Traditional GTM is breaking at every level.
Customer Acquisition Costs have doubled Since 2021, growth has halved, and only 40% of reps hit their quotas.
On the tactical side, outbound strategies have lost their punch. Tools have become overwhelming. Buyer journeys now span more stakeholders, touchpoints and channels than ever before.
GTM teams face an ultimatum: do more with less, in a market growing more complex by the day.
Every dollar demands ROI. Every campaign must drive impact. Every touchpoint needs precise timing.
The path forward isn't about working harder—it's about working smarter. We're entering an era of hyper-efficiency where success depends on mastering the intersection of technology and revenue generation.
AI and automation are rewriting GTM playbooks, demanding a new level of technical quotient to stay competitive.
This is a bit paradoxal, because AI democratizes technical capabilities — from scripting to web scraping to schema building.
Yet, you need to be way more tech friendly to use the right AI model, mastering prompt engineering, and orchestrating APIs to connect tools together.
This is where GTM Engineers step in.
Let's pause here for a bit
Since our last piece on the rise of GTM Engineers, many have adopted the term for basic list building or enrichment work.
Let's be clear about what it really means
GTM Engineering isn't about being a modern BDR — it's about architecting entire revenue systems that scale efficiently.
Take a look at these recent GTM engineer job postings from Preply and Userled.
Their role transcends list building tasks to focus on more advanced things like:
- Build an Advanced GTM Motion: Design and implement a sophisticated outbound strategy that combines custom data points, intent signals, and multi-channel creative to drive pipeline from key accounts.
- Develop and Implement TAM and Scoring Models
- "Design and build the Total Addressable Market (TAM) and scoring framework within our Customer Data Platform."
- "Leverage data insights to prioritize high-potential accounts, improving targeting accuracy for Sales and Marketing teams."
- Lead Allocation and Orchestration
- "Develop lead allocation processes to ensure optimal distribution of leads to sales reps and automated campaigns"
You may wonder, so what about the "engineer" part?
It's mostly engineering mindset first, technical skills second. GTM Engineers are problem solvers, whether it involve coding or not.
Have another look at the core missions: "building scoring models" require regression analysis, unifying the tech stack involve leveraging data engineering tools like Snowflake, leveraging signals require custom scrapping and so on..
That said, let's come back to the impact on the revenue organization.
We see a shift where before you would hire an army of BDRs to grow pipeline, nowadays you'll hire a GTM engineer coupled with full sales cycle AE to do the job.
As Justin Michael notes, 70% of sales activities can be automated, making this new model suitable, and way more efficient than the old Predictable Revenue model.
Let’s just have a quick reminder of who are GTM engineers before diving into the pillars of GTM engineering.
Who are GTM engineers
GTM Engineers are the architects of scalable, revenue-driving systems. They blend technical expertise with business acumen to design workflows, unify data, and activate GTM strategies — enabling companies to grow faster and more efficiently.
Those people come from two different backgrounds:
- Engineers who are transitioning smoothly into focusing on business outcomes.
- Business pros (working growth, sales operations, and revenue operations) who are learning technical skills (like JavaScript and SQL) to overcome technical challenges and gain more independence in their work.
GTM engineers are revenue architects, deciding what to build and orchestrating the entire customer journey. But what does this really mean in practice? It starts with three core pillars:
- A unified data foundation that powers everything
- Revenue orchestration that activates this data effectively
- A holistic model to measure and optimize revenue generation
Let's dive in
The foundation of GTM engineering.#
1. Data powers everything
Whether it's to fuel AI models, build robust automation, segmenting audiences, building reliable attribution of GTM activities, data powers everything.
GTM success hinges on unified data. Companies like Ramp, CultureAmp, and Gorgias built their competitive edge by mastering advanced data architecture.
Source: Winning through multi-channel, multi-person outreach
What separates average GTM motions from exceptional ones?
The ability to seamlessly connect, unify, and orchestrate data across the entire revenue engine.
Composable GTM stacks features four elements:
- Inputs: first & third party data sources
- Storage: Data warehousing
- Capabilities: Enrichment and analytics tools
- Federation: Data movement and orchestration
How "good" a given GTM stack is less about the quality of any of the individual tools and more about how well they play together, the orchestration or what Austin Hay refer to as stack flexibility.
