Meaning Design Series: A New Paradigm for Search Engines and Users to Be Properly Understood in the Age of AI.

You can read Part 1 here.

Keyword Stuffing No Longer Works.

SEO in the 2000s was a simple matter of repeatedly writing a keyword like "rental office" on a page to achieve higher search rankings. However, search engines have evolved, moving towards semantic search that understands user intent. And in 2024, Google's "AI Overview" has arrived, ushering in an era where AI-generated direct answers appear at the top of search results.

The Core of Meaning Design: Visualizing Prerequisites

AI cannot understand implicit contexts like humans do. The example given by Mr. Gunji, "It's hot today," illustrates this. The meaning of "hot" spoken in a Tokyo office on October 3rd is completely different from the meaning of "hot" spoken in Antarctica or the Sahara Desert. Clearly designing these prerequisites (who, where, when, why) is the most important element of "meaning design."

How the Transformer Architecture Understands

Modern AI understands meaning through three relationships: "key," "query," and "value":

• Query: The word of interest (e.g., "cold").
• Key: The perspective or subject (e.g., "water" or "person").
• Value: The meaning derived from that combination (e.g., "temperature" or "lack of empathy").

This mechanism allows AI to infer meaning from the relationships between words, not just keyword matching. However, it also carries the risk of assigning incorrect values if the relationship between the query and key is unclear in the content.

The "Fragmentation Problem" on Social Media and AI's Average Expressions

On social media, the "fragmentation problem" occurs, where prerequisites are omitted, and only fragmented information is spread. For example, the act of "deleting an account" has a completely different meaning depending on whether it is a bot account or a human account, but that context is often missing.

Furthermore, generative AI tends to choose the "most likely" words based on statistical patterns in the training data, inevitably leading to average, universal, and ordinary expressions. This is the biggest challenge that hinders business differentiation.

Practical Steps for Meaning Design

• Visualize Prerequisites: Clarify the target audience, context, location/time, and purpose.
• Clarify Queries, Keys, and Values: Organize user challenges, your company's services, and the value provided.
• Use Expressions that Prevent Context Disconnection: Always explicitly include important prerequisites.
• AI Verification: Have the AI analyze the content to confirm its understanding.

Part 2: Value Driven by Perspective — Practical Transformation of Ambiguity into Concreteness

Read Part 2 here.

The Relativity of Perspective: The "Crayon Shin-chan" Logic

The Value of a piece of content is not static; it transforms entirely based on the Key (the lens or intent) through which it is viewed. Using the anime Crayon Shin-chan as a model:

• User Intent (Child): Entertainment value ("Is it fun?")
• User Intent (Parent): Educational safety ("Is it appropriate?")
• Business Intent (TV Network): Performance metrics ("Are the ratings high?")
• Business Intent (Sponsor): ROI/Conversion ("Does it drive sales?")

Marketers often overlook these underlying premises, yet these "unconscious assumptions" are the primary reason content fails to be accurately indexed or valued by AI and search engines.

The Trap of "Commitment" (The Ambiguity Gap)

Abstract buzzwords like "quality," "sophistication," or the Japanese concept of Kodawari (uncompromising commitment) are subjective. Ten different users will have ten different interpretations. As noted in the "Egashira-san" (comedian) case study, a subject’s Value can be completely redefined simply by shifting the social context, even if the "product" itself remains the same.

Case Study: Semantic Design in Ultra-Soft Water & Onigiri

This example demonstrates a perfect execution of Meaning Design—translating features into tangible user benefits.

• Level 1 (Low Information Gain): Ambiguous Copy
  "We use special water and premium ingredients. These are rice balls made with true commitment."
  
  • SEO/Marketing Critique: Zero differentiation. The term "commitment" is a hollow descriptor that provides no mental model for the user.
• Level 2 (Better): Feature-Based Copy
  "We use ultra-soft water, which has an extremely low mineral content."
  
  • SEO/Marketing Critique: Better for technical indexing, but fails to trigger a "Value" conversion. The user is left asking, "So what?"
• Level 3 (Optimal): Semantic Design / High Information Gain
  "We use ultra-soft water. Because of its near-zero mineral content, the water penetrates the rice grain more deeply, resulting in a fluffier texture. This lack of mineral interference allows the natural aroma and sweetness of the rice to be the hero of the dish."
  
