There’s often a disconnect between what a webpage says it’s about and what its audience is actually searching for.

This mismatch has always existed. But the stakes are higher now.

If your page fails to match user intent, it won’t show up in AI-powered search surfaces. Search engines will find a page that delivers.

You can see the mismatch, but it’s hard to quantify. The data to measure it is already in your Google Search Console account. Below, you can analyze your own pages to see how closely your content aligns with what your audience is searching for.

Measuring the gap between positioning and demand

Most web content today is designed to accommodate multiple target audiences, tens or hundreds of keywords, and brand positioning. As a result, it drifts away from the problems people are trying to solve.

I’ve had this argument many times and learned that observations create interesting conversations, but numbers create urgency and action. In this case, the numbers you need are already in your data, and the intent gap analysis tool uses that data to measure them.

Google Search Console captures what your audience searches for when they find each page. The meta description captures what the page says it’s about. One is demand. The other is positioning.

Intent gap analysis scores the distance between your meta description and your audience’s queries. Vector embeddings make that score possible by measuring meaning rather than just matching words. The result is a single intent gap score (0-100) that shows how well your page aligns with what your audience is searching for. 

Connecting positioning to demand

Google’s Search Central documentation describes the meta description as “a pitch that convinces the user that the page is exactly what they’re looking for.”

The meta description also functions as a machine-readable signal. LLMs and generative engines consume it as a compact summary of what the page claims to deliver.

Achieving “durable visibility in AI ecosystems” requires “consistent metadata, provenance, and trust signals that can be interpreted by search crawlers and generative engines,” IDC’s December 2025 Market Note on brand visibility found.

Scoring a page’s meta description requires an anchor in audience behavior. Google Search Console provides that anchor — the queries where Google chose to surface your page, regardless of whether the page was built for that intent.

The intent gap analysis tool expresses the gap as a score. In the sample analysis below of LumonHR, a fictional SaaS platform inspired by Severance, the homepage scores a 32.

The meta description uses vague aspirational language that doesn’t match the functional, software-focused queries driving traffic. The page isn’t attracting the audience it targeted.

Dig deeper: How to use AI to diagnose and improve search intent alignment

Why intent is measurable now

Search engines now use vector embeddings as a core part of how they match content to queries. Intent matching runs on meaning, not just keywords. When a user searches, the engine embeds the query and compares it against content candidates in a shared vector space. 

Semantic similarity is one of the signals that determines whether your page gets surfaced, cited, or used to generate an answer, alongside authority, trust, freshness, and other ranking factors.

Vector embeddings let you see your page the way a search engine does.

Where existing tools stop

N-gram analysis and TF-IDF have been the standard tools for analyzing search queries. N-grams surface recurring phrases, revealing the vocabulary your audience uses. TF-IDF highlights which terms matter most in your query set. 

These approaches match words, not meaning. “Setting boundaries between office and personal time” and “maintaining employee work-life balance” share zero words. To a word-matching tool, they’re separate topics. To a search engine running on embeddings, they express the same intent. 

When brands match words and search engines match intent, you’re working at a disadvantage.

Measuring meaning, not words

Vector embeddings encode meaning. An embedding converts text into numbers, allowing you to create a map of relationships rather than a list of terms. When two pieces of text mean similar things, their vectors land close together in a shared mathematical space.

Once your meta description and your audience’s queries are plotted in the same space, the distance between them is measurable.

Queries close to the meta description align with the page’s positioning. Queries far from it represent demand the page wasn’t built for. That distance is the intent gap score.

The map below breaks the intent gap into clusters, showing where your page aligns with audience demand and where it doesn’t.

Dig deeper: SEO gap analysis: How to find content and keyword gaps

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What the intent gap reveals

Clustering your queries into topics reveals which audiences the page is reaching and which it’s missing. Each cluster has two properties: 

• How closely it aligns with the meta description.
• How much search demand it carries. 

Those two dimensions place every cluster into one of four quadrants: defend, create, optimize, or monitor.

Defend

High alignment, high demand. The audience is finding your page for the reasons you built it, and in volume. This is where your topical authority lives.

Protect and reinforce. Keep the content current, and update the meta description if the language has drifted from how the audience phrases their searches.

Create

Low alignment, high demand. The audience is arriving with intent the page was never built to serve. This is demand you’re visible for but not capturing.

Create new content for the clusters that fit your strategy, using the language your audience is already using. Ignore the ones that don’t. Each cluster that passes the filter is a signal for new content.

Optimize

High alignment, low demand. The page matches what these searchers need, but few are finding it. The content is right. The visibility isn’t.

Investigate the constraint. The alignment is there, but the audience is small. Rankings may be too low, the positioning too narrow, or the topic may need supporting content to grow.

Monitor

Low alignment, low demand. Some clusters may grow into Create or Optimize territory over time.

Watch for growth. This is often where emerging topics are first detected. If demand increases, re-evaluate.

Dig deeper: How and why to ‘be the primary source’ for organic search

Your data, your score: Running the intent gap analysis

Here’s the tool and how to run the analysis on your own pages.

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Step 1: Export your page data

In Google Search Console, navigate to Performance > Search results, filter by a single page, and export as a .zip file. 

Step 2: Upload and score

Upload the .zip file to the tool (your data is not stored) to get your intent gap score. The tool scrapes the meta description, scores every query against it, and clusters the results. 

Step 3: Explore the map

Each cluster is plotted by alignment and demand. Click any bubble to see the individual queries with clicks, impressions, CTR, and position.

Step 4: Review the breakdown

Every cluster in one view with its quadrant, alignment score, and performance metrics.

Step 5: Get rewrite recommendations

The tool generates recommended changes to your page’s title and meta description, grounded in the search language from your highest-demand clusters.

Step 6: Share your results

Download the table as CSV or use the “Copy as Image” buttons to share individual views with your team.

Dig deeper: How to master user intent with SEO personas

Turning the score into a decision

The intent gap score assigns a number to the disconnect, and that number gives it traction. It turns observations into actions you can take in stakeholder conversations, whether that means changing a page or defending it.

Your audience is already telling you what they need. That signal is always shifting. Now you can monitor it, measure it, and close the gap.

The tool featured in this article was created by Robin Tully, co-founder at Forecast.ing.