A leaked file reveals the user interactions that OpenAI is tracking, including how often ChatGPT displays publisher links and how few users actually click on them.
By the numbers. ChatGPT shows links, but hardly anyone clicks on them. For one top-performing page, the OpenAI file reports:
610,775 total link impressions
4,238 total clicks
0.69% overall CTR
Best individual page CTR: 1.68%
Most other pages: 0.01%, 0.1%, 0%
ChatGPT metrics. The leaked file breaks down every place ChatGPT displays links and how users interact with them. It tracks:
Date range (date partition, report month, min/max report dates)
Publisher and URL details (publisher name, base URL, host, URL rank)
Impressions and clicks across:
Response
Sidebar
Citations
Search results
TL;DR
Fast navigation
CTR calculations for each display area
Total impressions and total clicks across all surfaces
Where the links appear. Interestingly, the most visible placements drive the fewest clicks. The document broke down performance by zone:
Main response: Huge impressions, tiny CTR
Sidebar and citations: Fewer impressions, higher CTR (6–10%)
Search results: Almost no impressions, zero clicks
Why we care. Hoping ChatGPT visibility might replace your lost Google organic search traffic? This data says no. AI-driven traffic is rising, but it’s still a sliver of overall traffic – and it’s unlikely to ever behave like traditional organic search traffic.
About the data. It was shared on LinkedIn by Vincent Terrasi, CTO and co-founder of Draft & Goal, which bills itself as “a multistep workflow to scale your content production.”
Microsoft Advertising is rolling out Image Animation, a new Copilot-powered feature that automatically converts static images into short, dynamic video assets — giving advertisers a faster path into video without traditional production.
How it works:
Copilot transforms existing static images into scroll-stopping animated video formats.
The tool extends the lifespan of strong image creatives by repurposing them for video placements across Microsoft’s global publisher network.
The feature is now in global pilot (excluding mainland China) and accessible through Ads Studio’s video templates.
Why we care. Video continues to dominate digital attention, with the average American now watching more than four hours of digital video per day. As video becomes essential in performance campaigns, advertisers need scalable ways to produce it — especially when budgets or resources are tight.
This update reduces production barriers, extends the value of top-performing images, and unlocks broader inventory across Microsoft’s premium video network.
Between the lines. For many advertisers, the biggest bottleneck to entering video isn’t strategy — it’s production. Microsoft is positioning Copilot as a creative multiplier, letting performance marketers upgrade image-based campaigns with lightweight, AI-generated motion.
We’re always looking for new ways to help you understand your data and make smarter decisions when it comes
to Google Search. That’s why we’re happy to announce a new feature within the Search Console performance
reports: Custom annotations. This feature is designed to empower you to add your own contextual notes directly
to your performance charts. Think of it as a personal notebook for your Search data.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-11-17 05:00:002025-11-17 05:00:00Adding context to your Search Console data with custom annotations
If your email workflows are driving less than 70 percent of your total email revenue, you’re leaving money on the table. Automated email workflows meet your customer in the moment. When someone browses a product, adds something to their cart, or signs up for your list, they’re giving you a signal. And, if done right, your workflows are your chance to respond at scale, without adding more to your team’s plate.
You only need to set them up once, and they run in the background, working around the clock to nurture leads, recover sales, and turn interest into action. In this guide, we’ll break down how to build email flows that not only look good in your email service provider (ESP) but also drive revenue.
Key Takeaways
Every email workflow needs a trigger, filters, delays, and a clear exit condition to perform well.
The five core workflows that drive revenue are: welcome series, browse abandon, abandon cart, post-purchase, and win-back.
Workflows only run when triggered. Low performance could point to bigger issues like poor site traffic or weak segmentation.
Open rate, click rate, revenue per flow, and engagement drop-off are the metrics that matter most.
What Are Email Workflows and How Do They Work?
At its core, an email workflow is a series of automated emails triggered by a user’s behavior. This can include signing up for a list, abandoning a cart, or making a purchase. Each workflow is designed to align with where someone is in their customer journey. If they’re new, you welcome them. If they’re shopping but don’t check out, you nudge them. If they just bought, you follow up. It’s not a case about raising your email cadence, but sending the right emails at the right time.
These aren’t one-off blasts. They’re part of a drip system that sends a chain of multiple emails at an interval you set (every week, every month, on holidays, etc.). Each email is personalized and goal-oriented. The moment a contact meets the right condition, they automatically enter the workflow and progress through it based on delays, filters, and rules you set up in advance.
This is where automated email marketing becomes a real growth engine. Done well, workflows turn casual interest into conversions, without relying on constant manual effort. And unlike campaigns, which stop after you hit “send,” workflows continue to run. That’s what makes them such a scalable (and often overlooked) channel for revenue.
Email Workflow Types
Not all email workflows do the same job. The best ones align with specific moments in your customer journey. Here are five must-haves:
Welcome Series: Triggers when someone joins your list. Introduce your brand and guide them toward that first action.
Browse Abandon: Sends when a user views a product or page but leaves without engaging. Remind them what they were interested in, answer objections, and keep the conversation going.
Abandon Cart: Activates when a shopper adds something to their cart but doesn’t complete checkout. Use urgency or incentives to help them finish the purchase.
Post-Purchase: Follows up after a sale and makes the experience feel complete for your customer. After saying thank you, it’s also an opportunity to suggest cross-sells or ask for a review.
Win-Back: Targets subscribers who haven’t engaged in a while. Reignite interest with something personalized or exclusive before you lose them for good.
Each of these flows drives value differently, but together, they help automate the full customer lifecycle down the marketing funnel.
Elements of Email Workflows
Every successful email workflow is built from a few key components. Think of these as your toolkit for delivering the right message to the right person at the right time:
Trigger: The action that starts the workflow. This could be someone signing up, browsing a product, making a purchase, or going inactive.
Profile Filters: Rules that help control who enters the workflow. For example, only include first-time buyers, or exclude people who have already purchased recently.
Exit Condition: The goal. Once the subscriber completes the desired action, like buying or booking a call, they automatically exit the flow.
Delay: The time between emails in the sequence. Use delays to mirror your actual sales cycle or to avoid overwhelming your audience.
Trigger Splits: Branching logic based on how someone entered the workflow. Useful for segmenting based on cart value, source, or form response.
Conditional Splits: Branches based on profile or past behavior, like whether someone is a returning customer or how engaged they are.
Combining these elements makes your automations feel personalized, making your customers more likely to engage, and that’s what drives performance.
Measuring the Results
Identifying reliable metrics to track is essential for determining whether your email workflows are effective. Leveraging automation so you have less work to do is nice, but the real value kicks in when you can track and improve performance.
Here are the core metrics to watch:
Open Rate: This is the percentage of recipients who are opening your emails. More specifically, it tells you if your subject lines are getting people to engage. If your open rate is low, try a split test with different wording, timing, or preview text to see if you can improve it.
Click Rate: A good open rate means nothing without clicks. Look at your call-to-action, email layout, and how clearly you’re guiding users to the next step if you’re trying to improve this metric.
Revenue: The ultimate KPI. If revenue per workflow is lagging, revisit the relevance of your message and the landing page experience to ensure they align with your goals. If your numbers are still struggling, you can also look at your email timing as a possible solution.
Workflow Volume: Remember, automations only trigger when users take action. If your list isn’t entering flows, you may have an opt-in or segmentation problem.
Engagement Drop-Off: Monitor where users stop clicking. If most people bounce after receiving email two, consider tightening your message or testing a shorter sequence.
Don’t just set and forget. Use these signals to refine over time and keep your automations earning while you sleep.
FAQs
What is an email workflow?
