The Precise Formula for Discovering Your Most Profitable Customers

Patrick McFadden • August 28, 2016

Don’t waste time marketing and selling to people who will never take your business to the next level. Save time and energy by discovering your most profitable customers.

A great deal about marketing has changed over the last few years, but mostly what’s changed is the overall way people shop and buy and that’s what you have to understand in order to thrive in the world today.

Building the trust and rapport needed to convert a lead into a client can be a slow and difficult process—especially when you as the owner or your sales team has to adjust to changes in this buying environment. But what if instead of constantly struggling uphill with unqualified leads, every prospect in your pipeline was profitable right from the start?

Impossible? Hardly. All you need is the right formula to discover what profitable clients looks like in the most specific way possible.

The secret to increasing your profitability isn’t more marketing—it’s targeting. Don’t squander your marketing budget and hundreds of hours generating leads that take your business nowhere. Find your profitable client from the outset, and everyone wins.

Segment Your Client Base

Create a spreadsheet of your clients and focus on segmenting your client base between normal accounts and your most successful accounts. Your best clients or most successful accounts should have the following two key behaviors: they are profitable and also refer business to you.

Dive Deeper

From your client base above start looking at the characteristics of these successful accounts or best clients. You’re searching for any common characteristics that are shared by this client base.

Here’s what you are deep diving for:

  • Demographics – Business2Business (B2B) demographics could be the type of industry, the job title of that individual, the years that a company has been in business, and/or revenue levels. Business2Consumer (B2C) the demographics could be age, sex, illness, income, and a particular area of town.
  • Psychographics – Understand where do they hang out, what do they read, what do they listen to, what do they search online, what makes them tick, what triggers them to go looking for a solution
  • Challenges or Problem – Marketing is about solving customer problems, whether those are problems customers are currently facing, or problems they will face as their marketplace evolves and their needs change.
  • Real Quotes – Include a few real quotes taken during your interviews that represent your persona well. This will make it easier for employees to relate to and understand your persona.

Start Building Your Profitable Client Profile

Now armed with the information of what your best clients or most successful accounts look like and their characteristics, develop a detailed profile of your profitable clients. Then, show up in the right places (social media channels, networking events, publications, search engine, mobile) at the right time (when profitable clients are looking to solve a problem or research a solution). You may be featured in fewer publications and meet with fewer people, but you’ll close more sales.

Today’s buyers require more expertise, interaction, trust, and maintenance than ever before. So don’t waste your time courting the wrong clients. Consistently add something to the conversation: leads will listen, suspects will engage, and prospects will buy. You just have to make sure you’re talking to the right people first

