#35 — The rise of GTM Engineering

Why it matters: In today's hyper-competitive SaaS landscape, traditional go-to-market strategies are becoming less effective as inboxes overflow with automated outreach and markets flood with similar products. The emergence of GTM engineers represents a fundamental shift in how startups approach growth.§

The big picture: GTM engineers combine technical expertise with business acumen to build scalable growth systems across the entire customer journey, making them increasingly valuable in a post-ZIRP era where companies must "do more with less."§

By the numbers:§

• Over 33% of GTM Engineers started their roles in 2024, indicating rapid growth in this field
• Companies using GTM engineering approaches have seen conversion rates increase by up to 70%

Understanding GTM engineers

What sets GTM engineers apart

Different from growth marketers: While growth marketers focus primarily on acquisition and top-of-funnel metrics, GTM engineers orchestrate the entire customer journey from awareness to revenue and retention.§

Different from growth engineers: Unlike traditional growth engineers who focus on product experiments, GTM engineers integrate expertise in user journey, ICP identification, messaging strategy, and business acumen while connecting multiple systems across the revenue stack.§

Different from growth PMs: Growth PMs concentrate on product evolution and feature development, while GTM engineers focus on integrating product, marketing, and sales strategies into one unified growth system that drives measurable revenue outcomes.§

Different from sales ops: Sales ops professionals optimize existing processes, while GTM engineers build entirely new systems that span multiple departments and automate complex workflows.§

Core competencies every GTM engineer needs

Technical foundation:§

• Data architecture: Building unified customer data platforms using tools like Segment, Mixpanel, or custom APIs
• Automation scripting: Creating workflows in Python, JavaScript, or no-code platforms like Zapier and Make
• API integrations: Connecting CRMs, marketing automation, product analytics, and customer success platforms
• Database management: SQL proficiency for customer segmentation and behavioral analysis

Business intelligence:§

• Revenue forecasting: Working backward from ARR targets to build predictable pipeline generation
• Customer lifecycle mapping: Understanding every touchpoint from first visit to renewal/expansion
• Unit economics mastery: CAC, LTV, payback periods, and churn analysis across different segments
• Competitive intelligence: Monitoring market positioning and differentiation opportunities

Strategic thinking:§

• ICP refinement: Using data to continuously evolve ideal customer profiles beyond basic demographics
• Messaging optimization: A/B testing value propositions across different customer segments and channels
• Channel strategy: Determining optimal mix of inbound, outbound, product-led, and partnership-driven growth
• Resource allocation: Prioritizing high-impact initiatives based on data-driven ROI projections

What GTM engineers actually do: The complete workflow

Phase 1: Foundation building§

• Audit existing tech stack: Map all customer touchpoints and identify data gaps
• Implement unified tracking: Ensure every customer interaction is captured and attributed
• Build customer scoring models: Create lead scoring based on behavioral and demographic data
• Establish baseline metrics: Document current conversion rates, customer acquisition costs, and retention rates

Phase 2: System orchestration§

• Design automated workflows: Connect marketing, sales, and customer success processes
• Create dynamic segmentation: Build real-time customer segments based on behavior and intent signals
• Implement progressive profiling: Gradually collect customer data without overwhelming forms
• Build feedback loops: Ensure insights from closed deals inform marketing and product strategies

Phase 3: Optimization and scaling§

• Run systematic experiments: Test messaging, channels, and processes with statistical rigor
• Develop predictive models: Use historical data to forecast customer behavior and churn risk
• Build self-service experiences: Create product-led growth motions that reduce sales friction
• Scale successful playbooks: Systematize and automate processes that drive repeatable results

They own revenue metrics: GTM engineers forecast and work backward from key business metrics to build workflows that directly tie to the bottom line, taking responsibility for pipeline generation, conversion optimization, and revenue predictability.§

They master the full funnel: They're responsible for the entire buyer journey, from ICP identification to pipeline building, conversion, retention, and expansion—ensuring no customer touchpoint is optimized in isolation.§

