going into particularly data centers, chips and all of the infrastructure to make what we hope successful use cases actually happen.

00;07;44;00 - 00;07;49;23
Speaker 1
So much for holiday cheer the year.

00;07;49;23 - 00;07;52;21
Speaker 2
I'm being optimistic. I'm saying that some brands that are like.

00;07;52;22 - 00;07;59;18
Speaker 1
I'm just say you blood in the street. Yeah, let's let's keep it red. And look.

00;07;59;18 - 00;08;02;24
Speaker 2
At the billions. Look at the circular economy happening around. And video.

00;08;02;24 - 00;08;21;26
Speaker 1
Of course, it's a huge circle jerk. It's there's an inappropriate image of OpenAI and every AI company all involves. So, yes. Agreed. But whoof. It's one way to predict the future. Okay, Keanu, do you have another one?

00;08;21;28 - 00;08;25;27
Speaker 3
Obviously, I'm, I'm just, reacting to,

00;08;26;00 - 00;08;26;26
Speaker 1
In real time.

00;08;26;26 - 00;08;33;20
Speaker 3
I thought this was, Yeah, I thought this was a family friendly podcast, but, apparently not. Apparently, I'm quite violent.

00;08;33;23 - 00;08;39;07
Speaker 1
It's clean. Technically, we're going off the category.

00;08;39;09 - 00;09;01;19
Speaker 3
But I think I think you're both right. I think it's a real make or break season next year for both the companies adopting this technology and the companies making it themselves. Right. If the enterprise doesn't adopt it in the way that, OpenAI and Co hopes that they do, then it's going to be it's gonna be a hard time finding that road profitable, which, you know, these companies are burning a lot of money.

00;09;01;19 - 00;09;30;05
Speaker 3
But at the same time, you know, brands are very good at burning money that they don't need to burn as well. So, you know, it creates that culture, circular economy going. But I think, I guess there is value. I think there's value. But it's for the companies that take it seriously. Invest on going. You know, I think everyone likes the idea of a, a quick proof of concept with, AI agents, but all the, signs and all the, research we're doing and talking to brands shows that for the most part, that doesn't really exist.

00;09;30;23 - 00;09;34;14
Speaker 3
It's a bit of a misnomer. So that's where I think it's heading next year.

00;09;34;17 - 00;09;35;13
Speaker 1
That makes sense.

00;09;35;16 - 00;09;36;09
Speaker 2
Very smart.

00;09;36;11 - 00;09;55;14
Speaker 1
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00;09;55;14 - 00;10;13;24
Speaker 1
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00;10;13;27 - 00;10;15;12
Speaker 1
And now back to the hotseat.

00;10;15;19 - 00;10;18;17
Speaker 3
And I say all right are we ready for ready for number two.

00;10;18;20 - 00;10;19;21
Speaker 1
Oh yeah.

00;10;19;23 - 00;10;35;01
Speaker 3
Okay. The next one is around decisioning. So there's been something really interesting happening in the last sort of 18 months, I'd say, or 12 months to 18 months. So I'm sure you guys know about AI decisioning platforms, so.

00;10;35;01 - 00;10;37;07
Speaker 1
Never heard of it. Tell me what I was just.

00;10;37;07 - 00;11;00;24
Speaker 3
This, it's this little, little thing that's happening, in the biotech space, but, you know, these these AI decisioning platforms are springing out of primarily at a steady pace. So, machine learning, reinforcement learning platforms that you basically just set a target, you set the, the barriers that it has to work within, and it will go off and try and achieve that goal.

00;11;00;24 - 00;11;23;25
Speaker 3
So whether that goal is, maximize cross-sell for certain cohorts or I guess a lot of these goals, around, marketing and growth, but I think decisioning is at risk of becoming another organizational silo because of this. And if you think about it this way, so we have AI decisioning platforms. They're very targeted towards marketing people.

00;11;23;25 - 00;11;54;08
Speaker 3
So that's the entry point for these platforms into the organization is the marketing stakeholder. And then you've obviously got all these traditional complex decisioning platforms. So you know, sort of customer journey orchestration and real time interaction management. You think about like SAS pega, Thunderhead before is acquired. But by Medallia, these companies generally they're in into the organization is a KCS team or a chief customer officer or more on the customer side.

