Best of LinkedIn: MarTech Insights CW 16/ 17

00:00:00: This episode is provided by Thomas Allguyer and Frennus, based on the most relevant LinkedIn posts about MarTech from calendar weeks sixteen and seventeen.

00:00:09: Frenness is a B-to-B market research company that supports enterprise marketing teams in unlocking full potential of their customer data with the help of AI.

00:00:17: You can find more info in description.

00:00:20: So to kick things off I want you picture something.

00:00:22: Okay go for it.

00:00:23: Imagine dropping like five grand on this high-end super advanced smart fridge because your food just keeps boiling, right?

00:00:31: Right.

00:00:31: But you live in a house where the electrical wiring is literally held together by duct tape and to make it worse You haven't even bothered to plug in the oven that you bought last year.

00:00:41: I mean you would just call that completely irrational.

00:00:43: Like why would anyone do exactly

00:00:44: but in B to be Martek right now?

00:00:46: i mean That Is pretty much Exactly what marketing leaders are doing every single

00:00:50: day.

00:00:51: yeah which is why we're seeing this massive industry-wide reality check.

00:00:55: And that's exactly what were getting into today, where you are stepping away from the perfectly polished strategy slide decks to look at the actual state of BDB marketing technology.

00:01:04: Because honestly, the era of endless stack expansion it effectively dead?

00:01:10: The new mandate across the board is execution discipline.

00:01:13: Totally!

00:01:14: So if your a BBB marketing professional trying to navigate space We promise you a really focused no fluff deep dive into what is actually working right

00:01:24: now.

00:01:25: Yeah, we tracked the top trends across LinkedIn during calendar week sixteen and seventeen And were looking way past the hype of just acquiring shiny new tools.

00:01:33: Right one unpack how teams are achieving operational excellence The overarching theme that was found in our data.

00:01:40: Is this really painful but completely necessary pivot?

00:01:43: Yeah, Dan McGow pointed out this wild pattern he's seen.

00:01:46: He's worked with over I think five hundred companies and he says the cycle is almost always identical.

00:01:51: Oh yeah!

00:01:51: i saw that it so predictable It

00:01:53: really is.

00:01:54: a company goes out they buy a customer data platform They fail to connect it to their ad networks And then uh...they buy an analytics tool That literally nobody logs into.

00:02:04: Right

00:02:04: Nobody checks that.

00:02:05: Never, and then invariably like six months later someone sits in a strategy meeting And claims they need to buy yet another tool to fix their pipeline problem.

00:02:14: It's

00:02:14: just wild it-and the tragic part is that companies routinely try To you know by their way out of this massive data mass.

00:02:22: Yeah They just throw money at it.

00:02:24: Exactly there was this fantastic example shared by Nick Friedman Tabakhorst.

00:02:29: That Honestly, it should make every marketing leader listening to this just wince.

00:02:34: Oh the attribution platform story?

00:02:36: Yes

00:02:37: He talked about a client who was literally days away from signing one hundred thousand dollar contract for a brand new Attribution platform

00:02:44: A hundred grand Just casually

00:02:45: Right.

00:02:46: But when they finally sat down and actually mapped out their existing infrastructure They realized that already owned three different tools capable of doing attribution.

00:02:54: Three Tools Sitting completely idle

00:02:56: Yeah, so the fix for their pipeline visibility was not some shiny new purchase order.

00:03:02: It was literally just two weeks of dedicated integration work.

00:03:05: but you know because no one was actually maintaining the connections between the systems they already paid.

00:03:13: And when the data breaks down, the sales team immediately loses trust in their reporting.

00:03:18: Yep and then the panic reflex kicks-in.

00:03:20: they just throw more software at that problem Which

00:03:22: is the worst thing you can do because every new tool without proper integration creates complexity.

00:03:28: Complexity is what ultimately kills your execution speed.

00:03:31: We see how damaging this fragmented data is, especially in highly complex environments.

00:03:36: Alex Vembrillo highlighted this specific nightmare.

00:03:39: and healthcare marketing.

