{"id":19174,"date":"2025-12-16T08:30:47","date_gmt":"2025-12-16T13:30:47","guid":{"rendered":"https:\/\/www.data-mania.com\/blog\/?p=19174"},"modified":"2026-03-17T23:41:59","modified_gmt":"2026-03-18T03:41:59","slug":"the-20m-lesson-why-go-to-market-is-now-your-biggest-bottleneck","status":"publish","type":"post","link":"https:\/\/www.data-mania.com\/blog\/the-20m-lesson-why-go-to-market-is-now-your-biggest-bottleneck\/","title":{"rendered":"The $20M Lesson: Why Go-To-Market Is Now Your Biggest Bottleneck"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">George Rekouts hit $2 million in revenue in under a year.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">His first company, Mad Objective, was crushing it. IP-to-company mapping for visitor intelligence. Back when the category was still new, still exciting. He&#8217;d partnered with a distributor who had an established customer base. Sales came fast. Growth felt inevitable.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Then the distributor wanted to sell.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">George had no choice but to exit. No independent brand. No direct sales motion. Complete dependency on the partner who&#8217;d gotten him to $2M so fast.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">He watched the company grow to <\/span><b>over $20 million<\/b><span style=\"font-weight: 400;\"> post-acquisition.<\/span><\/p>\n<p><b>Here&#8217;s what I keep thinking about:<\/b><span style=\"font-weight: 400;\"> George didn&#8217;t fail. He succeeded fast, then got locked out of the upside because he&#8217;d outsourced the one function he thought he could afford to ignore: go-to-market.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Now he&#8217;s building <\/span><a href=\"https:\/\/discolike.com\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">DiscoLike<\/span><\/a><span style=\"font-weight: 400;\">, and this time he&#8217;s doing it differently. But the lesson he learned isn&#8217;t just about partnerships. <\/span><b>It&#8217;s about a fundamental shift happening right now across B2B: engineering used to be the bottleneck. Now it&#8217;s go-to-market.<img decoding=\"async\" class=\"aligncenter size-large wp-image-19180 lazyload\" data-src=\"https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2025\/12\/openart-ill-analyze-this-article-and-create-4-detailed-design-briefs-for-infographgrey-a9a9a8-icons-at-top-of-each-column-should-be-same-size-and-centered_eJX5i66h_upscaled-1024x765.jpg\" alt=\"\" width=\"800\" height=\"598\" data-srcset=\"https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2025\/12\/openart-ill-analyze-this-article-and-create-4-detailed-design-briefs-for-infographgrey-a9a9a8-icons-at-top-of-each-column-should-be-same-size-and-centered_eJX5i66h_upscaled-1024x765.jpg 1024w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2025\/12\/openart-ill-analyze-this-article-and-create-4-detailed-design-briefs-for-infographgrey-a9a9a8-icons-at-top-of-each-column-should-be-same-size-and-centered_eJX5i66h_upscaled-300x224.jpg 300w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2025\/12\/openart-ill-analyze-this-article-and-create-4-detailed-design-briefs-for-infographgrey-a9a9a8-icons-at-top-of-each-column-should-be-same-size-and-centered_eJX5i66h_upscaled-768x573.jpg 768w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2025\/12\/openart-ill-analyze-this-article-and-create-4-detailed-design-briefs-for-infographgrey-a9a9a8-icons-at-top-of-each-column-should-be-same-size-and-centered_eJX5i66h_upscaled-90x67.jpg 90w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2025\/12\/openart-ill-analyze-this-article-and-create-4-detailed-design-briefs-for-infographgrey-a9a9a8-icons-at-top-of-each-column-should-be-same-size-and-centered_eJX5i66h_upscaled-1536x1147.jpg 1536w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2025\/12\/openart-ill-analyze-this-article-and-create-4-detailed-design-briefs-for-infographgrey-a9a9a8-icons-at-top-of-each-column-should-be-same-size-and-centered_eJX5i66h_upscaled-2048x1529.jpg 2048w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2025\/12\/openart-ill-analyze-this-article-and-create-4-detailed-design-briefs-for-infographgrey-a9a9a8-icons-at-top-of-each-column-should-be-same-size-and-centered_eJX5i66h_upscaled-600x448.jpg 600w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2025\/12\/openart-ill-analyze-this-article-and-create-4-detailed-design-briefs-for-infographgrey-a9a9a8-icons-at-top-of-each-column-should-be-same-size-and-centered_eJX5i66h_upscaled-869x649.jpg 869w\" data-sizes=\"auto, (max-width: 800px) 100vw, 800px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 800px; --smush-placeholder-aspect-ratio: 800\/598;\" \/><\/b><\/p>\n<p><span style=\"font-weight: 400;\">And if you&#8217;re still relying on<\/span><a href=\"https:\/\/www.apollo.io\" target=\"_blank\" rel=\"noopener\"> <span style=\"font-weight: 400;\">Apollo<\/span><\/a><span style=\"font-weight: 400;\"> or LinkedIn data to fuel your outbound, you&#8217;re leaving money on the table in ways you probably don&#8217;t realize.