August 9, 2026

How to Use Google’s Merchant API for SEO and GEO

Google Search is no longer just a list of ten blue links.

As search queries became more complex, Google began separating results into different search surfaces based on the type of information and user intent behind a query.

This led to dedicated surfaces for news, maps, books, scholarly publications, images, and shopping.

For ecommerce businesses, Google Shopping is especially important because it provides a different view of how products and brands compete in search.

A product can rank well in traditional organic search but still lose clicks because another seller has a better price, stronger reviews, faster shipping, or better product information.

This creates an interesting opportunity for SEOs.

What if you could access Google’s Shopping data directly and use it to understand what products are gaining demand, which brands are dominating a category, and how your products compare with the wider market?

That is where Google’s Merchant API becomes useful.

In this guide, we will look at how SEOs and GEO practitioners can use the Merchant API to extract market intelligence and turn it into actionable SEO and GEO insights.

We will also look at a practical workflow using a Google Colab script and Chrome extension that makes the process easier.

---

What Are Merchant Listings and Google Shopping?

Merchant listings are product-focused results that can appear across Google Search.

They can show a product, the stores that sell it, and information that can influence a person’s purchase decision.

Depending on the product and the information available to Google, this can include:

• Product name
• Price
• Reviews
• Ratings
• Shipping information
• Estimated delivery dates
• Product availability
• Store information
• Other product attributes

Google Shopping is the dedicated shopping search surface where products are displayed in response to commercial and transactional searches.

For example, someone searching for:

“best running shoes”

has a different intent from someone searching for:

“how to choose running shoes.”

The first query has a stronger commercial intent.

Google therefore needs structured product information to decide which products to show and how to present them.

For ecommerce businesses, there are several ways to provide Google with this information.

One is structured data on product pages.

Another is a product feed submitted through Google Merchant Center.

This makes Merchant Center more than a platform for managing shopping campaigns.

It is also a major source of product information that Google can use across its shopping ecosystem.

---

Why Should SEOs Care About Merchant Center Data?

Most SEO workflows start with the website.

You crawl the site.

You research keywords.

You analyze competitors.

You look at Search Console.

You review rankings and traffic.

All of this is useful.

But there is another question worth asking:

What is actually happening in the market?

For example:

• Which products are becoming more popular?
• Which brands dominate a category?
• Which products are moving up the Shopping rankings?
• Are we selling products that are gaining demand?
• Are our products competitively priced?
• Which categories deserve more attention?
• Which products should receive more internal links?
• Are there differences between countries?

These questions are difficult to answer using traditional SEO tools alone.

Merchant API data provides another layer of market intelligence.

The API allows you to query Merchant Center reporting data programmatically and analyze it at a much larger scale than would be practical through a graphical interface.

This is where the Merchant API becomes particularly interesting for ecommerce SEO.

---

What Is Google’s Merchant API?

The Google Merchant API is a programmatic interface for interacting with Google Merchant Center.

Instead of manually navigating the Merchant Center interface, you can use code to authenticate with Google, send requests, retrieve data, and process the results.

This is particularly useful for large ecommerce websites.

Imagine that you are working with 50,000 products.

You may want to compare market demand across several categories, countries, and time periods.

Doing that manually would take a significant amount of time.

An API allows you to automate the process.

Instead of asking:

“What information can I see in Merchant Center?”

you can ask:

“What information can I extract from Merchant Center that can help me make better SEO decisions?”

That is the more useful question.

The API can also be connected to Python, Pandas, Google Colab, spreadsheets, databases, and other systems.

This means you can extract the data, analyze it, compare it across periods, and combine it with other SEO datasets.

---

What Can You Use the Merchant API For?

Google provides a wide range of Merchant API functionality.

For SEO and GEO, some of the most useful applications include:

• Managing Merchant Center data sources
• Managing product information
• Updating product attributes
• Managing inventory
• Managing prices
• Working with reviews
• Accessing reporting data
• Analyzing best-selling products
• Analyzing best-selling brands
• Reviewing price competitiveness
• Comparing markets
• Studying changes in product demand

Not every API capability is directly an SEO metric.

The real value comes from using the information to answer SEO questions.

For example, Google might show that a particular product cluster is gaining demand.

Google does not necessarily tell you:

“Create a new category page.”

That interpretation is the SEO’s job.

The API provides the market signal.

The SEO strategy turns that signal into an action.

---

A Simpler Way to Use the Merchant API

The Merchant API is powerful, but working with APIs can be intimidating if you are not a developer.

You have to deal with authentication, Google Cloud, service accounts, API requests, Merchant Center Query Language, reporting tables, date formats, and data processing.

That is why I built a workflow around the API that removes much of this technical work.

The workflow has two main components:

1. A Google Colab script
2. A Chrome extension

The Colab script handles the API connection and reporting.

The Chrome extension provides a simpler browser-based way to work with the resulting information as part of an SEO and GEO research workflow.

The idea is simple:

Merchant API → Market data → Analysis → SEO/GEO action

Instead of building the entire process yourself, you can use the tools to get from API access to useful market intelligence faster.

---

The Google Colab Script

The Google Colab script handles the technical part of communicating with the Merchant API.

Google Colab is useful because it allows you to run Python in your browser.

You do not need to configure a local Python environment to get started.

The script handles authentication and allows you to configure the main variables needed for the research.

These include:

• Merchant Center account ID
• Country
• Product category
• Start date
• End date
• Reporting granularity
• Number of results

For example:

ACCOUNT_ID = "000000000"
COUNTRY = "US"
CATEGORY_ID = None
START_DATE = date(2026, 1, 1)
END_DATE = date.today()
GRANULARITY = "WEEKLY"
TOP_N = 10

Once configured, the script can query Google’s best-seller reports.

The workflow currently focuses on two important datasets:

Best-selling product clusters

and

Best-selling brands

It can also be extended to other Merchant API reporting workflows.

The script processes the results and converts them into Pandas dataframes.