They build systems, not silos: GTM engineers create automated, data-driven systems that connect marketing, sales, product, and customer success, breaking down departmental barriers that typically slow growth.§

Between the lines: The most effective GTM engineers combine technical skills (coding, data analysis, API management) with strategic thinking and deep understanding of customer psychology, market dynamics, and revenue operations.§

The Gorgias playbook: GTM engineering in action

Event-Based Marketing foundation: Gorgias built their growth engine around an Event-Based Marketing (EBM) strategy that monitors detectable user actions as signals of elevated purchase intent, moving beyond traditional demographic targeting to behavioral triggers.§

Signal intelligence system: They developed a comprehensive intent scoring system that tracks:§

• Hiring signals: New CX, support, or ecommerce role postings
• Technology adoption: Installation of complementary tools in their stack
• Engagement patterns: Participation in relevant communities and forums
• Competitive research: Visits to competitor websites or review platforms
• Growth indicators: Funding announcements, expansion news, or traffic spikes

Signal-based outbound optimization: When Gorgias detected prospects with multiple intent signals (like hiring a CX lead while using complementary tools), their contract values were 3-4x higher than cold outreach, proving the power of data-driven prospecting.§

360-degree customer intelligence: Gorgias used Segment to build unified customer profiles that aggregated:§

• Product usage data: Feature adoption, time-to-value metrics, and engagement scores
• Marketing touchpoints: Email interactions, content consumption, and campaign responses
• Sales interactions: Meeting notes, demo feedback, and objection patterns
• Support history: Ticket volume, resolution times, and satisfaction scores

Hyper-personalization at scale: They developed 20+ different onboarding combinations tailored to each customer's tech stack and use case. For example:§

• Shopify + Recharge users: Received specific integration guides for subscription management
• High-volume merchants: Got dedicated CSM introduction and advanced feature tutorials
• Multi-channel sellers: Received unified inbox setup guidance and cross-platform automation tips

Automated demand generation engine: By 2024, Gorgias had built a fully automated system that:§

• Maps total addressable market: Using multiple data vendors to identify all potential customers
• Scores prospects continuously: Applying machine learning to prioritize outreach based on conversion probability
• Personalizes messaging automatically: Creating dynamic email sequences based on company characteristics and intent signals
• Optimizes send times: Using behavioral data to determine optimal engagement windows for each prospect

Measurable transformation: This GTM engineering approach helped Gorgias achieve:§

• Scale milestone: Growth to 9,000+ customers and over $25M in ARR
• Operational efficiency: Reduction of 15,000-16,000 manual support tickets through AI automation
• Pipeline acceleration: 70% increase in qualified pipeline through automated outbound messaging
• Resource optimization: 3x improvement in sales team productivity through better lead qualification

Building your GTM Engineering capability

The GTM engineer hiring playbook

What to look for:§

• Technical versatility: Experience with APIs, databases, and automation platforms
• Revenue responsibility: Previous ownership of pipeline or conversion metrics
• Cross-functional collaboration: Success working with product, marketing, and sales teams
• Analytical mindset: Comfort with statistical analysis and experimental design
• Customer empathy: Deep understanding of user behavior and journey optimization

Red flags to avoid:§

• Single-channel expertise: Candidates who only understand one growth lever
• Tactical focus only: Those who can't connect activities to business outcomes
• Tool obsession: Overemphasis on specific platforms rather than strategic thinking
• Siloed experience: Lack of cross-departmental collaboration in previous roles

Interview framework:§

• Case study: Present a real growth challenge and ask for a systematic solution
• Technical assessment: Test ability to design data flows and automation workflows
• Metrics discussion: Evaluate understanding of unit economics and forecasting
• Customer journey mapping: Assess ability to identify optimization opportunities across the funnel

Building vs. buying GTM engineering capabilities

When to hire internally:§

• Complex product: Multi-stakeholder B2B sales with long cycles
• Technical customers: Engineering or technical decision-makers who need deep product understanding
• Regulatory constraints: Industries with compliance requirements that need internal expertise
• Venture-backed scale: Companies with funding to invest in full-time specialized talent