00;11;54;08 - 00;12;32;05
Speaker 3
So I guess, I'm seeing this challenge where, organizations are adopting both or they have both in place, but decisioning decisioning for the most part is a centralized capability or in in organizations that do it. Well, it's a single point decisioning. It's a centralized capability. And I think the other challenge that, organizations are going to run into is, you know, if marketing has one vision of, decisioning and, you know, KCS or customer teams have, different vision is suddenly you have competing platforms, competing priorities.

00;12;32;07 - 00;12;49;24
Speaker 3
And I guess the way these platforms work is also quite different as well. So if you think of the traditional complex decisioning platforms, it's very deterministic. So a lot of it is rules based, which is, you know, there for a reason. And a lot of it is inbound. So it's reacting to customers interacting with the brand.

00;12;49;25 - 00;13;13;07
Speaker 3
AI decisioning platforms, for the most part, are more probabilistic. Just by the nature of how they're built and also more outbound. So, the first sort of channel that, most of these platforms are built on is email. Some do SMS and push and other outbound channels. But I guess the the challenge that I'm seeing here is that this becomes just a new organizational silo.

00;13;13;07 - 00;13;19;10
Speaker 3
It's like having to, single sources of truth or two different single customer views across different teams.

00;13;19;10 - 00;13;28;28
Speaker 1
It's almost as if history is repeating itself with the concept of a CDP, like, let's have our own data set based off of our existing data set. Let's pay for it in addition.

00;13;29;00 - 00;13;29;17
Speaker 3
Yeah.

00;13;29;19 - 00;13;30;25
Speaker 1
And, I see your point.

00;13;30;26 - 00;13;51;25
Speaker 3
Different team owning different versions of the truth or owning different, decisioning approaches. So I think, I think there's sort of two ways brands can go, well, there's actually three. The first one is, have two different versions of truth ready decisioning and see the, the mess that that causes. Number two, you know, I have one system that sits upstream of the other.

00;13;51;25 - 00;14;24;17
Speaker 3
So, one might be doing the more deterministic decisioning that perhaps a traditional decisioning or complex decisioning platform with an AI decisioning platform or something doing more probabilistic decisioning, downstream or just have one. So depending on the needs of your organization, you can just have one, one decisioning acting as the brain. But I guess, the other thing that I see happening here is the AI decisioning platforms adopting more of the complex decisioning from those traditional platforms as well.

00;14;24;17 - 00;14;44;11
Speaker 3
So you can see a world where they start to converge and their capabilities as well. But I think, if brands don't think about this in a serious plant way and, across the organization to the way, then they're going to fall foul of a lot of the the challenges of the past. Right? A lot of, you know, different teams, different silos.

00;14;44;11 - 00;14;49;00
Speaker 3
So, yeah, that's that's something that I'll, I'll be keeping a with it eye on next year.

00;14;49;05 - 00;15;13;21
Speaker 2
Yeah. It's curious to me how, there's so many new players in this space in the past 18 months. Not even. And what's causing that? Because you mentioned sort of the piggies and SAS and these companies that have been doing the decisioning for a long time upwards. I Pega is a 40 year old company. Go figure. You know, they've been around for a long time, but all of a sudden there's been this kind of rush to get into marketing, decisioning, not customer experience decisioning.

00;15;13;21 - 00;15;31;00
Speaker 2
And I think right now where there's a lot of big bets in this space, particularly from the vendors and not a lot of successful use cases, that's, you know, we've covered that in several episodes in the past, we were like, cool. The star customer for some of the vendors in the space, a customer, customers that, you know, they've been around for a couple of years and, you know, it hasn't been a lot of fresh blood in this space.

00;15;31;11 - 00;15;40;28
Speaker 2
I, I just kind of think that it's like, why now? Like, why is marketing decisioning becoming so important to the vendors of like, what does that actually tell us about the industry?

00;15;41;00 - 00;16;11;11
Speaker 3
Yeah, I think, I think there's a few a few reasons for that. One is just the ability to productize these types of machine learning tools and reinforcement learning tools. So when I started my career, we said 13 years ago we built a, basically something this AI decisioning, from the ground up for an automotive company. And it required so much data science and so much tweaking and fiddling under the hood.

00;16;11;12 - 00;16;31;02
Speaker 3
There was so much retraining. It was very, yeah, very, very manual process to to actually make these things work. I think over time, these marketing platforms and particularly city papers have found ways to productize it and to generalize it without losing some of that fidelity. I think to your point one, I think there are useful use cases here.

