How to Evolve GTM Operations

“Your GTM Isn’t a Product—It’s a Platform.” a $21M CEO asked me: “how did Snowflake grow from zero to $2B+ in revenue in one of the most crowded categories?” my response? “they didn’t just build a product. they built a GTM system that scaled with every stage of growth.” most companies stall after finding early traction— 📌 they scale revenue, but not operations 📌 they hit product-market fit but don’t evolve 📌 they rely on one channel, one persona, or one hero rep but the best companies don’t just grow. they transform—from product to platform. and they do it with a go-to-market system. when GTM is a system, it evolves across stages: problem → product → platform so how did Snowflake do it? 1️⃣ predictable demand generation → how do we create pipeline at every stage of growth? 🟠 at problem-market fit: ✅ messaging focused on separation of storage & compute ✅ technical founders led early education + sales ✅ first customers were data engineers & architects 🟡 at product-market fit: ✅ launched an enterprise sales engine ✅ paid + partner channels activated ✅ early wins in finance and healthcare verticals 🟢 at platform-market fit: ✅ category creation: “The Data Cloud” ✅ multi-cloud strategy + marketplace fueled demand ✅ C-level, IT, and data teams engaged in the same ecosystem 🚀 Snowflake didn’t chase channels. they aligned GTM with product maturity. 2️⃣ seamless pipeline conversion → how do we turn interest into enterprise deals? ✅ sales process aligned to data transformation roadmap ✅ layered in vertical use cases + security/compliance value ✅ sales + SE + customer success teams worked in pods ✅ weekly forecast + usage reviews to spot and accelerate deals 🚀 every pipeline stage mapped to buyer readiness, not internal quotas. 3️⃣ revenue retention & expansion → how do we grow customer value over time? ✅ usage-based pricing → aligned value to cost ✅ net revenue retention (NRR) > 130% ✅ platform expansion: analytics → governance → apps ✅ integrations + marketplace drove stickiness 🚀 they didn’t just retain customers—they expanded into entire ecosystems. final thoughts 📌 if your GTM strategy doesn’t evolve with your product—you’ll stall. 📌 if you treat GTM as a one-time play—you’ll never become a platform. 📌 if you don’t invest in the system behind the growth—your wins won’t scale. Snowflake didn’t win because of one product. they won because their GTM system evolved at every stage. so i’ll ask you: 👉 is your GTM built to evolve—or are you still selling like it’s day one? let’s discuss 👇 — love, sangram p.s. follow Sangram Vajre to learn how to scale your GTM from product to platform with GTM O.S. #gotomarket #gtm #growth #b2b #sales #marketing #snowflake #platform #nrr #categorycreation

Having spent more than a decade running GTM teams and functions, I can say that it's fundamentally changing. GTM teams used to be run like massive reporting machines. Every function will have a dashboard. Every meeting generates a status update. Everyone has a version of the truth. This is a surefire way to failure in the current era. You cannot play this giant game of telephone with manual inputs, biased summaries, and endless "sync" meetings. This is too slow and often wrong. Here's how to run GTM to succeed in the current scenario: 1. Ditch the reporting treadmill and start listening for signals. A signal-based GTM system doesn’t report the past. It reacts to the present. 2. Start using signals to automate workflows and update shared "sources of truth". 3. Use customer behaviors that indicate buying intent or engagement to refine your goals and priorities. > Visiting key pages on your website (e.g., pricing, product demos) > Engaging with marketing emails or content > Attending webinars or events > Updates on social channels about relevant team problems. 4. Start small and select a few high-impact signals and test their effectiveness before scaling up. Run it like a precision operation and not a large hierarchical reporting organization. #Signals #GTM #Future