00:03:40: Oh

00:03:40: yeah!

00:03:41: Healthcare's a perfect example for that.

00:03:42: It

00:03:42: really is because health care groups frequently grow through mergers and acquisitions right?

00:03:47: So a single marketing team might inherit like six different CRMs.

00:03:53: Six CRMs A dozen isolated call tracking systems And just massive messy overlaps in patient data.

00:04:00: I mean, reporting in that kind of environment just turns into a manual forty hour-a-week administrative black hole.

00:04:08: Exactly!

00:04:09: Membrillo's advice cuts straight to the core here.

00:04:11: he says you absolutely must define a single source of truth and consolidate your data before even entertaining the idea of customizing workflows or buying specialized add ons Right

00:04:22: which brings up real technical challenge for people actually managing these systems day-to-day.

00:04:28: Autish Narlawar noted that the hardest engineering work in Martek today is no longer evaluating and selecting vendors,

00:04:35: right?

00:04:35: Anyone can buy software

00:04:37: exactly!

00:04:38: The hardest work now is the glue...the

00:04:40: glue.

00:04:40: I love that term for it.

00:04:41: yeah the glue is the critical piece And we aren't talking about you know fragile duct tape connections using basic Zapier triggers that just break the second someone changes a field name.

00:04:51: Oh,

00:04:51: I've lived through that nightmare

00:04:53: we all have.

00:04:53: We are talking about building robust data pipelines and API connections That allow these platforms to communicate bidirectionally in real time without any human intervention.

00:05:03: But going back to their fridge analogy if your entire house is wired poorly Applying stronger glue isn't gonna fix the structural issue right?

00:05:11: No

00:05:12: not

00:05:12: at all And this is exactly why the entire philosophy of Martek architecture is shifting right now.

00:05:18: If the core problem, it's a bunch of fragmented tools acting like isolated silos The industry solution Is moving towards something called composable architecture

00:05:28: Right and Steve Chitwood introduced A brilliant concept to frame This shift.

00:05:32: he calls It the ambition gap

00:05:34: The Ambition Gap.

00:05:35: Okay break that down.

00:05:36: for me.

00:05:37: So the ambition gap is the massive distance between the fancy strategy deck your CMO presented to the board You know, the one promising real-time omni channel AI driven personalization.

00:05:48: I had a buzzword deck

00:05:49: exactly.

00:05:49: yeah The gap between that deck and what your systems can actually execute on a random Tuesday.

00:05:54: because on a Random Tuesday most teams are like manually uploading batch CSV files.

00:06:00: Yeah Exactly they're dealing with fragmented user identities writing Channel specific rules in Marketo That have absolutely no idea What is happening over in Salesforce?

00:06:08: It's all completely disconnected

00:06:10: right?

00:06:11: so Shitwood argues that your underlying architecture is your actual revenue infrastructure.

00:06:17: It sets the absolute ceiling on your growth.

00:06:20: So, the architecture you have dictates the strategy You can execute not the other way around

00:06:25: one hundred percent.

00:06:26: so to physically close That ambition gap we really have to look at how these systems are built.

00:06:32: yeah Josh Kanagi provided a really clear mental model for this in the posts we reviewed.

00:06:37: Yeah,

00:06:37: I love his take on that.

00:06:39: he

00:06:39: says you have to architect your stack from the data core outward.

00:06:42: let's break down how this actually functions compared of the old way.

00:06:46: historically A tool like, say your email platform.

00:06:50: It was a monolith

00:06:51: right?

00:06:51: it did everything.

00:06:52: Yeah I'd held the customer data and made all decisions about who gets what e-mail And then its sent to E-mail.

00:06:57: But in the new composable model that completely flips Your intelligence layer which includes you know identity resolution Audience building All of the decisioning logic That is pulled entirely out Of individual tools.

00:07:07: So where does he go ?

00:07:09: That brain now lives centrally In your data warehouse

00:07:12: meaning your engagement tools, whether that is your email platform or SMS gateway.

00:07:18: Your ad network.