It can then export the results as CSV files for further analysis.

The source script uses Google’s ReportServiceClient and the best_sellers_product_cluster_view and best_sellers_brand_view reporting tables.

This means the user does not have to manually construct every API request.

---

The Chrome Extension

The second part of the workflow is the Chrome extension.

The extension is designed to make the Merchant API research process easier to use from the browser.

This is important because SEO research rarely happens inside one tool.

You may be looking at:

• A Google Search result
• A competitor’s product page
• A category page
• Product information
• Market data
• Search results
• AI-generated answers

The extension provides a bridge between the raw Merchant API data and the browser-based research process.

Instead of treating Merchant API data as a separate spreadsheet that you look at once, the extension can become part of the way you investigate products, brands, categories, and competitive opportunities.

The broader workflow becomes:

1. Merchant API

Extract market information.

2. Colab script

Process and organize the information.

3. Chrome extension

Use the information during browser-based research.

4. SEO/GEO analysis

Identify opportunities.

5. Website action

Optimize the relevant pages and entities.

This is the main reason for having both tools.

The script handles the data extraction.

The extension helps bring the research into the working environment where SEO decisions are made.

---

Who Is This Workflow For?

You do not need to be a developer to use the workflow.

It is particularly useful for:

• Ecommerce SEOs
• Technical SEOs
• GEO practitioners
• SEO agencies
• Ecommerce growth teams
• Product marketers
• Competitive intelligence teams

It becomes especially valuable when you work with large product catalogs or multiple markets.

Instead of manually researching every category, you can automate the collection of market information and focus your time on interpreting the results.

---

How to Access the Google Merchant API

Before using the Colab script, you need to establish access to the Merchant API.

At a basic level, you need:

1. A Google Merchant Center account
2. Admin access to that account
3. A Google Cloud project
4. Merchant API enabled
5. A service account or another supported authentication method
6. Merchant Center access for the authenticated identity
7. Developer registration for the Merchant Center account

The setup looks complicated at first, but it can be broken into a few steps.

---

Step 1: Find Your Merchant Center Account ID

Log into Google Merchant Center.

Your account ID is displayed in the Merchant Center interface.

You will need this number when configuring the script.

For example:

ACCOUNT_ID = "5609626931"

Keep this number available.

You will use it when registering the developer project and when running reports.

---

Step 2: Create a Google Cloud Project

Open Google Cloud Console and create a project.

You can also use an existing project.

Once the project is ready, open:

APIs & Services → Library

Search for Merchant API.

Enable it.

If you skip this step, API requests can return a 403 error.

---

Step 3: Create a Service Account

Go to:

IAM & Admin → Service Accounts

Create a service account.

You can give it a name such as:

merchant-api-runner

Once it has been created, open the service account and select:

Keys → Add Key → Create new key → JSON

Google will generate a JSON credentials file.

Keep this file secure.

Anyone who has access to the credentials may be able to use the associated Google Cloud permissions.

---

Step 4: Add the Service Account to Merchant Center

Copy the email address of the service account.

It will normally look something like:

merchant-api-runner@project-name.iam.gserviceaccount.com

Then add it as a user in your Merchant Center account.

For the workflow described here, Admin access is the simplest setup.

If the API authenticates successfully but your queries return permission errors, check this step first.

---

Step 5: Authenticate the Colab Script

Upload the JSON credentials file to Google Colab.

The script can then point Google’s authentication libraries to the file.

For example:

import os
from google.oauth2 import service_account
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = (
 "/content/your-service-account-key.json"
)

The Merchant API scope used by the workflow is:

MERCHANT_API_SCOPE = (
 "https://www.googleapis.com/auth/content"
)

The Google authentication library can then use these credentials to create an authenticated Merchant API client.
One important detail is that Google Colab sessions are temporary.

When the session resets, the uploaded JSON file may disappear.

You will therefore need to upload it again when starting a new session.

---

Step 6: Register Your Developer Project

Before running the reporting workflow, Google requires the Merchant Center account to be linked to the Google Cloud project.

The script handles this using the:

developerRegistration:registerGcp

endpoint.

You provide:

ACCOUNT_ID = "Your GMC ID"
DEVELOPER_EMAIL = "Your Email"

The script then sends a POST request to Google’s Merchant API.

A successful response confirms that the project has been linked to the Merchant Center account.

This is generally a one-time step for the account.

After registration has been completed, you can move on to reporting.

---

Running Your First Merchant API Report

Once authentication and developer registration are complete, you can start querying Merchant API reporting data.

The Colab script has a configuration section where you define the market you want to investigate.

For example:

COUNTRY = "US"
CATEGORY_ID = None
START_DATE = date(2026, 1, 1)
END_DATE = date.today()
GRANULARITY = "WEEKLY"

There are four particularly important variables.

Country

This determines the market you want to investigate.

For example:

COUNTRY = "US"

Category

You can provide a Google product taxonomy category ID.

Or you can use:

CATEGORY_ID = None

to query across categories.

Date range

You define the period you want to investigate.

Granularity

You can use:

"WEEKLY"

or:

"MONTHLY"

The script handles the reporting dates automatically.

---

Report 1: Best-Selling Products

The first report uses:

best_sellers_product_cluster_view

This is one of the most valuable reports for ecommerce SEO research.

It can provide:

• Product rank
• Previous rank
• Relative demand
• Product title
• Brand
• Category
• Inventory status

The script queries these fields and organizes them into a dataset.

The important distinction is that this is market-level information.

You are not simply looking at which products sold best on your own website.

You are looking at products appearing in Google’s Shopping market data.

That creates a completely different SEO opportunity.

---

Finding Products That Are Gaining Demand

One of the most useful things you can do with this data is compare current rank with previous rank.

Imagine you find this:

Product	Previous Rank	Current Rank	Change
Product A	42	17	+25
Product B	28	31	-3
Product C	16	8	+8

Product A has made a significant move.

That should immediately trigger an SEO question:

Do we sell Product A?

