the gtm engineer

Listen on SpotifyIlija Stojkovski is the Chief Revenue Officer at HeyReach, one of the leading LinkedIn automation platforms that has grown from zero to $6M ARR in just two years. As the company's sole salesperson when he joined, Ilija ran 1,800 demos in 14 months, while helping transform HeyReach from a tiny startup into a platform that powers LinkedIn campaigns for some of the best companies in the world.Ilija joined in 2021 when the company was still a Reddit automation tool. After pivoting to LinkedIn automation, HeyReach's multi-account capabilities became a massive competitive advantage when LinkedIn restricted connection request limits. In this conversation, Ilija talks in depth about HeyReach’s growth - from pricing strategy and referral partnerships to what he learned across 1,800 sales calls. He also talks about LinkedIn outbound best practices based on 3 million connection requests of learnings as well as what he looks for in a GTM Engineer.In this podcast, we discuss:* Why HeyReach's multi-account capabilities became a competitive advantage when LinkedIn implemented connection request restrictions* How experimentation informed HeyReach’s pricing model* Building referral partnerships at scale with LinkedIn account vendors and integrations* Tracking closed lost reasons to inform the product roadmap* Learnings from running 8-9 sales calls per day for 14 months straight* Why personalization isn’t the most important thing for LinkedIn campaigns* Why business intuition is far more important than toolset skills for GTM engineersEpisode highlights:* HeyReach’s first revenue spike came from a bit of luck — when LinkedIn clamped down on connection request limits per profile. Their second spike was more intentional, when HeyReach ran hard at selling into agencies.* LinkedIn account vendors were charging clients per account, but HeyReach offered them unlimited accounts for a flat fee – letting them increase their margins, and, in turn, giving HeyReach a GTM flywheel.* Ilija systematically tracked every closed lost reason and ranked them by revenue impact, which he fed directly to product development. They built white-labeling, API access, and webhooks in one quarter, helping quickly grow revenue with the most critical features.* After running hundreds of demos, Ilija realized, "it's not about what you offer, it's about what they need" and shifted from showcasing features to understanding current challenges. This approach led to shorter, more effective demos.* To qualify the flood of free trial signups, Ilija hired GTM Engineers to build an enrichment system using Clay that analyzed company websites, founding dates, services offered, and target ICPs.* By analyzing 3 million connection requests, HeyReach discovered that the requests without messages saw a 27% acceptance rate compared to 22% with personalized messages.* When hiring GTM engineers, Ilija found most candidates could learn GTM tools but lacked business logic to understand what the company actually needed. He emphasizes that learning technical skills like Clay takes much less time than building business intuition.Where to find Ilija:* LinkedIn: https://www.linkedin.com/in/ilijastojkovski/* HeyReachTranscript details:(00:00) Intro(03:37) Ilija's background and journey to HeyReach(06:22) What HeyReach does and how it works(09:18) HeyReach’s market analysis and early-traction building(14:19) How HeyReach experimented to determine their pricing(16:23) Building referral partnerships with LinkedIn agencies and AI account vendors & tracking closed lost reasons(22:57) Deciding whether to test paid channels(23:55) Using Clay to qualify and target top free trial signups(28:34) Learnings from running 1800(30:26) Understanding and selling to GTM professionals(34:17) LinkedIn's future and why automation tools aren't going away(39:10) Best practices for LinkedIn outreach and connection requests(47:27) Why over-personalization is less important than focusing on relevance(48:58) Defining GTM engineers and the importance of business logic(51:00) How to build business intuition as a GTM engineer(53:09) Advice for companies hiring GTM engineers(54:42) Prediction for future of GTM engineering, underrated tools, and conclusionFor inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thegtmengineer.substack.com