When to partner with agencies:§

• Early-stage startups: Pre-Series B companies that need expertise but can't afford full-time senior talent
• Specific initiatives: Time-bounded projects like CRM implementation or automation setup
• Knowledge transfer: Situations where you want to build internal capabilities while getting immediate results
• Market testing: Validating new channels or customer segments before full commitment

Hybrid approach benefits:§

• Faster time-to-value: Agencies can implement proven frameworks immediately
• Risk mitigation: External expertise reduces learning curve and implementation mistakes
• Internal development: Team members learn alongside experienced practitioners
• Cost efficiency: Pay for results rather than salary and benefits during uncertain periods

Implementation roadmap for founders

Note: Depending on the size and complexity of your organization, Phase 1-3 can take just 90 days.§

Phase 1: Assessment and planning§

• Audit current state: Map existing tools, processes, and data flows
• Identify quick wins: Find immediate optimization opportunities with minimal investment
• Set baseline metrics: Establish current performance across the entire funnel
• Define success criteria: Set specific, measurable goals for GTM engineering initiatives

Phase 2: Foundation building§

• Implement tracking infrastructure: Ensure comprehensive data collection across all touchpoints
• Connect core systems: Integrate CRM, marketing automation, and product analytics
• Build initial automation: Create simple workflows that save time and improve consistency
• Train team members: Ensure everyone understands new processes and tools

Phase 3: Optimization and scaling§

• Launch systematic testing: Begin A/B testing messaging, channels, and processes
• Develop predictive models: Use accumulated data to forecast and optimize performance
• Expand automation: Build more sophisticated workflows that handle complex scenarios
• Measure and iterate: Continuously optimize based on results and feedback

Common pitfalls and how to avoid them

Tool sprawl without strategy:§

• The problem: Adding new tools without clear integration plans
• The solution: Start with strategy, then select tools that support specific outcomes
• Best practice: Limit new tool adoption to one per quarter and ensure full utilization

Data quality issues:§

• The problem: Making decisions based on incomplete or inaccurate information
• The solution: Implement data validation and regular auditing processes
• Best practice: Assign data ownership and create accountability for accuracy

Over-automation without human oversight:§

• The problem: Removing human judgment from processes that need nuanced decision-making
• The solution: Build automation with appropriate human checkpoints and override capabilities
• Best practice: Start with assisted automation before moving to full automation

Metrics vanity over revenue impact:§

• The problem: Optimizing for engagement metrics that don't correlate with revenue
• The solution: Always connect activities to business outcomes and customer lifetime value
• Best practice: Use a metrics hierarchy that prioritizes revenue-generating activities

Why this matters for founders

The competitive advantage: As AI and SaaS tools democratize individual marketing and sales functions, the differentiation comes from orchestrating these tools into cohesive growth systems that compound their effectiveness.§

Resource efficiency imperative: In today's funding environment, startups can't afford to hire specialists for every growth function. GTM engineers provide the strategic and technical capabilities to do more with smaller teams.§

What successful companies are doing: Companies like Clay (data enrichment), Cargo (revenue operations), Gorgias (customer service), and Datadome (security) are already leveraging GTM engineering to achieve outsized results with lean teams.§

The market evolution: The shift toward GTM engineering reflects broader pressures: tighter funding environments, AI democratizing technical capabilities, and increasingly saturated product categories all demand more sophisticated and efficient growth strategies.§

Investment priority: For founders looking to stay competitive, investing in GTM engineering capabilities—whether through hiring, training existing team members, or partnering with specialized agencies—represents one of the highest-leverage investments for sustainable growth.§

Reality check on talent: Finding experienced GTM engineers is challenging because they require a rare combination of technical expertise, business acumen, and growth experience. This scarcity makes them extremely valuable but also means you may need to develop these capabilities internally or through partnerships.§

The smart move: Start building GTM engineering capabilities now, even if it means starting small with automation and data integration projects. The companies that master this approach early will have significant advantages as markets become more competitive and efficient growth becomes essential for survival.§