If you’re serious about growing organic traffic in 2026, you can’t rely on rank trackers and gut feel alone. You need to see what Google actually sees. Google Search Console (GSC) is the closest thing to a direct line into Google’s understanding of your website. 

It shows what queries you appear for, which pages earn clicks, what gets indexed or excluded, and which technical issues reduce visibility. With the right setup and a clear process for reading its reports, GSC becomes a weekly tool for finding quick wins and protecting your search performance.

This guide covers how to use Google Search Console in 2026 from start to finish: how to set it up properly, and how to use its reports to make decisions that improve rankings and clicks.

What Is Google Search Console?

Google Search Console is a free Google tool that helps you monitor your website’s search performance and technical SEO health. It shows how your pages appear in search results, what queries trigger impressions, and whether Google can crawl and index your content correctly. 

In 2026, GSC data is especially valuable because it doesn’t only help with “rankings”—it helps you build the foundation that gets your pages surfaced in newer search experiences (including AI-driven ones).

What you can do with Google Search Console

• Track clicks, impressions, CTR, and average position
• See what Google has indexed vs. excluded (and why)
• Submit sitemaps and request indexing for updated pages
• Diagnose technical SEO issues (errors, redirects, canonical confusion, 404s)
• Monitor Core Web Vitals and page experience signals
• Validate structured data (Breadcrumbs, Videos, Products, etc.)
• Review external and internal linking signals

Google Search Console vs Google Analytics 4: What Each Tool Is For

People often mix Google Search Console and Google Analytics 4 (GA4) up, so here’s the clean way to think about it:

• GSC answers: How do people find you on Google Search, and how does Google understand your site?
• GA4 answers: What do people do after they land on your site? Engagement, events, conversions, and journeys.

They overlap a bit on “organic traffic,” but they’re measuring different parts of the funnel. If you want the full story, you use both.

How to Set Up Google Search Console (2026)

To start, you sign in with a Google account and add a “property” (the site you want to track). You’ll choose between:

• Domain Property (recommended for most businesses)
• URL Prefix Property (useful for specific sections like /blog/)

Option 1: Add a Domain Property (Best for full coverage)

A Domain property gives the widest view because it includes:

• http + https
• www + non-www
• all subdomains (like blog.yoursite.com)
• all paths

Steps (Domain Property)

1. In Search Console, choose Domain
2. Enter your root domain only (example: yoursite.com)
3. Copy the TXT record Google provides
4. Open your DNS provider (GoDaddy, Cloudflare, etc.)
5. Add a new TXT record:
   • Type: TXT
   • Host/Name: @ (or leave blank depending on provider)
   • Value: paste the TXT string from GSC
   • TTL: default / 1 hour is fine
6. Back in GSC, click Verify

Important: DNS updates can take minutes—or sometimes many hours. If it fails right away, it doesn’t always mean it’s wrong.

Option 2: Add a URL Prefix Property (For specific site areas)

A URL Prefix property tracks only one exact version of a site, like:

• https://www.yoursite.com/
• or https://www.yoursite.com/blog/

This is useful if you want to monitor a subfolder separately (or control access for teammates by scope).

Verification options (URL Prefix)

Google usually offers multiple methods, like:

• HTML file upload (often recommended)
• Meta tag
• Google Analytics
• Google Tag Manager
• DNS / domain provider

How to Use the HTML File Method

Best for: when you want a simple, direct, and stable verification method and you (or your web developer) can upload a file to the site’s directory.

1. Choose URL Prefix
2. Enter the full URL (example: https://www.yoursite.com/blog/)
3. Download the verification HTML file
4. Upload it to the correct root directory (based on the prefix you entered)
5. Click Verify

Note: Keep that verification method active. If you remove the file or tag later, you can lose access.

How to Use the Meta Tag Verification (HTML tag)

Best for: when you can edit your site’s <head> (CMS theme settings, plugin, header script field).

1. In GSC, choose URL Prefix and enter the full URL (e.g., https://www.yoursite.com/blog/), then click Continue
2. Select HTML tag
3. Copy the meta tag Google gives you (it looks like:
   <meta name=”google-site-verification” content=”…”>)
4. Add it to your site’s homepage <head> section:
   • WordPress: use an SEO plugin (like adding verification in the plugin’s “Webmaster Tools” area), or insert into header via theme settings / header scripts plugin
   • Shopify: Online Store → Themes → Edit code → theme.liquid → paste inside <head>
   • Wix/Squarespace: SEO / Marketing tools → Site verification → paste code
5. Publish/save changes
6. Go back to GSC and click Verify

Note: Don’t remove the meta tag later, or verification can break.

How to Use Google Analytics Verification (GA)

Best for: when GA is already installed and you have edit/admin access to that GA property.

1. In GSC, choose URL Prefix → enter the full URL → Continue
2. Select Google Analytics
3. Make sure GA is installed on the site using the same Google account (or you have the required permissions)
4. Confirm the GA tracking code is firing on the site (best to check the homepage loads GA)
5. In GSC, click Verify

Common reasons this method can fail:

• GA is installed via a different account (no permission)
• Tracking code isn’t on the verified URL/version of the site
• GA tag is blocked by consent mode until user accepts (can interfere)

How to Use Google Tag Manager Verification (GTM)

Best for: when the site runs tracking via GTM and you have container-level access.

1. In GSC, choose URL Prefix → enter the full URL → Continue
2. Select Google Tag Manager
3. Confirm GTM is installed properly (both the <head> and <body> snippets are placed as instructed)
4. Confirm you’re logged into the Google account that has the right GTM permissions
5. In GSC, click Verify

Common reasons this method can fail:

• Only one GTM snippet was installed (head OR body, not both)
• Wrong container is installed
• The GTM snippet is blocked/conditioned by cookie consent until opt-in

How to Use DNS / Domain Provider Verification (via provider)

Best for: when you can access DNS (same benefit as Domain verification, but used under URL Prefix options sometimes).

1. In GSC, choose URL Prefix → enter URL → Continue
2. Select Domain name provider (or DNS verification option)
3. Choose your provider if listed (some providers have guided steps)
4. Copy the TXT record value Google gives you
5. In your DNS settings, add a new TXT record:
   • Host/Name: usually @ (or blank depending on provider)
   • Value: paste Google’s TXT value
   • TTL: default / 1 hour
6. Save DNS changes
7. Return to GSC → click Verify

Tip: DNS propagation can take time. If it fails, retry later.

Owners, Users, and Permissions

Access matters because GSC isn’t just reporting—it can affect indexation workflows.

Owners

Owners can view all data, change settings, submit sitemaps, request indexing, and manage users.

• Verified owner: the one who completed verification
• Delegated owner: granted owner access by a verified owner

Users

Users can view data, but their abilities vary:

• Full user: broad data access + some actions
• Restricted user: limited visibility
• Associate: can perform specific tasks but may not access the full UI

How to add a user to your Google Search Console Property

1. Go to Settings → Users and permissions
2. Click Add user
3. Enter email + choose access level
4. Save

How to Submit a Sitemap in GSC

A sitemap helps Google discover your important URLs more efficiently.

Steps

1. Go to Sitemaps
2. Enter your sitemap URL (example: https://yoursite.com/sitemap.xml)
3. Click Submit
4. Check the status (Success vs errors)

If you get errors, fix the root cause first—then resubmit or validate.

Google Search Console Reports You Should Actually Use

Let’s break down the reports that move the needle.

Performance Report (Search Results)

This is where you see how your site performs in Google Search.

Key metrics inside:

• Clicks: how many visits you got from search results
• Impressions: how often your pages appeared
• CTR: clicks divided by impressions
• Average position: average ranking spot
• Queries: What keywords your website is showing up for.

What to look for (practical wins):

• High impressions + low CTR: Your page is showing, but not getting chosen. Improve title + snippet clarity, match intent, and make the “why click this” obvious.
• Queries you expected but don’t see: Either you’re not ranking at all (content gap), or Google doesn’t think your page matches the intent.
• Pages stuck around positions 8–15: These are prime “almost there” pages. A few upgrades (better answers, stronger internal links, updated content) can push them into the top results.

Pro tip: Compare time periods (e.g., last 28 days vs previous 28 days) to spot real movement.

AI-Powered Configuration Tool (New in Google Search Console)

Google Search Console added an AI-powered configuration feature inside the Performance report to help you build the exact view you need faster. Instead of manually clicking filters, dimensions, and comparisons, you describe what you want in plain language and GSC configures the report for you.

What the AI Configuration Tool does

The AI configuration tool can automatically set up:

• Filters (query, page, country, device, search appearance where available).
• Date ranges and comparisons (example: last 28 days vs previous 28 days).
• Metric selections (clicks, impressions, CTR, position).
• Table and chart configuration choices within the Performance report.

What it Does not do

Google is clear that this tool is for configuring the report, not for doing advanced analysis actions. It cannot do things like sorting the table by “highest clicks,” exporting data, or performing spreadsheet-style computations for you.

Where to Find it in GSC

1. Open Google Search Console
2. Click Search results (Performance report)
3. Look for the AI option such as a banner or button that lets you customize using AI
4. Enter your request in natural language and apply the suggested configuration

Prompts to Use

When I use this feature, the best inputs are specific. Include these elements in one request:

• Dimension: query or page
• Metric focus: clicks, impressions, CTR, position
• Time range: last 7 days, 28 days, 3 months
• Comparison: previous period, YoY if available in that range
• Context filter: device, country, page folder, branded vs non-branded

I also have an in-depth guide on how to use Google Search Console’s new AI configuration tool that you can use. 

URL Inspection Tool (Best for page-level troubleshooting)

If there’s one feature you’ll use during launches, updates, and debugging—it’s this.

URL Inspection tells you:

• Is the page indexed?
• When was it last crawled?
• Does it have issues with canonical, robots, or rendering?
• Is structured data detected and valid?

When to use “Request Indexing”

Use it when you:

• published a new page
• made major updates
• fixed an important technical issue

It’s still a request, not a guarantee—but it often speeds up reprocessing.

Page Indexing Report (Indexing → Pages)

This shows which URLs are indexed and which are excluded, plus the reasons.

Why it matters: If a page isn’t indexed, it won’t show up in search results. (No index = no organic visibility.)

Common “not indexed” reasons and what they mean

• Not found (404): the URL doesn’t exist. Redirect it if it used to matter.
• Redirected: normal if you intentionally redirected URLs.
• Duplicate / canonical issues: Google is choosing another version. Fix canonicals and internal linking.
• Crawled, currently not indexed: Google knows it exists but hasn’t indexed it yet (could be quality, duplication, crawl priorities).

After fixing index coverage errors, you can click Validate Fix so Google re-checks the set of impacted URLs.

Sitemaps Report (Indexing → Sitemaps)

This confirms whether Google can read your sitemaps and how many URLs it discovers from them.

Pay attention to:

• Status (Success, Has errors, Couldn’t fetch)
• Last read date
• Discovered pages count

If Google “couldn’t fetch,” use a live URL test or check if the sitemap is blocked, redirected, or returning the wrong format.

Core Web Vitals Report (Experience)

This report groups URLs into:

• Good
• Needs improvement
• Poor

Core Web Vitals focus on:

• LCP: how fast main content loads
• INP: responsiveness to user interaction
• CLS: layout stability (no unexpected jumps)

How to use this report well: When you see failures across many pages, it’s usually a template/site-level issue (theme, scripts, image handling), not “one page” being the problem.

Enhancements (Structured Data Reports)

Enhancements show structured data Google detects—like:

• Breadcrumbs
• Videos
• FAQs (if applicable to your implementation)
• Products / Reviews (site-dependent)

If items are invalid, Google may not show rich results. Fix the markup, then validate.

Manual Actions (Penalty Check)

This tells you if your site has a manual penalty due to spam policy violations.

• No issues detected: you’re clean
• Issues detected: address the violation and submit reconsideration (after fixing root causes)

If rankings collapse overnight, this report should be one of your first checks.

Links Report (External + Internal Links)

This report helps you understand:

• Top linked pages (backlinks)
• Top linking sites
• Common anchor text
• Internal linking distribution

Note: Internal links are the easiest authority lever you control. If your most important pages aren’t getting internal links, you’re making Google work harder than it needs to.

Shopping (If you’re an eCommerce / product site)

If you use product structured data, you may see Shopping-related reports such as:

• Product snippets
• Merchant listings
• Shopping tab eligibility issues

Fix invalid items, then test with Google’s Rich Results tooling.

Pro Tips: Getting More Value from GSC Data

Connect GSC with GA4

This gives you a fuller story: search queries and landing pages alongside engagement and conversions.

Use case: If a page ranks and gets clicks but doesn’t convert, you can improve content alignment, UX, offers, or CTAs.

Build dashboards in Looker Studio

If you want leadership-friendly reporting (and a clear narrative), Looker Studio is where GSC shines—especially when combined with revenue or CRM data.

Use the Search Console APIs (Advanced)

If you have dev support, APIs can automate:

• Search analytics extraction
• URL inspection at scale
• Sitemap monitoring workflows

FAQs About Google Search Console

What does Google Search Console do?

Google Search Console shows how your website appears in Google Search, what queries trigger your pages, and whether Google can crawl and index your URLs properly. It also highlights technical and experience issues that can reduce visibility.

Is Google Search Console free?

Yes. Google Search Console is a free tool available to any verified site owner or manager.

Should I choose Domain or URL Prefix property?

Choose Domain if you want full visibility across protocols and subdomains. Choose URL Prefix if you only want to track a specific site version or section (like a blog folder).

How long does it take for GSC data to appear?

Newly verified properties often take time to populate. Performance data may appear sooner than some technical reports, but it’s normal to wait before everything fills in.

Why is my page “discovered but not indexed”?

It usually means Google knows the URL exists but hasn’t indexed it yet. Common causes include low perceived value, duplication, weak internal linking, or crawl prioritization.

Does “Request Indexing” guarantee I will rank?

No. It requests crawling and potential reprocessing, but ranking still depends on relevance, content quality, competition, and technical signals.

Key Takeaway

GSC is one of those tools that feels “basic” until you realize it’s literally Google telling you what it sees—and what it can’t. In 2026, with search becoming more AI-shaped, your technical foundation and clarity of content matter even more.

