Marketing Automation Finally Grew Up
I have spent most of my career in the space where sales, marketing, and operations keep trying to behave like a single organism even though the incentives push them in different directions. On a good day, it feels like collaborative chaos. On a bad day, it feels like a custody battle over who owns the buyer. Somewhere along the way, I became obsessed with fixing the parts of the process that everyone tolerates even though they quietly drain the life out of a go to market team.
So when I started building a small experiment inside GTM Engine, I did not expect it to turn into something bigger. I began with a simple question. What would it look like if marketing finally behaved like it had been listening the entire time a deal was unfolding, instead of jumping in halfway with generic nurture paths and well intentioned guesswork. The project was supposed to be a quick test. It ended up revealing something I have been waiting years to see. Marketing automation that adapts in real time to buyer truth instead of living in a disconnected universe of personas and assumptions.
The moment it clicked, I realized this was not a workflow. It was the beginning of a different way to think about personalization, CRM accuracy, and the relationship between human conversations and automated communication. It was the first time the technology felt mature enough to match the complexity of actual selling.
Why I Put Zero Faith in Traditional Personalization
Personalization has become one of those words that lost meaning through overuse. Most platforms promise it, most teams claim to do it, and most buyers can tell immediately that none of it is actually personal. When I look at most personalization strategies, they rely on surface level identifiers that have no correlation with real buying behavior. If a system decides to personalize based on where someone went to college or the last whitepaper they downloaded, the output might technically be customized, but it is not relevant in any way that helps a buyer make a decision.
I prefer a more grounded approach. The only signals worth personalizing around are the ones with predictive value. A prospect repeating the same use case unprompted. A subtle but consistent reference to internal blockers. A metric that keeps resurfacing because it represents a real organizational priority. The faint stress in their voice when a competitor’s name comes up. These are the clues that reveal what will make or break the deal.
You cannot buy those signals. They do not sit in third party databases or enrichment tools. They only appear inside the sales process, during live conversations, when the buyer is letting their guard down because they believe they are talking to a human, not a system. That is the gold standard of data quality. It is also the data marketing almost never gets to use.
When I Connected the Dots
The more deals I watched run through GTM Engine, the more obvious the disconnect became. Sales hears everything that matters. Marketing hears almost nothing after the initial form fill. By the time a buyer reaches a meaningful stage in a deal cycle, we have a complete profile of their motivations, sensitivities, and priorities. Yet the content they receive from marketing still behaves like the only thing we know is their job title.
So I built something to collapse that gap. Instead of treating the CRM as a static repository of half completed fields, I turned it into a living reflection of the emotional, strategic, and tactical information we learn along the way. I mapped a series of custom properties in GTM Engine to their corresponding HubSpot fields so every insight captured from conversation flowed directly into the CRM. Suddenly, the CRM stopped being an afterthought. It became the anchor.
Once that foundation was in place, the next step almost built itself. If marketing could see what sales understood, it could speak the buyer’s language without forcing the rep to manually translate everything. That was the moment I realized the system could do more than organize data. It could generate actual relevance.
How the Consideration Stream Works Under the Hood
People often assume personalization engines are complicated. This one is not. The strength comes from the structure and the flow, not the complexity.
Inside GTM Engine, I created a dedicated set of fields designed specifically for email generation. Each field represents a component marketing needs to assemble a message that feels intentional. These include the subject line, preheader text, body content, call to action, primary theme, and destination URL. Each property mirrors an equivalent field I built inside HubSpot, which ensures the CRM remains the single source of truth.
From there, I constructed a series of workflows that operate on a simple loop. When a contact meets the criteria for the consideration stage, the engine pulls the record, evaluates the insights tied to that person, selects the dominant themes based on what we learned in previous conversations, and populates all six emails in the sequence. The system builds each email based on that individual buyer’s context.
This is not pseudo personalization. The output changes depending on the human at the other end. If someone consistently talks about data accuracy, their sequence leans into that theme. If someone worries about internal adoption, the content shifts accordingly. The system does not guess. It reacts.
The other key element is synchronization. Deals move quickly. Signals change. Priorities reorganize. So if a contact leaves the eligible stage or if new insights appear, the system recalibrates. It stops sending outdated messaging. It regenerates the relevant content. It keeps the email stream aligned with reality instead of running on an autopilot no one is monitoring.
That alignment is what makes the whole thing feel like intelligence instead of automation.
Why This Depends on a CRM
During the live demo, Jacob asked a question many people quietly wonder. If GTM Engine is driving all of this, why even bother with a CRM.