00;16;31;08 - 00;16;58;02
Speaker 3
So cross-sell is a great one. Finding folks in your database that prime for cross-sell. So, you know, whether that's trying to increase your, you know, average order value or increasing, customer lifetime value, which is a very worthy goal. But I guess this is where the the challenge comes in. If you've got a probabilistic method for guessing who should receive that cross-sell message, there are some people who shouldn't, but that's based on deterministic rules, right?

00;16;58;02 - 00;17;16;25
Speaker 3
So if you think about a, insurance, use case, there are plenty of people who are, you know, absolutely ripe for, picking another insurance product from you. But there are other reasons that you might not want to do that. There's regulatory reasons. They might be in distress or finance wise, they're not in a strong position.

00;17;17;03 - 00;17;39;27
Speaker 3
So this is where I talk about this. This difference between deterministic and probabilistic rules and, you know, recommendation algorithms around this. So I think part of it is, you know, these platforms have worked out how to productize, reinforcement learning in a positive way. I think marketing marketing has been very ab test, very manual, for, for far too long.

00;17;39;27 - 00;17;59;29
Speaker 3
You know, these these things have been around for a while, at least. The capability has been up in the head of, data scientists for a long time. But I got to say, I'm excited for the capabilities. I. I just think it's an org wide discussion. I think marketers are going to fall over if they don't have this discussion about decisioning at an organization level.

00;18;00;04 - 00;18;22;04
Speaker 3
They don't. They need to have it across customer service, any department that is interacting with customers. Because as I said, you know, it's always a big audience of people that would be ripe for a cross-sell message, message or any other type of goal that you're going to set in an AI decisioning platform. But there are other reasons that you might not contact them, or you might not contact them yet for that message.

00;18;22;06 - 00;18;28;11
Speaker 3
So I think there's there's risk if, if people don't have that conversation at the organization level.

00;18;28;13 - 00;18;51;26
Speaker 1
I agree with you. And also, I think of the beauty of certain CDPs and how more than just marketing can use it. And this is where I think AD could become that much better and not in a silo if we get more teams. To your point, using it, centralizing it more than just, we should use this for this email or this push notification.

00;18;52;00 - 00;19;20;29
Speaker 1
But, maybe we use this for this paid ad or this, you know, web experience and embedded component within an app. So there's more context for product for data science and just more components. And I really think of it as the third party ification of lack of observability tools and alerting and the guardrails that if you have an extremely strong tech team who is productize your own decisioning and machine learning equivalent, then that's great.

00;19;21;03 - 00;19;29;13
Speaker 1
But there's very few companies at scale that are able to do that. They might be able for one small component, but it really depends. Interested to hear your thoughts.

00;19;29;15 - 00;19;48;20
Speaker 3
Yeah, I think I think 100%. Right. And building these things yourself is expensive. I used to work with a, with a guy who data scientist got his PhD predicting the winner of the annual Sydney Yacht race, and you could predict it with like 99.8% accuracy or something. So like crazy. So you're talking.

00;19;48;20 - 00;19;52;24
Speaker 1
About that's a cool resumé headline.

00;19;52;26 - 00;20;02;03
Speaker 3
I was I was very fascinated by it. I always asked him questions about it. Yeah, always been off topic. We try to talk about marketing and I ended up talking about yacht racing. But but it's.

00;20;02;10 - 00;20;03;29
Speaker 1
Way more interesting.

00;20;04;17 - 00;20;25;22
Speaker 3
You know, this is, this is serious, serious and expensive talent and rightfully so. Yeah. This is where I think things like AI decisioning, actually, they they productize and they make that type of output from that type of expensive talent actually possible for, for other brands who don't have the inclination to hire that type of person or don't have the money to hire that type of person.

00;20;25;25 - 00;20;40;17
Speaker 3
So that's, that's I'm it's part of the beauty of, product izing this, this capability. But, with any new capability comes, great responsibility. So, yeah, companies are still working out how to deploy this in a way that works or glide.

00;20;41;07 - 00;20;58;24
Speaker 2
Yes, I agree with that. I'm, I'm a little bit more optimistic about this one than AI agents. And the reason is AI agents, still feel quite early in terms of its capability and the promise. But I decisioning does have a heritage. It does have a foundation. And there's like the chief commercial officer should care about AI decisioning.