<\/span><\/p>\n<h2><b>Apollo Isn&#8217;t Broken. But Here&#8217;s the Opportunity Loss<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Let&#8217;s get something straight: Apollo works. Thousands of companies use it successfully. George uses it. I&#8217;m not here to trash a tool that clearly has product-market fit.<\/span><\/p>\n<p><b>The real question is: what are you missing?<\/b><\/p>\n<p><b>Here&#8217;s what might surprise you:<\/b><span style=\"font-weight: 400;\"> LinkedIn covers about <\/span><b>one-third of the addressable market.<\/b><\/p>\n<p><span style=\"font-weight: 400;\">If you&#8217;re selling dev tools to Series A startups in SF, NYC, or Austin? Great. Apollo is probably fine. Those founders live on LinkedIn. They update their profiles. They care.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">But step outside that bubble and the coverage drops off a cliff.<\/span><\/p>\n<p><img decoding=\"async\" class=\"aligncenter size-large wp-image-19178 lazyload\" data-src=\"https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2025\/12\/openart-create-a-horizontal-instagramlinkedin-infographic-1350x1080px-54-landscape-layout-works-better-for-the-data-visualization-to-show-all-stats-at-once_Va0njfj-_upscaled-1024x765.jpg\" alt=\"\" width=\"800\" height=\"598\" data-srcset=\"https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2025\/12\/openart-create-a-horizontal-instagramlinkedin-infographic-1350x1080px-54-landscape-layout-works-better-for-the-data-visualization-to-show-all-stats-at-once_Va0njfj-_upscaled-1024x765.jpg 1024w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2025\/12\/openart-create-a-horizontal-instagramlinkedin-infographic-1350x1080px-54-landscape-layout-works-better-for-the-data-visualization-to-show-all-stats-at-once_Va0njfj-_upscaled-300x224.jpg 300w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2025\/12\/openart-create-a-horizontal-instagramlinkedin-infographic-1350x1080px-54-landscape-layout-works-better-for-the-data-visualization-to-show-all-stats-at-once_Va0njfj-_upscaled-768x573.jpg 768w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2025\/12\/openart-create-a-horizontal-instagramlinkedin-infographic-1350x1080px-54-landscape-layout-works-better-for-the-data-visualization-to-show-all-stats-at-once_Va0njfj-_upscaled-90x67.jpg 90w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2025\/12\/openart-create-a-horizontal-instagramlinkedin-infographic-1350x1080px-54-landscape-layout-works-better-for-the-data-visualization-to-show-all-stats-at-once_Va0njfj-_upscaled-1536x1147.jpg 1536w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2025\/12\/openart-create-a-horizontal-instagramlinkedin-infographic-1350x1080px-54-landscape-layout-works-better-for-the-data-visualization-to-show-all-stats-at-once_Va0njfj-_upscaled-2048x1529.jpg 2048w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2025\/12\/openart-create-a-horizontal-instagramlinkedin-infographic-1350x1080px-54-landscape-layout-works-better-for-the-data-visualization-to-show-all-stats-at-once_Va0njfj-_upscaled-600x448.jpg 600w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2025\/12\/openart-create-a-horizontal-instagramlinkedin-infographic-1350x1080px-54-landscape-layout-works-better-for-the-data-visualization-to-show-all-stats-at-once_Va0njfj-_upscaled-869x649.jpg 869w\" data-sizes=\"auto, (max-width: 800px) 100vw, 800px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 800px; --smush-placeholder-aspect-ratio: 800\/598;\" \/><\/p>\n<p><span style=\"font-weight: 400;\">Selling to legal? Construction? Medical devices? <\/span><b>LinkedIn penetration in those verticals is dramatically lower.<\/b><span style=\"font-weight: 400;\"> Lawyers and doctors don&#8217;t prioritize LinkedIn the way engineers and marketers do.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">And internationally? Forget it.<\/span><\/p>\n<p><b>LinkedIn company data\u00a0 by region:<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Germany and Norway: Weak<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">France: Better<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Japan: Nearly non-existent<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Asia-Pacific overall: Tiny sliver<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">George puts it bluntly: &#8220;If you don\u2019t target accounts outside of LinkedIn, you&#8217;re gonna be missing a lot.&#8221;<\/span><\/p>\n<p><b>Then there&#8217;s the accuracy problem.