00:07:20: they're treated merely as dumb receivers.

00:07:22: Dumb receivers exactly!

00:07:23: They don't do any of the thinking anymore.

00:07:25: so The Data Warehouse runs a query decides that you know user X needs to see message Y and it just sends A simple API instruction To the email tool saying hey send this specific template right now.

00:07:37: And Katie Ewan backed up why this structure is just so vital.

00:07:40: She emphasized that real-time behavioral infrastructure, it's the only thing that makes this composable canvas work because when a user clicks a link in an email That action doesn't just sit trapped In the email platforms reporting dashboard.

00:07:53: It feeds back into the central data core Continuously instantly updating the users profile.

00:07:58: So then the ad platform can immediately adjust its retargeting bid based on that email click.

00:08:03: Exactly!

00:08:04: Its all connected in real time.

00:08:05: Vindy S Raj also noted that this API-first modular approach completely outmaneuvers those old monolithic stacks when it comes to business agility.

00:08:14: But, I mean pulling the intelligence out of engagement interface and putting into a data warehouse creates massive human problem doesn't?

00:08:22: How so?

00:08:23: Well, if I am an ops manager who spent the last five years mastering the intricacies of a very specific email marketing interface... Ah!

00:08:32: ...I'm suddenly playing in the wrong sandbox.

00:08:34: Oh yeah that is tough pill to swallow.

00:08:38: So let me ask you uncomfortable question here If engagement tools are just receivers now does this mean marketers need stop obsessing over UI delivery platforms and focus almost entirely on their data infrastructure?

00:08:52: Honestly, that is the harsh reality.

00:08:53: The industry's waking up to right now.

00:08:55: Yes if your intelligence lives in your central data core the user interface of the delivery tool matters significantly less than its API capabilities.

00:09:03: Wow Yeah, the companies that win in this next era won't be the ones with the deepest most complex implementation Of a single and judgment product.

00:09:12: the winners are going to be the one whose data foundation Is so incredibly strong?

00:09:15: That every engagement product becomes completely interchangeable.

00:09:18: So if a vendor raises their prices you just What, unplug their API and plug in a competitor?

00:09:23: Exactly.

00:09:24: And your central brain doesn't skip a beat.

00:09:26: That is fascinating.

00:09:27: But you definitely cannot run this new composable data-first architecture with the legacy operating model Which is driving this massive reframing of how we view marketing operations talent.

00:09:41: Yeah, Ronald games brought up a really transformative concept here.

00:09:44: He says we need to start treating marketing ops systems as product not

00:09:47: just projects.

00:09:48: right We have to stop treating internal systems as temporary projects or are just like simple task lists.

00:09:53: So mechanically what does that actually look?

00:09:56: Like for team

00:09:57: it means applying actual product management principles to your internal tools.

00:10:02: If you build a lead routing workflow, You don't just launch it and walk away and forget about It.

00:10:06: Right?

00:10:06: You treat as the Lead Lifecycle Engine!

00:10:09: You define who its internal users are... You establish a product roadmap for feature updates And track specific outcomes in KPIs.

00:10:17: So when Ops shifts from taking support tickets to actively defining value through system capabilities They become an indispensable part of Revenue Engine.

00:10:28: Exactly

00:10:30: That level of rigor is pretty much the exact opposite of how most companies currently run.

00:10:35: Yeah, Matthew Volem delivered a pretty harsh reality check regarding revenue operations on LinkedIn.

00:10:41: he stated that most companies don't actually have governed execution

00:10:44: right.

00:10:44: they just Have.

00:10:45: what did you call it?

00:10:46: He said They have post-mortem ops and vibes

00:10:49: vibes.

00:10:50: that Is hilarious.

00:10:51: describing an operating model as vibes.

00:10:53: It's funny but deeply tragic for them.

00:10:56: Bottom line, honestly.

00:10:57: Definitely because what he means is that if your audit trail for a discount approval lives in some random slack thread or If you're sales reps have figured out how to manually bypass mandatory CRM feels just to push a deal through

00:11:10: which they always do Always

00:11:12: then You don't actually have governance?

