The 5 Biggest Referral Marketing Mistakes Small Companies Make

Patrick McFadden • August 16, 2016

Discover five of the biggest referral marketing mistakes that can kill your referrals and erode growth.

Usually I talk about how to think and approach marketing in a different way for better results whether those results are to get more customers, increase your conversion rate, differentiate your business, create content assets, etc.

But you also need to know about the factors that will erode your chance at generating new business by referral.

If you want your referral program to thrive, watch out for these five referral marketing mistakes. Get them straightened out and you’ll get your referral program on the road to robust good health.

1. Not Expecting Referrals

The first way to screw up generating a flood of new business for your organization is not expecting referrals. What I’m really saying here is that referrals should be a condition of doing business with you.  That’s right success starts with you—not the company, not your employees, and not your customers. From now on your mindset must be that you deserve referrals and you’re doing a disservice to your customers and strategic partners by not providing an easy way for their clients, family and friends to experience the same value or receive the same result as others.

2. Using a One Size Fits All Approach

Don’t be lazy and treat everyone the same.  Your customers have very different reasons and ways they would like to refer you than your strategic partners. You need a whole separate approach and offers for customers and strategic partners. Create a targeted approach and offer for your most successful clients and related businesses who can be motivated to refer.

3. Saying That Anyone Who Has Money is a Referral

Want to receive the worst referral possible and waste your time? Just mention that anyone is a referral for you. You’ll find it very true, that what you communicate out in the world is what you get in return. You can’t blame your referral source for sending that bad referral. You can only blame yourself.

We can’t or shouldn’t ask for referrals anyway, until we tell our referral sources in great detail  – exactly who makes a great referral for your business, why you deserve referrals, and what you are going to do with that referral.

4. Showing No Appreciation For the Attempt

This is something most marketing consultants don’t talk about: keeping your referral source motivated for the next one. Quite frankly I just have a different point-of-view on it. I believe that showing your appreciation for a referral that DOES NOT turn into a customer is one of the best ways to keep a source motivated to give you the next one. People who go out of their way to voluntarily tell someone about your business are generally motivated to do so because they like doing it rather than expecting something in return, but a simple show of appreciation is always a must.

5. Asking For Referrals

It’s been noted that, “you get more referrals by asking.” While I believe this is true, most dismiss the approach of what to ask for. Asking for referrals can get you plenty but the real point is to turn them into a new client.

The mistake most people make in the business of referrals is to ask for leads or referrals when they should be asking for introductions. If you want to make referrals a significant part of your lead generation you need to start asking for introductions and not just a list of names.

3 Keys to Asking Success

  1. The best time to ask for an introduction is when a client of yours is saying nice things about you. All you need to do to get the introduction is to then ask them how well they know ‘person x,’ and whether they feel able to give you an introduction.
  2. Provide a valuable piece of content to start the introduction. Being introduced is no guarantee that a relationship will then start to build. Therefore, you need to provide value and credibility before you actually meet. Never assume that the other person will take you up on your introduction.
  3. Instead of asking for a referral, ask for their advice. “I love working with people like you, particularly people who have x challenge. If you were in my position, what would you do to find more clients like you?”

The Missing Layer in AI: Why Judgment, Not Just Data, Will Define the Next Era of Intelligence

By Patrick McFadden • May 2, 2025
Everyone is scaling outputs. Almost no one is scaling judgment.

Everyone Talks About AI Judgment. We Built It.

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.

We Found the Missing Layer AI Has Been Waiting For

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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