Listen on SpotifyBrandon Camhi has played a pivotal role in Rippling's extraordinary growth journey over the past six years. He joined Rippling in November, 2019 when the company had 150 employees and had just hit $10 million in revenue. Starting as their first growth marketing IC, Brandon built out the new logo sales growth team, took on cross-sell to existing customers, and for the past 18 months has been leading all of marketing at Rippling. During his tenure, Rippling has grown from a $250 million valuation to over $16 billion—a 60x increase.Brandon's career began with a content marketing internship at OpenGov, where he learned to get inside buyers' heads and write compelling content. He then joined Hearth as their first marketing hire, helping scale the business to tens of millions in revenue before discovering Rippling's Series A memo and joining what he recognized as a company with massive potential. In this conversation, Brandon shares the tactics and strategies that drove Rippling's growth, why he believes understanding customers trumps growth hacks, and the key accelerants to career growth.In this podcast, we discuss:* Why deeply understanding your customer is the foundation of effective growth, and how to best understand customers* How Brandon prioritized his time in the early days of Rippling’s growth and how the team recovered when their top of funnel collapsed during COVID* The biggest wins and mistakes during Brandon’s 6 years at Rippling* The challenges and thrills of working at a hypergrowth compound startup* Brandon’s top career advice* How to balance AI automation with human judgment in modern GTMEpisode highlights include:* Brandon transformed Rippling's automated outbound program from 10-15% intent-driven demos to 60-70%. He talked to sales and studied customers to learn what signals prospects gave when they were ready to buy.* During his time at Hearth, after digging around roofer Facebook groups to see what content got the most engagement, Brandon discovered that memes were overwhelmingly popular. By creating ads that followed the same meme format, performance reached all-time highs overnight.* Rippling shifted from purely automated outbound to investing in human SDRs to augment their automated programs. This drove higher yield on the accounts that they were targeting.* To create true sales and marketing alignment, both teams report to the CRO and Rippling is working to eliminate language like "marketing sourced" vs "sales sourced."* People often underestimate the importance of the company they join when looking at their career. The right company will feel chaotic and if you can lean into the chaos, you will often find career magic.* Rippling has a principle called "go and see" to encourage everyone up to the C-Suite to go gather qualitative evidence instead of just looking at dashboards. Following this approach, Brandon still listens to 3-5 Gong calls weekly. In listening to calls, Brandon realized how much Rippling’s brand investment was being undervalued in attribution.Where to find Brandon:* LinkedIn: https://www.linkedin.com/in/bcamhi/Transcript details:(00:00) Intro(03:41) Brandon's career journey starting at OpenGov and why he decided to join Rippling(06:04) Why Brandon chose Rippling(08:12) Early challenges and building intent driven campaigns(12:09) Why intuition beats experimentation frameworks(14:28) Understanding your buyer(16:40) How Rippling navigated COVID and the top of funnel dropping to zero(18:10) A COVID growth hack gone wrong(19:43) How Brandon thinks about standing out in a competitive market(24:28) The most important decisions driving Rippling's growth(28:33) Why Rippling decided to invest in human-led outbound(30:46) Creating true sales and marketing alignment(34:40) What excites Brandon about Rippling's future potential(37:30) Brandon’s top career advice(42:10) The "go and see" principle at Rippling(43:48) Building business intuition in marketing(46:43) Operating principle learnings from Rippling and managing imposter syndrome(50:04) Brandon’s takes on AI in go to market(54:42) Underrated software tools, favorite growth hack, and conclusionFor inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thegtmengineer.substack.com