00:11:13: You Just have an illusion of control.

00:11:15: and That gap between What the system demands And what Actually happens on The floor Is where Your revenue and your data Trust leak right Out Of the Business

00:11:25: Of that lack of governance is incredibly high, especially for the new talent you bring in to fix it.

00:11:30: Ah!

00:11:30: Evan Kubicek warned about this.

00:11:32: he calls it The Invisible Tax of Undocumented Systems.

00:11:35: Oh...the

00:11:36: Indiana Jones story.

00:11:37: Yes He shared a story about an newly hired marketing director who spent her entire first thirty days playing Indiana Jones, just doing digital archaeology.

00:11:46: Doing

00:11:46: archaeology instead of actually

00:11:48: building... Exactly!

00:11:49: She was forced to reverse engineer why certain work flows were built three years ago.

00:11:53: she's discovering that critical automations we're firing off the completely wrong fields because the person who build them left zero documentation.

00:12:03: See, documentation isn't just some nice-to-have administrative chore.

00:12:06: It is the foundational infrastructure that dictates whether your next expense of hire actually builds anything valuable or if they've been their first six months excavating a broken database.

00:12:16: Right and all this friction is clearing away for brand new role we are seeing debated over LinkedIn right now.

00:12:22: The marketing engineer

00:12:24: Yes both Jason K Walsh and Nick Lafferty really explored this evolution.

00:12:29: Yeah

00:12:29: so define what it is.

00:12:31: So this is a marketer who builds systems, deploys AI agents and writes complex automations at the architectural level.

00:12:39: Okay but I want to push back on this little bit?

00:12:40: Sure Is marketing engineer really new thing or just a fancy inflated title upgrade for senior marketing ops manager?

00:12:50: It's

00:12:51: a fair pushback But Nick Lafferty makes a really critical distinction between the two roles.

00:12:56: Marketing operations is your bedrock foundation.

00:12:59: Ops ensures clean data hygiene, functional integrations reliable processes basically they keep the lights on and infrastructure running.

00:13:07: A marketing engineer, however takes that clean foundation and builds entirely new capabilities on top of it.

00:13:12: OIC.

00:13:13: so they are the ones writing like custom Python scripts to connect an AI decisioning engine directly to the ad bidding API.

00:13:19: precisely They combine a really deep understanding of marketing strategy with heavy technical fluency.

00:13:25: So they don't just maintain this CRM The invent new programmatic ways to go market That is standard out-of-the box tool could literally never accomplish.

00:13:33: And that Technical Fluency Is mandatory today because once you have the right data architecture and engineering talent to govern it, You can finally deploy AI effectively.

00:13:43: Right!

00:13:43: Let's go back our earlier kitchen analogy.

00:13:45: Deploying an AI agent is kind of like hiring a personal chef.

00:13:48: Yeah And then just throw random disconnected tasks at new human team member and expect them drive strategy.

00:13:55: Yeah, if your smart fridge is still unplugged and your pantry has a disorganized mess that chef's just going to stand there completely confused.

00:14:02: Exactly!

00:14:03: You give them the defined role and integrate into a shared workflow yet build architecture first.

00:14:09: And once that architecture is in place, the conversation around AI and MarTech moves rapidly away from just basic prompt engineering.

00:14:16: Yeah Ian Kim introduced a fantastic framework for this.

00:14:19: he says we need to design agentic marketing Around durable AI roles rather than just short-lived tactical use cases.

00:14:27: meaning if you Just use AI For tactical things like asking it To generate three subject lines for This one email.

00:14:34: The efficiency games just don't scale

00:14:36: Right.

00:14:37: But if you assign an AI agent the structural role of, say a data analyst and its permanent job is to constantly identify engagement patterns across all your channels and alert you to anomalies well then you create real collaborative operational model

00:14:52: An Aquamaraman highlighted mechanism that makes this team-wide collaboration actually function using feature called clogged skills.

00:15:01: I was reading about that.

00:15:02: so instead of fragmented prompting right?