The post Google Search Console Guide 2026: Setup, Reports, Indexing & SEO appeared first on SEO Services Agency in Manila, Philippines.

Google Search Console officially released its AI-powered configuration tool for users worldwide. This new feature is designed to simplify how marketers and SEO teams interact with complex search performance data every day.

Understanding website performance has traditionally required a lot of manual clicking. You had to set filters for dates, countries, and devices one by one. Now, we can simply “talk” to the tool (through prompts) and get the specific report configuration we need much faster.

The big shift here isn’t that AI is doing SEO for us. The shift is that AI is removing the repetitive parts of report setup so we can spend more time on what matters: decisions, insights, and action.

What is the AI-Powered Configuration Tool?

The AI-powered configuration tool is a new assistant found inside the Performance report in Google Search Console. It uses natural language processing to turn our sentences into technical report filters. Instead of navigating multiple menus, we type what we’re looking for in plain English.

This tool reduces the time spent on manual data mining. It helps us focus on making decisions rather than spending energy just to set up views, filters, and comparisons. It’s also a meaningful step toward making high-level SEO reporting more accessible to more users.

Author’s note: The tool’s purpose is configuration—building the report view—rather than interpretation or diagnosis. Treat it like a smart filter builder, not an SEO consultant.

How to Access the Tool

When you open your Performance report, look for a filter icon at the top besides the reset filter button. This button will lead you to customize your report using the power of artificial intelligence. Clicking this icon opens a simple prompt box where you can type your requests.

Once you enter a prompt, the AI instantly applies the correct filters for you. It can select metrics, set date ranges, and even compare different time periods. You then review the filters to ensure they match your original intent perfectly.

Author’s Note: Right after the AI applies a configuration, we recommend you verify these 3 things before making conclusions: 

• Did it use the exact period you intended (last 28 days vs last 30 vs calendar month)?

• Are you looking at Queries, Pages, Country, Device, or Search appearance? (Wrong dimension = wrong story.)

• If you asked for a comparison, confirm it’s actually “Compare” and not just a changed range.

Core Features of the AI Assistant

The tool is quite versatile and handles several different parts of report setup. It is built to understand common SEO questions and translate them into data views. Here are the main things the AI can do for you right now.

Smart Filtering and Date Comparison

You can ask the tool to show data for specific queries or pages. For example, you might ask to see searches from mobile devices in the Philippines. The AI will filter out everything else and show you exactly that specific data.

Here’s the result: 

Setting up date comparisons used to take several clicks and careful selection. Now, you can simply ask the AI to compare this month to last month. It can even handle year-over-year comparisons for specific quarters or busy seasons.

Author’s Note: If you want this feature to actually drive decisions (not just prettier reports), do this weekly:

• Prompt 1: “Compare last 7 days vs previous 7 days for clicks and impressions.”

• Prompt 2: “Show queries with impressions up but CTR down.”

Action: Update titles/meta for the top opportunities first, then re-check next week. (That’s a real feedback loop.)

Automatic Metric Selection

The tool knows which metrics are most important for your specific questions. If you ask about visibility, it will toggle the Impressions and Position checkboxes. If you ask about traffic, it will focus on Clicks and Click-Through Rate.

This ensures you are always looking at the most relevant data for your goals. You no longer have to manually check or uncheck boxes to clear your view. The AI streamlines the interface to show only what matters most to you.

Here’s an example: 

Author’s Note: A lot of teams get stuck reporting numbers that don’t change behavior. Here’s a simple rule:

• If your goal is growth, prioritize Clicks + CTR + Position (then ask what to improve).

• If your goal is awareness, prioritize Impressions + Position (then expand content coverage).

• If your goal is conversion, remember GSC won’t answer conversion questions—pair the report with GA/CRM data before making business calls.

At a Glance: Manual vs. AI Setup in GSC

Important Limitations to Remember

While the tool is very helpful, it is currently in an experimental phase. Users must understand what the tool can and cannot do to avoid errors. Relying too heavily on AI without checking the work can lead to mistakes.

Only for Search Results

Currently, the AI tool only works within the Performance report for standard Search results. It does not yet support data from Google Discover or Google News. If your site relies on these sources, you still need manual filters for now.

Configuration, Not Analysis

It is important to remember that this is a configuration tool, not an analysis tool. It builds the report for you, but it does not explain the data. It won’t tell you why your impressions dropped or how to fix the issue.

No Sorting or Exporting

The tool cannot yet perform actions like sorting tables or exporting data to CSV. You must still click the column headers to sort your data manually. If you need to download the report, you must use the standard export button.

Power Prompts for 2026

To get the most out of this tool, you should use clear and specific prompts. Using standard SEO terms helps the AI understand you better. Here are some prompts to try in your own account.

• “Show me my top 10 performing pages by clicks this month.”
• “Compare mobile versus desktop clicks for the last 90 days.”
• “Identify queries with high impressions but low click-through rates.”
• “Show performance for all URLs that contain the word ‘guide’ in them.”

Key Takeaway

The release of the Google Search Console AI-powered configuration tool represents a major shift in the SEO landscape of 2026. By automating the tedious work of setting filters and comparing dates, Google is giving you back hours of your work week. This time should be reinvested into high-level tasks that AI still cannot perform: understanding user intent, building human connections, and creating unique, lived-experience content.

The future of search belongs to those who can ask the right questions. The AI is now here to provide the answers in seconds, but your success still depends on your ability to interpret those answers and turn them into a winning business strategy. Don’t just use this tool to work faster—use it to work smarter.

The post Google Search Console’s New AI Upgrade: Pull 2-Hour Reports in 20 Seconds appeared first on SEO Services Agency in Manila, Philippines.

Search is changing fast. AI answers now pull insights from your content and deliver them directly to users without sending traffic to your site. Your brand may gain visibility while your clicks stay flat. Influence shows up, but attribution does not.

Marketers now need a new way to measure influence. The real task is creating attribution for AI answers that captures visibility and impact before the click.

This article introduces a simple framework built on four essentials: visibility, resonance, impact, and feedback.

Author’s Note

Before we get into it: this chapter is part of my ongoing AEO/GEO series on how content discovery and search behavior is changing, and what you need to do to stay visible on search. If you’d like the fuller foundation, here are the key posts referenced throughout this series:

Foundations of AI Search Behavior

• AEO Maturity Model
• How AI Overviews Impact CTR and SEO
• How Generative AI Is Changing Search Behavior
• The Psychology of Conversational Search
• Mapping Content to User Goals & Intent

AI Retrieval, Ranking & Synthesis

• How Generative AI in Search Works
• Structuring Content for AI Extraction
• Authority Signals & Schema Markup for AEO
• Structuring Content for Multi-Turn AI Conversations

Measuring AI Visibility & Performance

• How to Measure AEO Performance
• Building an AEO-Ready Team

When AI Answers Replace Clicks

Traditional analytics were built for a world where users visited your site to engage with your content. AI has changed that. Much of the interaction now happens off-site, inside AI-generated responses, with little to no trace in your reporting tools.

As generative search takes on more of the work, your content can shape decisions without triggering a single measurable action. The real signals are happening elsewhere, and marketers must account for the influence created before a user ever clicks.

Defining Attribution 2.0: From Traffic to Influence

Traditional attribution measures movement like clicks, pageviews, sessions. But today, influence often comes first, which means attribution should be about understanding and tracking momentum. It should be about seeing how ideas spread, how your brand authority grows, and how your content shapes perceptions before any measurable action occurs.

Marketers must then focus on how AI represents and amplifies content. Every summary, recommendation, or synthesized answer powered by AI can extend reach and impact, often without a direct click. 

Understanding how these systems interpret and showcase your content allows marketers to map influence across the broader ecosystem: capturing visibility, authority, and engagement that traditional analytics would otherwise miss.

The Framework: Measuring Influence in the AI Ecosystem

Measuring influence in the AI ecosystem means looking beyond traditional metrics. This framework focuses on four key dimensions: visibility, resonance, impact, and feedback.

Layer 1: Visibility

Visibility in this era is not just about showing up in the search results anymore, rather about how often your brand or content is noticed or referenced within AI-generated outputs. The goal here is simple: see if your ideas are being seen and recognized, even when users don’t click through to your site.

1. Audit your AI presence. Regularly search your brand, products, and key content topics across AI engines like ChatGPT, Gemini, Perplexity, Claude, and Copilot. Record where your content is mentioned, summarized, or notably absent to understand how AI systems are interacting with your content.
2. Track citations. Use LLM visibility trackers, or set up alerts for AI-generated mentions. These tools show when large language models (LLMs) such as ChatGPT, Gemini, Claude, or Perplexity reference your brand, cite your content, or recommend your products. This helps you understand where, how, and how often your brand appears across AI-generated answers. Some of the leading LLM visibility trackers available include:
   1. SE Ranking – combines traditional SEO tools with AI search monitoring. Its AI Search Toolkit tracks brand mentions, positions, and competitors across platforms like ChatGPT and Google AI, giving a clear view of your AI-driven presence.
   2. Ahrefs Brand Radar – shows how AI chatbots represent your brand across platforms like ChatGPT, Google AI, Perplexity, Gemini, and Microsoft Copilot. It helps businesses monitor AI mentions, benchmark against competitors, and uncover opportunities to strengthen their presence in AI-generated search results.
   3. Profound AI – provides advanced insights into how AI interprets content and optimizes product placement, helping companies achieve significant visibility growth in AI-driven search environments.
3. Enhance discoverability. Improve your content’s likelihood of being cited by implementing schema markup, ensuring factual clarity, and optimizing key entities. Clear, structured content helps AI models confidently reference your brand.
4. Benchmark visibility. Develop a monthly “AI Share of Voice” score that tracks the percentage of AI-generated answers where your brand appears for target queries. This provides a measurable way to monitor your growing influence over time.

Visibility is the first step in building influence. If AI systems don’t surface your content, it can’t shape decisions or guide conversations. 

Layer 2: Resonance

Visibility alone is not enough; your content also needs to resonate. Resonance is all about how your brand is understood and remembered. And in the world of AI, that means measuring whether AI systems cite your brand positively, accurately, and frequently, in a way that carries meaning and relevance.

Tracking resonance means looking at how frequently your content is used in AI outputs, whether it’s summarized correctly, and if the core ideas are preserved and represented effectively.

1. Monitor branded search volume. Track increases in searches for your brand or products using tools like Google Trends and Google Search Console. Look for correlations between AI-driven visibility spikes and upticks in search interest to see if AI exposure is influencing awareness.
2. Measure sentiment. Analyze the tone of conversations about your brand using social listening tools or AI-driven sentiment analysis platforms. Monitoring sentiment before and after major AI visibility events helps you understand how your brand is perceived and whether AI references are building positive authority.
3. Survey recall. Conduct periodic audience surveys or polls to measure unaided brand awareness within your category. Understanding how well your brand sticks in users’ minds provides a direct signal of resonance and influence.
4. Create a “Resonance Dashboard.” Combine sentiment, branded search data, and social conversation metrics into a single composite Influence Score. This view will allow marketers to track how effectively AI-driven visibility is translating into brand recognition, perception, and authority over time.

Paying attention to signals like citation frequency, sentiment, and source trust can let marketers start to understand not just if they’re being referenced, but how they’re being referenced. This helps monitor whether the brand is resonating in the AI conversation, building authority in ways that may not show up in clicks or pageviews but are essential to long-term influence.

Layer 3: Impact

Visibility and resonance are powerful on their own, but their true value shows when they translate into meaningful business outcomes. Impact is where AI-driven presence moves beyond awareness and perception, shaping real decisions, behaviors, and conversions. This is the stage where marketers look for proof that being cited, recommended, or surfaced by AI systems is driving tangible value: more qualified traffic, stronger leads, higher engagement, or even direct revenue lifts.

1. Run correlation analysis. Compare periods with high AI visibility against shifts in website traffic, conversions, lead quality, or pipeline growth to identify relationships between AI exposure and business performance.
2. Define proxy conversions. Track secondary indicators of influence such as increases in branded organic searches, direct type-in visits, social engagement, or repeat interactions, to capture the impact that happens before a user ever clicks.
3. Implement attribution modeling. Use regression, media mix models, or Bayesian inference to estimate the indirect contribution of AI-driven exposure, giving you a clearer picture of influence that isn’t captured by last-click metrics.
4. Report “Influence-Weighted ROI.” Layer your traditional ROI or ROAS with an influence multiplier based on inferred impact from AI visibility and resonance, creating a more complete assessment of how AI contributes to revenue and brand growth.

It’s not just about being seen or remembered by AI. It’s about whether that exposure changes what people do. When you tie AI visibility and resonance back to these measurable outcomes, you can clearly see how influence is contributing to your business growth.

Layer 4: Feedback

Influence is not a one-time achievement; it’s a cycle. And feedback is where true influence takes shape. It becomes the engine that keeps your visibility, resonance, and impact evolving. 

As AI systems adapt based on patterns, signals, and relevance, marketers must do the same. Take insights from AI mentions, audience reactions, and performance indicators, then feed them back into the content strategy to strengthen the signals that guide how AI engines interpret your brand. 

1. Map success signals. Identify which types of content (whether they are guides, data-backed studies, FAQs, or definitions) appear most frequently in AI-generated answers. This helps you understand what formats and topics AI engines perceive as most authoritative.
2. Strengthen high-performing entities. Identify the people, products, locations, or concepts that AI already associates with your brand. Expand these pages, improve internal linking, and reinforce supporting content so AI models develop an even stronger, more consistent understanding of these entities.
3. Improve factual density. Refine your top-performing content to be clearer, more concise, and richer in well-structured information. AI models favor content that’s easy to parse and confidently cite, so improving clarity and accuracy increases your chances of repeated inclusion.
4. Close the loop with regular audits. Refine your top-performing content to be clearer, more concise, and richer in well-structured information. AI models favor content that’s easy to parse and confidently cite, so improving clarity and accuracy increases your chances of repeated inclusion.

Influence is supported through repetition, clarity, and constant improvement. And feedback is the mechanism that keeps your authority alive, relevant, and growing.

Operationalizing the Framework

Putting the framework of measuring influence in AI into action simply means integrating each layer into what you already do. Visibility, resonance, impact, and feedback can be layered directly onto the processes you already use, transforming traditional analytics into a more adaptive, AI-aware discipline.