Listen on SpotifyBrian Swichkow went viral in 2014 for pranking his sword-swallowing roommate with hyper-targeted Facebook ads. When he wrote about the prank, it generated 450,000 impressions on Reddit in 72 hours. This experience shaped his marketing philosophy of doing things worth talking about and demonstrating value through action and storytelling.Since then, Brian has spent over a decade helping seed to Series B startups drive initial user adoption and he has consulted with thousands of startups + over 150 Fortune 1000 companies. Most recently, Brian is running a product studio creating unique products like MythOS, a storytelling platform for personal knowledge management. In this conversation, Brian shares his systematic approach to cold email, his framework for digital empathy in an AI-driven world, and why the intersection of creativity and analytics is the future of GTM.In this podcast, we discuss:* Brian’s formula for cold email copy that he names the Inigo Montoya method* Creating relevant personal context that sounds human even when working with scraped data* Why AI filtering tools will fundamentally change cold outbound strategies and the importance of constant innovation* How investing time in seemingly random curiosities fuels creative breakthroughs* Why Brian spends 2.5 hours each day talking to AI and how it impacts his human interactions* Using multi-agent AI prompting to better understand your target audienceEpisode highlights:* Brian's Inigo Montoya method structures every email with a polite greeting, relevant personal context, managed expectations, and a clear call to action.* To create genuine personal messaging from scraped data, Brian gives AI agents comprehensive information about himself and the target audience, then prompts them to write a single sentence that conveys a relevant connection. By keeping campaigns small and focused, the AI agents are able to craft more targeted, specific messaging that resonates.* After working with an education company whose ads failed when selling the learning process but succeeded when selling the outcome, Brian learned that testing distinct concepts is what changes campaign performance, not tweaking individual words. He now focuses on validating fundamentally different messages rather than wordsmithing when optimizing cold email campaigns.* Brian uses multi-agent prompting where one AI agent creates prompts for another agent, then validates outputs through a third agent, with each agent having its own specialized context and expertise. He even created Brian Bot Broadcast which synthesizes his email newsletters into a daily podcast in his own deep-faked voice.* Brian uses LLMs to help himself learn what kind of messaging resonates with buyer personas he doesn't understand. For instance when he was selling GLP-1s to Midwest conservatives, he gave an LLM an 800-page right-leaning ideology document to role-play the audience, which revealed that framing health in terms of legacy was the key selling point, not self-care as he initially assumed.* We talk about what digital empathy means and why even the small wording you use has a crucial impact on how you are perceived online.Where to find Brian:* LinkedIn: https://www.linkedin.com/in/brianswichkow/* MythOSTranscript details:(00:00) Intro(02:36) Brian's journey from content and marketing agency to a product studio with a growth community(3:55) Brian’s Facebook prank(06:07) What running a product studio looks like(08:40) Getting kicked off Notion and building MythOS(14:56) Copywriting philosophy and the Inigo Montoya method(21:19) Managing expectations in cold emails and avoiding direct sales(25:24) Developing unique communication styles(28:03) Digital empathy in the age of AI(32:21) Testing concepts vs. testing words in copy(35:57) Using AI to understand unfamiliar audiences(41:48) AI email filtering with Missive and the future of outbound as AI expands(47:35) The impact of poor prompting on human behavior(50:13) Cross-discipline creativity and borrowing from other fields(53:25) Balancing curiosity with productivity(58:48) The prompt that reveals how well AI knows you(01:01:09) Blending emotionality and logic as the future of GTM Engineering and conclusionFor inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thegtmengineer.substack.com