00:15:04: Exactly Currently, the typical marketing team uses AI in these isolated silos.

00:15:10: Five different marketers write five different prompts to write a blog post and you end up with five wildly inconsistent brand voices which

00:15:17: is a nightmare for brand consistency.

00:15:19: right but Claude skills changes that mechanism entirely.

00:15:22: it acts as a central repository almost like a master CSS file for our website so that marketing engineer we just talked about can build a single highly optimized shared workflow.

00:15:32: Wow.

00:15:33: So if they update the core prompt to reflect a new brand guideline, everyone's AI output across the entire department updates instantly?

00:15:40: Yes without the individual marketers having to change their behavior at all.

00:15:45: that is huge but introducing centralized Ai workflows requires really careful management.

00:15:51: Tim Moller brought up The concept of dividing your operations into the lab versus the factory.

00:15:56: The Lab and the Factory.

00:15:57: I Really like this framework

00:15:58: me too.

00:15:59: The lab is where you conduct your fast-paced, AI enabled experiments.

00:16:04: But the factory Is Where Your Proven Scalable Govern Revenue Operations Run

00:16:08: And his warning here is critical for anyone deploying AI!

00:16:12: The lab requires strict engineering discipline.

00:16:14: You have to time box your AI Experiments and define really clear exit criteria

00:16:19: Right.

00:16:20: so if an AI agent isn't producing reliable results within say a two week sprint you shut it down.

00:16:25: You have to, if you don't contain those experiments that indefinite tinkering just bleeds into your production environment and your factory completely grinds to a halt.

00:16:33: And the major platform vendors are absolutely recognizing this shift in completely redesigning their underlying systems.

00:16:39: Yeah,

00:16:40: Akande Davis and Kachikisum both shared some really profound observations from The Adobe Summit regarding Project Halo & Marketo.

00:16:47: They noted that platforms are being rebuilt from the ground up to be native agent-first environments.

00:16:52: And we aren't talking about a generic AI chatbot just bolted onto a settings menu somewhere?

00:16:58: No,

00:16:58: not at all!

00:16:59: These Native agents were designed to take a messy unstructured text brief form a product manager automatically turn it into multi channel campaign plan build actual assets and then QA entire program against complex organizational rules before human ever even clicks approve.

00:17:15: It is a fundamental rethinking of how marketing operations work gets executed inside a platform.

00:17:20: But you know, when you combine composable stacks, marketing engineers and native AI agents driving incredibly complex multi-touch campaigns at scale the old ways we measure success completely fracture under pressure.

00:17:34: Oh entirely!

00:17:35: The attribution models that we've relied on for these last decades simply do not map to this new reality.

00:17:40: Bill Hobbib made a really poignant observation about this disconnect.

00:17:43: He noted that while the MQL, The Marketing Qualified Lead isn't strictly dead absolutely no one trusts it as a leading indicator of revenue anymore.

00:17:51: Right when hobbib asked to room full of marketing leaders if they still report mqls To their executive teams almost everyone raised their hand naturally but then when he asked If They actually trust those mql's to predict future Revenue at the hands just vanished.

00:18:04: wow

00:18:05: yeah The unit of measurement has to evolve from the individual lead to the account level.

00:18:10: One person downloading a white paper is just an activity metric,

00:18:14: right?

00:18:14: It doesn't mean they're buying

00:18:15: exactly but three different stakeholders From the same target company engaging across a podcast and ad And email sequence over two weeks now.

00:18:24: that is a quantifiable buying group forming.

00:18:27: Petra Marino provided a really necessary reality check on why capturing that group behavior is so difficult though.

00:18:34: B-to-B measurement, it's just inherently messy.

00:18:36: Very messy.

00:18:37: Sales cycles stretch for eighteen months.

00:18:40: buying committees consist of like ten different people rotating in and out the process trying to assign a precise fractional dollar value.

00:18:47: every single marketing touch point into dashboard is virtually impossible

00:18:51: which leads to highly controversial but honestly necessary take from Pranav Piyush.