An email workflow is a sequence of automated emails triggered by a specific action, like signing up, abandoning a cart, or making a purchase. Each email is timed and tailored to guide the user toward a goal, eliminating the need for manual sends.
Conclusion
Most businesses barely scratch the surface of what email workflows can do. When your automations are dialed in, they work quietly in the background, turning browsers into buyers and first-time customers into loyal ones.
The key is building flows that reflect your actual customer journey, not just what your ESP makes easy to set up. Start with the essentials: welcome, browse abandon, cart abandon, post-purchase, and win-back. Then build from there.
If you’re ready to go deeper, check out our guides on how to write emails and craft targeted customer personas. Once you set up your automation sequences, dialing in your copy and targeting the right customers are great techniques to take your email revenue to the next level.
With the right creative and workflows, your emails won’t just support your marketing strategy; they’ll become one of its strongest revenue channels.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-11-14 20:00:002025-11-14 20:00:00Automations That Sell While You Sleep: Building High-Impact Email Flows
Did you know that even a one-second delay in page loading speed can cause up to 11% fewer page views? That’s right, you might have the best content strategy and a solid plan to drive traffic, but visitors won’t stay long if your site lags. Page speed is one of the biggest factors in keeping users engaged and converting.
In this guide, we’ll uncover the most common causes of slow websites and explore proven ways to boost website performance. Whether your site feels sluggish or you simply want to make it faster, these insights will help you identify what’s holding it back and how to fix it.
Key takeaways
Page speed significantly affects user experience and conversion rates, with even minor delays leading to increased bounce rates
What do we mean by ‘website performance’ and why is it important for you?
Website performance is all about how efficiently your site loads and responds when someone visits it. It’s not just about how fast a page appears; it’s about how smoothly users can interact with your content across devices, browsers, and locations. In simple terms, it’s the overall quality of your site’s experience that should feel fast, responsive, and effortless to use.
When your page loading speed is optimized, you’re not only improving the user experience but also setting the foundation for long-term website performance.
Here’s why it matters for every website owner:
Fast-loading sites have higher conversion rates and lower bounce rates
Attention spans are notoriously short. As the internet gets faster, they’re getting shorter still. Numerous studies have found a clear link between the time it takes a page to load and the percentage of visitors who become impatient while waiting.
By offering a fast site, you encourage your visitors to stay longer. Not to mention, you’re helping them complete their checkout journey more quickly. That helps improve your conversion rate and build trust and brand loyalty. Think of all the times you’ve been cursing the screen because you had to wait for a page to load or were running in circles because the user experience was atrocious. It happens so often, don’t be that site.
A fast page improves user experience
Google understands that the time it takes for a page to load is vital to the overall user experience. Waiting for content to appear, the inability to interact with a page, and even noticing delays create friction.
That friction costs time, money, and your visitor’s experience. Research shows that the level of stress from waiting for slow mobile results can be more stressful than watching a horror movie. Surely not, you say? That’s what the fine folks at Ericsson Research found a few years back.
Ericsson Mobility Report MWC Edition, February 2016
Improving your site speed across the board means making people happy. They’ll enjoy using your site, make more purchases, and return more frequently. This means that Google will view your site as a great search result because you are delivering high-quality content. Eventually, you might get a nice ranking boost.
Frustration hurts your users and hurts your rankings
It’s not just Google – research from every corner of the web on all aspects of consumer behavior shows that speed has a significant impact on outcomes.
Nearly 70% of consumers say that page speed impacts their willingness to buy (unbounce)
20% of users abandon their cart if the transaction process is too slow (radware.com)
The BBC found that they lost an additional 10% of users for every additional second their site took to load
These costs and site abandonment happen because users dislike being frustrated. Poor experiences lead them to leave, visit other websites, and switch to competitors. Google easily tracks these behaviors (through bounces back to search engine results pages, short visits, and other signals) and is a strong indicator that the page shouldn’t be ranking where it is.
Google needs fast sites
Speed isn’t only good for users – it’s good for Google, too. Slow websites are often inefficient. They may load too many large files, haven’t optimized their media, or fail to utilize modern technologies to serve their page. That means that Google has to consume more bandwidth, allocate more resources, and spend more money.
Across the whole web, every millisecond they can save, and every byte they don’t have to process, adds up quickly. And quite often, simple changes to configuration, processes, or code can make websites much faster with no drawbacks. That may be why Google is so vocal about its education on performance.
A faster web is better for users and significantly reduces Google’s operating costs. Either way, that means that they’re going to continue rewarding fast(er) sites.
Improving page speed helps to improve crawling for search engines
Modern sites are incredibly wieldy, and untangling that mess can make a big difference. The larger your site is, the greater the impact page speed optimizations will have. That not only impacts user experience and conversion rates but also affects crawl budget and crawl rate.
When a Googlebot comes around and crawls your webpage, it crawls the HTML file. Any resources referenced in the file, like images, CSS, and JavaScript, will be fetched separately. The more files you have and the heavier they are, the longer it will take for the Googlebot to go through them.
On the flip side, the more time Google spends on crawling a page and its files, the less time and resources Google has to dedicate to other pages. That means Google may miss out on other important pages and content on your site.
Optimizing your website and content for speed will provide a good user experience for your visitors and help Googlebots better crawl your site. They can come around more often and accomplish more.
Page speed is a ranking factor
Google has repeatedly said that a fast site helps you rank better. It’s no surprise, then, that Google has been measuring the speed of your site and using that information in its ranking algorithms since 2010.
In 2018, Google launched the so-called ‘Speed Update,’ making page speed a ranking factor for mobile searches. Google emphasized that it would only affect the slowest sites and that fast sites would not receive a boost; however, they are evaluating website performance across the board.
In 2021, Google announced the page experience algorithm update, demonstrating that page speed and user experience are intertwined. Core Web Vitals clearly state that speed is an essential ranking factor. The update also gave site owners metrics and standards to work with.
Of course, Google still wants to serve searchers the most relevant information, even if the page experience is somewhat lacking. Creating high-quality content remains the most effective way to achieve a high ranking. However, Google also states that page experience signals become more important when many pages with relevant content compete for visibility in the search results.
Google mobile-first index
Another significant factor in page speed for ranking is Google’s mobile-first approach to indexing content. That means Google uses the mobile version of your pages for indexing and ranking. This approach makes sense as we increasingly rely on mobile devices to access the internet. In recent research, Semrush found out that 66% of all website visits come from mobile devices.
To compete for a spot in the search results, your mobile page needs to meet Core Web Vitals standards and other page experience signals. And this is not easy at all. Pages on mobile take longer to load compared to their desktop counterparts, while attention span stays the same. People might be more patient on mobile devices, but not significantly so.
Take a look at some statistics:
The average website loading time is 2.5 seconds on desktop and 8.6 seconds on mobile, based on an analysis of the top 100 web pages worldwide (tooltester)
The average mobile web page takes 15.3 seconds to load (thinkwithgoogle)
On average, webpages on mobile take 70.9% longer to load than on desktop (tooltester)
A loading speed of 10 seconds increases the probability of a mobile site visitor bouncing by 123% compared to a one-second loading speed (thinkwithgoogle)
All the more reasons to optimize your website and content if your goal is to win a spot in the SERP.
Understanding the web page loading process
When you click a link or type a URL and press Enter, your browser initiates a series of steps to load the web page. It might seem like magic, but behind the scenes, there’s a lot happening in just a few seconds. Understanding this process can help you see what affects your page loading speed and what you can do to boost website performance.
The process of loading a page can be divided into three key stages:
Network stage
This is where the connection begins. When someone visits your site, their browser looks up your domain name and connects to your server. This process, known as DNS lookup and TCP connection, enables data to travel between your website and the visitor’s device.
