Cracking the Code: 7 Elements of Google Reviews That Improve Your Local Ranking

Patrick McFadden • January 4, 2024

Have you ever heard that your customers leaving a review on Google has no real impact on local search rankings and visibility?


Well, hold on to your screen because the truth is, these reviews pack a significant punch, and getting that coveted Google 5-star review should be a cornerstone in your local SEO strategy.

Now, before we dive into the nitty-gritty of how Google reviews affect local search rankings, let's dispel a big myth. Stuffing a bunch of keywords in owner responses to reviews won't make your business skyrocket through the rankings any faster. While it won't do much from an Local SEO ranking perspective, it does add a touch of finesse and acts as a reputation management element on Google, offering a know, like, and trust component for your small business.

So, what parts of a Google review can actually move the needle and improve your local ranking on Google? Let's break it down:

1. High Numerical Google Ratings (e.g., 4-5):

Ever heard the saying, "first impressions last"? Well, the same goes for online reviews. Higher numerical ratings build trust with potential customers and hold sway in Google's local search algorithm.


A business with a high numerical rating, say 4 or 5, indicates that customers are generally satisfied with the products, services, or overall experience provided by that business. Google considers these high ratings as a positive signal, and they can contribute to the business's credibility and trustworthiness in the eyes of potential customers.


In the realm of local SEO, search algorithms often take into account the average rating of a business when determining its local ranking. Therefore, businesses with consistently high numerical ratings are more likely to be favored in search results, as they are seen as providing a positive experience to customers. This is why maintaining a high average rating is considered one of the key elements for improving a business's local ranking on Google.

2. Quantity of Native Google Reviews (with text):

Numbers matter, but so does substance. Aim for a substantial number of reviews, especially those with detailed feedback. Reviews with text add depth to your online presence, offering valuable insights that resonate with potential clients.


The presence of text in reviews can offer more context and detailed information about the customer's experience, which is beneficial for both potential customers and search algorithms.

3. Recency of Reviews:

Keep the reviews flowing in. Regularly receiving new reviews signals to search engines that your business is actively engaged with customers. It's like telling Google, "Hey, we're here, and we're consistently making our customers happy!"


Recent reviews signal to search engines that a business is actively engaged with its customers. A steady stream of recent reviews indicates that the business is current and continues to provide products or services. This is why search algorithms favor businesses with recent reviews when determining local rankings — an active and engaged business is likely to be more relevant to current users.

4. Keywords in Native Google Reviews:

It's not just about the stars; it's about the words too. Relevant keywords in reviews can give your business an edge in local search rankings. Encourage your customers to express themselves naturally, weaving in those important keywords.


When users search for products or services related to your business, search engines analyze the content of reviews to determine relevance. If the reviews contain keywords that match the user's search query, it signals to the search engine that your business is relevant to those specific terms.

5. Positive Sentiment in Review Text:

Positive vibes matter. Positive sentiment in reviews not only attracts potential customers but also paints your business in a glowing light. Harness the psychological impact of positive reviews to win over new clients.


Search algorithms perform sentiment analysis on the text within reviews. Positive sentiments contribute to a business's positive online reputation, which, in turn, can positively impact its local ranking.

6. Quantity of Native Google Ratings (no text):

Even brief is better than nothing. Numerical ratings, even without accompanying text, play a role in enhancing your local rankings. The more ratings, the merrier!


The sheer number of ratings, even if they lack detailed comments, provides a clear indication of customer engagement with the business. A higher volume of ratings not only reflects popularity but also suggests the business is well-established and widely recognized.


This volume of ratings plays a pivotal role in how search algorithms assess the overall standing of a business. When determining local rankings, search algorithms consider the business's overall rating. Interestingly, even when reviews lack accompanying text, the numerical scores significantly contribute to the average rating.


This average rating is a crucial factor in local SEO, influencing the visibility and positioning of the business in search results. Therefore, the quantity of ratings, irrespective of the presence of text, holds substantial weight in shaping a business's online reputation and search engine ranking.

7. Quantity of Positive Google Review Attributes:

Specifics matter. Positive attributes mentioned in reviews contribute to your business's credibility. Encourage your customers to share detailed feedback on what they loved about their experience with your brand.


Ratings-only reviews pose several challenges. Firstly, they offer no meaningful information beyond the star ratings, providing neither Google nor the businesses being reviewed with valuable insights. Moreover, as Google accumulates numerous reviews, ratings-only entries contribute to a clustering effect, where results fall within a narrow range, hindering a comprehensive understanding of a business.


In response to this challenge, Google introduced review attributes. This tool prompts reviewers to click on specific words such as "professionalism," "responsiveness," or "timeliness" to serve as proxies for written reviews. The attributes are tailored to different categories; for instance, attributes valued in salons might include "cleanliness," while categories like insurance agents may emphasize "responsiveness."

Wrap Up

In conclusion, the elements mentioned above can work wonders for your business's local ranking on Google. Keep those positive reviews coming, respond thoughtfully, and maintain a stellar online reputation. After all, in the world of local SEO, your customers' voices are your strongest allies.

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.
More Posts