Listen on SpotifyEric Nowoslawski is the founder of Growth Engine X, one of the first Clay agencies that has served over 300 customers. As Clay's first marketing contractor when they had just four employees, Eric helped build their early YouTube and LinkedIn presence before launching his own agency. Growth Engine X is now Clay's largest user by enrichment volume and will send over 4 million emails this month.In this conversation, Eric shares his framework for creating high-converting campaigns for massive scale, why he believes signals aren't the silver bullet many think they are, and how to build offers so compelling that prospects would pay for the discovery call.In this podcast, we discuss:* The crawl walk run framework for getting started with Clay and cold outbound* Why offers matter more than personalization and how to craft irresistible value propositions* Growth Engine X's creative ideas campaign that remains their best performing approach across all clients* Why signals aren't a silver bullet and what to focus on for sustainable growth* Evaluating whether cold outbound is right for your business based on TAM size and unit economics* Finding and hiring top GTM engineering talent from the agency ecosystemEpisode highlights:* Eric's creative ideas campaign uses AI to generate three specific ways your product can help each prospect's business. To ensure the outputs remain consistent, he makes the AI focus on predetermined value props rather than generating random suggestions.* Eric crafts offers by asking what he could say that competitors can't say, focusing on creating value so compelling that prospects would pay for the discovery call itself. Every offer must answer why someone wouldn't respond and address their hidden objections upfront. Growth Engine X's free test campaigns exemplify this by removing all risk and proving results before any payment is required.* Growth Engine X always maintains backup inboxes equal to their sending capacity because no matter how good outbounding copy is, some will mark it as spam. When primary inbox delivery goes down, they instantly switch to warmed backups with zero downtime.* For a Google reputation management client, Growth Engine X achieved positive replies on 1 in 70 emails by finding businesses with 3.5 to 4.5 star ratings, pulling specific negative reviews, and offering to remove them with payment only after removal.* Eric recommends TAMs over 100,000 and customer lifetime values over $10,000 for cold outbound success. He emphasizes that a business’s customer acquisition cost to lifetime value ratio should ideally be 1 to 10, though 1 to 3 is acceptable. This ratio ensures cold outbound campaigns remain profitable with healthy margins.* To find GTM engineering talent, Eric targets small agency owners with teams under 10 employees who are tired of running a business but have proven outbound skills. These operators often make perfect full-time hires.Where to find Eric:* LinkedIn: https://www.linkedin.com/in/outboundphd/* Growth Engine XTranscript details:(00:00) Intro(02:55) Eric's journey from Clay's first marketing hire to agency founder(10:12) The crawl walk run framework for Clay and cold outbound(12:55) Infrastructure, list building, and crafting the right message(17:55) Building successful campaigns and understanding what conversion rates are “good”(24:39) Deciding if a business should do cold outbound or not(26:43) Defining offers and why they're crucial for success(29:41) Why signals are overrated for building a reliable outbound motion(32:35) Examples of strong offers(39:49) Personalization strategies for your entire database — firmographic vs person level data(41:14) Eric’s creative ideas campaign(48:34) Hiring GTM engineers(53:48) The future of cold email(58:25) Email deliverability and the importance of backup infrastructure(01:02:23) Favorite tools including Supabase and Pipedream(01:04:25) Predictions on the future of GTM engineering and conclusion This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thegtmengineer.substack.com

Davide Grieco, growth leader who built GTM engineering and revenue programs at Verkada. He talks about automating ABM landing pages, a campaign-in-a-box that drove millions in pipeline, cutting cold email volume while boosting precision, breaking sales-marketing silos, and how AI SDRs and idea-led teams will reshape go-to-market.