1. Create a unified “AI Attribution Dashboard” that combines key metrics from all four layers (visibility, resonance, impact, and feedback) into a single dashboard. Include AI mentions, sentiment analysis, and conversion or proxy data to create a holistic view of how your content is performing in AI-driven environments.
2. Set quarterly KPIs. These benchmarks provide clear targets and allow your team to gauge the effectiveness of their efforts. Define measurable goals to track progress over time such as:
   1. Increasing your “AI Share of Voice” by 20%
   2. Improving your overall Influence Score by 15%; or
   3. Achieving a 10% lift in traffic correlated with AI visibility. 
3. Align cross-functionality. Bring together SEO, content, brand, analytics, and communications teams to agree on influence metrics, share insights, and coordinate actions. Cross-functional collaboration ensures that AI attribution becomes a shared responsibility.
4. Document assumptions. Treat your AI attribution model as iterative. Clearly note any assumptions, such as how AI visibility or resonance is weighted, and refine these metrics as transparency and data from AI platforms improve. This approach keeps your model accurate and adaptable.
5. Educate stakeholders with influenced-based storytelling. Shift the narrative from traditional traffic- or click-focused reporting to one that emphasizes influence. Explain how AI visibility and resonance drive authority, shape perception, and contribute to measurable outcomes, helping them understand the full value of your AI-optimized content.

Integrating these steps into your regular workflow lets businesses create a system that consistently measures how AI represents a brand—and continuously improves the presence in AI-generated answers. This approach keeps your strategy adaptive, measurable, and aligned with how people now discover information.

Key Takeaway

The marketing landscape is shifting dramatically—from clicks to credibility, and from sessions to significance. Traditional metrics can no longer capture the full story of influence in an AI-driven world. Visibility, resonance, impact, and feedback provide a modern framework for understanding how your content shapes perception, builds authority, and drives results even when users never click.

In the age of AI answers, the brands that win are those that are recognized, referenced, and trusted. Influence now extends beyond what is seen on the page; it exists in the moments AI surfaces your expertise and shapes decisions.

The challenge for marketers is clear: measure the unseen, track the indirect, and embrace a new standard of attribution that values influence as much as traffic. Those who do will lead the way in defining success for the AI era.

The post Attribution in the Age of AI Answers appeared first on SEO Services Agency in Manila, Philippines.

AI search has quietly become mainstream, but most businesses have no clue how they appear in these AI-generated responses.

Unlike traditional SEO, where you can check your Google rankings, AI search is essentially invisible to marketers. When someone asks Perplexity or ChatGPT about your industry, you’re flying blind—no way to know if your brand gets mentioned, how you stack up against competitors, or why certain sources get cited over others.

This visibility gap has created an entirely new category of tools for SEO: AI search tracking platforms. I analyzed the best LLM visibility tools available and selected 8 that actually deliver value, from free starter tools to enterprise-grade solutions costing thousands per month.

I’ll show you exactly what each tool for LLM visibility does well, where it falls short, and how to pick the right one for your situation and budget. 

What Is LLM Visibility and Why It Matters in 2025

LLM visibility refers to how prominently your brand appears in AI-generated responses from ChatGPT, Google’s AI Overviews, AI Mode, Perplexity, Claude, and other large language models. Unlike traditional search results with clickable links, these AI systems provide direct answers without sending users to your website.

This differs from traditional SERP tracking. Instead of ranking for keywords, you’re optimizing for AI systems that decide which sources to trust. There are no fixed positions, and mentions don’t guarantee traffic.

When people ask ChatGPT “What’s the best CRM for restaurants?” they don’t browse through search results anymore. They’re getting a list that looks like advice from a knowledgeable friend. If your brand isn’t there, you’ve lost a potential customer.

LLM visibility tools help monitor these mentions and optimize accordingly. The best tools for LLM visibility track brand appearances across AI platforms, analyze competitors, and provide actionable insights—because if AI doesn’t recommend you, many customers will never find you.

Best LLM Visibility Analysis Software: Comparison

Methodology: How I Evaluated These LLM Visibility Analysis Software 

To find the best LLM visibility tool for different business needs, I put each platform through its paces across five key areas.

• Accuracy of detection: I checked how well each tool actually catches brand mentions and notices when AI responses change.
• Multi-platform support: The best LLM visibility tool should monitor multiple AI platforms where your customers actually search, so I examined coverage across Google AI Overviews, Bing Copilot, ChatGPT, Perplexity, Claude, Gemini, and more.
• Frequency of data updates: I tracked how often each LLM visibility software refreshes its data. Some updates in real-time while others take a week, which makes a big difference when you’re monitoring fast-moving conversations.
• UI/UX and report generation: I spent time in each dashboard to see how easy they are to navigate and whether you can actually create useful reports for your team. The most effective options don’t overwhelm you with data dumps.
• Pricing and scalability: What might seem like the best LLM visibility tracker due to extensive features could turn out to be an unnecessary expense that’s overkill for your actual needs. That’s why I broke down what each platform costs and specified who benefits the most.

I tested each best tool for LLM visibility with real brand searches and focused on whether the insights would actually help marketing teams make better decisions.

Top LLM Visibility Tracking Tools in 2025

SE Ranking

Source: SE Ranking

SE Ranking is a robust SEO platform, offering everything from rank tracking and backlink analysis to technical audits and competitor research. What sets it apart is its seamless integration of AI search optimization with traditional SEO, giving users precise and practical insights on how their brands perform in AI-generated results.

Its AI Search Toolkit includes dedicated solutions for monitoring your brand’s presence in AI-powered search engines like Google AI Overviews, AI Mode, and ChatGPT (and support for Perplexity, Gemini, and others on the way). These tools let you track brand mentions, links, positions, and competitors day by day and over time.

Key Features:

• AI visibility tracking across major platforms: Monitors brand mentions and links in AIOs, AI Mode, and ChatGPT, with more platforms coming soon.
• Precise keyword and prompt tracking: See which keywords and prompts trigger AI answers with your brand mentions and website links.
• Position and link analysis: Get placement data within AI answers, including the top 3 positions.
• Competitor monitoring: Checks who else appears alongside you in AI responses, so you can compare visibility and spot missed opportunities.
• Daily refreshes and historical trends: Updates metrics every day, plus historical data lets you spot shifts and measure campaign impact.
• Cached copies of AI answers: Users can see generated answers to better understand how their brands and competitors are presented.
• Data export: Users can export data or share guest links with teammates or clients.
• Research features: You can check any domain’s AI search presence and analyze what drives its visibility.

Pros:

• Granular visibility data with brand and link positioning
• Easy competitor comparison and benchmarking
• Cached AI answer snapshots to review exact mentions and citations
• Seamless transition from traditional SEO to AI SERP insights
• Daily data refreshes with access to historical trends
• Well-known brand with positive reviews

Cons:

• No sentiment tracking or AI agent behavior analysis yet
• Features come with higher pricing plans, but include generous limits

Pricing:

SE Ranking AI visibility trackers are included in:

• Pro: $119/month
• Business: $259/month
  

There’s also a 14-day free trial to test features at no cost. Users who opt for an annual subscription will get 20% off.

Best For:

SE Ranking is ideal for agencies, in-house SEO teams, content strategists, and brand managers who want a reliable, easy-to-use AI search visibility tracker to monitor their performance in answer engines and evaluate their efforts. It’s especially valuable due to daily updates and clear, actionable metrics.

Ahrefs Brand Radar

Source: Ahrefs

Ahrefs is one of the world’s most popular SEO platforms, primarily known for its advanced backlink analysis tools, keyword research capabilities, and search ranking monitoring. The company has recently expanded its offering with Brand Radar—a dedicated tool for tracking brand presence in AI-generated responses.

Brand Radar monitors how AI chatbots present your brand in AI search results, offering brand presence tracking across ChatGPT, Google AI Overviews, Perplexity, Gemini, and Microsoft Copilot. The platform helps businesses:

• Discover what AI chatbots say about their brand
• Compare visibility against competitors
• Identify opportunities to improve their presence in AI-driven search results

Key Features:

• Competitive visibility analysis: Compares your brand’s appearance frequency across AI platforms against industry competitors with trend tracking
• Market landscape mapping: Reveals which brands dominate AI conversations in your industry and identifies key competitive threats
• Hidden competitor identification: Automatically discovers brands that appear in similar AI contexts but may not be obvious traditional competitors
• Topic-based mention tracking: Tracks how often your brand appears in responses related to specific industry topics and user queries
• Sentiment tracking: Monitors the frequency and quality of brand mentions with positive/neutral/negative analysis
• Coverage gap identification: Highlights opportunities where competitors appear in AI results but your brand is absent, with strategic recommendations
• Brand association analysis: Shows what product categories, use cases, and topics AI models most commonly link to your brand
• AI vs. Organic comparison: Contrasts brand visibility patterns between AI-powered search and traditional organic search results

Pros:

• Backed by Ahrefs’ 14 years of search data expertise and infrastructure
• Seamless integration with existing Ahrefs SEO workflows and reporting
• Established platform with proven reliability and user base
• Direct comparison between AI search and traditional organic performance

Cons:

• Contradictory pricing information creates uncertainty about actual costs
• Requires an existing Ahrefs subscription
• Higher pricing compared to other LLM visibility tracking tools

Price: 

Main Ahrefs plans range from:

• Lite: $129/month
• Standard: $249/month
• Advanced: $449/month
• Enterprise: $1,499/month

All plans offer a 17% discount when paid annually.

Ahrefs presents conflicting pricing information for Brand Radar. The platform states that this tool is included in all pricing plans, but simultaneously lists it as a “$199/mo per index” add-on.

Best For: 

Brand Radar is the best LLM visibility tracker for businesses already using Ahrefs for SEO who want to add AI visibility tracking without switching platforms, particularly those with larger budgets who value the integration between traditional search data and AI mention tracking within a single ecosystem.

Profound AI

Source: Profound

Profound is an enterprise-focused tool for LLM visibility designed to help brands increase their appearance across AI-powered search engines and chatbots. The company positions itself as a solution for reaching millions of consumers who use AI platforms to discover new products and brands, with documented success stories including helping Ramp achieve a 7x increase in visibility and become the 5th most visible fintech brand in the world.

The platform specializes in enterprise-level AI visibility tracking with advanced features spanning multiple areas of AI search optimization, from understanding how AI crawlers interpret website content to optimizing product placement in ChatGPT Shopping experiences.

Key Features:

• Answer Engine Insights: Tracks brand visibility across AI platforms, analyzes AI responses about your brand, and identifies citation sources driving AI mentions
• Conversation Explorer: Reveals what people actually ask AI platforms, helping brands understand AI conversation trends versus traditional search patterns
• Agent Analytics: Monitors AI crawler access patterns, measures traffic from AI-driven search, and tracks which pages get referenced in AI responses
• ChatGPT Shopping optimization: Monitors how products appear in ChatGPT’s shopping feature, finds keywords that prompt product recommendations, and measures performance across different retail partners

Pros:

• Enterprise-grade security with SOC 2 Type II compliance and SSO integration
• Proven results with documented case studies
• One of the most comprehensive feature sets among dedicated LLM visibility trackers
• Extensive LLM coverage available in custom enterprise pricing

Cons:

• Requires an application for access with a review process
• No self-service or trial options available
• Expensive for a standalone tool compared to integrated solutions

Price:

Profound AI offers two pricing tiers:

• Profound Lite: $499/month: Covers ChatGPT, Perplexity, Google AI Overviews, and Microsoft Copilot
• Enterprise: Custom pricing: Includes all Lite platforms plus Google AI Mode, Google Gemini, Meta AI, Grok, and DeepSeek

A demo requires filling out a contact form and speaking with their expert for evaluation.

Best For:

Profound is the best LLM visibility analysis tool for large enterprises that need comprehensive AI visibility tracking with advanced security features, particularly those requiring SOC 2 compliance, SSO integration, and dedicated support for complex AI search optimization strategies.

seoClarity

Source: seoClarity

seoClarity is an enterprise SEO platform founded in 2007 that positions itself as “the world’s first AI-driven, enterprise SEO platform.”

Their Clarity ArcAI solution represents a comprehensive approach to AI search optimization, offering what they describe as the complete workflow from understanding how AI bots crawl your site to measuring the business impact of AI-driven traffic.

The platform focuses on actionable insights rather than just reporting. While most LLM visibility trackers tell you what happened, seoClarity’s ArcAI Insights claims to

Key Features:

• AI Search Visibility Tracking: Monitors brand mentions and variations across AI Mode, Perplexity, Gemini, ChatGPT, and AI Overviews with competitive benchmarking over time
• AI Mode and AI Overviews Analysis: Tracks Google’s AI Mode responses and AI Overviews at scale, measuring traffic impact down to the keyword and URL level
• AI Bot Tracking: Monitors crawl activity from AI search engine bots to ensure proper indexation
• Topical Gap Analysis: Identifies content gaps across thousands of prompts where AI engines aren’t surfacing your brand
• AI Search Site Analytics: Measures AI-driven traffic, engagement, and conversions with source attribution and performance comparison
• ArcAI Insights: Provides prioritized, actionable recommendations to improve AI search visibility
• AI Prompt Research: Reveals audience demographics, intent mapping, and comprehensive topic coverage requirements for AI optimization

Pros

• Comprehensive feature set covering the entire AI search optimization workflow
• Integrated task management and team collaboration tools
• Established platform with 18+ years of search optimization experience

Cons

• No free trial available
• All plans require custom quote requests
• Complex platform with a steep learning curve for teams new to enterprise SEO
• High pricing compared to other LLM visibility tracking tools
• Limited AI platform coverage

Price

seoClarity offers four main tiers with custom pricing:

• Rankings: Custom pricing based on keyword queries (minimum 2,000 keywords)
• Research & Content: Starts at $2,500/month
• Technical SEO: Starts at $3,200/month
• Enterprise: Starts at $4,500/month

All pricing requires requesting a custom quote—no free trial is available.

Best For

seoClarity works best for large enterprises that need comprehensive AI search optimization integrated with traditional SEO workflows, particularly those requiring enterprise-grade infrastructure, advanced technical capabilities, and coordinated optimization across global teams and multiple domains.