If you do, check the product’s organic visibility.

If you do not, ask whether the product represents an emerging opportunity for the business.

The script can calculate these movements across the reporting period.

It compares the first and latest appearances of products and calculates the rank change.

This makes it much easier to identify products that are gaining momentum.

---

Report 2: Best-Selling Brands

The second report uses:

best_sellers_brand_view

Instead of looking at individual product clusters, it aggregates the information at the brand level.

The report includes:

• Brand
• Rank
• Previous rank
• Relative demand
• Category

The script queries the brand data separately and produces a brand-level dataset.

This is useful for competitor research.

Suppose your ecommerce store sells products from 50 brands.

The Merchant API could help you identify which brands are gaining market visibility.

You can then compare those brands against your own SEO data.

For example:

Merchant API

Brand A is rising.

Search Console

Brand A is receiving more impressions.

Your website

You have a category page for Brand A but it has weak internal links.

SEO action

Improve the category and brand architecture.

This is a much more evidence-based way to prioritize work.

---

Report 3: Price Competitiveness

Price is another important factor in ecommerce.

A technically strong product page can still struggle if the product is significantly more expensive than competing offers.

Google provides price competitiveness reporting that can help you understand how your prices compare with market benchmarks.

This creates another useful diagnostic question:

“Is this an SEO problem or a commercial competitiveness problem?”

For example, suppose:

• Impressions are high
• Rankings are reasonable
• Product information is complete
• Organic traffic is healthy
• Conversion rate is poor

Price may be one of the factors worth investigating.

SEO should not be viewed separately from the commercial offer.

Visibility, product quality, availability, price, and demand all influence ecommerce performance.

---

How Merchant API Data Can Improve SEO Decisions

The most useful way to think about Merchant API data is as a source of market signals.

For example:

Market signal

A product moves from rank 50 to rank 15.

SEO question

Do we have adequate visibility for this product?

Website investigation

The product exists but has weak internal links and limited organic visibility.

SEO action

Improve the product page, category connections, internal links, structured data, and supporting content.

Measurement

Track organic impressions, clicks, rankings, conversions, and revenue.

The API does not perform the SEO strategy.

It gives you better information for deciding where the strategy should be applied.

---

Using Merchant API Data for Content Strategy

The same process can be applied to content.

Suppose you operate an ecommerce website selling skincare products.

Merchant API data shows that certain product clusters are gaining demand.

You can use that information to determine whether your content architecture reflects the market.

You might need:

• A stronger category page
• New product pages
• Supporting informational content
• Comparison content
• Buying guides
• Use-case pages
• Brand pages

The important thing is that the content strategy is being influenced by market data rather than only by keyword volume.

Keyword research tells you what people search for.

Merchant API data can help show what products and brands are actually important within the Shopping market.

Those are related but different signals.

---

Using Merchant API Data for Internal Linking

Merchant data can also help with internal linking.

Suppose the API identifies a group of products that are gaining demand.

You can check whether those products are adequately connected to the rest of the site.

For example:

Homepage → Category → Subcategory → Product

You may discover that an important product sits several clicks away from the main category.

Or perhaps the product has no contextual links from relevant informational content.

The market signal gives you a reason to investigate the internal link structure.

This is especially useful on large ecommerce websites where thousands of products make manual prioritization difficult.

---

Using Merchant API Data for GEO

The Merchant API is not a direct API for ChatGPT, Gemini, Claude, or other AI search engines.

It does not tell you which brand an AI system will recommend.

That distinction is important.

The GEO value comes from something different.

Merchant API data can help you understand which products, brands, categories, and markets matter.

You can then use those insights to improve how clearly your own website represents those entities.

For example, suppose the data shows that a particular product category is becoming important in the US.

You can ask:

• Do we have a page for this category?
• Is the category clearly defined?
• Are the products connected to the category?
• Are product attributes clearly stated?
• Is our structured data complete?
• Are the brand and product relationships clear?
• Do supporting articles explain the use cases?
• Is the same information represented consistently across the website and Merchant Center?

This creates a useful connection between Shopping intelligence and GEO.

---

Using Country-Level Data for International SEO and GEO

The Merchant API workflow can also be used to investigate different markets.

For example:

COUNTRY = "US"

could be changed to another supported country.

This allows you to compare market signals between countries.

That matters because product demand is not always universal.

A product can be highly competitive in the US but relatively weak in another market.

This can affect:

• International SEO
• Category prioritization
• Product assortment
• Localization
• Content strategy
• GEO strategy

Instead of creating the same ecommerce strategy for every country, you can use market-level data to identify where the biggest opportunities exist.

Choosing the Right Product Category

The script uses Google’s product taxonomy.

You can either query across all categories or specify a category ID.

A broad category is useful when you want to understand a large market.

A narrower category is useful when you want to investigate a specific niche.

For example, a broad report can help answer:

“What is happening in the overall market?”

A narrow report can answer:

“What is happening specifically within this product category?”

The script supports both approaches.

---

Working With Weekly and Monthly Data

The script supports weekly and monthly reporting.

There is an important technical detail here.

Weekly best-seller rows are associated with Mondays.

Monthly rows are associated with the first day of the month.

The script includes a date helper that automatically generates the appropriate reporting dates.

This matters because an apparently valid date range can still return no data if it does not contain an appropriate reporting date.

For example, if you request weekly data for a range that contains no Monday reporting date, an empty response does not necessarily mean something is broken.

---

Exporting the Data

Once the reports have been processed, the script creates Pandas dataframes.

The product and brand datasets can then be exported as CSV files.

For example:

my_category_best_sellers_products.csv
my_category_best_sellers_brands.csv

This gives you a portable dataset that can be used elsewhere.

You can:

• Open it in Excel
• Import it into Google Sheets
• Process it with Python
• Combine it with Search Console data
• Combine it with analytics data
• Build dashboards
• Create automated opportunity scoring

The script’s export section writes the datasets to CSV files after the reporting process is complete.