<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Here&#8217;s something that shocked me: the entire B2B data industry is powered by LinkedIn scrapes from essentially two top suppliers. That&#8217;s why every vendor claims &#8220;35 million companies&#8221; on their homepage. They&#8217;re all buying from the same source.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">When George runs domain status checks on these datasets, he consistently finds <\/span><b>20-28% of domains are no good.<\/b><span style=\"font-weight: 400;\"> Parked. Redirected to a new company name. Dead.<\/span><\/p>\n<p><b>One-fourth of your list is wasted before you even hit send.<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Think about that for a second. You&#8217;re paying for data, building sequences, personalizing copy. And <\/span><b>25% of it is going into a black hole.<\/b><\/p>\n<p><b>In other words:<\/b><span style=\"font-weight: 400;\"> Apollo isn&#8217;t broken. But if you&#8217;re outside the LinkedIn-heavy tech ecosystem, or if you&#8217;re trying to reach international markets, you&#8217;re operating with a massive blind spot and a significant data decay tax.<\/span><\/p>\n<h2><b>The Relevancy Trap: Why Keywords Can&#8217;t Capture Your ICP<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Even if Apollo&#8217;s coverage was perfect, there&#8217;s a deeper problem. <\/span><b>Keyword-based search forces you into spray-and-pray.<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Let me show you what I mean.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Say you&#8217;re selling to medical device companies. Is that &#8220;healthcare&#8221;? &#8220;Manufacturing&#8221;? &#8220;Software&#8221;? You&#8217;re stuck choosing a category that doesn&#8217;t actually capture what makes your ICP unique.<\/span><\/p>\n<p><b>Here&#8217;s where it gets worse:<\/b><span style=\"font-weight: 400;\"> A company making blood test equipment is radically different from a company building lung machines. Which is different from an EKG device manufacturer. But in a keyword-driven system, they&#8217;re all bucketed together.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">You end up with two terrible options:<\/span><\/p>\n<ol>\n<li><b> Go too broad<\/b><span style=\"font-weight: 400;\"> (search &#8220;manufacturing&#8221;) and drown in noise. CNC machining shops, packaging companies, industrial suppliers. None of whom care about your product.<\/span><\/li>\n<li><b> Go too narrow<\/b><span style=\"font-weight: 400;\"> (hyper-specific keywords) and miss half your addressable market because you couldn&#8217;t predict every term your ICP might use.<\/span><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">George&#8217;s take: &#8220;You need a semantic layer for search. You need a model that understands the concept, not just keywords or rigid categories.&#8221;<\/span><\/p>\n<p><b>Or put another way:<\/b><span style=\"font-weight: 400;\"> Traditional search tools assume your ICP can be described with a few industry tags and keywords. But real buyer intent doesn&#8217;t work that way. You need something that understands <\/span><i><span style=\"font-weight: 400;\">what a company does<\/span><\/i><span style=\"font-weight: 400;\">, not just <\/span><i><span style=\"font-weight: 400;\">what box they checked on their LinkedIn profile.<\/span><\/i><\/p>\n<h2><b>How Disco Sees 680 Million Secure Websites (And Why It Wasn&#8217;t Possible 5 Years Ago)<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Here&#8217;s where George&#8217;s story gets interesting.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Disco&#8217;s data advantage comes from something I&#8217;d never heard of in this context: SSL certificate infrastructure.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">You know that little lock icon in your browser? The one that says a site is secure? That&#8217;s an SSL certificate. And to get one, you have to prove to a certificate authority that you own the domain. No faking it.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This is the same technology that protects your banking transactions and Bitcoin trades. It&#8217;s bulletproof.<\/span><\/p>\n<p><b>Google forced everyone to switch to HTTPS<\/b><span style=\"font-weight: 400;\"> over the past several years. If you don&#8217;t have SSL, browsers block your site with scary warnings. So nearly every commercial website globally had to get a certificate.<\/span><\/p>\n<p><b>Here&#8217;s the hack:<\/b><span style=\"font-weight: 400;\"> Disco partnered with certificate authorities. They help flag malicious domains and fraud. In exchange, they get a real-time feed of <\/span><b>every secure website that goes live.<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Within <\/span><b>10 minutes<\/b><span style=\"font-weight: 400;\"> of a site launching, Disco sees it.