You don’t have much direct control over this stage, but technologies like content delivery networks (CDNs) and smart routing can make a big difference, especially if you serve visitors from around the world. For local websites, optimizing your hosting setup can still help improve overall page loading speed.
Server response stage
Once the connection is established, the visitor’s browser sends a request to your server asking for the web page and its content. This is when your server processes that request and sends back the necessary files.
The quality of your hosting, server configuration, and even your website’s theme or plugins all influence how quickly your server responds. A slow response is one of the most common issues with slow websites, so investing in a solid hosting environment is crucial if you want to boost your website’s performance.
One popular choice is Bluehost, which offers reliable infrastructure, SSD storage, and built-in CDN support, making it a go-to hosting solution for many website owners.
Browser rendering stage
Now it’s time for the browser to put everything together. It retrieves data from your server and begins displaying it by loading images, processing CSS and JavaScript, and rendering all visible elements.
Browsers typically load content in order, starting with what’s visible at the top (above the fold) and then proceeding down the page. That’s why optimizing the content at the top helps users interact with your site sooner. Even if the entire page isn’t fully loaded yet, a quick initial render can make it feel fast and keep users engaged.
Key causes that are causing your website to slow down
While you can’t control the quality of your visitors’ internet connection, most slow website issues come from within your own setup. Let’s examine the key areas that may be hindering your site’s performance and explore how to address them to enhance your website’s performance.
Your hosting service
Your hosting plays a big role in your website’s performance because it’s where your site lives. The speed and stability of your host determine how quickly your site responds to visitors. Factors such as server configuration, uptime, and infrastructure all impact this performance.
Choosing a reliable host eliminates one major factor that affects speed optimization. Bluehost, for example, offers robust servers, reliable uptime, and built-in performance tools, making it a go-to hosting choice for anyone serious about speed and stability.
Your website theme
Themes define how your website looks and feels, but they also impact its loading speed. Some themes are designed with clean, lightweight code that’s optimized for performance, while others are heavy with animations and complex design elements. To boost website performance, opt for a theme that prioritizes simplicity, efficiency, and clean coding.
Large file size
From your HTML and CSS files to heavy JavaScript, large file sizes can slow down your website. Modern websites often rely heavily on JavaScript for dynamic effects, but overusing it can cause your pages to load slowly, especially on mobile devices. Reducing file sizes, compressing assets, and minimizing unnecessary scripts can significantly improve the perceived speed of your pages.
Badly written code
Poorly optimized code can cause a range of issues, from JavaScript errors to broken layouts. Messy or redundant code makes it harder for browsers to load your site efficiently. Cleaning up your code and ensuring it’s well-structured helps improve both performance and maintainability.
Images and videos
Unoptimized images and large video files are among the biggest causes of slow websites. Heavy media files increase your page weight, which directly impacts loading times. If your header image or hero banner is too large, it can delay the appearance of the main content. Optimizing your media files through compression, resizing, and Image SEO can dramatically improve your website’s speed.
Too many plugins and widgets
Plugins are what make WordPress so flexible, but adding too many can slow down your site. Each plugin adds extra code that your browser needs to process. Unused or outdated plugins can also conflict with your theme or other extensions, further reducing performance. Audit your plugins regularly and only keep the ones that truly add value.
Absence of a CDN
A content delivery network (CDN) helps your website load faster for users worldwide. It stores copies of your site’s static content, such as images and CSS files, across multiple servers located in different regions. This means that users access your site from the nearest available server, reducing loading time. If your audience is global, using a CDN is one of the easiest ways to boost website performance.
Redirects
Redirects are useful for managing URLs and maintaining SEO, but too many can slow down your site. Each redirect adds an extra step before reaching the final page. While a few redirects won’t hurt, long redirect chains can significantly affect performance. Whenever possible, try to link directly to the final URL to maintain consistent page loading speed.
For WordPress users, the redirect manager feature in Yoast SEO Premium makes handling URL changes effortless and performance-friendly. You can pick from redirect types such as 301, 302, 307, 410, and 451 right from the dashboard. Since server-side redirects tend to load faster than PHP-based ones, Yoast lets you choose the type your stack supports, allowing you to avoid slow website causes and boost website performance.
A smarter analysis in Yoast SEO Premium
Yoast SEO Premium has a smart content analysis that helps you take your content to the next level!
How to measure page speed and diagnose performance issues
Before you can improve your website performance, you need to know how well (or poorly) your pages are performing. Measuring your page speed helps you identify what’s slowing down your website and provides a direction for optimization.
What is page speed, really?
Page speed refers to how quickly your website’s content loads and becomes usable. But it’s not as simple as saying, ‘My website loads in 4 seconds.’ Think of it as how fast a visitor can start interacting with your site.
A page might appear to load quickly, but still feel slow if buttons, videos, or images take time to respond. That’s why website performance isn’t defined by one single metric — it’s about the overall user experience.
Did you know?
There is a difference between page speed and site speed. Page speed measures how fast a single page loads, while site speed reflects your website’s overall performance. Since every page behaves differently, measuring site speed is a more challenging task. Simply put, if most pages on your website perform well in terms of Core Web Vitals, it is considered fast.
Core metrics that define website performance
Core Web Vitals are Google’s standard for evaluating how real users experience your website. These metrics focus on the three most important aspects of page experience: loading performance, interactivity, and visual stability. Improving them helps both your search visibility and your user satisfaction.
Largest Contentful Paint (LCP): Measures how long it takes for the main content on your page to load. Aim for LCP within 2.5 seconds for a smooth loading experience
Interaction to Next Paint (INP): Replaces the older First Input Delay metric and measures how quickly your site responds to user interactions like taps, clicks, or key presses. An INP score under 200 milliseconds ensures your site feels responsive and intuitive
Cumulative Layout Shift (CLS): Tracks how stable your content remains while loading. Elements shifting on screen can frustrate users, so keep CLS below 0.1 for a stable visual experience
How to interpret and improve your scores
Perfection is not the target. Progress and user comfort are what count. If you notice issues in your Core Web Vitals report, here are some practical steps:
If your LCP is slow: Compress images, serve modern formats like WebP, use lazy loading, or upgrade hosting to reduce load times
If your INP score is high: Reduce heavy JavaScript execution, minimize unused scripts, and avoid main thread blocking
If your CLS score is poor: Set defined width and height for images, videos, and ad containers so the layout does not jump around while loading
If your TTFB is high: Time to First Byte is not a Core Web Vital, but it still impacts loading speed. Improve server performance, use caching, and consider a CDN
Remember that even small improvements create a noticeable difference. Faster load times, stable layouts, and quicker interactions directly contribute to a smoother experience that users appreciate and search engines reward.
Tools to measure and analyze your website’s performance
Here are some powerful tools that help you measure, analyze, and improve your page loading speed:
Google PageSpeed Insights
Google PageSpeed Insights is a free tool from Google that provides both lab data (simulated results) and field data (real-world user experiences). It evaluates your page’s Core Web Vitals, highlights problem areas, and even offers suggestions under ‘Opportunities’ to improve load times.
Google Search Console (Page Experience Report)
The ‘Page Experience’ section gives you an overview of how your URLs perform for both mobile and desktop users. It groups URLs that fail Core Web Vitals, helping you identify whether you need to improve LCP, FID, or CLS scores.
Lighthouse (in Chrome DevTools)
Lighthouse is a built-in auditing tool in Chrome that measures page speed, accessibility, SEO, and best practices. It’s great for developers who want deeper insights into what’s affecting site performance.