The honest answer is that the CRM is the only place where this system can operate at scale. It is where the associations live. It is where the deal context lives. It is where the buyer record resides long after the initial outreach. But a CRM is only as strong as the accuracy of its data, and most CRMs get polluted not because teams are sloppy but because the expectations placed on humans are unrealistic.
Reps should not be saddled with the burden of perfect data entry. They already spend their days navigating calendars, conversations, shifting priorities, and internal pressure. Precision is not the problem. Bandwidth is.
What GTM Engine does is remove the requirement for manual rigor. Instead of hoping a rep associates the right contact with the right deal, the system handles it. Instead of expecting them to capture the blocker the buyer mentioned at minute forty two of a discovery call, the system extracts it automatically. Instead of relying on memory or habit, the system listens.
The CRM becomes reliable because the humans are no longer responsible for performing tasks they were never going to do consistently.
The Moment I Knew This Was Different
Every time I trigger the workflow on a test record and watch the fields populate in real time, I feel the same sense of clarity. This is what revenue intelligence should feel like. Not a dashboard with abstract insights. Not a firehose of raw transcripts. Not a one size fits all email sequence pretending to be tailored.
It feels like a synchronized system where each part amplifies the others. Sales uncovers the insight. Marketing operationalizes the insight. Automation scales the insight. The CRM stores the insight. Instead of sales and marketing running in parallel, they begin to function as a single system.
The Difference Between Relevance and Resonance
Marketing teams do not intentionally send bland emails. They do it because they lack the insight required to do anything else. I have written those campaigns. They tend to fall into predictable categories. Some rely on high level value propositions. Others push product features. Some lean on industry trends. All of them are strategically sound but emotionally hollow.
This system forces a different posture. It treats the buyer’s own words as the copy deck. It gives marketing the ability to write in a way that mirrors what the buyer already expressed. Instead of assuming, it reflects. Instead of convincing, it aligns.
Relevance is something an algorithm can approximate. Resonance requires understanding. This is the first time I have seen a process that can deliver both at scale.
Why I Think This Changes the Future of Go To Market Motion
Deals do not fall apart because of a lack of interest. They fall apart because of a lack of momentum. Silence becomes uncertainty. The rep waits for a signal. The buyer gets distracted. Marketing stays out of the loop because it has no idea what is happening.
This consideration stream fixes the quiet moments. It creates a continuous, intelligent presence in the process. It reinforces the themes the buyer cares about most. It keeps the narrative aligned between human conversation and automated touchpoints. Marketing finally supports the deal while the deal is alive, not after it closes.
What Happens When You Scale This System
The best part of this entire build is how undramatic scaling becomes. If five hundred people become eligible for the consideration stream, the system updates five hundred records. It does not break. It does not require manual intervention. It does not demand a new segmentation meeting or a scramble to build bespoke content.
It operates like an engine should. The humans supply the meaning and the machine supplies the precision. That is the partnership I have always wanted to build.
Where I Want This to Go Next
This started as an experiment. Now it feels like a blueprint for a different kind of revenue motion. A motion where sales learns the buyer, marketing reflects the buyer, operations connects the systems, and automation adapts as fast as the deal evolves.
It is not about replacing people. It is about making human insight scalable in a way teams have never been able to manage manually.
The most surprising part is how straightforward it ended up being. I wanted to see whether I could automate a six email sequence based entirely on what a buyer revealed in conversation. I built it. It worked. And the more I test it, the more convinced I become that it is the beginning of something bigger.
A future where revenue teams operate from a shared intelligence instead of shared frustration. Where the CRM becomes a living source of truth rather than a graveyard of incomplete entries. Where personalization stops being a promise and becomes a practice. This is only the first version, but it already feels like the version I wish I had years ago.
About the Author
Josh Roten is the Head of Marketing at GTM Engine. He and his team are building a brand and growth strategy centered on personalization at scale. Revenue teams don’t care about flashy messaging, they care about what actually works. That’s why clearly communicating GTM Engine’s core offering, and how it drives real results, is so important. Josh’s career has always lived at the crossroads of revenue strategy and storytelling. He’s built a reputation for turning messy data into clear marketing insights that fuel smart strategy. At GTM Engine, he’s putting that experience to work, helping shape a narrative that connects. He believes the future of go-to-market (GTM) isn’t about piling on more tools, it’s about finding better signals. After all, great marketing should feel like it was made just for you.
