Search Engine Risk Management

Published
Update
by Wayne Smith

Bridging Marketing Strategy and Search Performance

Good marketing and search engines ultimately want the same things: clarity, consistency, and trust. Algorithm updates may create headlines, but the underlying principles of effective content have remained stable for years.

The challenge is that content written for humans doesn’t always translate cleanly to rules-based algorithms or AI systems parsing entities, relationships, and context. Add the natural divide between content creators and link builders—many of whom avoid content because it’s outside their expertise—and strategic gaps inevitably appear. Concepts like thin content or E-E-A-T stay subjective until you convert them into measurable signals such as gain of knowledge, clarity, or relevance. Only then do they become actionable KPIs.

Rules-based systems also create their own catch-22s, forcing marketers to balance human readability with machine clarity at the same time.

Despite the complexity, the foundational risk-management KPIs remain straightforward: visibility, stability, and trust. Prioritize these, and marketing goals and search performance naturally move in alignment rather than conflict.

Google’s Hygienic Updates

The Helpful Content Updates (HCUs) that began rolling out in late 2023 targeted sites with weak user experiences—particularly those built around highly similar, repetitive content. Before these updates, many sites performed well by centering each page on a narrowly targeted keyword. But the sites hit hardest were those optimized around individual keywords instead of broader topics or user needs.

It’s also important to note that AI systems evaluate information at the entity level. When multiple pages duplicate the same entities, these systems struggle to distinguish their purpose—resulting in lower clarity, reduced relevance, or de-prioritization in both ranking and AI-driven summaries.

The core target of the hygienic updates is scaled, repetitive content where many pages offered marginal value by simply chasing minor keyword variations, rather than providing comprehensive, user-satisfying answers within a broader topic.

Crawling and Indexing

Most crawling and indexing problems boil down to technical. Bots need clean access to your content, and if the site throws errors or blocks them, you’re basically slamming the door in their face.

Then there’s the whole canonical mess. The same page can live at a bunch of different URLs — think:

Even parameterized URLs, like "example.com?shopping-cart=12345678," can essentially point to the same page. Without guidance, search engines may treat these as duplicates. In some cases, these may trigger soft 404-like issues when crawlers interpret the content as low value. See Fix Soft 404s.

For sites with more than 30 pages, a "sitemap.xml" helps guide crawlers and focus your crawl budget—but it won’t solve duplicate content or canonical issues on its own. The canonical tag shows which version is “official,” while "robots.txt" can steer bots away from sections of the site you don’t want crawled. Proper server configuration also helps, handling redirects between “www” and non-“www” versions or limiting unnecessary URL parameters. For a deeper dive on canonical tags, see Canonical tag.

Once canonicalization, indexing, and soft 404 issues are sorted, Google evaluates your pages for relevance—looking at keywords, context, and entities to decide where (and whether) your page appears in search results.

Keep in mind that even if pages are crawled correctly, they might not make it into the search index. That’s where factors like thin content, cannibalization, and intent matching come into play (see sections below).

Addressing Site Hygiene and Poor User Interfaces

Putting aside why keyword cannibalization or duplicate content happens, it’s frustrating for users when they try to return to something they were reading but can’t easily find it again. The underlying issue is usually a messy site structure: the same topic appears on multiple pages without a clear purpose. Which page is the one they actually need?

Search engines run into the same problem. They’re forced to choose a single page to rank, and sometimes they pick the wrong one. A human may notice a banner saying “deprecated archived version” and know to avoid it — but a bot or AI system can easily overlook that context.

The simplest fix is to mark archived pages as noindex and link users to the page you actually want visible. This helps people find the right content and prevents search engines from spending time on pages that shouldn’t compete in the first place.

Messy user interfaces

As a site grows, topics naturally start spreading across multiple pages. No blame here — new products launch, sections get added, features expand, and before you know it, one product’s content starts cannibalizing another’s. The fix? Spin up a new page — maybe a collections hub or a comparison page — and make sure it’s structured so it doesn’t step on existing pages.

Structured schema can be a big help here, too. It makes your site easier for search engines and AI systems to understand. Need a real-world example? Check out Stuctured Schema for topical pages on creating a collection page for the real-life aftertouch feature

Growth also means ongoing content maintenance for search engines. And remember — efforts depreciate over time. For a deeper look at content depreciation, see content depreciation.

Site Speed and Mobile

Site speed is often talked about like it’s a make-or-break factor, but the real question isn’t a few milliseconds on a lab test—it’s whether users leave before your page loads. That’s something search engines can measure, and it’s also just bad marketing if you ignore it.

Context matters, too. Some pages are naturally heavier — product pages with videos or detailed guides will take longer to load than simple contact or hours-of-operation pages. What really matters is whether the page meets user expectations.

Google now uses mobile-first indexing, meaning the mobile version of your page is evaluated. Consider what Navboost indicates about mobile engagement. Even if users do some research on desktop, your KPIs for engagement risk should prioritize mobile performance.

Framing it this way shifts focus from chasing arbitrary scores to measuring actual user impact. If someone just wants store hours and the page drags, that’s a lost opportunity. If they’re reading a 10-minute tutorial, they may wait.