Most GTM teams bleed revenue in the handoffs. Marketing drives leads → sales closes deals → CS handles the fallout. But what if all of that worked as one system? At AppFolio, CRO Marcy Campbell owns the entire customer journey – from first touch to long-term retention. She built “stream teams” that pull in marketing, sales, onboarding, CS, and support to run big initiatives together, end to end. The result: fewer silos, faster execution, and better outcomes for the business and the customer. Marcy (ex-PayPal, Boomi) came on the podcast to share how to build a truly unified GTM motion. Key takeaways any operator or founder can swipe: 1️⃣ Map the customer journey before you touch the org chart. Every CRO’s first job is to understand how customers discover, evaluate, buy, and use the product. Without this map, your GTM motions will misfire and misalign. 2️⃣ You can’t scale revenue with siloed execution. AppFolio built “stream teams” to run cross-functional initiatives end-to-end. Marketing, sales, onboarding, CS, and product move as one unit across the full journey. 3️⃣ Campaign performance = revenue + retention + LTV. Don’t stop at pipeline metrics. Track onboarding velocity, CS touch requirements, and downstream churn to understand the true ROI of your GTM motions. 4️⃣ Great CROs speak in customer verbs, not sales stages. Your process should mirror how the customer thinks – evaluating, comparing, adopting – not how your CRM is set up. Messaging and journey design should reflect their language. 5️⃣ Show your customer a single company, not your org chart. Buyers don’t care about internal handoffs. A unified experience means marketing ops, sales ops, and CS ops must operate from the same data and workflow foundation. 6️⃣ CRO success depends on the CMO relationship. AppFolio’s unified customer experience initiative started because of trust between CRO and CMO. Without mutual respect and shared metrics, sales and marketing stay misaligned. 7️⃣ Your best GTM asset might be your sales engineer. In one early startup, it was an engineer (not a seller) who gave the sharpest ICP filters based on what the product could actually deliver. Bring your builders into discovery. 8️⃣ Founders are de facto PMs until a repeatable motion exists. Early GTM is product management disguised as selling. Your job is to surface sharp use cases, value thresholds, and repeatable customer needs. 9️⃣ High-performing teams win because of process, not heroics. Individuals can brute-force short-term results. But consistent revenue growth comes from teams that operate with shared rituals, clear priorities, and metrics that matter. You'll also learn invaluable leadership lessons. More from Marcy in the full episode, available on the GTMnow website or wherever you get your podcasts by searching "The GTM Podcast" 🎧

The best GTM teams aren’t just scaling—they’re compounding. And the biggest shift we’re seeing? The rise of GTM engineering teams: squads of technical operators, SDRs, and RevOps pros who are blending product thinking, automation, and data to create a lasting advantage. They’re not waiting for better lists or warmer leads. They’re building systems. They’re building "GTM Alpha". That’s the label that Clay has created and I love it.  Maybe it’s because I come from the finance world originally but "alpha" is exactly the right way to think about differentiation.  What is GTM Alpha? It’s the edge you get when your go-to-market motion is "not easily replicable"—because it’s powered by proprietary data, real-time signals, and workflows your competitors can’t see (and probably can’t execute). Here are 5 lessons the best GTM orgs are putting into practice: 1. Treat GTM like product. Build. Ship. Iterate. Don’t just run sequences—run experiments. 2. Stop buying the same data as everyone else. Find your "unique data advantage"—behavioral signals, intent data, product usage, call transcripts. The best teams win on data others can’t access or act on fast enough. 3. Hire like you’re building a software team. The new SDR might be a former ops person. The new RevOps lead might write SQL. GTM engineers don’t “run campaigns”—they build infrastructure for scalable revenue. 4. Automate for leverage, not laziness. Don’t use AI to do what you shouldn’t be doing manually in the first place. Use it to unlock new channels, richer personalization, and faster learning cycles. 5. Build feedback loops. The best GTM machines are alive. They adapt. They respond. Real-time inputs → real-time adjustments → tighter market fit → higher conversion. This is how you build durable, differentiated growth. Not by hiring more. Not by spamming faster. But by engineering a better system—one that compounds. What’s your team doing to create GTM Alpha? Would love to hear what you’re experimenting with.

I have worked with 14 Seed-Series C companies in the last 12 months to help modernize their GTM strategy. Here are the 6 things I now suggest in every engagement, based on what we've learned: #1 → AI-enabled Account Tiering (ICP) This isn’t 2019. Stop using the same, basic attributes that everyone is using from their b2b database, like: location, industry, employee count, revenue. Instead, use rich* data points. AI can find these for you. *Examples: - Updated privacy policy page in the last 30 days  - $500M in online revenue and have lots of products being sold online  - Using snowflake, bigquery, databricks, or redshift (scraped from job descriptions) and have 5+ data engineers  - PLG SaaS company that has a pricepoint below $100/month, and at least 1 Researcher at the company Then, layer in the basic data points (location does still matter).  And finally, create a tiering system. This should be 100% automated in the background. Set it up so any new company added to your CRM gets enriched + tiered. #2 → AI-enabled Contact Sourcing + Categorization (Buyer Personas) Build a list (with the help of AI) of titles that you want to get in front of. Each title should translate to a “Persona”. For example, for Cursor, you may have the following Personas (and want to run different messaging/automations for each) - - Engineering Leaders (45+ title variants / keywords) - Engineering ICs - Product  - Founders  - Business users Build an ai-workflow so that every person at a company is found, with their title, and tagged with a ‘Persona’ that is written back to your downstream systems (eg: CRM and data warehouse). Y ou’ll use this persona for messaging (eg: ai prompts) and to determine the level of automation for certain plays. Also for ABM, reporting, and attribution. #3 → Signal-based sales plays (evergreen) Come up with a stack-ranked list of signals that show buyer intent, for your product. Many are these signals/plays are the same as everyone else (website visitors, champion tracking, new hires, social/public listening, first-party data). Getting easier to build these with modern tools like Clay, Common Room, Pocus, Unify, Warmly, etc. (and incumbents like Zoominfo and Apollo). But the alpha is in the stuff that 99% of other companies aren’t doing (because it’s not relevant to them). When I’m consulting, I cannot come up with these ideas, because I don’t know the business well enough. I’ve noticed the best ideas usually come directly from the founder, or the head of sales, or the top rep. #4 → AI-generated drafts of emails You have to customize the prompt to match your style (give it examples). And I recommend trying to get each unique message to generate ‘snippets’ within a message. Think of these as modular blocks used within a template. Things like: - Signal-based hook (relevance/timing)  - Persona-based pain + value prop - Segment-based social proofing. ...ran out of room, final two as a Comments below.