By Patrick McFadden May 2, 2025
Everyone is scaling outputs. Almost no one is scaling judgment.
By Patrick McFadden May 2, 2025
Ask anyone in tech where AI is headed, and they’ll tell you: “The next leap is reasoning.” “AI needs judgment.” “We need assistants that think, not just answer.” They’re right. But while everyone’s talking about it, almost no one is actually shipping it. So we did. We built Thinking OS™ —a system that doesn’t just help AI answer questions… It helps AI think like a strategist. It helps AI decide like an operator. It helps teams and platforms scale judgment, n ot just generate output. The Theory Isn’t New. The Implementation Is. The idea of layering strategic thinking and judgment into AI isn’t new in theory. The problem is, no one’s been able to implement it effectively at scale. Let’s look at the current landscape. 1. Big Tech Has the Muscle—But Not the Mind OpenAI / ChatGPT ✅ Strength: Best-in-class language generation ❌ Limitation: No built-in judgment or reasoning. You must provide the structure. Otherwise, it follows instructions, not strategy. Google DeepMind / Gemini ✅ Known for advanced decision-making (e.g., AlphaGo) ❌ But only in structured environments like games—not messy, real-world business scenarios. Anthropic (Claude), Meta (LLaMA), Microsoft Copilot ✅ Great at answering questions and following commands ❌ But they’re assistants, not advisors. They won’t reprioritize. They won’t challenge your assumptions. They don’t ask: “Is this the right move?” These tools are powerful—but they don’t think for outcomes the way a strategist or operator would. 2. Who’s Actually Building the Thinking Layer™? This is where it gets interesting—and thin. Startups and Indie Builders Some small teams are quietly: Creating custom GPTs that mimic how experts reason Layering in business context, priorities, and tradeoffs Embedding decision logic so AI can guide, not just execute But these efforts are: Highly manual Difficult to scale Fragmented and experimental Enterprise Experiments A few companies (Salesforce, HubSpot, and others) are exploring more “judgment-aware” AI copilots. These systems can: Flag inconsistencies Recommend next actions Occasionally surface priorities based on internal logic But most of it is still: In early R&D Custom-coded Unproven beyond narrow use cases That’s Why Thinking OS™ Is Different Instead of waiting for a lab to crack it, we built a modular thinking system that installs like infrastructure. Thinking OS™: Captures how real experts reason Embeds judgment into layers AI can use Deploys into tools like ChatGPT or enterprise systems Helps teams think together, consistently, at scale It’s not another assistant. It’s the missing layer that turns outputs into outcomes. So… Is This a New Innovation? Yes—in practice. Everyone says AI needs judgment. But judgment isn’t an idea. It’s a system. It requires: Persistent memory Contextual awareness Tradeoff evaluation Value-based decisions Strategy that evolves with goals Thinking OS™ delivers that. And unlike the R&D experiments in Big Tech, it’s built for: Operators Consultants Platform founders Growth-stage teams that need to scale decision quality, not just content creation If Someone Told You They’ve Built a Thinking + Judgment Layer™… They’ve built something only a handful of people in the world are even attempting. Because this isn’t just AI that speaks fluently. It’s AI that reasons, reflects , and chooses. And in a world that’s drowning in tools, judgment becomes the differentiator. That’s the OS We Built Thinking OS™ is not a prompt pack. It’s not a dashboard. It’s not a glorified chatbot. It’s a decision architecture you can license, embed, or deploy— To help your team, your platform, or your clients think better at scale. We’ve moved past content. We’re building cognition. Let’s talk.
By Patrick McFadden May 2, 2025
In every era of innovation, there’s a silent bottleneck—something obvious in hindsight, but elusive until the moment it clicks. In today’s AI-driven world, that bottleneck is clear: AI has speed. It has scale. But it doesn’t have judgment . It doesn’t really think . What’s Actually Missing From AI? When experts talk about the “thinking and judgment layer” as the next leap for AI, they’re calling out a hard truth: Modern AI systems are powerful pattern machines. But they’re missing the human layer—the one that reasons, weighs tradeoffs, and makes strategic decisions in context. Let’s break that down: 1. The Thinking Layer = Reasoning with Purpose This layer doesn’t just process inputs— it structures logic. It’s the ability to: Ask the right questions before acting Break down complexity into solvable parts Adjust direction mid-course when reality changes Think beyond “what was asked” to uncover “what really matters” Today’s AI responds. But it rarely reflects. Unless told exactly what to do, it won’t work through problems the way a strategist or operator would. 2. The Judgment Layer = Decision-Making in the Gray Judgment is the ability to: Prioritize what matters most Choose between imperfect options Make decisions when there’s no clear answer Apply values, experience, and vision—not just data It’s why a founder might not pursue a lucrative deal. Why a marketer might ignore the click-through rate. Why a strategist knows when the timing isn’t right. AI doesn’t do this well. Not yet. Because judgment requires more than data—it requires discernment . Why This Is the Bottleneck Holding Back AI AI can write. It can summarize. It can automate. But it still can’t: Diagnose the real problem behind the question Evaluate tradeoffs like a founder or operator would Recommend a path based on context, constraints, and conviction AI today is still reactive. It follows instructions. But it doesn’t lead. It doesn’t guide. It doesn’t own the outcome. And for those building serious systems—whether you’re running a company, launching a platform, or leading a team—this is the wall you eventually hit. That’s Why We Built Thinking OS™ We stopped waiting for AI to learn judgment on its own. Instead, we created a system that embeds it—by design. Thinking OS™ is an installable decision layer that captures how top founders, strategists, and operators think… …and makes that thinking repeatable , scalable , and usable inside teams, tools, and platforms. It’s not a framework. It’s not a chatbot. It’s not another playbook. It’s the layer that knows how to: Think through complex decisions Apply judgment when rules don’t help Guide others —human or AI—toward strategic outcomes This Is the Missing Infrastructure Thinking OS™ isn’t just about better answers. It’s about better thinking—made operational. And that’s what’s been missing in AI, consulting, leadership development, and platform design. If you’re trying to scale expertise, install judgment, or move from tactical to strategic… You don’t need a faster AI. You need a thinking layer that knows what to do—and why. We built it. Let’s talk.
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