---

Building an SEO Opportunity Score

Once you combine Merchant API data with your existing SEO data, you can start building an opportunity model.

For example:

Signal	Finding
Market rank	Improving
Relative demand	High
Organic visibility	Low
Product availability	Yes
Internal links	Weak
Conversion rate	Good

This would be a strong candidate for SEO investment.

You could create a simple scoring system based on:

Market demand + rank movement + organic visibility gap + commercial value

This is much more useful than prioritizing pages only by keyword volume.

---

A Practical Merchant API SEO Workflow

The complete workflow can be summarized like this:

1. Choose a market

Select the country you want to investigate.

2. Choose a category

Use Google’s taxonomy to define the product area.

3. Select a date range

Look at recent data or compare multiple periods.

4. Run the Colab script

The script retrieves the Merchant API reports.

5. Identify market movements

Look for products and brands gaining rank or demand.

6. Compare against your website

Check whether your site has adequate visibility for those opportunities.

7. Use the Chrome extension

Bring the findings into your browser-based SEO and GEO research workflow.

8. Prioritize actions

Decide whether the opportunity requires:

• New content
• Product page optimization
• Category expansion
• Internal linking
• Structured data improvements
• Product assortment changes
• Pricing investigation
• Better Merchant Center data

9. Measure the result

Track:

• Organic impressions
• Organic clicks
• Rankings
• Shopping visibility
• Product traffic
• Conversions
• Revenue

This creates a repeatable system rather than a one-time analysis.

---

Turning It Into an Automated System

The real value of the API appears when you use it repeatedly.

You could run the workflow every week.

For example:

Monday

Pull the latest best-seller data.

Tuesday

Compare it against the previous period.

Wednesday

Identify products with large ranking gains.

Thursday

Compare those products with your organic visibility.

Friday

Review the highest-value opportunities.

The process can eventually produce a table such as:

Product	Market Rank	Rank Change	Demand	Organic Visibility	Opportunity
Product A	12	+25	High	Low	High
Product B	24	+8	High	Medium	Medium
Product C	7	-2	Very High	High	Low

The Merchant API provides the market side.

Your SEO systems provide the website side.

Your strategy connects the two.

---

Important Limitations

There are several limitations you should understand.

Relative demand is not search volume

If Google reports HIGH or VERY_HIGH relative demand, do not treat it as a monthly search volume figure.

It is a directional demand signal.

Use it with keyword research, Search Console, analytics, and sales data.

Best-seller data is not your own sales data

The best-seller reports provide market information.

They do not simply represent what your store sold.

That distinction should remain clear throughout your analysis.

Empty results do not always indicate an API failure

A successful query can still return zero rows.

Possible reasons include:

• No data for the selected country
• No data for the category
• Invalid reporting dates
• Account eligibility
• Insufficient published data
• Permission issues

The script checks for empty datasets and provides a warning when no rows are returned.

Colab sessions are temporary

If you use a service account JSON file in Colab, you may need to upload it again after a session resets.

Keep credentials secure

Your service account JSON file should be treated as sensitive credentials.

Do not publish it publicly or commit it to a public code repository.

---

Common Errors

403 PERMISSION_DENIED

Check that:

• Merchant API is enabled
• The service account has access to Merchant Center
• The authenticated identity has sufficient permissions

401 UNAUTHENTICATED

Check your credentials and authentication scope.

If using Colab, confirm that the JSON file exists and the path is correct.

404 NOT_FOUND

Check your Merchant Center account ID.

Query succeeds but returns zero rows

Check:

• Country
• Category
• Date range
• Reporting granularity
• Account eligibility

For weekly reports, make sure your date range contains valid Monday reporting dates.

---

Merchant API vs Traditional SEO Tools

The Merchant API should not replace your existing SEO tools.

It should complement them.

Think of the different data sources this way:

Keyword research

What are people searching for?

Search Console

How does your website appear in Google?

Analytics

What happens after users arrive?

Merchant Center

How is your product data represented to Google?

Merchant API

What market and Shopping signals can be extracted programmatically?

Crawling tools

What technical problems exist on the site?

GEO monitoring

How is your brand represented across AI search and answer engines?

Each source answers a different question.

The Merchant API is valuable because it adds a market-level view to the SEO process.

---

Why This Matters for Ecommerce SEO

Traditional ecommerce SEO often starts with what already exists on the website.

You have a product catalog.

You have category pages.

You have existing rankings.

You then try to improve them.

Merchant API data gives you another approach.

You can start with the market.

What products are gaining demand?

Which brands are gaining ground?

Which categories are changing?

Where are competitors stronger?

Which products are missing from our strategy?

Then bring those findings back to the website.

This changes SEO from a purely website-focused exercise into a market-informed process.

---

Why This Matters for GEO

GEO requires a similar shift.

Instead of only asking:

“What keywords should we optimize?”

you can also ask:

“Which products, brands, categories, and entities matter in this market?”

Merchant API data can help answer part of that question.

If a product category is important in a specific country, your website should make its relationship with that category clear.

That may mean improving:

• Product pages
• Category pages
• Brand pages
• Product attributes
• Structured data
• Internal links
• Supporting content
• Merchant Center data

The API does not guarantee that an AI system will mention your brand.

No such guarantee should be made.

Instead, the opportunity is to use market intelligence to build a stronger information footprint around the entities that matter.

---

From Merchant API to an SEO and GEO Intelligence System

The most interesting opportunity is not using the Merchant API once.

It is turning it into a recurring intelligence system.

The basic model is:

Extract → Analyze → Prioritize → Optimize → Measure

The Colab script handles the extraction and initial processing.

The Chrome extension helps bring the information into your browser-based research workflow.

Your SEO tools provide website performance data.

Your analytics provide commercial outcomes.

Your GEO tools provide visibility across AI search environments.

Together, these sources can create a much richer picture of ecommerce demand.

You are no longer asking only:

“How do we rank this page?”