Writesonic AI Search Visibility (GEO) Tool

Source: Writesonic

Writesonic is primarily known as an AI writing and content creation platform, but has expanded into AI search visibility tracking with what they call the “Ahrefs for AI Search.” The company combines AI visibility monitoring with built-in content creation and SEO tools, allowing users to not just track their AI presence but actively create optimized content to improve it within the same platform.

What distinguishes Writesonic from other best LLM visibility analysis tools is their claim to track real AI conversations—they state their platform is powered by 120M+ actual AI conversations, showing exact user prompts and predicting search volume for AI queries. This data-driven approach extends beyond traditional keyword tracking to understand what people actually ask AI platforms.

Key Features:

• Brand Presence Explorer: Monitors brand visibility, sentiment, and share of voice across thousands of AI queries with competitor benchmarking
• AI Traffic Analytics: Reveals AI crawler visits invisible to Google Analytics, broken down by platform, with content performance insights
• Multi-platform tracking: Covers ChatGPT, Google AI Overviews, Google AI Mode, Claude, Perplexity, Grok, Gemini, and Microsoft Copilot.
• Real prompt analysis: Shows actual user prompts from 120M+ AI conversations with predicted search volumes for specific queries
• Citation source identification: Identifies which websites AI systems reference when mentioning your brand for outreach strategy
• ChatGPT Shopping optimization: Tracks product placement in AI-generated shopping results with optimization recommendations
• Actionable recommendations: Provides specific suggestions for new content, page refreshes, and outreach prioritized by potential impact

Pros:

• Comprehensive platform coverage, including newer AI search engines
• Built-in content creation and SEO tools eliminate the need for separate platforms
• Claims automatic addition of new AI platforms as they emerge to keep data complete

Cons:

• AI tracking available in only 3 out of 5 pricing plans
• Comprehensive AI platform coverage only available in Enterprise tier
• Full AI search visibility functionality limited to Enterprise version, which requires a demo and cannot be tested
• Content creation focus may dilute specialized AI visibility features

Price:

Writesonic offers five pricing tiers, but only three include AI search tracking capabilities (Lite and Standard provide only AI Bot Traffic Monitoring):

• Professional: $249/month—Tracks ChatGPT, Perplexity, and Google AI Overviews with 100 AI prompts monthly
• Advanced: $499/month—Adds Gemini tracking with 200 AI prompts monthly
• Enterprise: Custom pricing—Full platform coverage including Claude, Microsoft Copilot, Grok, DeepSeek, and Meta AI with custom prompt limits

All paid plans except Enterprise offer free trials. Enterprise requires requesting a demo.

Best For:

Writesonic is one of the best LLM visibility trackers for marketing teams, agencies, and consultants who want to combine AI visibility tracking with content creation capabilities, particularly those seeking a more accessible alternative to enterprise-only platforms with the added benefit of integrated content optimization tools.

Scrunch AI

Source: Scrunch AI

Scrunch AI is an AI search visibility platform that positions itself as helping brands “market to AI” rather than just people.

Scrunch offers three core products:

• Monitoring for AI visibility tracking
• Insights for optimization recommendations
• Agent Experience Platform (AXP) that creates AI-optimized versions of websites

What distinguishes Scrunch from other tools for tracking LLM brand visibility is its focus on AI personalization through configurable personas, showing how AI tailors responses for different user profiles. They also offer an innovative AXP solution that creates a parallel, AI-optimized version of your website served only to AI agents without affecting the human experience.

Key Features:

• AI visibility monitoring: Tracks brand presence, position, and sentiment across ChatGPT, Perplexity, Claude, Meta AI, Gemini, and Google AI Overviews
• Competitive benchmarking: Compares brand performance against competitors on specific prompts and topics with trend analysis
• AI persona insights: Shows how AI personalizes responses for different user profiles with configurable personas
• Citation and source tracking: Monitors first-party, competitive, and independent sources influencing AI search results
• Prompt analytics: Provides time-series performance tracking by prompt, topic, and persona with granular detail
• AI bot traffic monitoring: Tracks AI crawler activity with seamless integration to website providers and CDNs
• Technical compatibility audit: Assesses website compatibility for AI crawlers, including robots.txt validation and error tracking
• Agent Experience Platform (AXP): Creates invisible, AI-optimized versions of websites served only to AI agents (waitlist available)

Pros:

• Innovative AXP platform
• SOC 2 compliance and SSO integration
• Agency-focused features with multi-client management capabilities
• AI persona analysis that provides unique insights into personalized responses

Cons:

• Newer platform with less market presence compared to established SEO tools
• Enterprise focus may limit accessibility for smaller businesses
• No free trial or self-service options available

Price:

Scrunch AI offers custom pricing across multiple tiers:

• Starter: $300/month—350 custom prompts, up to 1,000 industry prompts, 3 personas
• Growth: $500/month—700 custom prompts, up to 2,500 industry prompts, 5 personas
• Pro: $1,000/month—1,200 custom prompts, up to 6,000 industry prompts, 7 personas
• Enterprise: Custom pricing—Fully customizable solution with SSO, Enterprise Data API, and dedicated support

All plans require contacting sales for setup and pricing confirmation.

Best For:

Scrunch AI is a great LLM visibility tracking tool if you’re running an agency with multiple clients or working at a mid-to-large company. It really shines when you need sophisticated AI visibility tracking that can adapt to different types of users. 

7. Semrush

Semrush AI Toolkit

Semrush AI Toolkit is a strategic insights add-on to the established Semrush SEO platform that analyzes brand presence across AI platforms like ChatGPT, SearchGPT, Google AI Mode, Gemini, and Perplexity. If you’re surprised to see a tool from one of the most powerful SEO platforms this far down my list, it’s because the AI Toolkit currently feels more like a standard entry into the AI visibility space rather than an innovative leader.

The platform covers the fundamentals well—tracking mentions, analyzing competitors, and providing strategic recommendations—but doesn’t bring the kind of unique capabilities that would make it a standout choice over specialized LLM visibility tools. For existing Semrush users, however, it offers the convenience of keeping AI insights within their familiar SEO ecosystem.

Key Features:

• Brand visibility analysis: Tracks your brand’s presence in AI conversations with competitive share analysis and improvement priority identification
• Market intelligence monitoring: Identifies which competitors are gaining AI visibility and reveals emerging market opportunities within your industry
• Multi-platform brand comparison: Shows how different AI systems present your products with sentiment tracking and feature mention analysis over time
• User query insights: Reveals what people ask AI about your brand and category, including how AI influences purchase decisions
• Strategic business recommendations: Provides specific suggestions for product development, marketing optimization, and customer-aligned positioning
• Trending topic analysis: Monitors shifting conversations and emerging concerns within your industry across AI platforms

Pros:

• Part of the well-known Semrush ecosystem
• Available as a standalone tool without requiring a full SEO toolkit purchase
• Clear priority labeling for actionable recommendations
• Interactive demo available to test functionality

Cons:

• No free trial available
• Requires a separate subscription for each domain 
• Additional cost for each new user accessing the tool
• Expensive for multiple domains and users

Price:

Semrush AI Toolkit is available as a standalone tool for $99/month per domain. 

Each user needs a separate subscription for every domain they wish to analyze. While there’s no free trial, Semrush offers a free demo report to showcase the tool’s functionality before purchase.

Best For:

Semrush AI Toolkit suits both existing Semrush customers who know the platform’s interface and new users, since you don’t need their main SEO package to get started. It’s a good fit for marketing teams and business managers who want straightforward AI visibility insights with solid reporting features, without dealing with overly complex LLM visibility analysis tools.

Xfunnel

Source: XFunnel

XFunnel is an AI search optimization platform founded by seasoned entrepreneurs Neri Bluman and Beeri Amiel, who bring deep expertise in AI strategy and B2B go-to-market optimization. 

The company positions itself as “pioneering the future of AI search” and focuses on four key areas: 

• Research
• Measure
• Analyze
• Optimize

With impressive scale credentials, including analysis of 1,500 companies, 5M+ responses collected, and 25M+ citations analyzed, XFunnel has built a platform designed to handle millions of queries weekly.

What sets XFunnel apart from other LLM visibility analysis tools is their focus on full funnel coverage—ensuring brand consistency across every stage of the AI-driven buying journey from problem awareness to purchase decisions. They claim to help businesses turn AI search engines into their most effective sales channel through proven optimization playbooks and hands-on implementation support. 

Key Features:

• AI query simulation: Uses 9 data sources to simulate buyer journey queries and map brand appearances across AI platforms
• Brand visibility tracking: Monitors brand mentions, rankings, and sentiment across ChatGPT, Gemini, Claude, Perplexity, and other major platforms
• Competitive benchmarking: Tracks competitor performance with share of voice analysis and head-to-head positioning comparisons
• Customer journey mapping: Analyzes how AI engines respond to different personas throughout the buying journey to identify conversion gaps
• Hallucination detection: Identifies errors in AI-generated search results about your products for brand safety monitoring
• Citation impact analysis: Examines citation sources and their influence on AI recommendations with source credibility tracking
• Strategic experimentation: Runs optimization experiments and measures their impact with proven visibility improvement playbooks
• Sentiment analysis: Tracks brand sentiment across AI platforms and monitors tone changes over time
• Alert notifications: Sends Slack and email alerts for visibility spikes, sentiment changes, or competitor movements
• Content optimization support: Provides AI-optimized content briefs and implementation assistance for visibility improvements

Pros:

• Extensive AI platform coverage
• Strong client roster including major brands like Monday.com, Wix, and Fiverr
• Free starter available with 50 one-time queries
• Real-time data processing with daily monitoring

Cons:

• No pricing plans between free and enterprise custom pricing, making it difficult to assess cost-effectiveness
• New company founded in 2025 with under 10 employees (according to LinkedIn)
• Limited online information available beyond the official website

Price:

XFunnel offers two main pricing tiers:

• Free Starter: $0—Includes ChatGPT, Gemini, Claude, and Perplexity tracking with 50 one-time queries and basic features
• Enterprise: Custom pricing—Comprehensive platform coverage including Google AI Overviews, Microsoft Copilot, Meta AI, Grok, and DeepSeek with unlimited queries and managed services

Best For:

XFunnel works well for businesses seeking free AI visibility monitoring options or those who enjoy testing new platforms and experimental approaches. Given its hands-on optimization focus and expert guidance, it particularly suits teams willing to invest time in experimentation and implementation rather than just passive monitoring of their AI visibility.

Key Takeaway

AI visibility isn’t optional—it’s inevitable. Sure, traditional search still drives traffic, but more people are asking ChatGPT about your industry than you realize. They’re getting product recommendations from Perplexity and trusting Google’s AI Overviews like they used to trust the first organic result.

Your SEO stack needs to evolve beyond keyword rankings and backlinks. The best tool for LLM visibility isn’t necessarily the most expensive one—it’s the one you’ll actually use to understand how AI platforms present your brand and take action on those insights.

Don’t get left behind as search transforms. The brands adapting now will have a significant advantage over those who wait until AI search becomes an obvious necessity. Pick the best LLM visibility tracking tool from my list, run your first report, and see where you stand.

Start somewhere. Test for a month. See what you learn. Because the conversations about your industry are already happening—the question is whether you’re part of them.

The post 8 Best LLM Visibility Trackers That Actually Work in 2025 appeared first on SEO Services Agency in Manila, Philippines.

Most SEO teams still operate on an outdated model built around long-form blogs, keyword lists, and gradual ranking gains. But search has shifted toward instant, answer-focused results. This is where Answer Engine Optimization (AEO) comes in – yet many teams aren’t structured nor equipped to apply it effectively.

To stay competitive, your SEO team must evolve into an AEO-ready team capable of producing clear, authoritative, and machine-readable content. The goal is simple: build a team and workflow that consistently creates the kind of precise, trustworthy answers AI systems and search engines choose to display first.

Author’s Note:

This article is part of my AEO/GEO series, which covers how websites can adapt to the changing landscape of AI-driven search. If you haven’t yet, you can check out my previous posts to better understand how AI retrieval, synthesis, and citation work. 

Catch up on the series

• How AI Overviews Impact CTR and SEO – A look at how Google’s AI-generated results are influencing click behavior and rankings.
• Mapping Content to User Goals – How to align structure and messaging with user intent to boost engagement.
• How Generative AI Is Changing Search Behavior – Why people search differently in the AI era and how that shift affects your strategy.
• How Generative AI in Search Works – A breakdown of how large language models (LLMs) retrieve, rank, and synthesize information.
• Structuring Content for AI Extraction – Practical tips on formatting content so AI systems can easily parse and reuse it.
• Using Authority Signals and Schema Markup for AEO Success – How structured data and expert attributions help AI trust and cite your content.
• How to Structure Content for Multi-Turn Conversations in AI Search – How to organize your content so it remains relevant and cited across multi-turn AI interactions.
• How to Measure AEO Performance – A guide to tracking visibility, citations, and authority across AI search environments.

Together, these articles form a complete framework for creating AI-optimized content that performs well in both traditional search and answer engine ecosystems.

Why SEO Teams Must Be Restructured for Answer Engine Optimization

AEO is not only about writing shorter answers. It is about mapping the entire content to deliver users’ goals: a definitive, structured answer to a defined question. That cannot be achieved if the team is configured like a standard blog production unit. 

Traditional content teams work in a linear chain. Strategy hands off to writing. Writing hands off to SEO. SEO hands off to publishing. Everything is sequential and disconnected. That workflow was built for keyword pages, not answer extraction. AEO requires a tightly aligned, collaborative structure where all roles share one objective: position zero. 

The Three Essential Roles in an AEO-Ready Team

To build a team capable of producing content tailored for answer extraction, you need three core roles working together:

SEO Specialist

The SEO Specialist is no longer just optimizing for Google’s ranking algorithms, they’re optimizing for AI visibility. Their job begins before a single word is written: identifying the right target keywords, understanding  search intent and shaping the blog or webpage so it aligns with how both search engines and AI models interpret relevance.

In this new landscape, the SEO Specialist’s role expands into AEO (AI Engine Optimization) — optimizing content to be cited and surfaced by AI systems like ChatGPT, Gemini, and Perplexity.

