How to measure goal posts using Google Search Console data

There is often a disconnect between what a web page says it is about and what its audience is actually searching for.

This disparity has always existed. But the numbers are higher now.

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

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

Measuring the gap between supply and demand

Most web content today is designed to carry 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 debate many times and learned that awareness creates interesting conversations, but numbers create urgency and action. In this case, the numbers you need are already in your data, and the objective gap analysis tool uses that data to estimate them.

Google Search Console captures what your audience is searching for when they find each page. The meta description captures what the page is about. Another quest. Another set.

Objective gap analysis measures the distance between your meta description and your audience’s questions. Vector embedding makes that scoring possible by measuring meaning rather than just matching words. The result is a single objective score (0-100) that shows how well your page matches what your audience is searching for.

Connecting the status of demand

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

The meta description also acts as a machine-readable signal. LLMs and search engines use it as a collective summary of what the page wants to deliver.

Achieving “long-lasting visibility in AI ecosystems” requires “consistent metadata, provenance, and signals that can be interpreted by search and production engines,” IDC’s December 2025 Market Note on product visibility found.

Finding the meaning of a page’s meta requires an anchor in the audience’s behavior. Google Search Console provides that guarantee — the queries where Google has chosen to display your page, regardless of whether the page was designed for that purpose.

The objective gap analysis tool presents the gap as a score. In the sample analysis below for LumonHR, an innovative SaaS platform inspired by Severance, the homepage gets a score of 32.

The meta description uses vaguely desirable language that doesn’t match the active, software-focused queries that drive traffic. The page is not appealing to the target audience.

Dig deeper: How AI is used to diagnose and improve search intent understanding

Why purpose is measurable now

Search engines now use vector embedding as a key part of how they match content to queries. Intent matching works on the description, not just the keywords. When a user searches, the engine embeds the query and compares it to content candidates in the shared vector.

Semantic similarity is one of the characteristics that determine whether your page appears, is cited, or is used to generate a response, in terms of authority, trust, freshness, and other ranking factors.

Vector embedding allows you to see your page the way a search engine does.

Where the tools are there they are

N-gram analysis and TF-IDF have been standard tools for analyzing search queries. N-grams are more than repetitive sentences, which reveal the vocabulary your audience uses. TF-IDF highlights which words are most important in your query set.

These methods correspond to words, not meaning. “Setting boundaries between office and personal time” and “maintaining an employee’s work-life balance” share zero words. In the word matching tool, they are different topics. In the search engine that works on embedding, they express the same purpose.

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

Measuring meaning, not words

Vector embedding includes definition. Embedding converts text into numbers, allowing you to create a relational map rather than a list of words. If two pieces of text mean the same thing, their vectors are drawn closer together in a shared statistical space.

If your meta description and your audience’s questions are arranged in the same place, the distance between them is measurable.

The questions next to the meta description are related to the page layout. Questions that are far from this mean that this page has not been created. That distance is the result of an objective gap.

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

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

What is revealed is the goal gap

Grouping your questions into topics reveals which audiences the page is reaching and which ones it’s missing. Each collection has two properties:

• How closely it matches the meta description.
• How useful it is in search.

Those two dimensions put every cluster into one of four quadrants: protect, create, prepare, or monitor.

Protect

High alignment, high demand. The audience finds your page for the reasons you created it, and the volume. This is where your title authority resides.

Protect and strengthen. Keep the content current, and update the meta description if the language deviates from how the audience ranks their searches.

Create

Low alignment, high demand. The audience comes with the intention that this page was not designed to be used. This is the need you see but don’t photograph.

Create new content for collections that fit your strategy, using the language your audience already uses. Ignore those who don’t. Each collection that passes the filter is a sign of new content.

Prepare

High alignment, low demand. The page is similar to what these searchers need, but few find it. The content is fine. Apparently not.

Flip the barrier. The alignment is there, but the audience is small. The standards may be too low, the format too small, or the topic may need supporting content to grow.

Take care

Low alignment, low demand. Some collections may grow into Create or Configure environments over time.

Look for growth. This is often found where emerging topics are discovered first. If the demand increases, re-evaluate.

Dig deep: How and why you can become a ‘prime source’ for organic search

Your data, your score: Initiating an objective gap analysis

Here’s a tool and how to run analytics on your pages.

Step 1: Submit your page data

In Google Search Console, navigate to Performance > Search resultsfilter by one page, and send as a .zip file.

Step 2: Upload and get points

Upload the .zip file to the tool (your data is not saved) to find your target gap. The tool clears the meta description, checks all queries against it, and aggregates the results.

Step 3: Check the map

Each collection is organized by alignment and need. Click on any bubble to see each question’s clicks, impressions, CTR, and location.

Step 4: Review the classification

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

Step 5: Get resume recommendations

The tool creates recommended changes to your page title and meta description, based on search terms from your most popular collections.

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 deep: How to know user intent with SEO personas

Converting points into decisions

The result of the objective gap assigns a number to the cutoff, and that number makes it a draw. It turns observations into actions you can take in stakeholder conversations, whether that means turning the page or defending it.

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

The tool featured in this article was developed by Robin Tullyfounder of Forecast.ing.