00;20;58;24 - 00;21;18;17
Speaker 2
That's what I care about, right. Is does this generate revenue? AI decisioning should more efficiently generate revenue for your business. That's the thinking here, right? Exactly. You're getting more sales, more lifetime value upgrades, less churn. You know, whatever those key metrics are that ladder up to, you know, growth I think I decision has a better shot comparison AI agents I think it's just too wild and fantastical.

00;21;18;17 - 00;21;38;24
Speaker 2
But AI decisions hard data science, you know, probabilistic reasoning around what's that right next experience, next offer for the customer 5 or 6 years ago was working on next best options. You know, around this, but it was extremely manual and slide. So I think that taking the effort out of it, making it more scalable, I think it's a winning formula for a lot of the adoption here.

00;21;38;24 - 00;21;48;20
Speaker 2
But but we should move on to our last prediction, which is a little bit of a hot take. Keanu I'm not too sure about this one. I'm not sure if we should talk about it, but, just tread lightly. That's all I have to say.

00;21;48;23 - 00;22;20;15
Speaker 3
I got told I was, in this, podcast to be the bull in the China store, so I'm, I'm going to go for it. But the next prediction is around the consolidation of CDPs and steps and, you know, ESG as well. So customer data platforms, customer engagement platforms and, email service providers. So the platforms that make first party data accessible to marketers, let them segment cohorts or build cohorts and, trigger it off via direct marketing and web experience, channels.

00;22;20;15 - 00;22;40;10
Speaker 3
I'm actually I'm not talking about at the market level. So, in terms of the market level, those different platforms, we've already seen consolidation. So, you know, there's a lot of CDP acquisitions at the start of the year and the turn of last year. But I'm actually talking about with in, enterprise stacks, we're going to see, consolidation here.

00;22;40;12 - 00;23;07;15
Speaker 3
And the reason is simple. So setups and KPIs and further down the chain as well, the capabilities have been converging for a number of years. So setups have been building the activation side. So being able to trigger, emails as well as push all the direct marketing channels as well as, personalized web experiences. So, recognizing who's hitting the website and giving them a different experience that has really been converging.

00;23;07;25 - 00;23;36;27
Speaker 3
And eating into the territory of the customer engagement platforms, which originally were more about the activation and less about the, data management behind it. And of course, you know, specific platforms for email triggering goes mess trigger or push triggering. Yeah, they're all trying to build the same consolidated first party data supply chain, which is first party data in, send it off to a platform or cohorts, build cohorts, send it off to a platform, trigger it to customer.

00;23;36;27 - 00;24;01;11
Speaker 3
So the calculus here for enterprise companies is, you know, you've got at the moment you might have three platforms in, in that chain, but with each of the players in this space building out capabilities that cover the whole, first party data supply chain, there's a lot more opportunities now to say, okay, great. We're just going to consolidate this into a single platform.

00;24;02;01 - 00;24;22;18
Speaker 3
You know, our CDP just, put in place, a bunch of email and SMS and activation capabilities. So we're going to consolidate. We don't need two platforms anymore, especially, you know, it's been a few years now. We've had tough macroeconomic conditions, less spend for for martech, you know, marketing teams in general, getting less, budget.

00;24;22;20 - 00;24;51;19
Speaker 3
So there's this very attractive situation where, you know, one of your vendors, subsumes the capabilities of another of your vendors. It's usually cheaper, usually easier for, for teams to manage because it's one platform rather than two. One vendor rather than two. And, trading is also, a consideration here. It's a lot easier to train people on one platform rather than two less handoffs of data between the platforms, more secure.

00;24;51;26 - 00;25;11;28
Speaker 3
So I think enterprise brands, I gotta say, you know, I don't need, three replications of my first party data in three different platforms to send an email. They're going to say, I only need one. My, my vendor, somewhere along that chain has, given me the option to consolidate. So I'm going to consolidate. That's that's what I think we're going to see more of.

00;25;12;04 - 00;25;26;04
Speaker 3
We're already seeing it this year. But the convergence of those platforms and their capabilities has really, ratcheted up this year, which means there's going to be a lot more opportunities for brands to consolidate in that space or consolidate within their stack, for sure.

00;25;26;04 - 00;25;27;11
Speaker 1
Definitely seems like we're.

00;25;27;11 - 00;25;36;01
Speaker 2
Already seeing this happen. Keanu, with several brands that we're chatting with, they call their customer engagement platform their CDP. Their executives call it the CDP. You know.