Key Responsibilities of an SEO Specialist

• AI Citation Analysis – Search visibility now depends on how generative AI systems select and cite sources. The specialist analyzes how LLMs retrieve and reference content — studying query fan-out patterns and synthetic subqueries to understand why some pages get cited more often.
• Competitive Intelligence – Focuses on AI citation gaps, uncovering why competitors’ content appears more in AI responses. Reviews semantic depth, entity coverage, and authority signals that shape AI selection probability.
• Platform-Specific Optimization – Each AI engine (ChatGPT, Gemini, Copilot, etc.) ranks and retrieves differently. The specialist adapts strategies for each — refining structured data, passage segmentation, and content embeddings to boost AI search visibility.
• Content Performance Tracking – Traditional SEO metrics like clicks and impressions don’t capture performance in AI-driven ecosystems. Instead, they need to start tracking metrics such as:

• • Chunk Retrieval Frequency (CRF): how often passages appear in AI retrievals
  • Embedding Relevance Score (ERS): how semantically aligned content is with AI queries
  • AI Citation Count (AICC): how often content is cited by AI systems

Blog Writer

The blog writer transforms strategic direction into clear, authoritative content. Their job is simple: answer the user’s question fast. The opening paragraph should deliver the core answer immediately, followed by supporting insights, data, and context. The goal isn’t length, it’s precision, clarity, and trust.

Key Responsibilities of a Blog Writer

• Clear, Immediate Answers – Opens every article with a direct, authoritative answer to the user’s query. Supporting details and data follow naturally, ensuring clarity and depth without unnecessary length.
• Structured Content Design – Organizes information in a way that aligns with how AI models parse and rank content, improving both human readability and AI search visibility.
• Prompt Engineering – Uses prompt engineering to test how AI systems interpret and surface content. This helps refine tone, structure, and phrasing to match real-world AI query behavior.
• AI Behavior Analysis – Experiments with different prompts to reverse-engineer AI decision-making, uncovering what formats, hierarchies, and signals improve AI citation potential.
• Collaborative Optimization – Works closely with SEO/AEO specialists to ensure each piece contributes to AI impressions, semantic coverage, and overall content authority across platforms.

Content Manager

The Content Manager ensures every piece meets the highest standards of accuracy, clarity, and strategic intent. They refine tone, enforce structure, and align the final product with AEO best practices — making sure it performs for both human readers and AI systems.

This role blends AI automation with human creativity, building scalable workflows that turn complex content strategies into repeatable success. The Content Manager also engineers content specifically for AI comprehension and citation, ensuring each piece is easy for large language models to parse, synthesize, and reference.

Key Responsibilities of a Content Manager

• Editorial Oversight – Review and refine drafts for accuracy, clarity, and alignment with brand and search intent. Guarantee consistency in tone and narrative flow across all content.
• AEO-Driven Structuring – Applies AI Engine Optimization principles to structure information in semantic units that AI systems understand and cite easily — balancing machine readability with human engagement.
• AI Synthesis Optimization – Creates content designed for AI synthesis, ensuring that when AI systems combine data from multiple sources, your content is selected and accurately represented.
• Semantic Engineering – Edits work to ensure that it follows explicit semantic triples (subject–predicate–object patterns) and clear logical relationships. This is needed to enhance how AI models interpret meaning and maintain coherence when content is chunked or embedded.
• Workflow Automation – Develops scalable content workflows that merge AI-assisted editing and human quality control, boosting both efficiency and creative quality.
• Cross-Team Alignment – Collaborates with SEO, writers, and strategists to ensure every asset meets performance goals — from AI search visibility and citation probability to audience engagement.

Website Developers & Engineers

Website developers and engineers are the technical backbone of AEO. They ensure content is not only accessible to users but also discoverable and interpretable by AI systems. 

While traditional SEO focused on optimizing for Google’s crawlers, today’s developers must design for a broader ecosystem, including AI crawlers, retrievers, and embeddings-based systems that each process content differently.

They must create infrastructure that performs flawlessly for both humans and machines, enabling speed, clarity, and seamless data exchange across AI-driven environments.

Key Responsibilities of Website Developers and Engineers

• AI Accessibility & Discoverability – Builds sites that AI systems can easily crawl, parse, and understand. This includes optimizing structured data, schema markup, and metadata for AI search visibility.
• Multi-Platform Optimization – Adapts site architecture and content delivery for various AI systems (e.g., ChatGPT, Gemini, Perplexity) — each with distinct retrieval and indexing methods.
• Performance Engineering – Ensures lightning-fast load times, clean code, and mobile responsiveness. Performance remains a key AI ranking and citation signal across all engines.
• Information Architecture – Designs logical, semantic site structures that help AI models identify relationships between pages and topics — improving AI citation probability and content synthesis accuracy.
• Technical AEO Infrastructure – Integrates APIs, vector databases, and structured embeddings to support next-generation AI-driven discovery and contextual retrieval.
• Human + Machine Alignment – Balances UX principles with machine readability, creating digital environments that satisfy users while communicating meaning clearly to AI systems.

If your team already includes these roles, it’s time to upskill them for an AEO-first approach — expanding beyond traditional SEO into workflows built for AI search visibility.

When these roles work in sync, your team creates content engineered for answer engines, not just optimized for clicks or page views.

Skills That Enable Effective AEO Teams

Beyond the roles, the people assigned to them must carry a specific set of skills. AEO does not reward generalists who produce generic blog posts. Your team needs competencies such as:

• Research precision instead of keyword volume fixation.
• Ability to understand searcher intent at different stages.
• Skill in simplifying complex information into one authoritative answer.
• Awareness of how to optimize for featured snippets and voice-assisted search answers.
• Editorial judgment to preserve accuracy and credibility.
• Familiarity with structuring content for AI extraction

These are the capabilities that increase the probability of being surfaced as the answer in search.

How an AEO Workflow Operates from Start to Finish

Once the team is in place, the process they follow determines the success of the strategy. An AEO workflow is not linear and isolated. It is collaborative and aligned to one output: the answer.

Step 1: Intent and Question Research

The process starts with understanding user intent, identifying the exact question your content needs to answer. The SEO Specialist leads this step by analyzing search behavior, using familiar SEO techniques such as exploring People Also Ask results, reviewing SERP snippets, and identifying LSI (latent semantic indexing) keywords to reinforce topical relevance within pillar content.

With AEO, the research goes further. Instead of stopping at keyword analysis, the specialist studies AI-generated answers to see which sources are cited and how they are structured. Often, answers generated by AI go beyond the root keyword, they provide additional information that they have deemed valuable to users. Always take a look at the full, generated answer produced by AI. This helps reveal what kind of content AI systems pull from when forming responses and how to position your content to be selected.

Take this example from looking up “SEO agencies in the Philippines” on Google:

Many AI search platforms like Perplexity or Gemini also display related or follow-up questions. These can guide your content roadmap by showing which questions to include within your article and which to reserve for future topics.

The goal is to align with both human and AI understanding: validate search intent, reflect real query phrasing, and create content that AI systems can easily parse, synthesize, and cite.

Step 2: Answer-First Content Writing

The writer delivers the answer at the beginning of the article. The rest of the content provides support, explanation, and proof. Context follows the conclusion, not the other way around.

The rest of the content should build around that answer, offering explanation, context, and credible proof to reinforce it.

A question-and-answer structure works best for both human readers and AI systems. This approach mirrors how people search and how AI engines extract and cite information. It also helps search systems easily identify distinct passages for indexing and retrieval.

To improve readability and AI search visibility, expand your content with:

• Logical subheads that guide readers through related ideas in sequence
• Bullet points and tables to simplify complex information or comparisons
• FAQs that respond to common or follow-up questions surfaced in AI tools like Perplexity or ChatGPT
• Short overview or summary sections that restate key insights and help AI systems capture structured meaning

This structure balances clarity and depth. It keeps content accessible for readers while improving how AI models interpret, segment, and cite your work. Well-organized content not only ranks better in traditional search but also increases the likelihood of inclusion in AI-generated responses and answer engine results.

Step 3: Review and Refinement

A strong review also includes competitive and AI-based testing. Search for your target keyword across multiple AI environments, such as ChatGPT, Gemini, or Perplexity. Study the AI-generated responses that appear for your query and compare them with your own content. This helps identify content gaps, missing context, or phrasing patterns that make competitors’ material more appealing to AI systems.

If competitors’ content is consistently cited or synthesized, look closely at what sets it apart. Consider factors like semantic structure, entity coverage, and answer clarity. Use these insights to refine your own piece so it is easier for AI models to parse, summarize, and cite.

The goal of this stage is to move from “good enough” to AI-ready. Well-reviewed content performs more reliably in both traditional search and AI-generated summaries, strengthening visibility and authority across platforms.

Step 4: Monitoring and Iteration

After publication, the team tracks how the content performs across both traditional and AI-driven search environments. The goal is to determine whether it earns featured snippets, AI Overview placements, or citations in generative responses. If performance is weak, the team refines the opening answer, strengthens keyword alignment, and improves clarity or structure to boost AI search visibility.

Because AI systems personalize and evolve constantly, results can vary from one test to another. Responses from tools such as ChatGPT, Perplexity, and Gemini often shift based on user history, sampling, or evolving index data. This makes continuous monitoring and iteration essential to maintaining visibility.

Effective AEO measurement combines active and passive tracking:

• Active Tracking – Test target queries in AI search tools like Google’s SGE, Gemini, or Perplexity. Note where your content appears, how it’s cited, and which prompts mention your brand.
• Passive Tracking – Use analytics and server logs to monitor AI crawler activity, visit frequency, and user engagement metrics such as dwell time and click depth to gauge long-term performance.

Some SEO tools are beginning to offer built-in AEO tracking features. I currently use SE Ranking’s AI Search Toolkit, which helps with:

• Monitoring when and where AI systems reference your brand or link to your content
• Detecting visibility gaps and uncovering new keyword opportunities across AI search engines
• Comparing AI inclusion rates and brand mentions against competitors
• Analyzing how AI-generated answers cite or paraphrase your content

Here’s a sample of how this toolkit tracks your current position on AI overviews, versus your position on traditional organic search results:

By combining these insights, teams can measure, iterate, and strengthen AI visibility over time. The goal is to treat AEO as an evolving process—one that adapts as search models and AI retrieval systems continue to change.

Step 5: Ongoing Authority Audits

Regular audits keep content credible and competitive. Revisit published pieces to update data, refresh examples, and remove outdated sources. Consistent maintenance signals trustworthiness and topical authority, both of which are key factors for ranking and AI citation.

This continuous improvement process makes AEO sustainable and scalable, ensuring your content stays accurate, relevant, and visible across evolving AI and search ecosystems.

Why This Team Model Works in Modern Search

Search engines favor clarity, not volume. They feature answers that are structured, intentional, and credible. They do not reward the longest article. They reward the most direct and trustworthy one.

An AEO team structure ensures that every piece of content is built with that standard in mind. This positions the brand to compete not only for ranking but for answer dominance. It prepares the organization for voice search, AI summarization, and future search models where only one answer is shown.

Brands that adopt this structure early will build authority ahead of competitors who are still writing as if search works the way it did five years ago.

Key Takeaway

Winning in modern search is no longer about publishing more content. It is about publishing the right content in the right structure with the right team behind it. When you build an AEO-ready team and align the workflow around answer-first publishing, you are not just reacting to search changes. You are positioning your brand to be the source that search engines choose to display.

In a world where users see only one answer, the team that learns to produce that answer first will own the future of visibility.

The post The AEO Team Framework: Restructuring SEO for the Answer-First Era appeared first on SEO Services Agency in Manila, Philippines.

As search engines evolve into AI-driven search ecosystems, the old ways of measuring performance are starting to fall short. Metrics like clicks and impressions only show part of the story. They don’t reveal how your content actually shows up, educates, or engages within AI-generated results.

That’s where Answer Engine Optimization (AEO) comes in. AEO shifts the focus from surface-level stats to a deeper understanding of how your brand appears and is referenced in AI summaries, chat-based answers, and contextual responses. It’s a new way to evaluate visibility and influence in a world where generative search is rewriting the rules.

The real question today isn’t “Do I rank?” — it’s “am I seen and cited by AI” This layer of search is fluid and unpredictable; mentions can appear or vanish depending on small changes in user intent, phrasing, or the model’s retrieval logic. Mastering AEO means learning to navigate that shifting terrain and ensuring your brand stays visible within it.

Author’s Note:

This article is the eighth entry in my AEO/GEO series, which explores how websites can keep up with new search ecosystems. If you’re new to the series, I recommend starting with the earlier pieces to understand how AI-driven retrieval, synthesis, and citation are reshaping the fundamentals of SEO.

Catch up on the series:

• How AI Overviews Impact CTR and SEO – How Google’s AI-generated results are changing click behavior and rankings.

• How Generative AI Is Changing Search Behavior – Why people search differently in the age of AI and what it means for your strategy.

• Mapping Content to User Goals – How to align structure and messaging with user intent to improve engagement.

• How Generative AI in Search Works – A breakdown of how large language models (LLMs) retrieve, rank, and synthesize information.

• Structuring Content for AI Extraction – A practical guide on formatting content so AI systems can easily parse and reuse it.

• Using Authority Signals and Schema Markup for AEO Success – How structured data and expert attributions help AI trust and cite your content.

• How to Structure Content for Multi-Turn Conversations in AI Search – How to design and organize your content so it remains relevant and referenced across multi-turn AI interactions.

Moving Beyond Traditional SEO Metrics

Search is no longer just about ranking on page one. It is now about being part of the answer. Answer Engine Optimization, or AEO, takes optimization a step further by making sure that your content is not just for improving search engine visibility, but for inclusion in AI-generated answers that directly respond to user queries as well. 

Also, with AI-driven search experiences now, the search journey has transformed: users are no longer just searching, they are conversing. These platforms then curate and deliver quick, summarized, and context-rich responses directly within the results without always requiring a click to visit a website. 

Visibility now means more than appearing in search listings. It’s about being cited and recognized by the very AI models shaping what users see and trust, and these have become just as valuable as traditional organic rankings, signaling a shift in how we define engagement and discoverability.

As search becomes more intelligent, success now is not measured by clicks alone. A website may experience fewer visits, yet achieve greater reach and influence by being referenced or featured within AI answers. 