<\/span><\/p>\n<p><b>680 million secure websites.<\/b><span style=\"font-weight: 400;\"> About <\/span><b>68 million<\/b><span style=\"font-weight: 400;\"> of those are commercial sites (after an LLM classifier filters out blogs, personal sites, etc.).<\/span><\/p>\n<p><b>Think about what this means:<\/b><span style=\"font-weight: 400;\"> While Apollo is scraping LinkedIn profiles that might be months out of date, Disco is watching the entire internet in near-real-time through first-party infrastructure.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">George told me this wouldn&#8217;t have been possible even five years ago. The HTTPS mandate created the conditions for this data advantage. It&#8217;s an infrastructure-level moat that&#8217;s incredibly hard to replicate.<\/span><\/p>\n<p><b>The hard part is:<\/b><span style=\"font-weight: 400;\"> This isn&#8217;t cheap. Disco&#8217;s hardware footprint (GPUs for LLM processing plus petabytes of storage) is much higher than most B2B SaaS startups. They underestimated the cost.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">But the result is a dataset that&#8217;s fundamentally different from anything built on scraped LinkedIn and Google Maps data.<\/span><\/p>\n<h2><b>Why Disco Built a Custom LLM (And Why It&#8217;s Easier Than You Think)<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">When George mentioned &#8220;custom LLM,&#8221; I assumed he meant something on the scale of Bloomberg or OpenAI. Years of R&amp;D, massive compute budgets, the whole thing.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Turns out, it&#8217;s not like that at all.<\/span><\/p>\n<p><b>Here&#8217;s what most people get wrong about large language models:<\/b><span style=\"font-weight: 400;\"> We think &#8220;LLM&#8221; means &#8220;<\/span><a href=\"https:\/\/chat.openai.com\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">ChatGPT<\/span><\/a><span style=\"font-weight: 400;\">.&#8221; We think it means chatbots.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">But LLMs existed before ChatGPT, and they can do a lot more than generate text. Classification. Data extraction. And in Disco&#8217;s case: <\/span><b>search.<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Disco didn&#8217;t build a reasoning engine. They built a <\/span><b>search engine.<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Here&#8217;s how it works:<\/span><\/p>\n<p><b>Step 1:<\/b><span style=\"font-weight: 400;\"> Grab text from a website and convert it into embeddings (numerical representations of meaning).<\/span><\/p>\n<p><b>Step 2:<\/b><span style=\"font-weight: 400;\"> Run a similarity search across embeddings from your search query and <\/span><b>68 million business websites<\/b><span style=\"font-weight: 400;\">.<\/span><\/p>\n<p><b>Step 3:<\/b><span style=\"font-weight: 400;\"> Return top results before similarity drop, as the closest conceptual matches.<\/span><\/p>\n<p><b>The difference between chat and search models:<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Chat models<\/b><span style=\"font-weight: 400;\"> (like ChatGPT) use embeddings to predict the next token. They&#8217;re trained to generate text word by word.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Search models<\/b><span style=\"font-weight: 400;\"> (like Disco&#8217;s) compare embeddings. They&#8217;re trained to find similarity, not generate sentences.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Same foundational architecture (LLM embeddings). Different inference path.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">George&#8217;s point: &#8220;Building embeddings isn&#8217;t hard. You can use existing models and apply additional transfer learning, in Disco\u2019s case focusing on business-specific data. The trick is swapping inference from &#8216;next token prediction&#8217; to &#8216;similarity matching.&#8217;&#8221;<\/span><\/p>\n<p><b>What this unlocks:<\/b><span style=\"font-weight: 400;\"> You can search by <\/span><i><span style=\"font-weight: 400;\">concept<\/span><\/i><span style=\"font-weight: 400;\">, not just keywords. You describe what you&#8217;re looking for in plain language. &#8220;Medical device companies specializing in diagnostic imaging for hospitals.&#8221; And the model understands the semantic intent, not just the literal words.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This is why keyword search breaks down and semantic search works.<\/span><\/p>\n<h2><b>The Clustering Reveal: Why You Don&#8217;t Know Your Best Customers<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Here&#8217;s my favorite story from the conversation.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">One of Disco&#8217;s clients sells VCR software for commercial video recording. Think hundreds of cameras, simultaneous multichannel recording. They had about <\/span><b>8,000 customers.