Thin Content, Gain of Knowledge, and fresh content

“Thin content” gets tossed around a lot, but it’s really just a catch-all term. The truth is, the label isn’t all that useful because it’s contextual and not directly quantifiable. You’ve probably seen plenty of pages in the index and thought, “Wait, how is this not considered thin?”

Think of it like “E-E-A-T” — the term itself isn’t something you can measure directly. In contrast, concepts like “gain of knowledge” or “real-world experience” are quantifiable, making them far more actionable when using a rules-based model to assess content quality.

Here’s the nuance: thin content isn’t about word count or page size. It’s about what already exists online when your page goes live. If your content doesn’t add new knowledge, perspective, or value beyond what’s already out there, algorithms may treat it as thin — even if it looks “substantial” on the surface.

That’s why reframing thin content as a “gain of knowledge” test works better. Ask yourself: does this page give the reader (and by extension, the index) something they didn’t already have? If yes, it passes the bar.

And remember — long doesn’t automatically mean strong. Big pages that simply rehash existing material, or sites that aggregate content from elsewhere, can still be “thin” if they don’t deliver fresh knowledge. On the flip side, short pages that deliver something new, fill a content gap, or provide news-worthy value aren’t thin at all — because the rule isn’t size, it’s knowledge gained.

Additional Considerations for Gain of Knowledge

Content syndication can influence rankings. For example, enabling an RSS feed in WordPress or distributing content to other trusted sites can help or hurt your page:

Content Depreciation

Content depreciation is essentially content that, over time, has become thin. Even pages that once added value can lose relevance if they stop contributing meaningful knowledge or fail to reflect updated perspectives. An uptick in pages labeled “Crawled – currently not indexed” often coincides with Core Updates, though pages can fall into this category at any time.

On closer inspection, these depreciated pages typically don’t add new insights, contain outdated information, or are linguistically unclear, making it harder for search engines to evaluate their value. The fix is straightforward: refresh the content with updated materials, clarify the language, and ideally incorporate insights from experts who can provide real-world guidance.

These attributes—freshness, clarity, and expertise—are quantifiable and directly address the gain-of-knowledge criteria, helping your content maintain relevance and search visibility over time.

When a noindex is the best course of action

There are situations where “thin content” provides value to site visitors but isn’t useful to search engines. In these cases, the pages should be removed from the index without removing them from the site.

To conserve crawl budget, the most effective method is to disallow these pages in the robots.txt file. An on-page meta noindex can also be used, but it only takes effect after the page is crawled. This means it doesn’t immediately save crawl budget, making it more suitable for pages that are likely to be updated soon.

Structured Schema and linguistic clarity as tools to mitigate risks.

Risk management in today’s AI-aware search environment puts extra emphasis on on-page content. Pages optimized for keywords that appear in AI-driven “above-the-fold” sections can lose rankings—or even be removed from the section—if the language isn’t clear, making linguistic clarity a challenging but critical risk factor to assess.

For AI and LLM systems, linguistic clarity means clearly defining relationships between elements within the same text block and using pronouns in ways that are unambiguous.

Not to drift off topic ... AI evaluates content section by section, block by block, to determine the “gain of knowledge” each segment provides. At a basic level, it parses sentences as discrete blocks—e.g., “George Washington was the first president of the United States”—and associates the entity “George Washington” with “first president” and “United States.” This process does not merely look for the inclusion of George Washington, First President, and United States ... but uses linguistic models to process blocks.

A comparable process occurs in AI image generation systems, where prompts are analyzed to produce coherent outputs. This guide demonstrates how AI image generation interprets structured information and context, and provides an in-depth example of using schema as a practical mitigation for off-page content: AI-Aware Image Optimization

Example of ambiguous entities that are problematic for LLMs

Consider the “knobbly monster” scenario often seen in the British press. Editors sometimes introduce a new term (what AI would treat as a distinct entity) instead of repeating the original word. For example:

Text block: "The crocodile walked onto the sidewalk, and people called the police because the knobbly monster was scaring people."

For an LLM or AI image generator, this can be confusing. The system may fail to associate “knobbly monster” with “crocodile,” potentially leading to misinterpretation or hallucination. This illustrates how entity ambiguity and editorial style can create real linguistic risks for AI systems.

The solution

When writing for AI-aware search, review text blocks with an eye to entity clarity. Ask: Where is the relationship between this entity (a person, place, or thing) and its reference clearly defined? If the connection isn’t explicit, restating the entity instead of substituting with a creative synonym ensures both readability for humans and interpretability for AI. Pronouns can also be used when they are not ambiguous.

"The male crocodile walked onto the sidewalk, and people called the police because he was scaring people."

The usage of the pronoun "it" becomes ambiguous because it could refer to the sidewalk.

Alternative microdata schema solution

This inline microdata schema declaires that a "knobbly monster" is the same as thing as a crocodile. The description property is being used instead of a sameAs because not all LLM recognised entities have a wikipedia page.

See From JSON-LD to Microdata: What Changes, What Stays the Same for examples of how to implement schema microdata.