To understand how to measure AEO success, we need to recognize the limitations of current analytics, define the right metrics, and track how our content appears, contributes, and delivers value across the AI-powered search ecosystem.

The Limitations of Click-Based Metrics

Generative search experiences are is reshaping how people look for and receive information. Users now receive AI-generated answers instantly, often without visiting a website. As a result, traditional organic funnels are showing fewer clicks, even when overall visibility and influence may be increasing.

A decline in traffic doesn’t necessarily mean a decline in reach. Content that is cited, summarized, or referenced within AI-generated responses still reaches a wide audience. These mentions strengthen brand authority and awareness, even among users who never leave the search results.

Current analytics platforms are not yet equipped to measure this new layer of visibility. Google Analytics can track traffic from search, but it cannot distinguish between a visit from a traditional link and one originating from an AI Overview. Google Search Console provides impressions and clicks but offers no insight into whether your content was used to inform a generative summary.

This presents two key challenges. First, the generative answer layer remains invisible to most analytics tools. Second, even when visibility can be detected, it is often unstable — a query that cites your content today may produce different results tomorrow, even without changes to your site.

For SEO and AEO professionals, the next step is to develop new measurement frameworks that uncover this hidden layer, track performance over time, and connect these appearances to meaningful business outcomes such as engagement, brand recognition, and trust.

AEO Performance Indicators

Measuring AEO success requires a wider lens than traditional SEO metrics can provide. Clicks and impressions alone no longer capture the full story of visibility in AI-driven search. To understand how to measure AEO performance, brands need to track new indicators that reveal how content performs beyond the click.

AI Impressions

AI impressions reflect how often your content or brand appears within AI-generated summaries or conversational responses, even when it isn’t directly linked or quoted. They are similar to traditional SEO impressions, but with a crucial difference: they measure how frequently your content becomes part of the AI-generated answer itself.

This metric highlights when your content is recognized and surfaced by AI systems, boosting brand exposure even without a click. Each mention within an AI Overview or generative search result signals that your content is being treated as a reliable and contextually relevant source, trusted by both users and the AI models delivering the results.

Tracking AI impressions is still an emerging practice as generative search technology continues to evolve. 

However, there are a few practical ways to start gathering insights: 

• Monitor visibility within AI overviews through early access tools or beta reports, such as Google Search Console’s SGE experiments (when available).
• Use third-party SEO platforms that are beginning to introduce generative search tracking, detecting when your content appears in AI summaries or overviews.
• Set up brand and content mention monitoring to identify instances where AI systems reference or summarize your content across different platforms.

Understanding where your content surfaces requires a closer look at how AI search interfaces work. AI Overviews usually appear as embedded panels within a standard search results page, triggered for queries where Google determines a synthesized answer would be useful.

Within it, you can search for anchor tags pointing to your domain or textual overlaps with your content. However, because these panels change dynamically with user context, model updates, and testing conditions, a single spot-check provides only a snapshot. Long-term tracking is necessary to build an accurate picture of your visibility trends.

Meanwhile, AI Mode introduces a more conversational environment. Unlike static summaries, it generates multi-turn responses designed to engage users in dialogue. As a result, the retrieval patterns are broader and more reasoning-driven, often drawing from different sources than AI Overviews. 

Measuring your presence here requires capturing the entire conversational output and identifying every linked or referenced source. Comparing results between AI Overviews and AI Mode can reveal content biases, preferred sources, and topic coverage gaps that influence how often your content is selected.

A practical way to quantify your visibility is through AI share of voice. For instance, if you track 100 keywords, and find that AI Overviews appear in 25 of them, and your content is featured in 10, your AI share of voice is 10%. 

This metric establishes a baseline for understanding how frequently you appear across generative search experiences. Over time, tracking this percentage helps measure the impact of your optimization efforts and identify opportunities to strengthen your presence within the evolving AI-driven search ecosystem.

Summary Inclusions

Summary inclusions highlight how often your brand or content is directly cited within AI-generated answers, whether in Google’s AI Overviews or other generative platforms. It serves as a measure of authority, relevance, and credibility, showing that AI not only recognizes your content but also trusts it enough to include it in its response. 

The more frequently your brand is referenced in these summaries, the stronger your visibility and trust become in the eyes of both algorithms and users. 

Monitoring summary inclusions provides valuable insight into how effectively your content aligns with what AI deems useful and reliable. 

However, tracking for AI overviews and on AI mode is primarily manual for now, as most analytics platforms are still adapting to AI-driven search reporting. Some practical ways to monitor when your content is being cited or referenced in AI-generated summaries include:

• Manually monitor AI-generated results for your target keywords using tools like Google’s SGE or Bing Copilot to see if your content is cited or linked in summaries.
• Use third-party tracking tools that are beginning to offer generative search visibility features, which detect brand mentions or links in AI overviews.
• Set up brand and URL mention alerts through platforms like Google Alerts or Mention to capture instances where AI-generated content references your site. Though these tools don’t yet directly track AI results, they can still monitor where and how your brand or content is being referenced online, which can indirectly indicate when AI systems are pulling information from your site.
• Document and compare appearances over time to identify trends in how frequently and where your content is being featured in AI answers.

Conversational Engagement

Conversational engagement is another AI performance indicator that reflects how actively users interact with your brand within AI-powered chat experiences. 

Rather than simply appearing in an AI-generated summary, this metric measures the depth of interaction: how often users mention your brand, ask follow-up questions, or continue queries related to your offerings. 

And in tracking metrics for conversational engagement, here are a few effective ways to monitor how users interact with your brand within AI chat environments:

• Track branded follow-up prompts. Observe how often users continue the conversation with additional questions or prompts that mention your brand. This is done by manually testing AI chat interfaces (like Google’s SGE or ChatGPT) using your key topics or brand-related queries, then noting if the AI generates follow-up suggestions or if your brand reappears in subsequent dialogue threads.
• Monitor repeated mentions in AI chat threads. Identify instances where your brand or content is referenced multiple times throughout a single AI interaction. You may test queries around your target keywords and observe whether the AI continues to cite or mention your brand across follow-up responses. Consistent repetition indicates stronger brand association and relevance within conversational contexts.
• Analyze rephrased or expanded branded queries. Look for users who refine or restate their questions involving your brand, showing deeper curiosity or intent. Experiment with variations of branded queries in AI chat interfaces and note if the system continues to associate your brand with related topics or reintroduces it in expanded answers.

Take this exchange I had with ChatGPT as an example:

SEO Hacker was mentioned in both the first and second answer, which means that we are possibly getting multiple brand mentions even throughout follow-up prompts from users. 

Dwell Time and Content Interaction

Dwell Time and content interaction measure what happens after visibility: how long users stay and engage once they reach your content through AI-driven results. Even in an AI-first search environment, time spent reading or interacting with your page remains a powerful indicator of relevance and satisfaction.

In hybrid search experiences where users discover information through both SERPs and AI overviews, higher dwell time signals that your content not only earned visibility but also fulfilled user intent — proving its depth, usefulness, and credibility in the moments that matter most.

While AI platforms don’t yet provide direct analytics, there are ways to measure these AI performance indicators:

• Use Google Analytics or GA4. Monitor average engagement time and scroll depth for pages frequently surfaced in AI results.
• Track referral sources. Identify sessions that originate from AI-driven search experiences (like Google’s SGE or Bing Copilot) if labeled in traffic sources.
• Analyze session duration trends. Look for increases in on-page time or interactions (clicks, video plays, form fills) on content recently appearing in AI overviews.
• Observe behavioral flow reports. See whether users explore additional pages after landing on your site, which may indicate that AI-driven visitors find the content relevant and worth exploring.

These insights help you gauge whether your content is simply being seen, or genuinely engaging users who arrive through AI-enhanced search experiences.

AI Bot Activity

One of the most overlooked signals of visibility is bot activity. Tracking how often AI-related crawlers visit your site can help you understand how frequently your content is being indexed, retrieved, or evaluated for use in generative search results.

Bots such as ChatGPT-User, ClaudeBot, or PerplexityBot regularly scan or request web pages to collect information or serve user queries. A consistent crawl rate typically indicates healthy visibility, while a sudden decline could mean your site has been deprioritized or excluded from certain retrieval pipelines. 

By reviewing server logs or bot analytics, you can spot potential issues before they show up in your downstream performance metrics.

Common AI Bots to Track

Below is a summary of major AI crawlers, what they do, and how to manage their access:

How to Measure AEO Performance 

Understanding AEO performance indicators is only the first step. The real impact comes from turning insights into measurable action, which requires a structured approach.

Tracking key metrics such as AI impressions, summary inclusions, conversational engagement, and dwell time involves creating dashboards that will give you a clear picture of how your content performs in AI-driven search environments. 

Identify Key AEO Metrics

Start by determining which performance indicators matter most for your goals. Focus on metrics such as AI impressions, summary inclusions, conversational engagement, and dwell time. These metrics capture your overall AI-driven search presence.

Combine Active and Passive Tracking Methods

Measuring performance in Answer Engine Optimization (AEO) requires using both active and passive tracking approaches. Together, they help capture the full spectrum of visibility, engagement, and authority signals that reflect how your content performs within AI-driven search environments.

Active tracking involves hands-on observation of where and how your content appears within generative search results. 

This can include running your target keywords in tools such as Google’s Search Generative Experience (SGE), documenting when your brand or pages are mentioned, and testing branded variations of your queries to identify follow-up prompts and conversational references. 

Regular testing helps uncover new opportunities and spot shifts in AI-driven ranking behavior.

Passive tracking, by contrast, collects data automatically through system logs and analytics platforms. 

This approach reveals how users and AI systems interact with your content behind the scenes. By analyzing server logs, you can see when AI crawlers fetch your pages, how frequently they return, and whether those visits change over time. 

Combined with analytics data—such as dwell time, engagement rates, and interaction patterns—passive tracking gives you a deeper view of how your content performs once it’s surfaced by AI systems.

Modern SEO and analytics tools are beginning to offer specialized features for AEO tracking. Platforms like SE Ranking’s AI Search Toolkit can help you:

1. Monitor when and where AI tools mention your brand or link to your pages.
2. Identify visibility gaps and opportunities across multiple AI search engines.
3. Compare your domain’s inclusion rate and brand mentions against competitors.
4. Review how AI-generated answers reference your content for specific prompts.
5. Evaluate and benchmark overall AI search visibility across multiple domains.

Build the Dashboard Framework

Organize your collected data into a dashboard that brings together all key AEO metrics such as AI impressions, summary inclusions, conversational engagement, and dwell time. This framework makes it easier to identify patterns, track trends, and compare performance across different indicators. 

A well-structured dashboard turns raw data into actionable insights, helping you make informed decisions to optimize your content for AI-driven search.

To enrich your dashboard with deeper insight, consider integrating additional data sources and monitoring methods:

• Clickstream data from trusted third-party providers can help approximate visibility by observing user behavior across the broader web. Monitoring AEO-affected queries in this way allows you to estimate click-through rates (CTR) and identify where your content is likely being cited or surfaced in AI responses.
• Server log analysis offers visibility into how AI bots interact with your site. By filtering logs for known AI user agents, you can measure crawl frequency and detect any drops or spikes that may signal changes in retrieval or ranking behavior.
• Direct monitoring provides the most accurate view of your presence in generative search. Using browser automation frameworks can automate your target queries, capture full generative outputs, and extract citations from AI-generated responses. Repeating this process regularly creates a longitudinal dataset that tracks how your inclusion in AI results evolves over time.

Creating an integrated dashboard helps you connect technical data with real performance results. Over time, it becomes your main hub for tracking AEO, making it easier to measure impact, spot changes in AI behavior, and keep improving your optimization strategy.

Visualize and Analyze Data

Use charts, tables, and trend lines to interpret your AEO metrics clearly and effectively. Visualizing relationships will help you which content truly resonates with users. 

Analyzing these patterns allows you to identify high-performing content, spot opportunities for improvement, and make data-driven decisions that enhance visibility, authority, and engagement within AI-powered search environments.

Optimize Based on Insights

Finally, use your dashboard to refine strategies. Identify content gaps, enhance authority, and adjust messaging to improve visibility and engagement across AI-driven platforms. This approach bridges the gap between theory and practice, transforming abstract AEO concepts into measurable actions that strengthen your brand’s presence in the AI-driven search landscape.

Repeated Tracking

Tracking share of voice in AEO  is more complex than it is in traditional search because there’s no fixed results page to measure. In classic SEO, rankings were stable enough to scrape and compare over time. You could identify changes tied to updates or competitors with relative confidence.

In generative search, however, results are dynamic. The same query can return different answers from one test to the next, even under identical conditions. The AI’s retrieval and synthesis processes constantly shift what’s displayed based on random sampling, evolving index data, and personalization signals.

Because of this, share of voice can no longer be viewed as a static percentage of rankings held—it’s better understood as a probability distribution of visibility over multiple observations. Measuring it effectively means running repeated tests, aggregating the results, and looking for patterns in how often and where your brand appears.

Repeated tracking also helps validate and strengthen other AEO metrics. For example:

• It provides context for AI impressions, showing how often your content is surfaced over time rather than in a single instance.
• It clarifies fluctuations in AI citations or mentions, revealing whether they are temporary or part of a longer trend.
• It supports dashboard-level insights, connecting short-term volatility with long-term performance averages.

By combining repeated tracking with your dashboard metrics—such as impressions, inclusion rates, conversational engagement, and crawl frequency—you can develop a more accurate picture of your brand’s true presence within generative search. This ongoing, iterative approach ensures you’re measuring visibility as a living system rather than a single static result.

Interpreting Answer Engine Analytics

The data you track in your AEO dashboard provides a framework for understanding how to measure AEO performance beyond traditional metrics. When your brand appears in AI-generated summaries or when dwell time on linked pages increases, it signals that your content is both relevant and trusted by AI systems.

AI favors authoritative, well-structured, and semantically rich content, so tracking which pages are cited or surfaced helps reveal what performs best. These insights can guide improvements in content structure, topic depth, and schema optimization, ensuring your brand earns not only visibility but also authority and engagement.