00:18:57: He argued that we need to stop chasing perfect attribution dashboards altogether.

00:19:02: Just

00:19:02: abandon them!

00:19:03: Pretty much, he pointed out the mechanical reality of digital tracking today.

00:19:08: between browser privacy updates ad blockers cookie deprecation Digital Tracking only captures about twenty percent Of The real nonlinear human journey.

00:19:17: Twenty

00:19:17: percent?

00:19:18: That is nothing

00:19:19: right.

00:19:20: so the other eighty percent happens in dark social.

00:19:22: It happens in private Slack communities, offline conversations text messages between colleagues and untrackable browser sessions.

00:19:30: So if you only optimize for the twenty percent you can actually see You are effectively starving your most effective marketing channels

00:19:36: Exactly!

00:19:37: Piyusha's advice is to start waiting perfect tracking data.

00:19:41: instead run bold experiments with strong hypotheses to actively create the data you need to prove your business impact.

00:19:49: But let's pause and consider the actual friction point here for a second, if you are a marketing leader preparing for a board meeting next Tuesday?

00:19:57: And you admit to your CEO that your attribution dashboard misses eighty percent of the buyer journey?

00:20:04: You're completely abetting.

00:20:08: How does a marketing leader actually defend a multi-million dollar marketing budget in that boardroom?

00:20:15: I mean, that is the single most important question for a marketing leaders today.

00:20:19: And you defend it with what Habib called confidence and pipeline value.

00:20:23: You have to stop talking about top of funnel activity metrics entirely.

00:20:29: So aligning with sales?

00:20:30: Deeply aligning.

00:20:31: With sales, you defend the budget by focusing on credible account level signals that demonstrably result in accepted opportunities.

00:20:38: You show The board controlled experiments That prove how marketing materially accelerated deal velocity or increased average contract value.

00:20:46: It is a significantly higher bar to clear than just reporting.

00:20:50: Hey we got five hundred new leads but it Is A much more honest reflection of marketing's true impact On business.

00:20:56: Absolutely Well.

00:20:57: To wrap up this deep dive There is a final, forward-looking insight from the sources that perfectly bridges everything we have covered today.

00:21:04: From composable architecture to real time data cores... To directly impacting revenue!

00:21:10: Yeah, Emile Birnscour provided brilliant analysis of payment infrastructure company Audien acquiring Talon One which is a realtime promotion and loyalty engine.

00:21:20: Right

00:21:21: because historically Loyalty programs and promotion logic have lived squarely inside the MarTech stack.

00:21:27: They sit in your CRM or your campaign manager, calculating points sending out discount codes via email way upstream from where money actually changes hands.

00:21:36: But look at mechanism of this acquisition.

00:21:37: It signals that loyalty and promotions are moving directly into real time transaction architecture.

00:21:43: So if an AI agent decides a customer is flight risk.

00:21:46: it doesn't just you know, send them an email coupon for their next visit.

00:21:49: No!

00:21:49: The system calculates a personalized margin discount in milliseconds and applies it dynamically at the exact moment that customer swipes credit card to check out

00:21:58: layer which leaves us with highly provocative thought.

00:22:02: as these systems become infinitely faster and fully composable.

00:22:07: We

00:22:11: are rapidly moving toward one unified commercial decisioning layer across the entire enterprise.

00:22:17: So, the question you have to ask yourself is this... Are your data foundation strong enough to support AI agent making a real-time margin impacting decision during a live transaction?

00:22:28: Because if your data is still sitting in disconnected silos just waiting for a duct taped Zapier integration to fire an update to your CRM You're going be too late.

00:22:38: Before you go out and buy another high-end smart fridge, make sure your house actually has the wiring to plug it in.

00:22:43: If you enjoyed this episode new episodes drop every two weeks!

00:22:47: Also check our other editions on account based marketing field marketing channel marketing AI and BDB marketing Go To Market & Social Selling.

00:22:55: Thanks so much for joining us on these deep dives into the reality of MarTech.

00:22:58: Don't forget to hit subscribe And we'll catch next time.