Listen on SpotifyKevin White runs GTM Strategy at Common Room, a platform helping GTM teams find intent signals, track users across platforms, and turn that data into full funnel campaigns. Before Common Room, Kevin was head of marketing at Retool and worked in growth at Segment, giving him deep insight into how modern GTM teams operate.Kevin believes that while AI avatars and personalization get the attention, the real differentiator for GTM teams is how they track, store, and use data. We explore how to find and harvest demand in the watering holes your prospects already live in, why identity resolution is the "unspoken linchpin" of go-to-market, and how to stack signals to create micro-campaigns that break through the noise.In this pod, we discuss:* Why proprietary data is your biggest GTM differentiator in the age of AI* How to build micro-campaigns by stacking multiple intent signals* The rise of dark social channels and why traditional MQL tracking is no longer the entire answer* Identity resolution as the foundation for modern GTM* What GTM problems AI hasn't solved yet and why humans remain critical in the loop* How to find the specific triggers that indicate your prospects are in-market for your solutionEpisode highlights:* Kevin advocates for micro-campaigns with highly specific commonalities rather than broad campaigns covering one generic intent signal (like funding or job change). He argues that specificity is what breaks through pattern recognition in crowded inboxes.* Modern marketing's role is expanding beyond driving form fills or event badge-scans into generating trackable signals in dark social channels. Prospects often indicate they are in-market to buy on the “dark social channels” where they already operate — places like LinkedIn, Slack communities and GitHub.* Identity resolution is the unspoken linchpin of modern GTM. The increasing number of signals across dark social channels are only as valuable as your ability to tie them back to a specific user. You can level up identity resolution with modern software (like Common Room) or thoughtfully-orchestrated internal tooling.* The best GTM teams Kevin works with have a lot of first-party data, they know it's a competitive advantage, and they obsess over finding ways to put it to good use.* Kevin warns that shiny object syndrome distracts even the best companies from the fundamentals needed to build a strong foundation of buyer data, intent and identity resolution.* Kevin and his teammate Josh Lind built playgent.ai, which intakes a company’s domain and returns suggested intent-based campaigns they can run.Where to find Kevin:* LinkedIn: https://www.linkedin.com/in/kevbosaurus/Transcript details:(00:00) Introduction, Kevin’s background, and the journey to Common Room(07:46) Why data infrastructure is the real GTM differentiator(10:24) Building effective micro-campaigns through signal stacking(21:51) Real examples of non-obvious data points that drive results(24:32) Dark social content and the evolution of marketing's role(29:38) Identity resolution as the unspoken linchpin of GTM(37:12) What sets apart the best GTM organizations(42:08) Getting started when you're behind on modern GTM practices(44:25) Building playagent.ai with AI orchestration(46:48) Evaluating which GTM companies will win long-term(48:36) The most important unsolved problems in GTM tech(50:43) Favorite tools, predictions for GTM engineering and conclusionFor inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thegtmengineer.substack.com

Listen on SpotifyPatrick Spychalski is a co-founder of The Kiln, one of the original Claygencies — an agency that primarily uses Clay’s software to add value to their clients. The Kiln helps companies build scalable inbound, outbound, and data enrichment systems. He's been pushing the boundaries of GTM engineering since Clay's early days, creating innovative campaigns that go far beyond basic personalization.Patrick started as Clay's first marketing contractor when they had just four employees, building their early YouTube and LinkedIn presence before launching The Kiln. In this conversation, Patrick shares his framework for high-impact campaigns, his approach to finding GTM alpha, and why he thinks the most underutilized Clay use case has nothing to do with cold outbound.In this podcast, we discuss:* Why the offer matters more than personalization in cold outbound campaigns* How to find personalizations based on your distinct value prop* The most important questions to ask about your buyers to build winning outbound campaigns* How to learn Clay and practical tips for using the product* Why data cleaning and CRM enrichment is the biggest missed opportunity for most companies* How to structure a modern GTM organization and when to hire vs. use an agencyEpisode highlights:* Patrick built a viral campaign using Lovable's API and Clay to automatically generate custom web apps for each prospect at scale.* Patrick's framework for creative campaigns involves deeply understanding the client's value prop and available offers, then working backwards to find data points in Clay that can quantify and personalize those benefits for each prospect.