<\/b><\/p>\n<p><span style=\"font-weight: 400;\">For years, they were convinced police stations were their number one customer. That&#8217;s where the memorable deals came from. That&#8217;s who they pitched to investors. That&#8217;s how they thought about their market.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">George ran their customer list through Disco&#8217;s clustering model.<\/span><\/p>\n<p><b>The results:<\/b><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Shopping malls<\/b><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Commercial parking lots<\/b><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Hospitals<\/b><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Warehousing and manufacturing<\/b><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Police stations<\/b><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">Police were a <\/span><b>distant fifth.<\/b><\/p>\n<p><span style=\"font-weight: 400;\">The founders had no idea. They&#8217;d been operating on anecdotal evidence. Memorable sales conversations, not data. And with 8,000 accounts, there&#8217;s no way to manually cluster and spot the pattern.<\/span><\/p>\n<p><b>Here&#8217;s what makes this possible:<\/b><span style=\"font-weight: 400;\"> Disco uses a specialized clustering model (not ChatGPT, which George says chokes beyond about <\/span><b>50-100 companies<\/b><span style=\"font-weight: 400;\">). The model segments your customer list automatically, revealing which verticals actually drive revenue.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Once you know your top segments, you run similarity search for each one. Upload five example domains from your best segment, generate an ICP description, and Disco finds precise matches across its 68 million commercial sites in 48 languages<\/span><\/p>\n<p><b>The workflow:<\/b><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Segment existing customers<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Discover hidden revenue drivers<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Run lookalike search per segment<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Export hyper-targeted prospect lists<\/span><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">This is the opposite of spray-and-pray. This is surgical.<\/span><\/p>\n<h2><b>Steal This: The One-Evening Validation Framework<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">George wanted to test whether open-source intelligence companies would buy Disco&#8217;s data as a side revenue stream.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Instead of spending weeks researching the market, he did this:<\/span><\/p>\n<p><b>Evening 1:<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Used Disco to find 20 OSINT companies<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Messaged founders on LinkedIn with a simple pitch<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Got responses within minutes<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">That&#8217;s it. One evening. He validated (or invalidated) an entire vertical.<\/span><\/p>\n<p><b>Here&#8217;s the framework George uses:<\/b><\/p>\n<p><img decoding=\"async\" class=\"aligncenter size-large wp-image-19179 lazyload\" data-src=\"https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2025\/12\/openart-create-a-vertical-instagramlinkedin-infographic-1080x1350px-45-portrait-wi-dominating-step-number-boxes-on-the-left-should-be-compact-but-readable_wiSS7j1__upscaled-1024x765.jpg\" alt=\"\" width=\"800\" height=\"598\" data-srcset=\"https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2025\/12\/openart-create-a-vertical-instagramlinkedin-infographic-1080x1350px-45-portrait-wi-dominating-step-number-boxes-on-the-left-should-be-compact-but-readable_wiSS7j1__upscaled-1024x765.jpg 1024w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2025\/12\/openart-create-a-vertical-instagramlinkedin-infographic-1080x1350px-45-portrait-wi-dominating-step-number-boxes-on-the-left-should-be-compact-but-readable_wiSS7j1__upscaled-300x224.jpg 300w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2025\/12\/openart-create-a-vertical-instagramlinkedin-infographic-1080x1350px-45-portrait-wi-dominating-step-number-boxes-on-the-left-should-be-compact-but-readable_wiSS7j1__upscaled-768x573.jpg 768w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2025\/12\/openart-create-a-vertical-instagramlinkedin-infographic-1080x1350px-45-portrait-wi-dominating-step-number-boxes-on-the-left-should-be-compact-but-readable_wiSS7j1__upscaled-90x67.jpg 90w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2025\/12\/openart-create-a-vertical-instagramlinkedin-infographic-1080x1350px-45-portrait-wi-dominating-step-number-boxes-on-the-left-should-be-compact-but-readable_wiSS7j1__upscaled-1536x1147.jpg 