WebPageTest
WebPage Test lets you test how your website performs across various networks, locations, and devices. Its ‘waterfall’ view shows exactly when each asset on your site loads, perfect for spotting slow resources or scripts that delay rendering.
Chrome Developer Tools (Network tab)
If you’re hands-on, Chrome DevTools is your real-time lab. Open your site, press F12, and monitor how each resource loads. It’s perfect for debugging and understanding what’s happening behind the scenes.
A quick checklist for diagnosing performance issues
Use this checklist whenever you’re analyzing your website performance:
Run your URL through PageSpeed Insights for Core Web Vitals data
Check your Page Experience report in Google Search Console
Use Lighthouse for a detailed technical audit
Review your WebPageTest waterfall to spot bottlenecks
Monitor your server performance (ask your host or use plugins like Query Monitor)
Re-test after every major update or plugin installation
Speed up, but with purpose
As Mahatma Gandhi once said, ‘There is more to life than increasing its speed.’ The same goes for your website. While optimizing speed is vital for better engagement, search rankings, and conversions, it is equally important to focus on creating an experience that feels effortless and meaningful to your visitors. A truly high-performing website strikes a balance between speed, usability, accessibility, and user intent.
When your pages load quickly, your content reads clearly, and your navigation feels intuitive, you create more than just a fast site; you create a space where visitors want to stay, explore, and connect.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-11-13 12:22:002025-11-13 12:22:00From slow to super fast: how to boost site speed the right way
Ad intelligence software promises to show you everything your competitors are doing: their keywords, budgets, creatives, and landing pages.
But many surface insights you could get for free.
Meta’s Ad Library shows what advertisers are currently running. Google’s Transparency Center does the same for search and YouTube. TikTok’s Creative Center reveals top performers by industry.
So, when does paid software earn its cost?
You’re tracking multiple competitors across platforms and losing hours to manual checks
You need historical data on which ads they tested and killed
You rely on spend benchmarks and real-time alerts to catch shifts before your clients do
That’s the gap paid tools fill. If they’re good.
Many aren’t. They bury useful insights under dashboards that create more work than they save. The data looks complete until you actually try to use it.
This guide covers six platforms that deliver real intelligence (if you know what you’re looking at).
We’re not promising magic improvements. We’re showing what each tool reveals, who it’s built for, and what you give up at each price point.
What Is the Best Ad Intelligence Software?
Ad Intelligence Tools
Best For
Price
Similarweb
Best for stalking competitors’ ads at scale — plus, their SEO, traffic, and market moves
$649+/month. (Only higher-tier plans come with ad intelligence.)
Semrush Advertising Toolkit
Best for multi-platform ad intelligence, from Meta and TikTok to Google Shopping
$99-$220/month
SpyFu
Best for affordable Google Ads intelligence with deep historical data
$39-$249/month
PowerAdSpy
Best for analyzing ad engagement across social media platforms
$69-$399/month
Adbeat
Best for tracking competitor display ads and landing pages
$249+/month
Pathmatics
Best for enterprise-level ad spend intelligence across mobile, social, and video
Pricing is not publicly available
1. Similarweb
Best for stalking competitors’ ads at scale — plus, their SEO, traffic, and market moves
Similarweb reveals your competitors’ complete paid strategies, from their winning ad creatives to their most successful publishers.
It also includes SEO and competitive intelligence tools in every subscription, so you get the full picture of how your rivals attract and convert traffic across every channel.
This cross-channel context is especially helpful if you already use native ad libraries but want scalable intel that ties everything together.
Learn Your Competitors’ Highest-Performing Publishers and Ad Networks
If your competitors are running ads, Similarweb shows you where (and how to beat them).
You’ll see:
Which ad networks and placements work best for your top competitors
Where their ad budgets go, broken down by channel
Industry-wide trends that reveal missed opportunities
Say Similarweb shows that multiple competitors spend over 50% of their display budgets on a single publisher.
That’s a data-backed signal you can’t ignore.
Use that data to target the same publisher to test similar placements. Or find underused publishers in the same category for more affordable traffic.
Get Inspired by Proven Ad Creatives
Similarweb’s database makes it easy to browse display ads by publisher, network, and format.
See the messaging and offers competitors use to get conversions
Learn how many days each ad was active, so you know which ones excelled (and which ones failed)
Find out which formats your competitors are using, including product, display, and video ads
Of course, copying your competitors’ ads word-for-word isn’t the goal.
The real value is in spotting patterns: the hooks they repeat, the formats they invest in, and the offers they continually test.
These insights let you design campaigns that build on what already works in your market.
When you’re juggling multiple accounts, this saves hours of creative testing, and points you directly toward proven formats.
Reverse-Engineer Competitors’ Search and Shopping Campaigns
Similarweb shows you which keywords your competitors bid on and how much they’re spending.
This helps you identify high-value keywords that drive conversions and avoid wasting budget on terms that don’t perform.
From there, you can build stronger landing pages that target your competitors’ most successful keywords and match intent.
Pros and Cons
Pros
Cons
Tracks 500M+ ads across publishers, networks, and formats for deep competitive insights
Ad intelligence tools only available with the most expensive plan
Uncovers competitors’ top-performing publishers and ad placements
Can feel overwhelming for smaller teams due to the platform’s depth
Includes SEO, traffic, and market data for a full competitive picture
If you only want ad intelligence, you’ll be paying for much more than you need
Pricing
Similarweb offers multiple plans, but only the most expensive one includes dedicated tools for ad intelligence.
This plan costs $649/month ($540 billed annually). Similarweb also offers business and enterprise plans, but pricing and tools are not publicly available online.
Best for multi-platform ad intelligence, from Meta and TikTok to Google Shopping
When you’re managing multiple clients or campaigns, switching between Meta, TikTok, and Google dashboards gets messy fast.
Semrush’s Advertising Toolkit consolidates that chaos into one workspace — letting you analyze competitor campaigns and build your own in the same place.
You’ll get deep intel on keywords, budgets, ad copy, and creative trends.
Plus, actionable advice on how to turn that data into high-performing campaigns.
Track Competitor Keywords and Budgets
The Advertising Research tool reveals everything, and we mean everything, about your competitors’ Google Ads strategies.
Enter any domain and you’ll see:
Estimated ad traffic
Cost per click (CPC)
Highest- and lowest-performing keywords
Organic search position
Keyword difficulty
URL
No more wasting ad budget on terms that don’t perform. You’ll know exactly which ones to target in your next campaign.
The tool also tracks keyword trends over time.
See which keywords competitors continuously invest in month after month.
When a keyword consistently appears in their paid strategy with stable or growing volume, that’s a clear sign it’s profitable.
With this data, you might test variations of the keyword in multiple ads to capitalize on its success.
Or use them to inform your broader content strategy beyond paid campaigns.
Spy on Google Shopping Ads
Have an ecommerce brand?
The PLA Research tool shows you which products your competitors promote most heavily in Google Shopping.
You’ll see position, volume, price, product titles, URLs, and trend data for each listing.
When a product shows up month after month, it’s likely a top seller.
If you don’t carry that product yet, you might consider adding it to your catalog.
Already sell it? Increase your Shopping ads to compete directly.
You can also view all of your competitors’ Google Shopping ads in one place.
Analyze their copy, images, and offers.
Then, apply these insights to your own listings:
Adjust your product titles to match high-performing formats
Test pricing strategies that undercut or match theirs
Prioritize ads for products where you have a competitive advantage. Think better reviews, faster shipping, or exclusive features they don’t offer.
Here’s another cool feature:
Instead of bouncing between tools, Semrush’s AI-powered Ad Launch Assistant lets you create and optimize Google and Meta ads directly inside the platform.