  • SEO/Marketing Critique: This creates a definitive Query-Key-Value chain. It provides "Information Gain" that search engines reward and builds a consistent mental model across 100% of your audience.

Two Psychological Levers for Consumer Action

1. Self-Efficacy & Retention: Users who feel they have made a "smart" or "informed" choice show higher LTV (Lifetime Value) and subscription retention. When a user can justify a purchase based on logic rather than just price, they are more likely to leave detailed, positive reviews.
2. Sunk Cost Effect through Education: When content teaches a user a new "schema" or framework (e.g., how water hardness affects taste), the user wants to "realize" that knowledge. Buying the product becomes the final step in validating the effort they spent learning.

Overcoming Price Wars via Multi-Reference Points

The core of Meaning Design is to move the battleground away from price. By introducing new reference points—such as mineral density in water or breeding environments for livestock—you provide the consumer with a sophisticated toolkit for comparison. This is the ultimate strategy for De-commoditization and building a defensible brand moat.

Part 3: The Evolution of Competition on the Web — Scaling Sales Intelligence into Content

Read Part 3 here.

The Competitive Paradigm Shift: Why Energy Drinks Compete with Bath Salts

In the digital space, competitors are no longer defined by Product Categories, but by Audience Overlap. Consider the energy drink market:

• Real-World Competitors: Other energy drink brands (Direct Competitors).
• Web Competitors: Sleeping, taking a bath, massages, or supplements (Indirect/Functional Competitors).

Any solution that provides the same Benefit—in this case, "fatigue recovery"—is a competitor. A "tired user" has a finite budget and limited time; they will choose only one solution from this pool. On the web, you are competing for the "Fatigue Recovery" search intent, not just shelf space.

The B2B Buying Revolution: 80% of the Deal is Done Before the First Call

According to research released by Google (ZMOT), over 80% of B2B buying decisions are finalized before a sales representative ever sets foot in the room.

• The Sales Reality: The sales meeting is no longer for "information gathering"—it’s for "confirmation."
• The 6% Rule: Direct interaction with sales reps accounts for only 6% of the total buying journey.
• The 94% Opportunity: The remaining 94% of the journey is spent consuming web content, reading reviews, and researching third-party social proof—all of which happen outside of your direct control.

B2C Subscriptions: Self-Efficacy as a Retention Engine

A Think with Google survey of 20,000 users revealed a stark correlation between Self-Efficacy (user confidence in their choice) and Retention:

• High Self-Efficacy Users: >90% retention rate.
• Low Self-Efficacy Users: <50% retention rate.

Users who are educated by your content feel more confident in their purchase. These "expert" users write detailed, high-quality reviews that act as User-Generated Content (UGC) to assist other buyers. Conversely, consumers who only understand "Price" as a metric will only write "It was cheap," which accelerates your brand’s commoditization.

Scaling Sales Intelligence: From 10 to 100,000

Your sales team holds a goldmine of First-Party Data. They hear the raw pain points and latent needs of customers every day. However, this knowledge is usually siloed within an individual.

• The Math of Scaling: A sales rep might visit 30–40 clients a month. A piece of high-quality content can reach 10,000 or 100,000 potential leads simultaneously.
• The Strategy: Transforming sales insights into evergreen content is the ultimate strategy to prevent "knowledge waste" and achieve exponential scale.

Actionable Implementation: From Sales Floor to Content Asset

To turn sales field intelligence into high-performing marketing assets, follow this workflow:

1. Extract the "Query": Identify the raw, unfiltered problems and questions customers ask during sales calls.
2. Clarify the "Key": Systematize the specific solutions and frameworks the sales team uses to address those problems.
3. Define the "Value": Verbalize the concrete ROI and benefits the customer achieved.
4. Scale the Asset: Package these insights into Case Studies, FAQs, and White Papers to capture organic search and social traffic.

Part 4: Google’s "Likelihood" Algorithm — The Paradigm Shift Toward Authority & Authorship

Read the final Part 4 here.