Connecting AI visibility to outcomes like traffic, conversions, and revenue brings the full picture into focus. In Google Analytics, start by segmenting landing pages tied to queries that trigger AI panels. If traffic declines while conversions hold steady, your content may be capturing only the most intent-driven users.

Even without clicks, AI citations still drive value. Mentions in authoritative answers can increase branded search and direct visits over time. Tracking these assist signals helps quantify how generative visibility contributes to broader brand lift and long-term growth.

Key Takeaway

Success in AEO goes beyond clicks and rankings. It’s about being seen, cited, and trusted within the AI layer of search, where users engage directly with generative results. True performance is measured not just by traffic, but by visibility, authority, and meaningful engagement across AI-driven platforms.

To measure AEO performance effectively, focus on AI-native KPIs such as AI impressions, summary inclusions, conversational engagement, and dwell time. These metrics reflect how your content participates in the

The post How to Measure AEO Performance: Key Metrics and Strategies for the AI-Driven Search Era appeared first on SEO Services Agency in Manila, Philippines.

Search is no longer just about matching keywords. Before, it was all about finding pages that contained the exact keywords you entered. But today, search engines focus on understanding the intent and context behind your query, and deliver answers that feel almost human. 

Thanks to generative AI in search, we can now receive direct, context-rich answers tailored to their intent, changing the way we discover, consume, and interact with information online. It has been transforming the search experience into something smarter, faster, and more intuitive than ever before.

Author’s Note: 

This article is the fourth installment in my ongoing AI + SEO (AEO) series. To get the full picture, I highly recommend checking out the first two parts before diving in:

• How AI Overviews Impact CTR and SEO – Discover how Google’s AI-powered overviews are changing click-through rates and reshaping search strategies.

• Mapping Content to User Goals: A Guide to Intent Orchestration – Learn how to structure content around user intent to maximize engagement and conversions.

• How Generative AI Is Changing Search Behavior – Understand how AI-driven search is reshaping the way people look for and interact with information.

By reading in order, you’ll not only understand what’s changing in search but also learn how to adapt your content strategies step by step.

What is Generative AI in Search?

Generative AI in search is a type of artificial intelligence designed to generate new content rather than simply analyzing or classifying existing data. And when applied to search engines, this means producing original, synthesized answers instead of just ranking and displaying a list of links. 

Unlike traditional search, generative AI in search uses advanced systems such as large language models (LLMs). These models are trained on massive datasets and use advanced algorithms to understand context, style, and structure. As a result, these can generate responses that sound natural, conversational, and human-like, instead of robotic or generic. 

The big difference is that instead of simply “retrieving” or pulling out an answer word-for-word from a database, AI can understand the intent of a question, adapt its tone, and deliver insights that feel more useful and intuitive to people.

Some of the most visible applications of generative AI in search:

• Google’s Search Generative Experience (SGE): Generates an AI-powered “snapshot” at the top of the results page. This snapshot works by pulling together key information from different sources, then gives you a quick, context-rich answer, which makes you understand a topic faster without clicking through several sites from the search listings.

• ChatGPT Search (OpenAI): This combines real-time web search with the conversational abilities of GPT models. Instead of only relying on pre-trained knowledge, this can fetch up-to-date information from the internet, summarize it in a natural, conversational style, and even provide citations to sources.

• Perplexity AI: This generative AI tool takes a more focused and straightforward approach compared to others. Instead of long summaries or conversational deep dives, Perplexity delivers concise, straight-to-the-point answers.

• Gemini (previously known as Bard): Google’s generative AI chatbot and search assistant, which is designed to produce AI-driven responses that go beyond simple facts. It offers creative content, summaries, and context-aware answers.
• Microsoft’s Bing Copilot: This tool is Microsoft’s version of combining generative AI with traditional search. It is built into Bing search and the Edge browser, which allows users to ask complex, natural-language questions and get back conversational, AI-generated answers instead of just a list of links.

These platforms demonstrate how generative AI is moving beyond theory into everyday tools, redefining user expectations for speed, accuracy, and usability in search.

The Science Behind Generative Search

As we’ve noticed, traditional search engines often struggle with limited contextual understanding, making it difficult to grasp the true intent behind a query. This can result in even irrelevant or frustrating results for users.

But generative AI in search changes the game by using large language models (LLMs) like ChatGPT and Gemini, which are trained on datasets to interpret queries, understand context, and deliver responses that feel natural and human-like. So from keyword dependency in search algorithms, the generative experience relies on these advanced models to understand context and intent, providing more meaningful responses to user queries.

To better understand how this transformation works, it helps to break down the science behind it.

Retrieval vs. Synthesis

In generative AI in search, retrieval and synthesis work together to transform how information is delivered. 

Retrieval refers to the AI’s ability to find relevant information by pulling relevant documents, data, or sources from a huge knowledge base to look for facts and data points that might answer the query. Think of it as the AI’s research phase: gathering everything it might need. 

Traditional search engines actually rely on retrieval, presenting users with lists of links and documents that match keywords. 

Synthesis, on the other hand, is what sets generative AI apart. It involves blending the retrieved information into coherent, context-rich answers that directly respond to the user’s intent. So instead of just listing facts or copying text, it organizes the information, explains it in natural language, and provides context. This is what makes AI answers conversational, readable, and intuitive, rather than just a jumble of data.

In essence, generative search systems seamlessly merge retrieval and synthesis: they first gather the most relevant information, then process and combine it into meaningful insights. This not only improves accuracy and relevance but also elevates the user experience, offering answers that are concise, actionable, and tailored to the query.

Understanding Latent Intent

Generative AI doesn’t just look at the exact words in your search query. It tries to understand your latent intent, or the underlying meaning or goal behind your query that goes beyond the keywords that you typed. 

So when you enter a query, an LLM can analyze the wording and context to pick up subtle clues, and predict the underlying goal (or the latent intent). Then, it will generate responses that address the hidden intent rather than just the literal keywords.

For example, when a user searches for “best SEO tools 2025,” the literal meaning of the query is simply a list of SEO tools. However, the latent intent goes deeper: the user is likely looking for tools that are up-to-date, reliable, and easy to use, ideally with pros, cons, and recommendations. 

Essentially, generative search reads between the lines of user queries to give answers that are actually helpful, rather than just matching keywords. It delivers answers that are context-aware, actionable, and relevant to the users, which makes information easier to understand.

The Mechanics of Query Fan-Out

One key technique behind generative AI in search is query fan-out, which refers to the process that AI uses to break a single user query into multiple related sub-queries to explore different angles and sources of information—some directly derived, others inferred from user context and intent.

So instead of relying on a single, straightforward search, generative AI “fans out” the query into several angles, interpretations, or related questions. The AI gathers a richer set of data points, uncovering insights that may not be immediately obvious from the original query alone.

For example, if the query is “What are the best strategies for increasing website traffic?” generative AI can fan out the query into related sub-queries like:

• “SEO strategies for website traffic”
• “content marketing tips”
• “social media tactics to boost traffic”
• “email marketing strategies for engagement”

Each of these sub-queries collects focused information, and the AI arranges the results into a comprehensive, context-aware response that covers multiple aspects of the original question. These answers are also more contextually relevant than a simple keyword-based search, delivering a user experience that feels thorough, personalized, and intelligently curated.

Transforming Content Planning and Audit Workflows

The rise of generative AI in search also touches on the approach in transforming content planning and audit workflows by leveraging its ability to understand context, latent intent, and user needs.

In content planning, the combination of retrieval and synthesis enables more effective topic ideation, allowing teams to plan around questions and intent clusters rather than focusing solely on keywords. This approach identifies what audiences truly want, guiding the creation of content that is relevant, comprehensive, and strategically aligned with search behavior.

Then, during content audits, AI streamlines the evaluation process by identifying gaps, redundancies, and areas where existing content may not fully satisfy latent intent. With the application of query fan-out and synthesis, it can highlight missing subtopics or perspectives that would enhance coverage. 

Key Takeaway

Generative AI is more than just the next step in search. It’s redefining how we discover information in ways that feel natural, intelligent, and deeply personalized. For SEO marketers today, this is actually a toolkit for thriving in the AI-driven era. They can move beyond keyword stuffing and start crafting content that truly aligns with what users seek. 

Success in this new landscape means thinking like AI: anticipating intent, covering topics from multiple angles, and continuously refining strategies. Those who embrace these shifts won’t just keep up with change, rather they’ll set the pace for the future of search marketing.

Next in my AEO/GEO series: How to Structure Content for AI-Powered Search

The post How Generative AI in Search Works: Understanding the Fundamentals appeared first on SEO Services Agency in Manila, Philippines.

Screaming Frog has just rolled out its most significant update yet with SEO Spider 22.0—and it’s not just another routine update. It introduces AI-powered features that elevate how SEOs analyze, audit, and optimize websites, thanks to advancements in semantic analysis and intelligent content discovery.

SEO Spider 22.0 offers powerful enhancements that are designed to streamline workflows and reveal deeper and more meaningful SEO opportunities. Let’s take a closer look at what’s new in this game-changing update from Screaming Frog.

Semantic Similarity Analysis

In previous versions, Screaming Frog only relied primarily on detecting exact keyword matches to assess content relevance. 

But with the newest update, with the integration of AI and large language models, Screaming Frog can now analyze the actual meaning and context of your page content. Instead of just checking for exact words, it evaluates how closely a page aligns with a specific topic or query, even if the phrasing differs.

This semantic similarity analysis is the standout feature of version 22.0 and marks a significant leap beyond traditional keyword-based matching.

Semantically Similar Pages

Imagine two blog posts delivering the same message with different phrasing. These might have slipped through unnoticed. But now, the AI-driven analysis of this new version can detect those subtle overlaps and hidden duplicates by comparing their meaning, not just language. 

This is especially useful for spotting hidden duplicate content, content cannibalization, or opportunities to consolidate and strengthen pages that may be competing for the same keywords. It gives SEOs a smarter way to clean up and refine site content for better performance.

Low-Relevance Pages

The new update can also help in identifying pages that do not align well with the overall theme and context of your website. 

For instance, if your website is an online clothing store but includes a page about car repair, running it through Screaming Frog will show and flag it as off-topic using semantic similarity analysis. 

Therefore, auditing your website using this version will ensure that your content stays focused and relevant to your niche—helping your website maintain topical consistency while signaling stronger thematic relevance to search engines.

Content Cluster Visualization

Another standout feature in SEO Spider 22.0 offers a powerful way to visually understand how your website content is organized. 

Note: To access, go to Visualisations > Content Cluster Diagram

Think of it as a map that groups your pages based on their meaning rather than just keywords. Pages that cover similar topics are positioned close together, forming natural content clusters. If a page appears far from the rest, it could be an outlier—either off-topic or lacking strong connections to related content.

This feature makes it easier to identify gaps in your content strategy, spot isolated pages that may need better internal linking, and ensure your website is clearly structured around well-defined themes and topics.

Semantic Search Feature

The new version also involves the Semantic Search Feature, which takes content discovery to a whole new level. Instead of using the find function (“CTRL + F”) to search for specific words, you can now type a concept or idea—and the tool will show pages that are related in meaning.

For example, if you run a car repair shop and search for “engine trouble,” the tool might highlight pages that mention “car won’t start,” “strange engine noises,” or “check engine light”—even if they don’t use the exact term. 

This smarter search capability makes it easier to locate related content, identify gaps, and improve internal linking across related topics.

Enhanced AI Integration

SEO Spider 22.0 takes automation and workflow efficiency to the next level. With improved integrations and smarter tools, analyzing, generating, and organizing data across your SEO projects has been easier. It works more smoothly with leading AI platforms such as OpenAI, Gemini, and Ollama. 

This upgrade allows users to tap into advanced AI capabilities directly within their SEO workflows, enabling more advanced content generation and semantic analysis during your website audits. It’s a useful tool in this age of AI-driven SEO. 

Here’s what’s new with the enhanced workflow features:

AI-Powered Image Extraction

Instead of relying on traditional methods to pull image data, the update enables AI to intelligently analyze and extract images and related information from your website. This is particularly useful for auditing alt text, flagging missing or duplicate images, and ensuring your visual content aligns with SEO best practices

Custom Export Configuration

Rather than sifting through cluttered spreadsheets filled with irrelevant columns, you can now define exactly which data points to include so you get only the data you need. This lets you save time and keep your reports focused.

Google Sheets Integration

This update enables you to automatically sync your crawl data to a shared spreadsheet in real time. This makes collaboration smoother by giving your team or clients instant access to the latest audit results—no need for manual exports or email attachments.

Multiple Sitemap Downloads

In previous versions, Screaming Frog could only crawl one XML sitemap at a time, which limits efficiency, especially for larger or more complex websites. But with version 22.0, you can now download and crawl multiple sitemaps simultaneously. This is particularly useful for websites that use sitemap index files or have separate sitemaps for different content types, such as products, blog posts, and categories. 

This update speeds up large-scale audits, ensures broader coverage, and helps you spot inconsistencies or errors across your entire sitemap architecture. 

It is actually a time-saving enhancement designed for more comprehensive and scalable SEO analysis.

Column Configurator

This new Screaming Frog feature gives you complete control over the data you see in your crawl reports. 

Instead of scrolling through dozens of default columns (many of which may not be relevant to your specific audit), you can now customize the interface to display only the metrics that matter to you. This helps reduce visual clutter, speeds up analysis, and ensures your exports are clean, concise, and aligned with your reporting goals. 

The result? A more efficient auditing process and clearer insights for you and your team.

Key Takeaway

Screaming Frog SEO Spider 22.0 isn’t just an update—it’s a major leap forward, transforming the way we approach audits and optimization. Now, when you start crawling websites, you will begin to discover more valuable insights, from broken links and missing metadata to deeper content gaps and structural issues, all of which can significantly impact your website’s performance and search visibility.

With smarter tools and deeper insights at your fingertips in this version, you will uncover opportunities you might have missed, giving you the edge to stay ahead in the competitive SEO landscape.

The post What’s New in Screaming Frog SEO Spider 22.0? A Major Update Introducing AI-Powered Features appeared first on SEO Services Agency in Manila, Philippines.

For SEO experts, our toolkit is crucial. It’s how we make sure we can quickly and effectively assess how well our websites are performing. Using the best tools can put you way ahead of other SEOs. One example (and one tool I’ve personally been using for years) is Screaming Frog. It’s a powerful, straightforward, and insightful website crawler tool that’s indispensable for finding technical issues on your website.