* Patrick considers CRM data cleaning the most underutilized, and lowest hanging fruit, Clay use case because a clean CRM underpins your entire GTM success* Marketing leaders should use Clay for inbound lead scoring to verify they're attracting the right audience and save sales teams research time by qualifying leads and prepping call context.* Modern GTM tools enable radical automation: The Kiln rebuilt an entire recruiting firm's business function in Clay, while Patrick automated his complete sales follow-up workflow with n8n from call transcripts to proposal generation.* We discuss how to evaluate GTM engineering candidates by looking for curiosity and avoiding those stuck in outdated ways of thinking.Where to find Patrick:* LinkedIn: https://www.linkedin.com/in/patrickspychalski/* The Kiln: https://thekiln.com/Transcript details:(00:00) Introduction and Patrick's creative cold outbound campaigns(02:32) Patrick's journey from Clay contractor to co-founding The Kiln(06:57) Building custom web apps at scale with Lovable's API(11:17) Framework for coming up with creative campaign ideas(13:47) Why offers matter more than personalization(20:56) Finding the right data points for your personalizations(22:32) Tips for learning Clay and understanding APIs(28:08) Finding GTM alpha and when strategies saturate(31:33) The Kiln’s shift from cold outbound to RevOps and data cleaning(33:46) Why CRM data cleaning is massively underutilized(37:26) Inbound lead aggregation and scoring strategies(40:53) How to structure your organization for GTM success(47:25) Why n8n is Patrick's favorite underrated tool(50:19) The future of GTM engineering and conclusionFor inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thegtmengineer.substack.com

Listen on Spotify Osman Sheikhnureldin runs GTM operations at Clay, and he's redefining the role of sales and marketing ops within tech.He was the first GTM operations hire at Clay and has built the GTM ops function while Clay has grown from 25 to ~150 people. In this conversation, Osman shares his view on the modern GTM operations org, and we talk about the cutting edge data capture and automations his team has built to support Clay’s growth.In this podcast, we discuss:* Why modern GTM operations should start with data architecture, not CRM admin* How to automate the most painful parts of sales workflows using LLMs* How to extract nuanced competitor intelligence from sales calls and the value of JSON schemas* How to build and track your own custom data points and use them to create self-improving LLM-generated battle cards* Marketing’s role in the new world of GTM Engineering* How to evaluate and hire your first GTM engineerEpisode highlights:* Osman and his team automated closed won hand offs between sales and customer success team to reduce manual work and speed up the post-deal transition.* Osman uses large language models to automatically extract competitor mentions, renewal dates, and buyer pain points.* Clay has built their own custom data points to get more granular about their ICP. They track sales org maturity, who their prospects sell to, where their prospects sell, and much more.* The GTM Ops team at Clay is working to build battle cards that write themselves — closed-won (and lost) patterns are fed back into Clay’s system, allowing automated generation of pre-call prep and objection handling guides.* Marketers at Clay have built personalized landing pages to target their top enterprise prospects.* We cover how to evaluate GTM engineering candidates by testing them for curiosity and digging into how they think about creative intent signals.Where to find Osman:* LinkedIn: https://www.linkedin.com/in/osmansheikhTranscript details(00:00) Introduction to Osman, his role at Clay, and the scope of his GTM Ops team(03:17) Osmans’s career journey and GTM Ops at Clay(8:44) The ideal GTM Ops org structure with examples from Clay as they’ve grown(22:02) Automating manual workflows from the Clay sales team(26:13) Extracting competitor data from sales transcripts and the importance of JSON schemas(32:57) Lightweight data engineering tips to store data at scale(36:30) Creating and tracking custom data points for your GTM needs(43:19) Building custom, self reinforcing battle cards based on call learnings(56:44) Marketing's Role in GTM Engineering(01:03:38) Building and evaluating GTM Engineering Skills(01:14:38) The long term value of a CRM and conclusionFor inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thegtmengineer.substack.com

