Best of LinkedIn: MarTech Insights CW 38/ 39

00:00:00: This episode is provided by Thomas Allgaier and Frennis, based on the most relevant LinkedIn posts about MarTech in calendar March, thirty-eight and thirty-nine.

00:00:08: Frennis is a B to B market research company helping enterprises gain the market, customer, and competitive insights needed to drive growth and success.

00:00:17: Welcome back to the deep dive.

00:00:21: Today, we're going to take a look at what B to B marketing technology folks were really talking about, maybe even arguing about, on LinkedIn these past couple weeks.

00:00:29: Yeah, we've sifted through quite a bit.

00:00:31: We definitely have.

00:00:31: And we've tried to pull out the key trends, give you a shortcut to what's actually moving the needle right now.

00:00:37: And honestly, if there's one big takeaway from all of it, it feels like the conversation around MarTech has really grown up.

00:00:44: We're kind of leaving that, let's just play with AI.

00:00:46: phase behind.

00:00:47: The novelties worn off a bit.

00:00:49: Exactly.

00:00:49: Now it's about operational AI.

00:00:51: Yeah.

00:00:51: And that shift, it's forcing everyone to get serious about the, well, the boring stuff, data foundations, governance, orchestrations, and really focusing on outcomes you can measure, not just collecting tools.

00:01:02: The honeymoon is over, and now we need to see the results.

00:01:06: So, okay, let's start with maybe the toughest part, because like you said, everything else kind of depends on it.

00:01:11: Data foundations, identity, and governance.

00:01:15: Why is this suddenly the critical thing, especially with everyone rushing towards AI?

00:01:20: Well, it's pretty straightforward, isn't it?

00:01:21: Garbage in, garbage out.

00:01:23: But with AI, it's more like garbage in, scaled garbage out.

00:01:27: AI needs data, obviously.

00:01:29: But if your data layer isn't secure, unified, governed, well, your AI efforts aren't just ineffective.

00:01:36: They're potentially risky.

00:01:37: Risky

00:01:38: how?

00:01:38: Untrustworthy results, data leaks.

00:01:40: Yeah.

00:01:40: Take digital identity, for instance.

00:01:42: Rob Girdin had a great point about how fragmented user identity is online, you know, across different devices, cookies disappearing, different platforms.

00:01:49: It makes attribution a total nightmare.

00:01:51: Yeah, tell me about it.

00:01:52: So how do you fix that fragmentation, though?

00:01:54: You basically got to sketch it back together.

00:01:55: Yeah.

00:01:55: You mean moving past, just relying on, say, third-party cookies or fuzzy matching.

00:02:00: You need those solid deterministic identifiers to think secure logins, verified emails.

00:02:05: and combine that with things like cross-domain tracking.

00:02:09: The goal is to build unified user profiles that are actually accurate.

00:02:15: If you can't reliably track one person from seeing an ad, all the way to closing a deal, well, your personalization isn't really personal, and your attribution is just a guess.

00:02:24: That guessing game, I think that ties into another big risk people were talking about, just having too many tools.

00:02:30: tool proliferation.

00:02:32: Oh, absolutely.

00:02:32: Niels van Meert Janssen flagged this.

00:02:35: every single new tool you add is another potential data leak, another point of failure for data integrity.

00:02:41: It's like leaving doors unlocked all over the place.

00:02:43: Precisely.

00:02:43: Each tool often copies and moves data, which just multiplies your risk.

00:02:48: The solution that's gaining ground, and frankly needs more attention, is this idea of zero copy architecture.

00:02:54: Zero copy.

00:02:54: Yeah.

00:02:55: Keep the data in one secure, governed place, usually a cloud data warehouse, and let the tools query it when they need it instead of making their own copies.

00:03:03: So the tools come to the data, the data doesn't go to the tools.

00:03:05: Exactly.

00:03:06: Yeah.

00:03:06: It's a big shift, architecturally no doubt, probably expensive up front.

00:03:10: But it might be the only sustainable way to manage data integrity as things get more complex.

00:03:16: That sounds like a major investment, but maybe necessary.

00:03:20: And this whole need for clean data, it really hits home when you talk about ROI, right?