You can start asking:

“Where is the market moving, and is our website positioned to capture it?”

That is a much more powerful SEO question.

---

Final Thoughts

Google’s Merchant API is more than a technical interface for Merchant Center.

For ecommerce SEOs and GEO practitioners, it can become a source of market intelligence.

You can use it to identify best-selling product clusters, track ranking changes, understand brand competition, analyze relative demand, investigate price competitiveness, and compare markets.

The important part is what you do with the information.

The API itself does not create an SEO strategy.

It gives you data that can improve the strategy.

With the Colab script, much of the technical work involved in querying the API can be automated.

With the Chrome extension, the resulting information can become part of your browser-based SEO and GEO research workflow.

The resulting process is simple:

Google Merchant API

↓

Colab script

↓

Market intelligence

↓

Chrome extension

↓

SEO and GEO analysis

↓

Website optimization

↓

Measurement

That is the real opportunity.

Instead of relying only on keyword volume, rankings, or what competitors are doing on their websites, you can bring Google’s own Shopping market data into the SEO decision-making process.

For ecommerce businesses operating across thousands of products, multiple categories, or multiple countries, that can turn Merchant API data into a repeatable source of SEO and GEO opportunities.

February 24, 2026

AI Visibility, Occupancy Scores, and LLM SEO – Complete Guide

Ever since ChatGPT was launched in late 2023, information retrieval on the internet has undergone a significant transformation. Rather than clicking on links in search engine results pages (SERPs), people now prefer to save time by interacting directly with LLM chatbots. This trend has altered search activity on Google, with ChatGPT processing 1.7 billion search-like requests per day, accounting for 12% of global search traffic.

To respond to this shift, Google launched multiple AI products, including Gemini, AI Mode, and AI Overviews. Combined with the rise of alternative LLMs like Grok, DeepSeek, Claude, and Perplexity, AI-generated responses have continued to expand, displacing the clickable blue links that previously dominated results pages.

This shift has created a new SEO focus: visibility is increasingly about mentions and citations within AI responses, not just traditional rankings.

Why AI Responses Matter for Marketing and Traffic

AI responses are important both as a traffic source and as a marketing channel because:

1. People increasingly use AI to discover and research products, tools, and companies.
2. The traffic benefits of first-page rankings are diminishing, as AI responses push top-ranking pages further below the fold.
3. AI results are more volatile than traditional SERP rankings, creating opportunities for smaller or lesser-known entities to appear in front of their target audience.
4. AI responses are highly query-relevant and often more trusted than information found in standard blog posts.
5. AI adoption is accelerating across apps, websites, and devices, including customer service chatbots, voice assistants, and IoT gadgets.

These factors make LLM visibility an essential competency in modern SEO and digital marketing.

What is AI Invisibility?

Many brands, people, and organizations are well-known among humans, yet there can be a gap between human knowledge and AI model recall. This phenomenon is known as AI invisibility.

AI invisibility is defined as the gap between human recall and model recall:

Human Recall−Model Recall=AI Visibility Gap\text{Human Recall} – \text{Model Recall} = \text{AI Visibility Gap}Human Recall−Model Recall=AI Visibility Gap

Human recall depends on advertising, education, and cultural exposure, while model recall is based on training data, which includes encyclopedias, research articles, books, and public content.

A gap emerges when AI models fail to recognize entities that are widely known by humans, often due to limitations in their training data or the way information is structured online.

Factors Contributing to AI Invisibility

Four main factors contribute to AI invisibility:

1. Source Asymmetry

If references to an entity are behind paywalls or in closed-source datasets, AI models may underrepresent it. For example, a popular politician who only publishes paywalled articles may be less visible in AI responses than in human awareness.

2. Category Misalignment

How an entity defines itself may differ from how models categorize it. Differences between self-perception and public perception can create misalignment, affecting AI visibility.

3. Absence of Structured Data Anchors

Structured data, such as Product, Person, or Article schema, helps AI models identify and classify entities. Without these “anchors,” AI may fail to recognize important entities.

Analogy: Structured data is to AI what uniforms and badges are to humans. Without them, a random observer might underreport the number of soldiers on a road; similarly, AI underreports entities without schema.

4. Decay Tendencies

AI responses can diminish over time due to model updates, competitive content, or changes in data ingestion. Even repeated prompts may see declining mentions over 30, 60, or 90-day periods.

Why Traditional SEO Metrics Fall Short

Standard SEO metrics are insufficient for measuring AI visibility because:

• Snapshot Bias: AI responses are less consistent than SERPs. The same prompt can produce different answers, making static ranking measures unreliable.
• Framing Bias: Minor changes in prompt wording can significantly change AI responses, unlike traditional keyword rankings.
• Dynamic Decay: AI mentions can disappear or shift as models update, unlike relatively stable search rankings.

To measure AI visibility effectively, new metrics such as occupancy scores have been developed.

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Occupancy Scores: Measuring AI Visibility

Occupancy scores are standardized metrics for capturing entity mentions within LLM responses. There are two main types:

1. Answer Space Occupancy

This measures the share of answer surfaces where an entity appears when the same prompt is repeated. For example:

Prompt: “What is the oldest civilization on earth?”
If “Egyptian civilization” appears in 3 out of 4 responses, its Answer Space Occupancy is 75%.

2. Prompt Space Occupancy

This measures the share of related prompts within a category or vertical that mention the entity. Dimensions include:

• Occupancy: % of prompts citing the entity
• Positioning: Average rank of the entity in responses
• Decay: Persistence over time (30/60/90 days)

Example: For acne treatment brands, prompts like “best cream for pimples” and “best cream for blackheads” form a prompt space. If Neutrogena appears in 6 of 7 prompts, its prompt space occupancy is high.

How to Improve Your AI Visibility & Occupancy Scores

1. Be Mentioned in Credible Sources

LLMs learn from reputable content. Secure citations in:

• Media outlets
• Industry publications
• Academic and technical reports
• Reputable directories and knowledge bases

Consistency is key; repeated mentions build credibility with AI models.