To design flexible GTM stack, focus on the FRIC framework:
- Focus: Each tool solves one specific problem
- Redundancy: Strategic tool overlap for critical functions
- Interoperability: Seamless communication through APIs
- Coupling: Minimal dependencies between tools
The power of a flexible stack?
You can swap tools without rebuilding workflows. Switch from Salesloft to Outreach? Just update where the data flows - your core process orchestration stay intact.
“At Ramp, after decoupling Salesforce and HubSpot, we connected both to Redshift. This made it easy to keep our source of truth (Salesforce) up to date and let each tool push and pull data as needed. Tools had push, pull, and audience integrations, allowing us to move data efficiently between the ETL, CDP, and other tools into our warehouse.“ - Austin Hay
To build such composable stack, there are some technical components to handle
-
Modern Data Stack (MDS): The modern data stack emerged as a solution to fragmented data challenges. It combines best-of-breeds tools for extracting, storing, and activating data across the organization. As tools proliferated and definitions diverged across teams, the modern data stack became the architecture for maintaining clean, reliable, and consistent data. AI's rise reinforces MDS importance: the better your data foundation, the more powerful your AI applications. From predictive scoring to automated personalization, clean unified data is what makes AI truly valuable for GTM teams.
Source: - APIs & API documentation: APIs are the universal language of software communication. Every modern tool interaction - from a simple workflow automation to complex data pipelines - relies on APIs. Think of them as the building blocks of modern GTM stacks. You can read this beginner-friendly introduction to APIs. APIs power how AI and LLMs connect with your GTM stack. Mastering them unlocks sophisticated automation that basic integrations can't match.
“As we move from AI generating content to AI taking action, trust becomes even more critical. That requires grounding your AI on a foundation of trusted customer data, knowledge, and service policies. The AI revolution is really a data revolution“
Ryan Nichols - CCO Salesforce
The end goals?
First, golden records - a single source of truth for every business entity. No more fragmented or conflicting data across tools. Every contact, company, and opportunity has one complete, enriched profile.
Second, a central hub to orchestrate your entire revenue engine. One place to design, execute, and optimize GTM processes across marketing, sales, and customer success.
Now let's dive a bit more into revenue orchestration.
2. The new standard of revenue orchestration
Efficient GTM execution demands orchestration. Generic sequences and basic routing don't cut it anymore.
The way to approach it is becoming way more granular, with specific strategies tailored to different motions, personas, and account tiers.
Orchestration = Right message, Right person, Right time, Right channel.
Product-Led Sales or advanced Outbound provides a great example where GTM orchestration operates on multiple levels:
Account Tiers:
Source: Winning with multi-threaded outreach
- Enterprise or Shiny logos (Tier 1): High-touch, personalized engagement
- Mid-Market (Tier 2): Mix of human and automated touches
- SMB (Tier 3): Automated, scalable motions
Look at Snowflake: Their orchestration matches account potential with engagement intensity. Tier 1 prospects get dedicated teams, while smaller accounts flow through automated nurture tracks that can escalate based on engagement signals.
Persona Tiers:
Within each account, approaches vary by role, as multi-threading is necessary for any deal. The larger the company, the more stakeholders involved.
- CXOs: White-glove, high-context and personalized outreach
- Champions: Educational, value-driven content, hybrid outreach
- End Users: Product-led, self-serve flows, AI-driven automated sequences
Handling the complexity of different personas and account tiers requires tailored treatment. That's where orchestration becomes the backbone of GTM efficiency, built on 4 pillar including lead enrichment, scoring, routing and AI enablement.
-
Waterfall Enrichment: Sequenced data enrichment processes are now the standard. No one relies solely on tools like ZoomInfo or Cognism anymore. As a GTM engineer, your mission is to ensure consistent enrichment rates and deeper account intelligence.
- Basic firmographics (company size, industry, location)
- Advanced data (technographics, signals)
- Custom enrichment: AI-generated insights that matters for your business, and that traditional enrichment solutions won't provide like the target market, if the company is sales led or product led, if the company is SOC 2 compliant and so on.
-
Scoring: Scoring is about prioritizing leads based on behavior, intent signals, and account fit—or flag accounts at risk of churn. Scoring models can be used as triggers to surface high-priority accounts or leads or as embedded in workflows to ensure each lead gets the right treatment.