00:03:25: Dan McGaw pointed out that frustration from the C-suite.

00:03:28: Why should they trust marketing ROAS when the CRM says one thing, the ad platform says another, and analytics says something else entirely?

00:03:34: It just breeds chaos and kills trust.

00:03:37: You can't have that strategic conversation if nobody believes the numbers.

00:03:40: The fix, again, goes back to that foundation.

00:03:42: Anchor everything to a single governed custom review, usually in a CDP or the warehouse.

00:03:48: Right.

00:03:48: And that clean data.

00:03:49: It directly impacts AI quality too.

00:03:52: Jean-Claude Pitcho made the point that quality data always beats quantity, especially zero-party data the stuff customers give you willingly.

00:04:00: That's gold for training AI models that are focused and less biased, which leads to better personalization.

00:04:05: We also saw folks focusing on making data usable, not just clean.

00:04:10: Ali Restielo shared how teams are using platforms like OpenPrize, she mentioned, to fill in those annoying data gaps.

00:04:16: Things like getting accurate job functions or personas.

00:04:20: So they can segment better and work around limitations in other platforms.

00:04:24: Like Salesforce, apparently locking down campaign object dates.

00:04:28: That can really mess up historical tracking.

00:04:30: So it's about making the data you do have work harder.

00:04:33: Yeah, getting operational with it.

00:04:35: And Franz Reimersma offered some really practical advice here.

00:04:38: Look, cleaning all your data.

00:04:40: Yeah, probably not realistic for most

00:04:42: definitely does overwhelming right.

00:04:43: so instead of trying to you know boil the ocean He suggests using follow the money questions to prioritize.

00:04:49: ask Who's our biggest customer?

00:04:51: What do they buy most?

00:04:52: What's the margin on that?

00:04:54: Clean the data related to your most profitable segments first.

00:04:57: It ties governance directly back to revenue impact.

00:05:00: I like that.

00:05:00: It makes a huge task feel more manageable.

00:05:03: All right, so let's say we've got that governed data foundation starting to shape up.

00:05:07: Now let's pivot to how AI is changing the actual doing part.

00:05:11: Operational AI, agent orchestration, and the shifting GPM stack.

00:05:15: We're moving past just experimenting, really.

00:05:17: We

00:05:17: really are.

00:05:18: This is where the day-to-day of B to B execution starts to look different.

00:05:22: Bill Staffthopoulos emphasized something important.

00:05:25: the fastest growing go-to-market teams.

00:05:28: They aren't choosing between AI and humans.

00:05:30: It's a hybrid.

00:05:31: AI takes on the speed, the scale, the repetitive stuff.

00:05:34: That frees up the humans to focus on the strategic side, better targeting, building real relationships.

00:05:39: And that AI scale can be pretty powerful.

00:05:42: Chase Diamond gave examples, like using Apollo's AI Assistant.

00:05:45: You can give it a natural language prompt, like find me series AB to be sauce companies that raised between five and thirty million dollars in the last month.

00:05:52: And it spits out a highly targeted.

00:05:59: And that kind of power, that efficiency, is exactly what's fueling some of the big anxieties about the Martek stack itself.

00:06:06: Scott Brinker talked about AI engines being the latest Armageddon scenario for Martek.

00:06:10: Armageddon?

00:06:11: Bit dramatic, maybe.

00:06:12: Well, the idea is that these AI engines could potentially just... take over the entire end-to-end digital experience, not just doing tasks, but making the decisions.

00:06:23: Okay.

00:06:23: Yeah, that feels a bit more threatening, especially when you hear news like what Andrew Frank shared, OpenAI, apparently looking into building its own ad infrastructure.

00:06:32: Exactly.

00:06:33: Think about that.

00:06:34: The company controlling the AI that generates content might also control the platform that distributes and optimizes the ads for that content.

00:06:41: That's a lot of control.

00:06:43: It

00:06:43: really is.

00:06:43: It's not just another ad tool.

00:06:45: Thomas Ipsale and Lillia Yalucchenko actually dug into job postings from ChatGPT's growth team and the roles they're hiring for.

00:06:53: They basically outline a full-blown AI ads manager system, meaning campaign management tools, real-time attribution, automated spend optimization.