The tool generates copy and visuals tailored to your brand, from attention-grabbing headlines to conversion-focused descriptions.
Instead of writing everything from scratch, all you have to do is review each element:
Headlines
Descriptions
Site links
Callouts
Images
Videos
Simply refine the voice and messaging as needed. You’ll be able to test multiple variations in minutes instead of hours.
Unlock Deeper Insights with AdClarity
AdClarity is Semrush’s advanced cross-channel ad intelligence tool.
Need complete visibility into competitor display, social, and video campaigns?
This is where you’ll find it.
You’ll get a lot of data with this tool.
Including how much rivals spend, which publishers drive the most impact, and the exact creatives they’re using across platforms:
Facebook
Instagram
X
Google Display
Pinterest
YouTube
TikTok
LinkedIn
Say a competitor suddenly doubles their TikTok spend. You’ll spot the shift immediately and can adjust your strategy in real time.
AdClarity also automatically identifies your competitors’ top publishers and campaigns.
So there’s no guessing or testing which ones work well for your target audience.
Pros and Cons
Pros
Cons
Combines robust multi-site ad intelligence with Meta and Google campaign execution in one platform
The base plan includes only Google and Meta ad intelligence
Google Shopping insights are especially strong for ecommerce brands
AdClarity is only included the higher-tier plan
AdClarity offers advanced ad intelligence across display, social, and video
Doesn’t include SEO tools; you’ll need a separate toolkit for that
Pricing
The Semrush Advertising Toolkit is $99 per month.
It includes Advertising Research, PLA Research, Ads Launch Assistant, and more.
The higher-tier plan ($220/month) includes AdClarity, along with all of the above.
3. SpyFu
Best for affordable Google Ads intelligence with deep historical data
SpyFu is built for one thing: uncovering Google Ads strategies.
If your strategy leans heavily on Google, it’s one of the most detailed and budget-friendly advertising intelligence software options available.
Download Competitor Keywords Without Limits
SpyFu shows you everything your competitors do on Google Ads — and lets you export it all with no limits.
Many ad intelligence platforms cap your keyword downloads, so this is a plus.
Type in any competitor’s domain and you’ll see:
Every keyword they’ve ever bought on Google Ads
Estimated monthly clicks and CPC
Total spend on paid search
For example, say you’re in SaaS project management and Asana is your top competitor.
Search their domain, and SpyFu shows you their current and historical ad keywords. We’re talking thousands of terms, not just the top 50 or 100.
Download the complete dataset and…
Feed it into your analytics tools or Google Sheets
Share it with your team for campaign planning
Build custom reports for leadership
Cross-reference it with your CRM to see which keywords actually convert
Spot Overlaps and Waste in PPC Strategies
SpyFu’s Kombat tool compares your PPC strategy against up to two competitors at once.
But instead of having to sift through 10,000 keywords, the ad intelligence tool automatically groups them into helpful buckets:
Core Keywords: Terms all competitors are bidding on
Consider Buying: Valuable keywords they use, but you don’t
Potential Ad Waste: Terms that neither competitor uses but you do
So, you know exactly which terms to focus on (and which to remove from your campaigns).
This is especially helpful if you’re newer to paid campaigns.
Or have limited time (or tolerance) for turning data into actionable insights.
SpyFu also tags certain terms as “Great Buys” and estimates how many impressions you’ll get for each one.
Plus, it shows which competitors already bid on them, so you can piggyback on proven opportunities.
For example, the report below reveals that Asana’s competitor, Monday.com, uses “top task management apps” and “work time tracker app” in its ad strategy.
Asana could (and probably should) target both terms since SpyFu’s data shows they’re worth the investment.
Learn From Ads That Worked (or Failed)
SpyFu’s Ad History tool shows every ad variation competitors have tested for a given keyword.
If an ad copy ran for 14 consecutive months, you know it was effective.
If it vanished after a week? Probably a dud.
This kind of insight lets you write ads with fewer flops and faster wins.
This is especially valuable if you handle multiple accounts. You can skip obvious mistakes and start from proven winners.
Pros and Cons
Pros
Cons
Unlimited keyword exports with no download caps
Focused exclusively on Google Ads; no social or display coverage
10+ years of historical ad data for deep competitive analysis
Historical data (10+ years) requires paying for higher-tier plans
Kombat tool automatically identifies keyword overlaps and wasted spend
The base plan doesn’t come with unlimited downloads
Pricing
SpyFu offers three main plans, all of which come with ad intelligence and SEO reports.
The most affordable plan is $39 per month.
However, you’ll need to upgrade to a higher tier to get 10+ years of historical insights ($59-$249/month).
You’ll also see landing page insights, so you get intel on the complete customer journey.
See Which Landing Pages Are Actually Converting
Adbeat shows you which landing pages drive the most ad traffic. And how long each page has been live.
For example, Squarespace’s longest-running landing page has been active for 794 days.
That’s over two years.
When a page stays live that long, you know it’s consistently converting.
This intel helps you see which page layouts, offers, and messaging are worth replicating.
If you work for an agency and have multiple clients, this is particularly valuable. It’s a fast way to benchmark what “good” looks like in each vertical.
Reveal Media Buying Strategies and Publisher Insights
The Advertiser Dashboard breaks down where competitors allocate their budgets across channels, networks, and publishers.
You’ll also see share-of-voice data to understand their market presence.
For example, Adbeat found that Squarespace ran 524 ads in one month.
And 78% of their spend went to programmatic ads.
Details like this highlight which channels matter most in your niche. And where you can reallocate budget to get better performance for your own campaigns.
Benchmark Campaign Performance and Spot Trends
Adbeat’s ad intelligence software lets you monitor how your competitors’ budgets shift over time.
But what’s especially helpful is that they break it down by ad type: standard, native, and video.
For example, Squarespace’s longest-running video ad has been live for 413 days.
If they’ve kept it running that long, it’s a moneymaker.
In other words, it’s worth considering if you’re investing enough in video ads. And studying individual high performers for hooks, visuals, and offers.
Pros and Cons
Pros
Cons
Lets you analyze ads and landing pages together for complete funnel insights
Limited coverage of search and social campaigns
Reveals media spend, publisher performance, and traffic sources
Pricing is higher than ad-creative-only tools
Great for agencies, affiliate marketers, and display-heavy advertisers
Enterprise pricing is not publicly available
Pricing
Adbeat’s pricing starts at $249 per month for display, programmatic, and native ad intelligence.
For advanced filters, alerts, and historical data, you’ll need the higher plan ($399 per month).
There’s also an enterprise plan, but pricing isn’t listed publicly.
6. Pathmatics by Sensor Tower
Best for enterprise-level ad spend intelligence across mobile, social, and video
Pathmatics is built for large teams and big brands.
Household names like P&G and Unilever use this platform, so expect enterprise-level pricing and complexity.
But if you’re managing high-volume spend or reporting to leadership, it offers the transparency and benchmarking you can’t get from native tools.
Uncover Competitors’ Ad Spend Across Every Channel
Pathmatics shows you where every ad dollar goes in a pretty granular way.
It breaks down spend by platform, campaign, or creative — and tracks impressions, reach, and frequency over time.
Say you notice a competitor’s Instagram spend suddenly increased by a significant amount in a single week during Q4.
That signals a major campaign launch — possibly holiday shopping or Black Friday prep.
With this data, you can adjust your strategy immediately. And compete head-to-head with your main competitors.
Pathmatics also lets you benchmark your ad spend against multiple competitors at once.
If you’re investing $500K on display while your top three competitors each spend $2M+, you’ll see that gap.
Use this data to justify budget increases to leadership.
Or to identify where smaller reallocations could close the gap faster.