1536w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2025\/12\/openart-create-a-vertical-instagramlinkedin-infographic-1080x1350px-45-portrait-wi-dominating-step-number-boxes-on-the-left-should-be-compact-but-readable_wiSS7j1__upscaled-2048x1529.jpg 2048w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2025\/12\/openart-create-a-vertical-instagramlinkedin-infographic-1080x1350px-45-portrait-wi-dominating-step-number-boxes-on-the-left-should-be-compact-but-readable_wiSS7j1__upscaled-600x448.jpg 600w, https:\/\/www.data-mania.com\/blog\/wp-content\/uploads\/2025\/12\/openart-create-a-vertical-instagramlinkedin-infographic-1080x1350px-45-portrait-wi-dominating-step-number-boxes-on-the-left-should-be-compact-but-readable_wiSS7j1__upscaled-869x649.jpg 869w\" data-sizes=\"auto, (max-width: 800px) 100vw, 800px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 800px; --smush-placeholder-aspect-ratio: 800\/598;\" \/><\/p>\n<h3><b>Step 1: Find 20 Hyper-Targeted Companies<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Not 1,000. Not 500. Just <\/span><b>20 companies<\/b><span style=\"font-weight: 400;\"> that perfectly match your ICP for a specific segment.<\/span><\/p>\n<h3><b>Step 2: Message Founders on LinkedIn<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Use the <\/span><b>2-2-1 structure:<\/b><\/p>\n<p><b>First 2 lines:<\/b><span style=\"font-weight: 400;\"> Hook them. State the problem or opportunity.<\/span><\/p>\n<p><b>Next 2 lines:<\/b><span style=\"font-weight: 400;\"> Show how you&#8217;re different.<\/span><\/p>\n<p><b>1 CTA:<\/b><span style=\"font-weight: 400;\"> Low-friction value offer. No hard sell.<\/span><\/p>\n<p><b>George&#8217;s example:<\/b><\/p>\n<p><i><span style=\"font-weight: 400;\">&#8220;You&#8217;re targeting one-third of your market with LinkedIn data. I can show you 60% more. Want me to prove it? No strings attached.&#8221;<\/span><\/i><\/p>\n<h3><b>Step 3: Measure Response<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Out of 20 messages, George typically sees:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>12 connections<\/b><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>6 responses<\/b><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">If your ICP is tight, the response rate is shocking. If you get silence, your targeting is off. Or the vertical doesn&#8217;t care about your problem. Either way, you know within hours, not months.<\/span><\/p>\n<h3><b>Step 4: Iterate or Pivot<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">If it resonates, go deeper. If not, test the next vertical tomorrow night.<\/span><\/p>\n<p><b>George&#8217;s philosophy:<\/b><span style=\"font-weight: 400;\"> &#8220;People overthink how easy this is. You have your offer. You find the companies. You ping the best 20. Done.&#8221;<\/span><\/p>\n<h2><b>The Bottleneck Just Flipped: Engineering to Go-To-Market<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Here&#8217;s the shift George sees happening right now:<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For <\/span><b>25-30 years<\/b><span style=\"font-weight: 400;\">, engineering was the bottleneck. Every other function (sales, marketing, ops) moved at the speed of the product team. You had to wait for engineers to build the thing before you could sell it.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">AI changed that.<\/span><\/p>\n<p><b>You can build a product in six months now.<\/b><span style=\"font-weight: 400;\"> Wrap ChatGPT. Launch an MVP. Validate quickly. Maybe you swap in a custom model later, maybe you don&#8217;t. The point is: the product isn&#8217;t the constraint anymore.<\/span><\/p>\n<p><b>The new bottleneck is go-to-market.<\/b><span style=\"font-weight: 400;\"> Reaching the right users. Testing messaging. Finding your best segments. Distribution.<\/span><\/p>\n<p><b>Think you need the perfect product before reaching out?<\/b><span style=\"font-weight: 400;\"> Here&#8217;s why that&#8217;s costing you months of learning.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">George&#8217;s advice to founders: <\/span><i><span style=\"font-weight: 400;\">&#8220;Start reaching out as soon as you can. Literally, don&#8217;t be shy. Think of the vertical, build the list, test it. You can validate a vertical in one evening.&#8221;<\/span><\/i><\/p>\n<p><b>The hard part is:<\/b><span style=\"font-weight: 400;\"> Most technical founders resist this (myself included, as a licensed professional engineer). We want the product to be perfect first. We want elegant architecture. We want to solve hard technical problems.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">But if no one knows you exist, none of that matters.