00:07:02: It's like a blueprint for advertising where AI runs the whole show, potentially cutting out a lot of existing players.

00:07:08: So if AI is managing the creative, the bids, the targeting, What happens to all the point solutions we use now?

00:07:15: It seems like the bigger platforms have to shift towards orchestrating all this.

00:07:18: That's exactly the trend.

00:07:20: Shashi Bellamkanda pointed to Optimizely's new Opal platform as an example.

00:07:26: The goal is shifting towards using AI agents to orchestrate entire workflows, not just individual tasks.

00:07:32: Its focus might move from managing hundreds of tools to orchestrating maybe one or a few core platforms.

00:07:38: Okay,

00:07:39: orchestration.

00:07:39: And it gets even more futuristic.

00:07:41: Julian Imchinsky talked about agent swarms.

00:07:43: Agent swarms sounds like sci-fi.

00:07:45: A little.

00:07:46: But the concept is multiple specialized AI agents coordinating in real time.

00:07:51: Imagine one agent drafting personalized emails, another adjusting ad spend on pipeline velocity, a third updating the CRM, all collaborating instantly across sales, marketing, ops.

00:08:01: That's mind-bending, but it also sounds like a lot of current jobs might change or even disappear.

00:08:07: Sianta Ghosh had a pretty stark prediction.

00:08:08: Ninety-five percent of today's GTM tools gone by twenty thirty.

00:08:13: Yeah, that number catches your attention.

00:08:14: Ninety-five

00:08:15: percent.

00:08:15: That seems incredibly fast.

00:08:17: How does a company even manage the risk of ripping out almost everything they use?

00:08:21: Well, the prediction isn't necessarily that you throw out every tool overnight.

00:08:25: It's more that the functionality gets absorbed into these higher level automated orchestrated layers.

00:08:31: The risk management comes from shifting your internal expertise now.

00:08:35: Okay,

00:08:36: how?

00:08:36: You need people who can architect this transition, who understand the strategy behind it.

00:08:41: The role shifts, like scientists suggested, towards RevOps, three point oh.

00:08:45: You move from being an operator of tools, knowing which buttons to click, to being an architect of the revenue engine.

00:08:52: You focus on strategy, pipeline health, retention, the how gets automated, the what and why become paramount.

00:08:57: If you don't make that shift, you risk being stuck maintaining a clunky manual system while the competition automates.

00:09:03: That makes sense.

00:09:04: You architect the automation.

00:09:05: You don't just operate the old tools, which leads us perfectly into our last theme, strategy, tool rationalization, and marketing ops leadership.

00:09:15: If AI handles more execution, Then strategy, smart tool choices, and strong ops leadership become the real differentiators.

00:09:23: Absolutely.

00:09:23: And the first step is stopping the bleeding, right?

00:09:26: Stopping the accumulation of operational debt.

00:09:29: Jenny Hervison and Alan Gonsonhauser both hit on this, just adding more tools without a clear strategy.

00:09:34: It just creates chaos, data silos, complexity.

00:09:38: makes everything harder.

00:09:39: Yeah, we've all seen stacks that look like science projects.

00:09:42: Exactly.

00:09:43: And Jeff Kay offered some great practical advice.

00:09:45: Before you even think about walking into the CMO's office to talk about specific tools, you need to have led a conversation about the business strategy first.

00:09:53: The strategy has to drive the tech choices, not the other way around.

00:09:56: That sounds obvious, but it's so often ignored in the rush to buy the next shiny object.

00:10:02: And the buying process itself has pitfalls.

00:10:04: Luke Amberzetti had a brilliant term for one of them.

00:10:07: Check box functionality.

00:10:08: Oh,

00:10:08: I loved that term.

00:10:09: Right.

00:10:09: It's that feature of vendor builds just so they can tick the box on an RFP, but in reality, it's weak or barely usable.

00:10:17: It looks good on paper, but fails in practice.

00:10:20: It's

00:10:20: the software equivalent of a movie set facade looks real, but there's nothing behind it.

00:10:26: And it costs companies a fortune because they buy these massive platforms hoping for an all in one solution.