2. Increase Industry Centrality

Appear alongside leading entities in your category. Achieve this by:

• Guest writing on respected platforms
• Collaborating with known organizations
• Speaking at conferences and events

Network centrality improves AI recognition.

3. Make Your Website AI-Friendly

Ensure your content is readable and structured:

• Mobile-optimized and fast-loading
• Clear headings and structured layout
• Avoid paywalls on key entity information
• Include About pages, FAQs, and detailed product descriptions

4. Strengthen Associations

Repeatedly link your brand with its industry, category, and geography.
Example: A fintech company in Nigeria should appear alongside African fintech, digital payments, Flutterwave, and Paystack.

5. Use Structured Data

Implement schema markup for:

• Company name and location
• Products or services
• Founders and social profiles

Structured data clarifies identity and relationships for AI models.

Conclusion

LLM SEO is no longer optional—it is essential for brands aiming to be discoverable in AI-driven search. By understanding AI invisibility, measuring occupancy scores, and strategically improving mentions, structured data, and centrality, you can ensure your brand is visible and trusted across LLMs like ChatGPT, Claude, and Perplexity.

Traditional SEO metrics are no longer sufficient; success in AI visibility requires a strategic, multi-layered approach that integrates content, backlinks, schema, and network positioning.

February 15, 2026

How To Use the Copyscape API for SEO With this Python Script

If you are serious about SEO, making sure your content is original is key. Duplicate content can hurt your search engine rankings and reduce your website traffic. One tool that helps solve this problem is the Copyscape API. This tool allows you to check your content for duplication across the web in a programmatic way.

What is the Copyscape API

Copyscape is a popular plagiarism detection service. It helps you find content that has been copied or duplicated from your website or any other source. The API version of Copyscape lets developers integrate its plagiarism detection capabilities into software or scripts. This is especially useful for websites with many pages or for agencies managing multiple clients.

Some of the main features include:

• Plagiarism detection: Check if content has been copied anywhere on the internet.
• Batch processing: Check multiple URLs or content pieces at once.
• Flexible integration: Use multiple programming languages to work with the API.

These features make it a valuable tool for anyone involved in SEO, content publishing, or digital marketing.

Why SEO Professionals Use the Copyscape API

The Copyscape API is useful for different groups:

• Content publishers: Verify content originality before publishing.
• SEO agencies: Monitor client websites to protect content from plagiarism.
• Educational institutions: Check student submissions for academic integrity.
• Content aggregators: Filter out duplicated content from multiple sources.

In addition, detecting duplicated content can help prevent negative SEO tactics. Some people copy content from high-ranking websites and publish it elsewhere to reduce the original site’s authority. By regularly checking your content, you can identify and address this type of issue.

How to Get Started with the Copyscape API

To start using the Copyscape API, follow these steps:

1. Create a Copyscape account and purchase credits. Each search costs a small fee, usually around $0.03 per search.
2. Obtain your API key from your account. This key allows you to access the Copyscape servers programmatically.
3. Prepare a list of URLs you want to check. This is usually done in an Excel file with a column called URL.
4. Use a Python script to send requests to the API and gather duplication data.

Here is a simple example using Python:

This script reads a list of URLs, sends them to Copyscape, and collects duplication information in an Excel file. You can then review which content has been copied and take action if necessary.

Interpreting Results

Once the script runs, the output Excel file will show:

from urllib.request import urlopen
from bs4 import BeautifulSoup
import pandas as pd

# Copyscape credentials
username = "your_username"
myapikey = "your_api_key"

# Load URLs from Excel
df = pd.read_excel('urls.xlsx')
list_urls = df['URL'].tolist()

# Store results
all_data = []

for url in list_urls:
    try:
        page = urlopen(f"https://www.copyscape.com/api/?u={username}&k={myapikey}&o=csearch&c=10&q={url}")
        soup = BeautifulSoup(page, 'xml')
        results = soup.find_all("result")
        for result in results:
            data = {
                'URL': result.find("url").text,
                'Title': result.find("title").text,
                'Text Snippet': result.find("textsnippet").text,
                'Min Words Matched': result.find("minwordsmatched").text,
                'View URL': result.find("viewurl").text,
                'Percent Matched': result.find("percentmatched").text
            }
            all_data.append(data)
    except Exception as e:
        print(f"Error processing {url}: {e}")

df_combined = pd.DataFrame(all_data)
df_combined.to_excel('results.xlsx', index=False)

print("Data extraction complete. Excel file saved as 'results.xlsx'.")

• The original URL
• Titles of copied content
• A snippet of the matched text
• How many words matched
• The percentage of duplication

With this information, you can determine which content needs to be rewritten or protected.

Conclusion

Using the Copyscape API for SEO is a smart way to maintain content originality and protect your website from plagiarism. Whether you are managing a single blog or a large site, the API makes it easier to detect duplication, monitor client content, and take action when necessary. By integrating it into your workflow, you can improve your SEO strategy and keep your content unique.

November 16, 2025

How to Use Python Scripts to Get TF IDF Scores for SEO Content Audits

If you want to improve your SEO performance, you need to understand how your content compares to other pages that talk about the same topic. One simple way to do this is to use TF IDF. TF IDF stands for term frequency inverse document frequency. It is a statistical method that shows how important a word is inside one page and across a group of pages.

In this guide, you will learn what TF IDF means, why it matters for SEO, and how to use a Python script to calculate TF IDF scores for any list of URLs.

What TF IDF Means

TF IDF is a combination of two parts:

1. Term Frequency

This measures how often a word appears on one page. A word that appears many times will have a higher term frequency.

2. Inverse Document Frequency

This measures how rare or common the word is across all the pages you are comparing. If a word appears in every page, it is not special. If a word appears in only one or two pages, it is more important.

When you multiply these two parts, you get the TF IDF score. A high TF IDF score means the word is important in that page and is not too common across the other pages.