<\/span><\/p>\n<p><b>Or put another way:<\/b><span style=\"font-weight: 400;\"> Stop perfecting your product. Start testing your market.<\/span><\/p>\n<h2><b>The List-First Cold Outreach Formula<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">I&#8217;ll be honest. I&#8217;ve always been skeptical of cold outreach. I&#8217;ve built my career on inbound. I get hundreds of cold emails a week, and most of them annoy me.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">But George&#8217;s framework made me rethink this.<\/span><\/p>\n<p><b>His thesis: List quality matters 10x more than copy quality. Your list is the message<\/b><\/p>\n<p><span style=\"font-weight: 400;\">If you&#8217;re reaching the right people who have a real problem you can solve, even mediocre copy works. They&#8217;ll respond because the timing is right, the fit is obvious, and you&#8217;re offering something they actually need.<\/span><\/p>\n<p><b>Here&#8217;s the structure George uses:<\/b><\/p>\n<h3><b>First 2 Lines: Hook Them<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">People won&#8217;t read beyond two lines unless you nail the hook. State the problem or opportunity clearly. No fluff.<\/span><\/p>\n<p><b>Example:<\/b> <i><span style=\"font-weight: 400;\">&#8220;Most dev tool companies are only reaching one-third of their addressable market because they rely on LinkedIn data.&#8221;<\/span><\/i><\/p>\n<h3><b>Next 2 Lines: Show Differentiation<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">How are you different? What can you do that they can&#8217;t get elsewhere?<\/span><\/p>\n<p><b>Example:<\/b> <i><span style=\"font-weight: 400;\">&#8220;We use SSL certificate infrastructure to see 68 million business websites in real-time. Including the two-thirds LinkedIn doesn&#8217;t cover.&#8221;<\/span><\/i><\/p>\n<h3><b>CTA: Low-Friction Value Offer<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Don&#8217;t go for the hard sell. Offer value with no strings attached.<\/span><\/p>\n<p><b>George&#8217;s go-to:<\/b> <i><span style=\"font-weight: 400;\">&#8220;How about we test it and you see if you find more data with us? No commitment, just proof.&#8221;<\/span><\/i><\/p>\n<p><b>The psychology here:<\/b><span style=\"font-weight: 400;\"> You&#8217;re not asking them to buy. You&#8217;re offering to prove your claim. If your targeting is right, they&#8217;ll want to see the proof.<\/span><\/p>\n<p><b>George&#8217;s hit rate:<\/b><span style=\"font-weight: 400;\"> 20 messages \u2192 12 connections \u2192 6 responses (when ICP targeting is tight).<\/span><\/p>\n<p><b>The insight I&#8217;m taking away:<\/b><span style=\"font-weight: 400;\"> I&#8217;ve been so focused on perfecting inbound funnels that I dismissed outbound entirely. But if you&#8217;re validating messaging or testing new segments, George&#8217;s framework is faster and cheaper than running ads or paying for focus groups.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">You just need the discipline to keep your list hyper-targeted.<\/span><\/p>\n<h2><b>What George Would Tell His 2015 Self<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">We circled back to the Mad Objective story at the end of our conversation.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">I asked George what he&#8217;d tell his 2015 self. The version of him who was about to partner with that distributor and race to $2 million in under a year.<\/span><\/p>\n<p><b>His answer: &#8220;Own your go-to-market. Don&#8217;t outsource it, no matter how tempting the short-term speed is.&#8221;<\/b><\/p>\n<p><span style=\"font-weight: 400;\">The distributor gave him instant access to customers. It felt like a shortcut. And it was. Until it wasn&#8217;t. When they sold, George had no leverage. No independent brand. No way to keep building.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">He left money on the table. A lot of it.<\/span><\/p>\n<p><b>George&#8217;s other lesson on partnerships:<\/b><span style=\"font-weight: 400;\"> If you have first-party data, sell it yourself. Don&#8217;t give it to someone else to monetize while they collect the margin. If you need data, buy it. Don&#8217;t build in-house unless it&#8217;s your core differentiator. Focus on what makes you unique.<\/span><\/p>\n<p><b>Here&#8217;s the broader lesson:<\/b><span style=\"font-weight: 400;\"> The bottleneck shifted from engineering to go-to-market, but most founders are still operating like it&#8217;s 2015. They&#8217;re perfecting the product, optimizing the architecture, waiting for the right moment to &#8220;do marketing.&#8221;<\/span><\/p>\n<p><span style=\"font-weight: 400;\">But the founders who win now are the ones who embrace distribution from day one. Who test verticals in an evening. Who build their own customer relationships instead of depending on partners.