Old GTM playbook: -Overreliance on new business acquisition  -Disconnected “assembly line” selling with broken handoffs  -Customer Success is a siloed function -Growth at all costs  -‘Spray and pray’ prospecting and marketing -PreSales reduced to demo machines  -Sellers avoid proof of concepts at all costs  -Rev Ops is limited; reactive to owning tools and running reports New GTM playbook:  -Intentional land-and-expand strategy  -Full-cycle GTM; account teams own the entire customer relationship -CS is everyone's job — Sales, Support, Pro Serv -Efficient, profitable growth  -Targeted outbound using market and audience signals  -Solutions act as a strategic copilot to Sales  -POCs integrated into the sales process -RevOps is a strategic growth driver, fueling decisions at the CRO level SaaS is changing FAST — and if you’re still running the same playbook from 5 years ago, you’re asking to get left behind. The good news? The companies willing to evolve and embrace this new GTM playbook are already winning.

Your old GTM playbook is soon dead. AI agents are writing the next one. Based on my extensive background as an Enterprise Software CRO, many CEO’s and revenue leaders are asking me: “Which AI tool should we add to our stack?” Wrong question. The real shift is this: AI is turning GTM from manual playbooks into autonomous systems that plan, act, and learn across your entire revenue engine. The AI technologies that matter most for GTM aren’t just dashboards and copilots. They are: • Agentic AI for execution – AI agents that run parts of your GTM: qualifying leads, handling outreach, routing opportunities, cleaning CRM, orchestrating campaigns. Not “assistants”, but autonomous doers owning workflows end‑to‑end. • Predictive & prescriptive intelligence – Models that stop telling you what happened and start telling you what to do next: which accounts to prioritize, what motion to use, and how to allocate resources across segments and regions. • Generative AI for content and proposals – Systems that generate hyper‑personalized emails, pages, collateral, and proposals in minutes, at scale, tuned to each account, persona, and stage of the journey. • Conversational AI and call intelligence – Digital teammates on every call and every page: qualifying visitors in real time, surfacing battlecards, capturing objections, and feeding those insights back into product, marketing, and enablement. • AI‑driven ABM and journey orchestration – Engines that detect buying intent long before a form fill, then personalize every touchpoint across ads, website, email, sales outreach, and product. • AI‑native “revenue brains” on top of the stack – A meta‑layer that sits above CRM, MAP, CS tools and continually optimizes GTM like a living system: testing offers, channels, territories, and messaging, then redeploying what works automatically. The provocation is simple: If your GTM still depends on humans stitching together disconnected tools and spreadsheets, you’re competing against organizations whose GTM is literally learning faster than yours every day. This is no longer about “augmenting reps.” It’s about redesigning GTM so that AI owns the repetitive work and humans own trust, creativity, and strategy. My recommendation to GTM leaders: • Map where AI agents can own workflows end‑to‑end, not just produce recommendations. • Decide where humans truly create differentiated value—and remove them from everything else. • Start treating your GTM like a product: instrumented, experiment‑driven, and continuously improved by an intelligent core. The next competitive moat won’t be your playbook. It will be the learning speed of your AI‑powered GTM system. Are you still adding tools to your stack, or are you building a revenue brain? #AI #GTM #Sales #Marketing #SaaS #AgenticAI #RevenueLeadership