Benchmark Market Share and Share of Voice
Pathmatics tracks your share of voice against competitors in your industry and region.
If three brands dominate 80% of impressions in your category, you’ll see who owns what percentage.
This data helps you understand your position in the market.
Are you a distant fourth? Or neck-and-neck with the leader?
You can also identify which competitors dominate specific channels and spot opportunities where they’re underinvesting.
If the market leader owns Facebook but ignores TikTok, that’s your opening.
Evaluate Creatives That Resonate
Every ad includes details like format, placement, messaging, CTAs, and audience profiles.
See which creatives competitors keep running and which ones they kill after a few days.
Track the exact messaging and offers that stick around for months or years.
Use these insights to refine your own creative strategy.
Double down on formats that consistently deliver, and try localized messaging in new markets where your competitors are seeing success.
Pros and Cons
Pros
Cons
Provides cross-channel visibility across social, display, mobile, video, and OTT
Pricing is custom and can be expensive for smaller teams and startups
Combines creative data with detailed spend, reach, and audience insights
Steeper learning curve due to platform depth and data complexity
Ideal for enterprise-level teams, app publishers, and multi-channel marketers
Some users report data accuracy issues
Pricing
Pathmatics’ pricing is custom.
Request a quote if you’re interested.
Turn Competitive Intel into Campaign Wins
The right ad intelligence software isn’t the one with the most features.
It’s the one you can trust.
This means reliable data, less manual work, and the ability to scale campaigns across platforms with ease.
On a budget and focused mainly on Google Ads? Start with SpyFu.
Need deep, multi-site advertising intelligence across search and social with campaign execution built in?
Go for Semrush’s Advertising Toolkit.
Once you’ve picked your platform and gathered competitive intel, the next step is making sure your paid and organic strategies work together.
Learn how to align SEO and PPC to maximize visibility, reduce wasted spend, and improve your ROI.
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We’re excited to announce that we’re now expanding the options for merchants to provide shipping
and returns information, even if they don’t have a Merchant Center account. Merchants can now
tell Google about their shipping and returns policies in two distinct ways: by configuring them
directly in Search Console or by using new organization-level structured data.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-11-12 10:00:002025-11-12 10:00:00More ways to share your shipping and returns policies with Google
October showed just how fast AI is reshaping how brands connect, convert, and stay visible. OpenAI turned chats into checkout experiences. Google tested AI-written snippets and agent-driven search. The line between platforms, ads, and transactions keeps disappearing.
Creators gained new credibility. Rebrands proved riskier than ever. Data-driven PR entered a new era.
Here’s what mattered most and how to stay ahead.
Key Takeaways
• AI is officially a channel, not a tool. Search, shopping, and PR are all happening inside AI environments now.
• Authenticity outperforms aspiration. Whether you’re selling luxury goods or refreshing your brand, identity, and connection drive growth.
• Visibility depends on AI citations and structure. The brands getting mentioned in AI results are building more trust and traffic everywhere.
• Automation is powerful, but it still needs control. As Google’s AI Max expands, you need to balance efficiency with oversight to protect budgets and brand safety.
• Every brand action is a public statement. From rebrands to creator partnerships, perception moves fast. Plan your narratives or risk losing control of them.
Search & AI Evolution
Search has moved beyond discovery. October’s updates from OpenAI and Google show how AI is collapsing the gap between queries and actions. Visibility means something different now.
OpenAI launches in-chat purchases
OpenAI rolled out Instant Checkout in ChatGPT. U.S. users can now buy products directly inside the chat. Powered by Stripe, the feature starts with Etsy listings and will expand to more merchants soon. Sellers on Shopify are auto-enrolled. Others can join by connecting product feeds and enabling Stripe checkout.
Our POV: ChatGPT shopping changes product discovery completely. If your product data isn’t complete, detailed, and conversational, you won’t show up. The most visible listings will have rich attributes and language that reflects how users naturally describe what they want.
What to do next: Audit your product feeds. Fill every field. Use detailed, long-form descriptions that anticipate real-world queries. Give the e-commerce agent what it needs to surface your products.
<h3> Google tests AI-written meta descriptions <h3>
Google began testing AI-generated snippets powered by Gemini. Instead of pulling your written meta description, the model writes or summarizes one based on on-page content.
Our POV: Google’s been rewriting descriptions for years. AI just made it smarter and less predictable. Treat your page intros as the new meta description because that’s what AI will pull from.
What to do next: Front-load the first 150 words of each key page with a clear summary of what the page delivers and why it matters. Tighten headings and intros, monitor CTR shifts, and adjust language when AI summaries drift from your brand’s tone.
<h3> Google Search Labs adds Agentic AI <h3>
Google’s AI Mode now lets users book restaurants and other services directly from results. Search is moving from recommending to acting.
Our POV: This isn’t a traffic killer. But signals are shifting. AI will handle the click path. The brands that win will have structured, verified, action-ready data.
What to do next: Audit structured data, integrate local feeds, and make sure your listings are up to date across booking platforms. When the search agent starts acting on your behalf, data hygiene becomes your conversion strategy.
Paid Media & Automation
AI is taking over ad delivery. Control is the new currency. You have to balance efficiency with visibility to keep performance from becoming unpredictable.
Google doubles down on AI Max
Google refreshed its AI Max ad pitch. The system is fully automated: it matches intent, rewrites copy, and routes users to brand assets. Powerful, but still a black box.
Our POV: Automation doesn’t replace strategy. Advertisers need visibility, not just results. Without strict guardrails, budgets can leak into low-value placements or off-brand creative.
What to do next: Run low-risk tests first. Add negative keyword lists, set URL exclusions, and manually review creative. Monitor performance closely until you can prove control before scaling.
Apple launches dedicated Games app
Apple introduced a standalone Games app with iOS 26, bridging Game Center and the App Store. Developers can now feature their games, run dual search visibility, and analyze engagement with new metrics later this year.
Our POV: This isn’t a small tweak, Apple’s essentially building a second storefront. Game publishers who adapt early will own discoverability.
What to do next: Refresh creatives, optimize In-App Events, and plan for dual indexing between the Games app and App Store. When analytics arrive, use them to refine ASO and campaign timing.
Social & Content Trends
Creators and consumers are rewriting the rules. Authenticity, identity, and emotional connection drive engagement across platforms that once ran on aspiration and polish.
TikTok reframes luxury branding
TikTok’s new research shows luxury audiences care more about self-expression than status. It’s about showing who you are, not showing off.
Our POV: That shift goes way beyond luxury. Audiences in every category now expect brands to reflect their identity. Connection beats aspiration. Authenticity beats polish.
What to do next: Reevaluate your brand’s emotional identity. Work with creators who reinterpret your message through their lens. Build content that feels participatory, not performative.
UK YouTubers contribute £2.2B to the economy
YouTube creators generated £2.2 billion for the UK economy last year, supporting over 45,000 jobs. Parliament even launched a cross-party group to represent them.
Our POV: Creators aren’t influencers anymore. They’re small businesses with real economic weight. Partnering with them means investing in industries, not individuals.
What to do next: Build collaborations that help creators grow beyond campaigns. Shared education, joint products, or community-driven initiatives create deeper, longer-term value.
PR, Reputation & Brand Risk
Reputation management has become real-time and AI-measurable. From LLM citation tracking to brand backlash, every communication choice now echoes faster and louder.
A first-of-its-kind industry partnership between these two companies now offers a tool that tracks how often press releases are cited by LLMs like ChatGPT and Gemini. It finally gives brands visibility into their “AI footprint.”
Our POV: PR just gained a measurable seat in AI discoverability. Knowing when AI cites your releases helps you shape future narratives.