Listen on SpotifyTed Eltringham worked in growth at Rippling and Samsara before starting Architect to build an agentic AI website. He has been using AI in his go to market campaigns since the day that (OpenAI first model) launched. We talk about how to use AI judges to find the best models and eliminate hallucinations, his learnings from growth at Rippling & Samsara, and how to become a GTM engineer.In this podcast, we discuss:* Using AI to build automatic reply handling for cold outbound campaigns* The concept of an AI judge to pick the best models and reduce hallucinations at scale* Some of Ted’s most creative growth campaigns while at Rippling and Samsara* How to start building GTM engineering skills and evaluate those skills* One practical piece of advice every company can follow to better integrate AI in their businessEpisode highlights:* Ted built automatic reply handling for his cold outbound campaigns to reply within minutes and increase reply rates.* Finding the best AI models for your needs by finding an initial winner and comparing all new model launches against the baseline.* Ted uses multiple AI judges for any of his more complicated or important workflows. This reduces the chance of error, and it allows him to train an agent on what amazing looks like.* Using traffic accident APIs to build cold outbound campaigns at Samsara and how to build customer lookalike audiences after every closed won deal.* The concept of an AI automation engineer that can hop between different business departments to help them automate workflows and better incorporate AI.Where to find Ted Eltringham:* LinkedInTranscript details:(00:00) Introduction to Ted and the conversation(02:36) Ted's journey into growth and GTM engineering work (05:04) Incorporating AI in GTM work, including reply handling and how to structure prompts(06:43) AI in Outbound and Email Automation (09:29) Finding the best models and using AI Judges to QA at scale(15:27) The most important skills to maintain with the rise of AI (17:50) What it’s like working in growth at first growth startups and some of Ted’s most creative experiments (26:23) Building valuable GTM engineering skills (31:53) How to build the GTM engineering muscle inside of your business and evaluate talent(37:23) Underrated tools and the future of GTM engineering (40:50) Architect as the first agentic website and conclusionFor inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thegtmengineer.substack.com

Listen on SpotifyEmre Kavaloglu started Waterfall.io to help companies more efficiently and cost effectively find, store and maintain their company and contact databases. He works with some of the fastest growing companies in tech and has a comprehensive view about building your TAM (total addressable market). We get into the nitty gritty details about TAM building - from new ways to find good-fit companies, to building a system that gets smarter over time. Emre also shares his views on common misconceptions about GTM engineering and how to become an effective GTM engineer.In this podcast, we discuss:* Why most companies overlook 20–40% of their TAM—and how to find it* How to pick (or build) a data stack and why you should be thinking about opportunity cost, not price per contact* How much effort to put into TAM building based on your company size, stage, and product market fit* All of the places that a GTM engineer can add value along the entire funnel* How to build GTM engineering skills from scratch, and advice for founders looking for their first GTM engineerEpisode highlights:* Smaller business without product market fit shouldn’t be concerned about maximum TAM coverage. Instead, they can think about the need for TAM building as a function of the different active acquisition channels. For outbound or cold email, having a large database is materially more important than if you’re driving all of your revenue through partnerships.* To find the right company list for your business, you need to look beyond the basic industry, location and employee size demographics. You can find more companies by looking for good-fit contact titles (e.g. “VP of human resources”), and by deploying manual enrcihment efforts against possible-fit lists until you identify the patterns that can scale with AI.* Decide what you are going to use intent data for before spending time and money capturing it. Once you start tracking intent, aim to retrieve + act on it as quickly as possible after the signal actually happens.* GTM Engineers can (and should) be thinking about how to deliver value across the entire funnel, not just cold outbound. The fastest way to do this is to go talk to internal folks interacting with customers (customer success, AEs, AMs, etc), be curious, and find ways to add value.* If you want to build your own GTM engineering skillset - be curious, play with workflow tools like N8N, and be intentional about honing your business intuition.Where to find Emre:* LinkedIn* Waterfall.ioTranscript details:(00:00) Introduction to Emre and the conversation(05:15) Defining what GTM engineering is NOT (12:45) Framework for building and maintaining your TAM (18:29) Practical ways to increase your TAM coverage(23:55) Database hygiene best practices(25:46) The common mistakes and best practices when tracking intent signals(30:15) Using intent across the entire funnel of your business (32:05) Account prioritization frameworks (36:56) TAM building for early stage companies (39:55) Other parts of the business a GTM engineer can add value (47:29) Building GTM engineering skills (52:46) Hiring and scaling GTM engineering for founders (55:41) Underrated tools and the future of GTM engineering (59:35) Conclusion and Final ThoughtsFor inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit thegtmengineer.substack.com