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">George&#8217;s second time around, he&#8217;s doing it differently. Product-led growth. Direct user acquisition. No dependencies. And a dataset that&#8217;s genuinely differentiated because it&#8217;s built on first-party infrastructure, not scraped LinkedIn profiles.<\/span><\/p>\n<h2><b>P.S. The Question I Didn&#8217;t Ask<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">After we stopped recording, I kept thinking about something George said: <\/span><i><span style=\"font-weight: 400;\">&#8220;You can test a vertical in one evening.&#8221;<\/span><\/i><\/p>\n<p><span style=\"font-weight: 400;\">I&#8217;ve spent years building inbound funnels. SEO. Content. Paid ads. All of it designed to attract the right people over time. And it works. But it&#8217;s slow.<\/span><\/p>\n<p><b>What if I&#8217;m overthinking it?<\/b><\/p>\n<p><span style=\"font-weight: 400;\">What if the fastest way to validate messaging isn&#8217;t running A\/B tests on landing pages? What if it&#8217;s just finding 20 people in my ICP and asking them directly?<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Last Tuesday I pulled a list of 18 companies in a vertical I&#8217;ve been curious about. Messaged their founders. Got 7 replies in 36 hours. Three wanted to see demos. Two became paying customers within a week.<\/span><\/p>\n<p><b>Here&#8217;s what I learned:<\/b><span style=\"font-weight: 400;\"> I&#8217;ve been hiding behind &#8220;perfect product development&#8221; when what I really needed was just to talk to people.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">If you want to try George&#8217;s framework, here&#8217;s where to start:<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Use<\/span><a href=\"https:\/\/discolike.com\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\"> DiscoLike<\/span><\/a><span style=\"font-weight: 400;\"> (or any tool that lets you build hyper-targeted lists) to find 20 perfect-fit companies<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Message their founders on LinkedIn with the 2-2-1 structure<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Measure response rate within 48 hours<\/span><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">If you get crickets, your targeting or messaging is off. If you get replies, you&#8217;ve validated something real.<\/span><\/p>\n<p><b>The hard part is:<\/b><span style=\"font-weight: 400;\"> You have to be willing to hear &#8220;no&#8221; quickly instead of hiding behind perfect product development.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">But that&#8217;s the shift. That&#8217;s the new bottleneck.<\/span><\/p>\n<p><b>Want to see how Disco works?<\/b><span style=\"font-weight: 400;\"> They have pre-configured sample queries on their site so you can test the output before committing. No free trial anymore (George got burned by people mining their GPUs), but you can browse sample data to see if it&#8217;s a fit.<\/span><\/p>\n<p><a href=\"https:\/\/discolike.com\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">Check out DiscoLike here<\/span><\/a><span style=\"font-weight: 400;\"> |<\/span><a href=\"https:\/\/www.linkedin.com\/in\/grekouts\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\"> Connect with George on LinkedIn<\/span><\/a><\/p>\n<hr\/>\n<p><em>Want a clean, repeatable system for measuring B2B growth? Get the free <a href=\"https:\/\/www.data-mania.com\/growth-metrics-os-email-course\/\"><strong>Growth Metrics OS<\/strong><\/a> \u2014 a 6-day email course for technical founders and operators who want to measure growth and make better decisions.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Most founders think Apollo data is good enough. Here&#8217;s what that assumption actually costs you.<\/p>\n","protected":false},"author":1,"featured_media":19178,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[830],"tags":[],"class_list":["post-19174","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-convergence-feature"],"_links":{"self":[{"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/posts\/19174","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/comments?post=19174"}],"version-history":[{"count":2,"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/posts\/19174\/revisions"}],"predecessor-version":[{"id":20407,"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/posts\/19174\/revisions\/20407"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/media\/19178"}],"wp:attachment":[{"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/media?parent=19174"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/categories?post=19174"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.data-mania.com\/blog\/wp-json\/wp\/v2\/tags?post=19174"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}