What to do next: Integrate AI citation metrics into your analytics stack. Identify which stories get surfaced and refine future language to match the tone that earns citations.
Rebrands are riskier than ever
Cracker Barrel’s attempted rebrand backfired almost instantly. Modest design updates triggered outrage and political backlash—proof that brand refreshes now carry reputational stakes.
Our POV: Rebrands still matter, but they demand foresight. A design tweak is a message, whether you mean it or not.
What to do next: Before launching a new look, test reactions across audience segments and scenario-plan your communication strategy. Shape the story before the internet does.
Olivia Brown automates PR outreach
A new AI platform called Olivia Brown is automating nearly every part of digital PR, from writing press releases to pitching journalists and sending aggressive follow-ups. It promises to “democratize publicity,” but its bulk-send approach is flooding inboxes and straining relationships between brands and reporters who value relevance and trust.
Our POV: Rebrands still matter, but they demand foresight. A design tweak is a message, whether you mean it or not.
What to do next: Before launching a new look, test reactions across audience segments and scenario-plan your communication strategy. Shape the story before the internet does.
SEO 2.0: The New Search Game
Traditional rankings are giving way to AI visibility. The brands that master structure, credibility, and omnichannel authority are the ones AI systems will learn to trust and users will keep choosing.
Rankings + AI Citations
Traditional SEO metrics can’t capture how visible you are inside AI systems. NP Digital’s SEO 2.0 approach tracks AI citations alongside rankings to see how content performs in generative search.
Our POV: Rankings aren’t the endgame anymore. Visibility inside AI summaries is. The brands that get cited are the ones shaping what users read next.
What to do next: Create original, data-backed content that builds authority across multiple platforms: YouTube, Reddit, TikTok, and forums. These are the signals AI models use to decide who to trust.
<America’s favorite new query: “Is it good or bad?”
SEMrush found that U.S. users are now searching in binary terms. Tens of millions of queries every month ask if something is “good” or “bad.”
Our POV: AI Overviews have trained users to expect clear answers. If your content hedges or buries the lead, you’ll lose clicks and credibility.
What to do next: Structure pages for speed and certainty. Use FAQ blocks, schema markup, and straightforward intros that deliver the verdict early. This is how you earn trust in zero-click environments.
Conclusion
AI is rewriting the rules of visibility, discovery, and trust. Success no longer depends on who publishes most. It depends on who provides the clearest data, most credible voice, and strongest structure. The brands investing in AI-ready content, authentic storytelling, and measurable strategy will own the next wave of search, social, and PR.
Need help applying these insights? Talk to the NP Digital team. We’re already helping brands adapt as things develop.
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Look, I get it. Every time a new search technology appears, we try to map it to what we already know.
When mobile search exploded, we called it “mobile SEO.”
When voice assistants arrived, we coined “voice search optimization” and told everyone this would be the new hype.
I’ve been doing SEO for years.
I know how Google works – or at least I thought I did.
Then I started digging into how ChatGPT picks citations, how Perplexity ranks sources, and how Google’s AI Overviews select content.
I’m not here to declare that SEO is dead or to state that everything has changed. I’m here to share the questions that keep me up at night – questions that suggest we might be dealing with fundamentally different systems that require fundamentally different thinking.
The questions I can’t stop asking
After months of analyzing AI search systems, documenting ChatGPT’s behavior, and reverse-engineering Perplexity’s ranking factors, these are the questions that challenge most of the things I thought I knew about search optimization.
When math stops making sense
I understand PageRank. I understand link equity. But when I discovered Reciprocal Rank Fusion in ChatGPT’s code, I realized I don’t understand this:
Why does RRF mathematically reward mediocre consistency over single-query excellence? Is ranking #4 across 10 queries really better than ranking #1 for one?
How do vector embeddings determine semantic distance differently from keyword matching? Are we optimizing for meaning or words?
Why does temperature=0.7 create non-reproducible rankings? Should we test everything 10 times over now?
How do cross-encoder rerankers evaluate query-document pairs differently than PageRank? Is real-time relevance replacing pre-computed authority?
These are also SEO concepts. However, they appear to be entirely different mathematical frameworks within LLMs. Or are they?
When scale becomes impossible
Google indexes trillions of pages. ChatGPT retrieves 38-65. This isn’t a small difference – it’s a 99.999% reduction, resulting in questions that haunt me:
Why do LLMs retrieve 38-65 results while Google indexes billions? Is this temporary or fundamental?
How do token limits establish rigid boundaries that don’t exist in traditional searches? When did search results become limited in size?
How does the k=60 constant in RRF create a mathematical ceiling for visibility? Is position 61 the new page 2?
Maybe they’re just current limitations. Or maybe, they represent a different information retrieval paradigm.
The 101 questions that haunt me:
Is OpenAI also using CTR for citation rankings?
Does AI read our page layout the way Google does, or only the text?
Should we write short paragraphs to help AI chunk content better?
Can scroll depth or mouse movement affect AI ranking signals?
How do low bounce rates impact our chances of being cited?
Can AI models use session patterns (like reading order) to rerank pages?
How can a new brand be included in offline training data and become visible?
How do you optimize a web/product page for a probabilistic system?
Why are citations continuously changing?
Should we run multiple tests to see the variance?
Can we use long-form questions with the “blue links” on Google to find the exact answer?
Are LLMs using the same reranking process?
Is web_search a switch or a chance to trigger?
Are we chasing ranks or citations?
Is reranking fixed or stochastic?
Are Google & LLMs using the same embedding model? If so, what’s the corpus difference?
Which pages are most requested by LLMs and most visited by humans?
Do we track drift after model updates?
Why is EEAT easily manipulated in LLMs but not in Google’s traditional search?
How many of us drove at least 10x traffic increases after Google’s algorithm leak?
Why does the answer structure always change even when asking the same question within a day’s difference? (If there is no cache)
Does post-click dwell on our site improve future inclusion?
Does session memory bias citations toward earlier sources?
Why are LLMs more biased than Google?
Does offering a downloadable dataset make a claim more citeable?
Why do we still have very outdated information in Turkish, even though we ask very up-to-date questions? (For example, when asking what’s the best e-commerce website in Turkiye, we still see brands from the late 2010s)
How do vector embeddings determine semantic distance differently from keyword matching?
Do we now find ourselves in need to understand the “temperature” value in LLMs?
How can a small website appear inside ChatGPT or Perplexity answers?
What happens if we optimize our entire website solely for LLMs?
Can AI systems read/evaluate images in webpages instantly, or only the text around them?
How can we track whether AI tools use our content?
Can a single sentence from a blog post be quoted by an AI model?
How can we ensure that AI understands what our company does?
Why do some pages show up in Perplexity or ChatGPT, but not in Google?
Does AI favor fresh pages over stable, older sources?
How does AI re-rank pages once it has already fetched them?
Can we train LLMs to remember our brand voice in their answers?
Is there any way to make AI summaries link directly to our pages?
Can we track when our content is quoted but not linked?
How can we know which prompts or topics bring us more citations? What’s the volume?
What would happen if we were to change our monthly client SEO reports by just renaming them to “AI Visibility AEO/GEO Report”?
Is there a way to track how many times our brand is named in AI answers? (Like brand search volumes)
Can we use Cloudflare logs to see if AI bots are visiting our site?
Do schema changes result in measurable differences in AI mentions?
Will AI agents remember our brand after their first visit?
How can we make a local business with a map result more visible in LLMs?
Will Google AI Overviews and ChatGPT web answers use the same signals?
Can AI build a trust score for our domain over time?
Why do we need to be visible in query fanouts? For multiple queries at the same time? Why is there synthetic answer generation by AI models/LLMs even when users are only asking a question?