Why TF IDF Matters for SEO

Before search engines began using advanced language models, TF IDF was one of the main ways they measured relevance. Even today TF IDF can help you understand how your content focuses on important keywords.

Here is what TF IDF can help you do:

• Discover the words your page truly emphasizes
• Check if your page aligns with the keywords you want to target
• Compare your page with competitor pages
• Identify content gaps
• Improve on-page SEO

TF IDF gives you a more objective picture than simple keyword counts.

What You Need Before Running the Script

To calculate TF IDF scores with Python, you need:

• A list of URLs
• A Python environment such as Google Colab
• A few libraries like TextBlob, BeautifulSoup, Pandas, and Cloudscraper

You can paste as many URLs as you want. Some users work with twenty pages. Others go up to hundreds.

The Python Script That Calculates TF IDF

Below is the script used in the video demonstration. It does three main things:

!pip install cloudscraper

import cloudscraper
from bs4 import BeautifulSoup
from textblob import TextBlob as tb

list_pages = [
    "https://emmanueldanawoh.com/how-to-use-google-bert-scores-in-seo-content-writing/",
    "https://emmanueldanawoh.com/how-to-avoid-being-a-victim-of-domain-squatting-homograph-attacks/",
    "https://emmanueldanawoh.com/seo-content-writing-how-to-optimize-for-entity-salience/",
    # Add more URLs as needed
]

scraper = cloudscraper.create_scraper()

list_content = []

for x in list_pages:
    content = ""
    html = scraper.get(x)
    soup = BeautifulSoup(html.text)

    for y in soup.find_all('p'):
            content = content + " " + y.text.lower()

    list_content.append(tb(content))

import math
from textblob import TextBlob as tb

def tf(word, blob):
    return blob.words.count(word) / len(blob.words)

def n_containing(word, bloblist):
    return sum(1 for blob in bloblist if word in blob.words)

def idf(word, bloblist):
    return math.log(len(bloblist) / (1 + n_containing(word, bloblist)))

def tfidf(word, blob, bloblist):
    return tf(word, blob) * idf(word, bloblist)

import nltk
nltk.download('punkt')

list_words_scores = [["URL","Word","TF-IDF score"]]
for i, blob in enumerate(list_content):
    scores = {word: tfidf(word, blob, list_content) for word in blob.words}
    sorted_words = sorted(scores.items(), key=lambda x: x[1], reverse=True)
    for word, score in sorted_words[:5]:
        list_words_scores.append([list_pages[i],word,score])

import pandas as pd

df = pd.DataFrame(list_words_scores)
df.to_excel('filename.xlsx', header=False, index=False)

1. Scrapes the content of each URL
2. Extracts all paragraph text
3. Calculates TF IDF scores and stores the top words for each page

What the Output Means

When the script finishes running, you will get an Excel file with three columns:

• URL
  The page the script analyzed
• Word
  The most important words on that page
• TF IDF score
  A score that shows how strongly each word stands out

This is helpful because it shows you which terms your page is truly known for. If the top TF IDF terms on your page do not match the target keywords you want to rank for, you may need to adjust your content.

---

How to Use TF IDF in Your SEO Process

Here are practical ways to use these scores:

1. Improve keyword targeting

Check if your page highlights the right phrases.

2. Compare against competitors

Run the script for competitor pages. Compare their top terms with yours.

3. Guide content rewrites

If your high-value keywords are missing, you will know exactly where to focus.

4. Spot content strengths

Some pages may already have a strong topical focus. TF IDF helps you identify them.

Final Thoughts

TF IDF is simple but powerful. It gives you a clear understanding of how your content communicates its main ideas. When combined with Python, you can run large content audits quickly and with very little manual work.

If you want to take your SEO work to the next level, learning how to calculate TF IDF with Python is a great step forward.

November 16, 2025

How to Use the Wayback Machine API for SEO

If you work in SEO, you already know how important it is to understand what changed on a website over time. Sometimes a site drops in ranking, and you need to know why. Other times you want to check how a competitor changed their content or design. The Wayback Machine is one of the best tools for this job. It stores snapshots of millions of websites so you can travel back in time and see older versions of any page.

In this guide, you will learn what the Wayback Machine does, why it matters for SEO, and how you can use its API along with a simple Python script to pull historical snapshots at scale.

What the Wayback Machine Does

The Wayback Machine is a digital archive of the internet. It crawls websites and saves snapshots of pages at different points in time. You can visit the website, enter any URL, and browse how that page looked on specific dates.

Here are the main things it offers:

• A large archive of snapshots from many years ago
• A date selector that lets you choose a specific day
• A search feature that works across URLs and domains

With this tool, you can study any website and see its past content, layout, and structure.

Why the Wayback Machine Matters for SEO

SEO changes all the time. When a site drops in traffic, the problem may be something that changed months ago. The Wayback Machine helps you find clues.

Here are ways SEOs use it:

1. Analyze historical content

You can check what your content looked like before rankings changed. Maybe a section was removed. Maybe keywords disappeared. Maybe the structure changed.

2. Recover lost content and backlinks

If a page was deleted or rewritten, older versions may still exist in the archive. This helps you restore useful content or rebuild lost link value.

3. Study competitor strategy

Competitors are always updating their pages. By checking their old snapshots, you can study their design choices, their content growth, and the changes they made over time.

4. Audit site performance

Large SEO audits often need long term data. The Wayback Machine can reveal patterns that help explain traffic drops or improvements.

The Practical Use of the Wayback Machine API

Checking one or two URLs is easy. Checking hundreds is not. This is where the API helps. The API lets you interact with the Wayback Machine using code so you can pull snapshots for many URLs at once.

The Wayback Machine offers three main APIs:

• JSON API
• Memento API
• CDX API

In this guide, we will focus on the Memento API because it is simple to use and works well with Python.

What You Need Before Running the Script

To use the Python script, prepare two things:

1. An Excel file that contains all the URLs you want to study
2. A date range that defines how far back you want to look

For example, you can select a one-year period, such as June 2023 to June 2024.