If you are trying to scale GTM by adding tactics, you are already too late. More channels More campaigns More experiments Before you have a system That mistake quietly kills momentum. It’s not that the ideas are bad. But there’s nothing underneath that can carry the weight. This map is not a list of things to implement. It is a maturity order. You do not skip layers. You move bottom → up. You do not climb it sideways. You do not cherry-pick blocks. Layer 1: Build This is where GTM either exists or doesn’t. → Who it’s for → What problem you solve → What you sell → How you explain it If these are fuzzy, everything above becomes interpretation. No amount of campaigns fixes that. Rule: Pick one domain and start at the bottom block. Ask: does this truly exist and is it written down? If not, stop. Build it. Lock it. Only then move one block up. Layer 2: Operate Now the question changes: Can the team run this without heroics? → Shared language → Clear handoffs → Repeatable motions → Decisions that do not live in people’s heads If your system needs constant explaining, it is not operational. Rule: Do not add scale until the motion runs without force. Layer 3: Grow Only now does growth make sense. → More channels → More segments → Pricing leverage → Expansion as a system Growth is not adding complexity. Growth is applying pressure to something stable. Most GTM failures are not execution failures. They are premature scaling failures. Foundation first. Then operations. Then growth. Never the other way around.

I’ve spoken to 15+ revenue leaders last week. Almost all of them struggle with the same issue: misaligned GTM leadership. Here’s what I heard: • Marketing is chasing MQLs, not revenue. • Sales is closing deals but failing to hand them over. • Partnerships is competing with the direct sales team. • Customer success is chasing internal buy-in post-sale. Silos aren’t the problem. Misaligned leadership is. GTM is one function with one goal: to create recurring revenue through recurring customer impact. How do you fix GTM misalignment? I asked it the GTM experts in my 6,000+ LinkedIn library: 1. Create a GTM leadership council     Your CRO, CMO, VP of customer success, and head of revenue operations should meet bi-weekly to align on one revenue strategy. If GTM leaders optimize for their own KPIs, the entire system breaks. 2. Shift from siloed metrics to a shared GTM scorecard     If marketing tracks MQLs, sales tracks closed deals, and CS tracks retention, you are misaligned. All teams should track one set of revenue-driving KPIs or you will never operate as one GTM function. 3. Embed cross-functional pods instead of silos     Segment GTM teams by customer segment (e.g., mid-market, enterprise), not by function. When marketing, sales, and CS collaborate at the account level, handoffs disappear. 4. Prioritize net revenue retention over new logos     Acquiring customers is step one. Keeping and expanding them is step two. The best SaaS companies scale by growing existing accounts, not just chasing new ones. 5. Move beyond lead generation and focus on buyer enablement     GTM is not about collecting leads. It’s about helping buyers make confident decisions. If your marketing team still celebrates MQL volume, it’s time to rethink the goal. 6. Revenue operations is the glue that aligns GTM     If marketing, sales, and CS are operating from different data sets, you are not aligned. RevOps should own one GTM data model, ensuring every team makes decisions based on revenue impact. GTM is not marketing. GTM is not sales. GTM is not customer success. It is one revenue function with one mission. How are you solving for GTM alignment right now?

Most people treat Google Tag Manager as a Technical Task. I treat it like a product. Because the moment you stop thinking of your GTM setup as a living system with users, dependencies, and lifecycles is the moment your data quality starts to decay. → Dashboards don’t fail because of bad charts. → They fail because the product feeding them your tagging system has stopped evolving. Here’s how I manage GTM like a product, not a project: 1- Define Clear Use Cases Before Building: Every tag must serve a purpose: what business question will this event answer? I never tag just in case. Each implementation starts with an objective, a stakeholder, and a measurable outcome. 2- Run Release Cycles for Tag Updates: Instead of ad-hoc changes, I use structured sprints. Test new tags in staging, validate through GA4 DebugView, and roll out with version notes. This eliminates the chaos of surprise data shifts. 3- Measure Tag ROI Over Time: Just like any product feature, a tag should earn its place. If it’s not helping decisions, conversions, or optimization, it gets deprecated. Clean systems scale. Cluttered ones collapse. → Your GTM container isn’t a one-time setup - it’s an evolving ecosystem. → Treat it like one, and it’ll pay you back in clarity, consistency, and trust. ↷ I’m Neil Shapiro, Founder of Zen Digital Analytics. ↷ I Help Agency Founders and Business Owners treat their data setups like products - so their measurement stays accurate, adaptive, and built to last. ➡️ What best describes your current GTM approach? A) Ongoing product mindset B) One-time setup C) Somewhere in between