How often do AI systems refresh their understanding of our site? Do they also have search algorithm updates?
Is the freshness signal sitewide or page-level for LLMs?
Can form submissions or downloads act as quality signals?
Are internal links making it easier for bots to move through our sites?
How does the semantic relevance between our content and a prompt affect ranking?
Can two very similar pages compete inside the same embedding cluster?
Do internal links help strengthen a page’s ranking signals for AI?
What makes a passage “high-confidence” during reranking?
Does freshness outrank trust when signals conflict?
How many rerank layers occur before the model picks its citations?
Can a heavily cited paragraph lift the rest of the site’s trust score?
Do model updates reset past re-ranking preferences, or do they retain some memory?
Why can we find better results by 10 blue links without any hallucination? (mostly)
Which part of the system actually chooses the final citations?
Do human feedback loops change how LLMs rank sources over time?
When does an AI decide to search again mid-answer? Why do we see more/multiple automatic LLM searches during a single chat window?
Does being cited once make it more likely for our brand to be cited again? If we rank in the top 10 on Google, we can remain visible while staying in the top 10. Is it the same with LLMs?
Can frequent citations raise a domain’s retrieval priority automatically?
Are user clicks on cited links stored as part of feedback signals?
Are Google and LLMs using the same deduplication process?
Can citation velocity (growth speed) be measured like link velocity in SEO?
Will LLMs eventually build a permanent “citation graph” like Google’s link graph?
Do LLMs connect brands that appear in similar topics or question clusters?
How long does it take for repeated exposure to become persistent brand memory in LLMs?
Why doesn’t Google show 404 links in results but LLMs in answers?
Why do LLMs fabricate citations while Google only links to existing URLs?
Do LLMs retraining cycles give us a reset chance after losing visibility?
How do we build a recovery plan when AI models misinterpret information about us?
Why do some LLMs cite us while others completely ignore us?
Are ChatGPT and Perplexity using the same web data sources?
Do OpenAI and Anthropic rank trust and freshness the same way?
Are per-source limits (max citations per answer) different for LLMs?
How can we determine if AI tools cite us following a change in our content?
What’s the easiest way to track prompt-level visibility over time?
How can we make sure LLMs assert our facts as facts?
Does linking a video to the same topic page strengthen multi-format grounding?
Can the same question suggest different brands to different users?
Will LLMs remember previous interactions with our brand?
Does past click behavior influence future LLM recommendations?
How do retrieval and reasoning jointly decide which citation deserves attribution?
Why do LLMs retrieve 38-65 results per search while Google indexes billions?
How do cross-encoder rerankers evaluate query-document pairs differently than PageRank?
Why can a site with zero backlinks outrank authority sites in LLM responses?
How do token limits create hard boundaries that don’t exist in traditional search?
Why does temperature setting in LLMs create non-deterministic rankings?
Does OpenAI allocate a crawl budget for websites?
How does Knowledge Graph entity recognition differ from LLM token embeddings?
How does crawl-index-serve differ from retrieve-rerank-generate?
How does temperature=0.7 create non-reproducible rankings?
Why is a tokenizer important?
How does knowledge cutoff create blind spots that real-time crawling doesn’t have?
When trust becomes probabilistic
This one really gets me. Google links to URLs that exist, whereas AI systems can completely make things up:
Why can LLMs fabricate citations while Google only links to existing URLs?
How does a 3-27% hallucination rate compare to Google’s 404 error rate?
Why do identical queries produce contradictory “facts” in AI but not in search indices?
Why do we still have outdated information in Turkish even though we ask up-to-date questions?
Are we optimizing for systems that might lie to users? How do we handle that?
Where this leaves us
I’m not saying AI search optimization/AEO/GEO is completely different from SEO. I’m just saying that I have 100+ questions that my SEO knowledge can’t answer well, yet.
Maybe you have the answers. Maybe nobody does (yet). But as of now, I don’t have the answers to these questions.
What I do know, however, is this: These questions aren’t going anywhere. And, there will be new ones.
The systems that generate these questions aren’t going anywhere either. We need to engage with them, test against them, and maybe – just maybe – develop new frameworks to understand them.
The winners in this new field won’t be those who have all the answers. There’ll be those asking the right questions and testing relentlessly to find out what works.
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Sir Tim Berners-Lee, who invented the World Wide Web, is worried that the ad-supported web will collapse due to AI. In a new interview with Nilay Patel on Decoder, Berners-Lee said:
“I do worry about the infrastructure of the web when it comes to the stack of all the flow of data, which is produced by people who make their money from advertising. If nobody is actually following through the links, if people are not using search engines, they’re not actually using their websites, then we lose that flow of ad revenue. That whole model crumbles. I do worry about that.”
Why we care. There is a split in our industry, where one side thinks “it’s just SEO” and the other sees a near future where visibility in AI platforms has replaced rankings, clicks, and traffic. We know SEO still isn’t dead and people are still using search engines, but the writing is still on the wall (Google execs have said as much in private). Berners-Lee seems to envision the same future, warning that if people stop following links and visiting websites, the entire web model “crumbles,” leaving AI platforms with value while the ad-supported web and SEO fade.
On monopolies. In the same interview, Berners-Lee said a centralized provider or monopoly isn’t good for the web:
“When you have a market and a network, then you end up with monopolies. That’s the way markets work.
“There was a time before Google Chrome was totally dominant, when there was a reasonable market for different browsers. Now Chrome is dominant.
“There was a time before Google Search came along, there were a number of search engines and so on, but now we have basically one search engine.
“We have basically one social network. We have basically one marketplace, which is a real problem for people.”
On the semantic web. Berners-Lee worked on the Semantic Web for decades (a web that machines can read as easily as humans). As for where it’s heading next: data by AI, for AI (and also people, but especially AI):
“The Semantic Web has succeeded to the extent that there’s the linked open data world of public databases of all kinds of things, about proteins, about geography, the OpenStreetMap, and so on. To a certain extent, the Semantic Web has succeeded in two ways: all of that, and because of Schema.org.
“Schema.org is this project of Google. If you have a website and you want it to be recognized by the search engine, then you put metadata in Semantic Web data, you put machine-readable data on your website. And then the Google search engine will build a mental model of your band or your music, whatever it is you’re selling.
“In those ways, with the link to the data group and product database, the Semantic Web has been a success. But then we never built the things that would extract semantic data from non-semantic data. Now AI will do that.
“Now we’ve got another wave of the Semantic Web with AI. You have a possibility where AIs use the Semantic Web to communicate between one and two possibilities and they communicate with each other. There is a web of data that is generated by AIs and used by AIs and used by people, but also mainly used by AIs.”
On blocking AI crawlers. Discussion turned to Cloudflare and their attempt to block crawlers and its pay per crawl initiative. Berners-Lee was asked whether the web’s architecture could be redesigned so websites and database owners could bake a “not unless you pay me” rule into open standards, forcing AI crawlers and other clients across the ecosystem to honor payment requirements by default. His response:
“You could write the protocols. One, in fact, is micropayments. We’ve had micropayments projects in W3C every now and again over the decades. There have been projects at MIT, for example, for micropayments and so on. So, suddenly there’s a “payment required” error code in HTTP. The idea that people would pay for information on the web; that’s always been there. But of course whether you’re an AI crawler or whether you are an individual person, it’s the way you want to pay for things that’s going to be very different.”
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/11/ai-ad-supported-web-j6x7xf.png?fit=1920%2C1080&ssl=110801920http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-11-11 18:34:402025-11-11 18:34:40Tim Berners-Lee warns AI may collapse the ad-funded web