Your Excel sheet should have:

• No empty rows
• No empty columns
• A header in the first row
• URLs starting from the second row

The Python Script That Pulls Wayback Machine Data

Here is the script used to collect snapshots:

# Install the necessary libraries
!pip install --upgrade wayback
!pip install pandas openpyxl

import wayback
import pandas as pd
from datetime import date
from openpyxl import load_workbook  # Import for reading Excel files

# Define paths and date range
excel_file = "time_travel_pages.xlsx"  # Replace with your Excel file path
sheet_name = "Sheet1"  # Replace with the sheet name containing URLs
date_from = date(2023, 6, 1) # date( Year, Month, Day)
date_to = date(2024, 6, 1) # date( Year, Month, Day)

# Initialize a list to store records
records_list = []




# Create Wayback Machine client
client = wayback.WaybackClient()

# Read URLs from Excel
wb = load_workbook(filename=excel_file, read_only=True)
sheet = wb[sheet_name]  # Access the specified sheet

# Loop through each row in the sheet (assuming URLs are in the first column)
for row in sheet.iter_rows(min_row=2):  # Skip the header row (row 1)
  url = row[0].value  # Assuming URLs are in the first column (index 0)
  if url:  # Check if there's a value in the cell
    # Search the Wayback Machine
    for record in client.search(url, from_date=date_from, to_date=date_to):
      # Construct memento URL (optional, if needed)
      # memento_url = f"http://web.archive.org/web/{record.timestamp}/{record.url}"

      # Collect data
      record_data = {
          'original_url': record.url,
          'timestamp': record.timestamp,
          # Use memento_url if needed, otherwise use view_url
          'memento_url': record.view_url  # Or memento_url if constructed
      }
      records_list.append(record_data)

# Create DataFrame and export to Excel
df = pd.DataFrame(records_list)
df['timestamp'] = df['timestamp'].dt.tz_localize(None)
df.to_excel('wayback_records.xlsx', index=False)

print("Data exported to wayback_records.xlsx")

When you run the script:

• It reads your Excel file
• It checks the Wayback Machine for each URL
• It collects snapshots that fall within your date range
• It exports all results into a spreadsheet

Your output file will contain:

• The original URL
• The exact snapshot timestamps
• A memento link you can click to see how the page looked on that date

This gives you a clean archive of snapshot data for your entire URL list.

How This Helps You in SEO

With your output spreadsheet, you can now:

• Compare content across dates
• Detect structural changes
• Restore old high-performing copy
• Track competitor updates
• Run timeline-based audits

This process speeds up SEO analysis and makes it easier to explain historical issues to clients or teammates.

Final Thoughts

The Wayback Machine is one of the most powerful but underrated tools in SEO. When paired with the API and a simple Python script, it becomes even more useful. You can collect large amounts of historical page data in minutes and use it to improve rankings, recover content, and study competitors.

If you want to level up your SEO practice, start using the Wayback Machine API. It gives you the power to see the past and improve the future.

November 23, 2025

How to Use the PageSpeed Insights API for SEO

In today’s digital world, page speed is more than just a convenience for visitors. It has become a key factor in search engine rankings. Google uses page speed as a signal of user experience, and understanding how your website performs can make a huge difference in SEO. That is where the PageSpeed Insights API comes in. It allows you to check the performance of multiple web pages at once and get actionable suggestions to improve them.

Why Page Speed Matters for SEO

Google cares about how fast your website loads. Slow websites can hurt your rankings and prevent your pages from appearing at the top of search results. Metrics such as Time to First Byte, First Contentful Paint, Largest Contentful Paint, and Cumulative Layout Shift all play a role in measuring page speed. By analyzing these metrics, you can understand what might be holding your site back.

For SEO professionals, this means having a tool that can inspect hundreds or even thousands of URLs quickly. Checking pages one by one is not practical for large websites. The PageSpeed Insights API allows you to do this efficiently and gain insights that can directly impact your SEO strategy.

Understanding the PageSpeed Insights API

The PageSpeed Insights API analyzes the content of a web page and provides suggestions to improve performance. It produces a performance score that ranges from zero to 100, measures core web vitals like LCP, FID, and CLS, and provides diagnostic information about your site’s compliance with best practices. You also get recommendations on how to improve speed and estimates of the impact of those changes.

Getting Started with the API

To use the API, you first need a Google API key. You can get this key by visiting the Google PageSpeed Insights documentation page. Once you have the key, you will include it in your Python script to authenticate your requests.

Next, you will need a list of URLs you want to analyze. This can be a handful of pages or thousands of URLs. If your list is large, you can use a spreadsheet to organize your URLs in a format that the script can read. Each URL should be wrapped in quotation marks and separated by commas.

Running the Script

The Python script fetches data from the API for each URL in your list. It extracts performance metrics from the API response and saves them in a CSV file. You can then open this file in Google Sheets or Excel to analyze the data.

Each row of the CSV file includes the URL, the metric name, and the numeric value of that metric. Metrics can include DOM size, modern image formats, unused JavaScript, off-screen images, boot-up time, network RTT, duplicated JavaScript, and many others. This allows you to compare different pages on your site and identify areas for improvement.

Analyzing Your Data

Once your data is in a spreadsheet, you can start to see patterns. Some pages may have slow loading times because of large images, unoptimized CSS, or too much JavaScript. Others may perform well in some metrics but need improvement in others. Using this information, you can prioritize fixes that will have the biggest impact on both speed and SEO performance.

Conclusion

The PageSpeed Insights API is a powerful tool for SEO professionals. It allows you to inspect many URLs at once, get a detailed look at performance metrics, and uncover actionable insights. By using this tool, you can improve your website speed, enhance user experience, and increase your chances of ranking higher in search results.

Even if you are not a developer, this API can be a game-changer. With a little setup, you can automate performance checks and make data-driven decisions for your website SEO strategy.