-
Routing: Automatically directing opportunities to the right teams or tools for next best action. The goal is to get faster response times, better lead coverage, and higher conversion rates. This includes:
- Lead allocation to the right sales rep for high-touch engagement.
- Pushing leads to marketing nurture sequences
- Adding accounts to targeted ad audiences
- Adding leads to automated sales sequence
-
AI Enablement: AI is here to assist GTM teams or engage leads automatically.
- Personalization: Sequences, outreach messaging
- Intelligence: Pre-meeting briefs, market research, competitive analysis, account research
- Content: Custom one-pagers, case studies, sales collateral
- Data mining: Extracting unique insights from unstructured sources
By automating enrichment, scoring, routing, and using AI, GTM Engineers ensure every lead gets the right touch, on the right medium, at the right time—without burdening GTM teams with manual processes.
With orchestration handled through a single hub, GTM teams can focus on optimizing these core metrics and improving their GTM execution. This brings us to the Bowtie framework - a blueprint for measuring and optimizing B2B revenue engine.
3. Holistic view of the funnel
The Bowtie funnel maps every stage of your revenue journey, from first touch to expansion, with clear metrics at each step. By unifying financial and GTM data, it translates executive vision into actionable metrics that GTM engineers use to optimize the entire revenue engine.
The BOWTIE model has three core components:
- VM are volume metrics that will be adapted for each stage: Education metrics (VM2) could be MQL or PQL depending of your GTM motion, while VM4 would be number of closed won deals.
- CR are conversion metrics that are key indicators measuring efficiency across the customer journey. An example would be "Lead to opportunity" to assess the conversion between awareness to selection stage.
- TM are Time metrics, used to measure the duration it takes for a customer/prospect to go from one stage to another (or from one metric to another). For instance, it could take on average 50 days to go from opportunity to closing (=sales cycle)
The unified data layer ensures these metrics drive accountability across teams, keeping focus on unit economics and revenue impact.
Every GTM initiative follows this framework:
- Identify target stage > 2. Set baseline metrics > 3. Implement solutions/tactics > 4. Measure impact > 5. Scale what works
Let's see this in action: Your MQL to SQL conversion is stuck at 1%.
What's a GTM Engineer would think about?
Some possible experiments:
- Build a robust ICP scoring model that combines behavioral data with firmographics data to surface the most qualified accounts for sales. This can be extended to third party signals (fundraising, new position, hiring, tech stack)
- Surface and fast-track MQLs from targeted account lists (= super shiny logos)
- Instant routing to assigned AE if MQL matches "dream 100" account list
- Auto-enrich all stakeholders in that account
- Trigger multi-channel plays (ads, email, LinkedIn)
- Automate stakeholder mapping when ICP accounts engage with content - moving from single content lead to full buying team for multi-threaded outreach.
- Design tiered lead routing to maximize coverage and resource allocation:
- Tier 1 (High ICP fit): Instant account executive assignment, personalized outreach + retargeting
- Tier 2 (Medium fit): SDR outreach + automated nurture
- Tier 3 (Low fit): Automated nurture
This systematic approach ensures GTM Engineers focus on what moves revenue metrics, not just what's technically interesting.
While revenue metrics are core, GTM Engineers adapt their metric focus based on the stage of the business:
- In early-stage startups, the focus might lean more heavily on metrics like pipeline growth and experimentation velocity. > - In growth-stage companies, metrics like net revenue retention (NRR) or average revenue per user (ARPU) may take center stage as expanding accounts becomes a priority. > - In mature organizations, focus shifts to efficiency metrics like gross margin or LTV-to-CAC ratios, ensuring sustainable scaling.
This can sounds like a very broad scope and many tools to manage for each experiment, but that's not necessarily the case.
If you want to learn more and connect with other high performing GTM engineers from around the world, access exclusive office hour and workshops and discuss with like-minded peers, feel free to apply here
Introducing Cargo: The #1 platform built for GTM engineer
GTM Engineers need a platform that matches their ambition. Cargo combines the two critical layers we've explored:
- A unified data foundation that connects your entire GTM stack into one source of truth, enabling seamless orchestration from a single hub.
- An orchestration layer for end-to-end revenue operations - enrichment, scoring, lead assignment and routing - powering sophisticated GTM use cases across the Bowtie funnel.
And if you want to learn more about this movement, feel free to join the community for GTM engineers
MaxNov 29, 2024