00:10:33: only to find the secondary features are terrible.

00:10:35: So what's the alternative?

00:10:36: Ambrazzetti advises finding your true center of gravity solution.

00:10:40: Maybe it's your CRM, maybe it's a specialized CDP, whatever is core to your needs, and then integrating truly best-of-breed tools around that core for the specific functions you need.

00:10:50: Right.

00:10:51: Don't rely on the weak checkbox features of one single mega platform.

00:10:55: Be strategic about integration.

00:10:56: Okay, so be strategic, find your core, integrate smartly, and who makes sure this whole complex integrated system actually delivers a good experience?

00:11:04: Muhammad Harris made a strong case for marketing operations being the most underrated role right now.

00:11:09: I completely agree with that.

00:11:10: He said, branding is the story you tell, but marketing ops is the customer's actual experience of that story.

00:11:16: They're the ones making sure every email fires correctly, every landing page loads fast, every touch point is smooth.

00:11:23: That execution builds trust.

00:11:25: It absolutely does.

00:11:26: And that's why that operational skill set is becoming incredibly valuable.

00:11:30: Jen Phillips shared examples of women in tech who leverage their seemingly boring ops expertise, understanding data flows, system integrations to pivot into really high value, future proof roles.

00:11:41: Like what?

00:11:42: Like AI governance lead or data as product leader.

00:11:44: They took their deep operational knowledge and added skills like AI ethics or data architecture on top.

00:11:50: Their ability to manage complex systems is becoming a leadership pathway in the AI era.

00:11:55: That's empowering.

00:11:56: And it connects to a prediction from Rebecca Corlis, which Phil Gamas shared.

00:11:59: The future Agentech marketing org won't push ops aside.

00:12:02: Instead, it puts lifecycle marketers, those focused on the end to end customer journey at the center, and positions ops as the critical architecture for growth.

00:12:10: Ops moves from being C as maintenance to being strategic design.

00:12:14: It has to.

00:12:15: But, and this is a big but, we have to loop all the way back to the very beginning, all this amazing tech, the AI, the agents, the orchestration, the governance, it all assumes one critical thing, that you actually know who your customer is.

00:12:28: Right,

00:12:29: back to basics.

00:12:30: Dan

00:12:30: Spearing gave a really blunt reminder.

00:12:33: Your CRM, your marketing automation platform, your ABM tool, they all assume you've done your homework on your ideal customer profile.

00:12:41: You need to define that ICP based on real data.

00:12:45: win rates, deal speed, retention.

00:12:47: And

00:12:47: if you don't?

00:12:48: Well, as he put it, if you feed a poorly defined ICP into these sophisticated automated systems, the tools won't just miss the mark, they'll scale the mess.

00:12:57: You'll just automate your strategic mistake faster and wider than ever before.

00:13:00: Ouch.

00:13:01: That's a sobering thought.

00:13:02: It really sums things up, doesn't it?

00:13:03: The overwhelming message we heard from B to B leaders is that operational excellence, solid data governance, and clear strategic thinking.

00:13:10: These are the real competitive advantages now.

00:13:13: It's less about the specific tool logos.

00:13:16: And much more about the underlying architecture and the smart people designing and managing it.

00:13:20: It's like finding the unplugged microphones in your current setup, the manual processes, the dirty data, the ungoverned areas.

00:13:27: Those are the weaknesses AI is going to expose.

00:13:29: Which brings us to maybe a final provocative thought for you to take away from this deep dive.

00:13:35: Yeah.

00:13:35: Given how fast AI orchestration and these AI ad managers are moving, basically taking over end to end execution.

00:13:43: Yeah.

00:13:44: How quickly does your organization need to get its entire operational house in order?

00:13:49: How fast do you need to plug in all those microphones and bring everything under governance to make sure you don't end up just being a fulfillment service, taking orders from the AI agents upstream?

00:13:57: A challenging question indeed.

00:13:59: If you enjoyed this deep dive, new episodes drop every two weeks.

00:14:02: Also, check out our other editions on account-based marketing, field marketing, channel marketing, AI and B to B marketing, go to market and social selling.

00:14:10: Thank you for joining us for the deep dive and don't forget to subscribe.