Everything is an Equation: Beyond Subjectivity

It is a common misconception that AI evaluates content based on a "vague feeling." In reality, search rankings are governed by explicit evaluation axes and mathematical formulas. Google publishes a 180-page document (the Search Quality Rater Guidelines) that outlines these exact standards.

E-E-A-T: The Four Pillars of Trust

• Experience: Is the information rooted in first-hand, real-world usage?
• Expertise: Is the creator a subject matter expert?
• Authoritativeness: Is the site or author a recognized leader in this niche?
• Trustworthiness: Is the source reliable, transparent, and accurate?

Google evaluates these abstract qualities through a "Likelihood/Verisimilitude" approach.

Assessing Expertise via Signals (The "Pro-Wrestler" Logic)

Consider a pro-wrestler: If a random person claims to be a wrestler, you likely won't believe them. However, if a muscular individual makes the same claim, you accept it as likely.

• The SEO Lesson: Google doesn't necessarily verify the "truth" of your identity in real-time; instead, it analyzes Signals that suggest expertise (e.g., specific terminology, depth of technical detail, and citations). These signals are often detailed in Google’s patents. If you trigger enough of these "Expertise Signals," your score increases.

The Essence of Quality: Comprehensive Topic Coverage

In Google’s eyes, "Quality" equals Topic Coverage. * Example: An article about "Cars" is considered high-quality if it covers more than just manufacturers. It must address environmental impact, TCO (Total Cost of Ownership), design aesthetics, resale value, and the used car market.

• The Risk of Bias: A narrow or biased perspective is flagged as low quality. This aligns with our Part 2 discussion on the Relativity of Perspective.

The Major Shift: From Content Evaluation to Author/Entity Evaluation

The most critical paradigm shift in modern SEO is that AI now evaluates who is speaking.

• Legacy SEO: Focus on the quality of the individual page/content.
• Modern SEO: Focus on the Trust Score of the Author and the Entity (Organization).

Websites that consistently publish diverse, multi-perspective content while adhering to technical standards earn "Trust" from the AI. Consequently, if two sites sell the same product, Google will recommend the one owned by the "Trusted Entity."

Technical Implementation: Structured Data (Schema.org)

While humans read your content, AI reads your Structured Data. Implementing Schema.org is no longer optional; it is the technical bridge that helps AI accurately map your content into its Knowledge Graph, ensuring you are indexed and ranked for the right entities.

Conclusion: The "Semantic Design" Strategy for the AI Era

We have moved from a "Keyword Frequency" era to a "Quality of Meaning" and "Entity Trust" era.

The 3 Pillars of Semantic Design

1. Contextual Visibility: AI cannot infer "hidden" context. You must explicitly define the Who, Where, When, and Why.
2. The Query-Key-Value Chain: Eliminate ambiguity. Replace vague buzzwords with concrete mechanisms (e.g., Ultra-soft water → Low minerals → Deep penetration → Fluffy texture).
3. Holistic Coverage: Build EEAT by covering diverse viewpoints, ensuring your Entity becomes a trusted authority.

Organizational Transformation

• Scalable Sales Knowledge: Convert 1-on-1 sales expertise into 1-on-100,000 digital assets.
• Redefining Competition: Your competitors are every solution solving your user's pain point, not just direct product rivals.
• Long-term Trust: Sustainable SEO is about the consistent output of multi-perspective, accurate information to build a "Trust Moat" with AI.

The Fusion of Tech and Strategy: Combining technical implementations like Structured Data with strategic frameworks like Semantic Design is not just an "SEO trick"—it is a total redesign of your business's communication strategy for the AI-first world.

Would you like me to create an E-E-A-T checklist based on these pillars to help your content team audit their next batch of articles?

A Final Message

In an era where AI-driven search engines exert "dominant intervention" over visibility, the survival of your business or brand no longer hinges on keyword density. Instead, "Semantic Quality" and "Authorial Trust" have become the ultimate deciders of success.

We invite you to leverage this new paradigm—a fusion of 20 years of SEO expertise and cutting-edge AI technology—within your own business. We hope this dialogue serves as your strategic compass for navigating content strategy in the age of AI.