And the good news is that it keeps getting better. Screaming Frog just released its 20th major version of the software, which includes new features based on feedback from SEO professionals.

Here are the main updates:

1. Custom JavaScript Snippets
2. Mobile Usability
3. N-Grams Analysis
4. Aggregated Anchor Text
5. Carbon Footprint & Rating

Custom JavaScript Snippets

One of the standout features in this release is the ability to execute custom JavaScript snippets during a crawl. This functionality expands the horizons for data manipulation and API communication, offering unprecedented flexibility.

Use Cases:

• Data Extraction and Manipulation: Gather specific data points or modify the DOM to suit your needs.
• API Communication: Integrate with APIs like OpenAI’s ChatGPT from within the SEO Spider.

Setting Up Custom JS Snippets:

• Navigate to `Config > Custom > Custom JavaScript`.
• Click ‘Add’ to create a new snippet or ‘Add from Library’ to select from preset snippets.

• Ensure JavaScript rendering mode is set via `Config > Spider > Rendering`.

Crawl with ChatGPT:

• Leverage the `(ChatGPT) Template` snippet, add your OpenAI API key and tailor the prompt to your needs.
• Follow our tutorial on ‘How To Crawl With ChatGPT’ for more detailed guidance.

Sharing Your Snippets:

• Export/import snippet libraries as JSON files to share with colleagues.
• Remember to remove sensitive data such as API keys before sharing.

Introducing Custom JavaScript Snippets to Screaming Frog SEO Spider version 20.0 significantly enhances the tool’s flexibility and power. Whether you’re generating dynamic content, interacting with external APIs, or conducting complex page manipulations, these snippets open a world of possibilities. 

Mobile Usability

In today’s mobile-first world, ensuring a seamless mobile user experience is imperative. Version 20.0 introduces extensive mobile usability audits through Lighthouse integration. 

With an ever-increasing number of users accessing websites via mobile devices, ensuring a seamless mobile experience is crucial. Google’s mobile-first indexing highlights the importance of mobile usability, which directly impacts your site’s rankings and user experience.

 Mobile Usability Features:

• New Mobile Tab: This tab includes filters for regular mobile usability issues such as viewport settings, tap target sizes, content sizing, and more.
• Granular Issue Details: Detailed data on mobile usability issues can be explored in the ‘Lighthouse Details’ tab.
• Bulk Export Capability: Export comprehensive mobile usability reports via `Reports > Mobile`.

Setup:

• Connect to the PSI API through `Config > API Access > PSI` or run Lighthouse locally.

Example Use Cases:

• Identify pages where content does not fit within the viewport.
• Flag and correct small tap targets and illegible font sizes.

With these new features, Screaming Frog SEO Spider version 20.0 streamlines the process of auditing mobile usability, making it more efficient and comprehensive. By integrating with Google Lighthouse, both via the PSI API and local runs, the tool provides extensive insights into the mobile performance of your website. Addressing these issues not only enhances user experience but also improves your site’s SEO performance.

N-grams Analysis

N-grams analysis is a powerful new feature that allows users to analyze phrase frequency across web pages. This can greatly enhance on-page SEO efforts and internal linking strategies.

Setting Up N-grams:

• Activate HTML storage by enabling ‘Store HTML’ or ‘Store Rendered HTML’ under `Config > Spider > Extraction`.
• View the N-grams in the lower N-grams tab.

Example Use Cases:

• Improving Keyword Usage: Adjust content based on the frequency of targeted N-grams.
• Optimizing Internal Links: Use N-grams to identify unlinked keywords and create new internal links.

Internal Linking Opportunities:

The N-grams feature provides a nuanced method for discovering internal linking opportunities, which can significantly enhance your SEO strategy and site navigation.

The introduction of N-grams analysis in Screaming Frog SEO Spider version 20 provides a tool for deep content analysis and optimization. By understanding the frequency and distribution of phrases within your content, you can significantly improve your on-page SEO and internal linking strategies.

Aggregated Anchor Text

Effective anchor text management is essential for internal linking and overall SEO performance. The aggregated anchor text feature in version 20.0 provides clear insights into how anchor texts are used across your site.

Using Aggregated Anchor Text:

• Navigate to the ‘Inlinks’ or ‘Outlinks’ tab.
• Utilize the new ‘Anchors’ filters to see aggregated views of anchor text usage.

Practical Benefits:

• Anchor Text Diversity: Ensure a natural distribution of anchor texts to avoid over-optimization.
• Descriptive Linking: Replace generic texts like “click here” with keyword-rich alternatives.

The aggregated anchor text feature provides powerful insights into your internal link structure and optimization opportunities. This feature is essential if you are looking to enhance your site’s internal linking strategy for better keyword relevance, user experience, and search engine performance.

Carbon Footprint & Rating

Aligning with digital sustainability trends, Screaming Frog SEO Spider version 20.0 includes features to measure and optimize your website’s carbon footprint.

Key Features:

• Automatic CO2 Calculation: The SEO Spider now calculates carbon emissions for each page using the CO2.js library.
• Carbon Rating: Each URL receives a rating based on its emissions, derived from the Sustainable Web Design Model.
• High Carbon Rating Identification: Pages with high emissions are flagged in the ‘Validation’ tab.

Practical Applications:

• Resource Optimization: Identify and optimize high-emission resources.
• Sustainable Practices: Implement changes such as compressing images, reducing script sizes, and using green hosting solutions.

The integration of carbon footprint calculations in Screaming Frog SEO Spider signifies a growing recognition of digital sustainability. As more businesses adopt these practices, we can collectively reduce the environmental impact of the web while driving performance and user satisfaction.

Other Updates

In addition to major features, version 20.0 includes numerous smaller updates and bug fixes that enhance functionality and user experience.

Rich Result Validation Enhancements:

• Split Google Rich Result validation errors from Schema.org.
• New filters and columns provide detailed insights into rich result triggers and errors.

Enhanced File Types and Filters:

• Internal and external filters include new file types such as Media, Fonts, and XML.

Website Archiving:

• A new option to archive entire websites during a crawl is available under `Config > Spider > Rendering > JS`.

Viewport and Screenshot Configuration:

• Customize viewport and screenshot sizes to fit different audit needs.

API Auto Connect:

• Automatically connect APIs on start, making the setup process more seamless.

Resource Over 15MB Filter:

• A new validation filter flags resources over 15MB, which is crucial for performance optimization.

Page Text Export:

• Export all visible page text through the new `Bulk Export > Web > All Page Text` option.

Lighthouse Details Tab:

• The ‘PageSpeed Details’ tab has been renamed ‘Lighthouse Details’ to reflect its expanded role.

HTML Content Type Configuration:

• An ‘Assume Pages are HTML’ option helps accurately classify pages without explicit content types.

Bug Fixes and Performance Improvements:

• Numerous small updates and fixes enhance stability and reliability. 

Screaming Frog SEO Spider version 20.0 is a comprehensive update packed with innovative features and enhancements that cater to the evolving needs of SEO professionals like us. From advanced data extraction capabilities with Custom JavaScript Snippets to environmental sustainability with Carbon Footprint and Rating, this release sets a new benchmark in SEO auditing tools.

Key Takeaway

Add this to your toolbox, or update to version 20 to explore the rich array of new features from Screaming Frog to optimize your website’s SEO, usability, and sustainability. It’s a no-fuss tool with tons of features that will help you stay ahead of your competitors, and ensure your websites perform optimally in terms of user experience and search engine visibility.

The post What’s New with Screaming Frog SEO Spider 20.0? appeared first on SEO Services Agency in Manila, Philippines.

What is Google My Business?

Google My Business (GMB) is a free tool that business owners can use to manage their online presence across Google Search and Google Maps.

This profile also puts out important business details, such as address, phone number, and operating hours, making it easily accessible to potential customers. 

When you click on a business listing in the search results it will open a detailed sidebar on the right side of the screen, providing comprehensive information about the business. 

This includes popular times, which show when the business is busiest, a Q&A section where potential users can ask questions and receive responses from the business or other customers, and a photos and videos section that showcases products and services. Customer reviews and ratings are also displayed, which are crucial for building trust and credibility.

Using Google My Business for Local SEO

Having an optimized Google Business Profile ensures that your business is visible, searchable, and can attract potential customers who are looking for your products and services.

• Increased reliance on online discovery: More consumers are going online to search and find local businesses, making it crucial to have a GMB listing.
• Be where your customers are searching: GMB ensures your business information is accurate and visible on Google Search and Maps, helping you stay competitive.
• Connect with customers digitally: GMB allows customers to connect with your business through various channels, including messaging and reviews.
• Build your online reputation: GMB makes it easy for customers to leave reviews, which can improve your credibility and trustworthiness.
• Location targeting: GMB enables location-based targeting, showing your ads to people searching for businesses in your exact location.
• Measurable results: GMB provides actionable analytics, allowing you to track your performance and optimize your listing.

How to Set Up Google My Business

If you already have a profile and need help claiming, verifying, and/or optimizing it, skip to the next sections.

If you’re creating a new Google My Business profile, here’s a step-by-step guide:

Step 1: Access or Create your Google Account:

If you don’t already have a Google account, follow these steps to create one:

• Visit the Google Account Sign-up Page: Go to the Google Account sign-up page and click on “Create an account.”
• Enter Your Information: Fill in the required fields, including your name, email address, and password.
• Verify Your Account: Google will send a verification email to your email address. Click on the link in the email to confirm your account.

Step 2:  Access Google My Business

• Go to the Google My Business website and click on the “Manage now” button.

Step 3: Enter Your Business Name and Category

• Type in your exact business name. Google will suggest existing businesses as you type
• If your business is not listed, fully type out the name as it appears
• Search for and select your primary business category

Step 4: Provide Your Business Address

• If you have a physical location where customers can visit, select “Yes” and enter your address.
• If you are a service area business without a physical location, select “No” and enter your service area.

Step 5: Add Your Contact Information

• Enter your business phone number and website URL
• You can also create a free website based on your GMB information

Step 6: Complete Your Profile

To complete your profile, add the following details:

• Hours of Operation: Enter your business’s operating hours to help customers plan their visits.
• Services: List the services your business offers to help customers understand what you do.
• Description: Write a detailed description of your business to help customers understand your offerings.

Now that you know how to set up your Google My Business account, all that’s left is to verify it. 

Verification is essential for you to manage and update business information whenever you need to, and for Google to show your business profile to the right users and for the right search queries. 

If you are someone who wants to claim their business or is currently on the last step of setting up their GMB, this guide will walk you through the verification process to solidify your business’ online credibility and visibility.

How to Verify Google My Business

There are several ways you can verify your business, including:

• Postcard Verification: Google will send a postcard to your business address with a verification code. Enter the code on your GMB dashboard to verify.
• Phone Verification: Google will call your business phone number and provide a verification code. Enter the code on your GMB dashboard to verify.
• Email Verification: If you have a business email address, you can use it to verify your listing.
• Instant Verification: If you have a Google Analytics account linked to your business, you can use instant verification.

How to Claim & Verify an Existing Google My Business Profile

If your business has an existing Google My Business profile, and you want to claim it, then follow these steps:

Step 1: Sign in to Google My Business

Access Google My Business: Go to the Google My Business website and sign in with your Google account. If you don’t have a Google account, create one by following the sign-up process.

Step 2: Search for Your Business

Enter your business name in the search bar to find your listing. If your business is already listed, you will see it in the search results.

Step 3: Claim Your Listing

If your business is not already claimed, you will see a “Claim this business” button. Click on this button to start the claiming process.

Step 4: Complete Your Profile

Once your listing is verified, you can complete your profile by adding essential business information such as:

• Business Name: Ensure it matches your business name.
• Address: Enter your business address accurately.
• Phone Number: Enter your business phone number.
• Hours of Operation: Specify your business hours.
• Categories: Choose relevant categories that describe your business.
• Description: Write a brief description of your business.

Step 5: Manage Your Listing

Regularly check and update your listing to ensure it remains accurate and up-to-date. Respond to customer reviews and use the insights provided by Google Analytics to improve your business.

Step 6: Verification 

Verify your business through postcard, email, or phone numbers as stated above. 

Now that you have successfully set up and verified your Google My Business listing, it’s time to optimize it for maximum visibility and effectiveness. By doing this, you can improve your local search rankings, increase customer engagement, and drive more conversions.

How to Optimize Google My Business

Here are the tips that I usually do when I’m optimizing my GMB account: 

1. Complete Your Profile: Start by ensuring every section applicable to your business is filled out with accurate and up-to-date information. Use your real business name without keyword stuffing to avoid suspension. Ensure your address and phone number are consistent with those on your website and other online directories, and add a link to your website and social media accounts.
2. Optimize for Keywords: Integrate relevant keywords into your business description, services, and posts. However, avoid stuffing your GMB profile with keywords, as this can appear spammy and reduce readability.
3. Add Backlinks: Encourage local websites, blogs, and business directories to link to your GMB profile. 
4. Select Appropriate Categories: Choose the most relevant primary category for your business to help Google understand what your business is about. Additionally, add secondary categories that accurately describe your business’s offerings to capture more relevant search traffic.
5. Encourage and Manage Reviews: Ask satisfied customers to leave positive reviews on your profile, as reviews significantly influence potential customers. Respond to all reviews, both positive and negative, in a professional and timely manner. Addressing negative feedback shows that you value customer opinions and are willing to improve.
6. Add High-Quality Photos and Videos: Use high-quality images for your profile and cover photos that represent your business well. Upload additional photos of your products, services, team, and premises. Adding short, engaging videos can give potential customers a virtual tour or highlight key services, enhancing their interest.

By following this comprehensive guide, you have successfully set up, verified, and optimized your GMB profile. Remember to continuously maintain and update your profile to ensure maximum impact and success.

Key Takeaway: 

With more and more people turning to Google for all their needs, creating, verifying, and optimizing your Google My Business profile is a must if you want your business to be found. 

Follow this guide to Google My Business, and you’re going to see increased online presence across Google Search and Google Maps in no time.

The post The Ultimate Guide to Google My Business (GMB) appeared first on SEO Services Agency in Manila, Philippines.