00;25;36;23 - 00;25;39;20
Speaker 1
That's just misinformation.

00;25;39;23 - 00;25;55;23
Speaker 2
I actually think that, you know, if it works like a duck, talks like a duck, kind of looks like a duck. You know, I think that's what's going on here is that especially if you look at like a vendor like braze, which is like, definitely integrating with the enterprise warehouse, doing the AI, decisioning stuff, you know, they are moving into this space.

00;25;55;23 - 00;26;16;07
Speaker 2
Yeah, sure. Like you could probably get away with a lot of use cases just by using braze as a CDP, right? Like I, I would genuinely think so. Right. Like but to your point, Keanu, like the activation platform. Is not necessarily going to be the best place to store all your customer identity. You, you know, long term profiling, you know, all of the activation history and things like that.

00;26;16;09 - 00;26;32;29
Speaker 2
Analytics, you know, all those things that sort of really matter. Yeah. It's it's a very interesting, challenging time, I think, because a lot of these platforms are starting to sound like each other. You know, that's what's going on. It makes that all the same noises, you know, they're all quacking like a doc and we're like, which one's a real doc?

00;26;33;02 - 00;27;05;13
Speaker 1
Oh man. I'm about. I almost did my, Donald Duck. Response. Because I can do the accent or the noise, but I'm not going to because that would be embarrassing. I agree that everyone too. It's like a race to the middle. However, I see the counter-argument of that doesn't mean it's a good thing, because I see the power of true CDPs, and I think most folks don't actually have a right definition of what a CDP is, but I see it as being used across the company, not just for a marketing use case.

00;27;05;13 - 00;27;22;27
Speaker 1
And so I actually prefer the Tetris building blocks and the modularity of being able to plug and play. But I can see the attractiveness of the all in one suite, because that's what everyone is kind of used to. If you go all in Salesforce, you go all in Adobe pros and cons. And so to that end, I like competition regardless.

00;27;22;27 - 00;27;28;21
Speaker 1
And that is CDP versus CFE at the same time. But I don't disagree with your prediction.

00;27;28;23 - 00;28;00;06
Speaker 3
Yeah. And I think, I think it's it's slightly different to the suede. And I think, I think the one big difference here is the unified data layer. So the thing that is companies like getting them right is building a unified data layer that powers everything in that first party data supply chain. Right. So you know, we've talked about Salesforce today, but part of their challenge with acquiring data platforms and then sort of building on top of them and then scrapping it, was that there was never this unified data layer.

00;28;00;06 - 00;28;18;25
Speaker 3
So you had challenges between the platform. So I think there is a true contraction of that first party data supply chain around the unified data layer that these platforms are building. But, you know, it's something of a suite. You're, you're putting more of your eggs in one basket. So there is of course, risks and, and challenges there.

00;28;19;20 - 00;28;32;07
Speaker 3
But it's, I know, I know, brands are always looking at the bottom line. And, if they can consolidate, they will always consider it, or a lot of them will. So that's, that's where I think it's heading next year.

00;28;32;09 - 00;28;35;28
Speaker 2
That's awesome Keanu. So much for your insights.

00;28;36;00 - 00;28;37;14
Speaker 1
Thank you.

00;28;37;17 - 00;28;55;14
Speaker 2
We love wheeling Keanu out because he's just the brains of the operation. You can really. It's true. He's spending so much time with the enterprise thinking through the strategies. What's coming up next in martech. So we're very, very, very honored to have him join us today to share a little peek into what's happening in 2026. As we close this podcast out.

00;28;55;14 - 00;29;13;08
Speaker 2
This is our last office hours for the year. Thank you for listening. Thank you for being part of that journey. Making Sense of MarTech podcast really launched again this year and it's been amazing to see the growth and reception globally around this podcast. We're still trying to keep up with the engagement and the questions and everything that comes in, every single week.

00;29;13;08 - 00;29;31;09
Speaker 2
So thank you so much for listening. If you want to stay connected to us, you can, follow our LinkedIn page, Making Sense of MarTech. You can also subscribe on the MarTech weekly.com can subscribe just to get podcast episodes in your inbox. Join our Reddit community. A lot of great things happening there and great discussions around martech, but for now, stay curious.

00;29;31;15 - 00;29;37;08
Speaker 1
Happy holidays!

00;29;37;11 - 00;29;37;20
Speaker 1
And.