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Category: Analytics & conversion

Harnessing SEO: Focus on High-Intent Traffic for Greater Impact

I’ve noticed that not every organic visit deserves the same consideration these days. It’s become evident that I need to hone in on high-intent pages to truly measure SEO success and its impact on my business.

Recently, HubSpot rebranded its flagship conference from INBOUND to UNBOUND. This change wasn’t merely cosmetic; it represented a strategic pivot away from old-school SEO strategies that emphasized top-of-funnel traffic.

Modern search dynamics are nudging us closer to a zero-click environment. Trust me, the click-through rate curve is rapidly evolving. Studies show that around 60% of searches now conclude without a single click leading to the open web.

I’ve also observed that the discovery layer of search has shifted significantly. Nowadays, buyers are researching vendors within platforms like ChatGPT and Perplexity before they even consider clicking a traditional blue link.

Attribution has become increasingly complex. The modern buyer journey is fragmented, often starting with AI-assisted search and only finalizing on my website when the prospect is ready to make a decision.

This shifting landscape distorts my SEO reports if I focus solely on traffic as a success indicator. I’ve decided it’s time to pivot and redefine how I present traffic data to marketing leadership, ensuring that my reports align more closely with business impact.

A lively discussion on LinkedIn, led by Peter Rota, debated whether to completely retire organic traffic as an SEO metric. The consensus, I’ve found, is to use traffic with caution, always considering intent and the actual revenue it drives.

While organic traffic isn’t inherently bad, relying on it solely as a KPI lacks context and could be misleading. Adam Heitzman pointed out that it’s essential for traffic metrics to come with intent-based context for more accurate reflections of performance.

In a situation where low-intent traffic is reduced and focus is shifted towards high-intent pages, I’ve noticed that although overall visits might drop, conversions and revenue can actually increase due to better-quality traffic.

This understanding has led me to differentiate between top-of-funnel visits and more meaningful page interactions, thereby filtering out the data noise and focusing on what really matters in my dashboards.

Rand Fishkin beautifully summarized that top-of-funnel marketing feels like ‘rented land’—and he’s right. Buyers are now finding most basic information elsewhere, opting for instant answers on platforms like Reddit, TikTok, and within LLMs.

As of now, generic informational traffic is dwindling. Ironically, many SEO efforts are still devoted to content types most vulnerable to AI-driven change, such as FAQs and long-form articles.

Given this shift, it’s crucial for me to track pages based on their transactional value—those that AI can’t easily replace. I’ve narrowed my focus to four main areas: homepage, pricing pages, products and solutions pages, and money content pages.

Focusing my reporting on these key pages allows me to cut through the noise and concentrate on the traffic truly affecting my business’s bottom line.

For example, when a prospective B2B buyer starts searching for a modern CX platform, they’ll go through AI search, Google verification, and eventually land in the dark funnel for conversion.

Understanding these layers helps me recognize which organic traffic is significant enough to report, enhancing my insights into customer journeys and how they interact with my website content.

I know I must move away from outdated traffic analysis techniques to embrace more effective, modern reporting standards that focus on directional trends and macro shifts indicative of real business impact.

By focusing on page health instead of unreliable keyword-level reporting and monitoring branded search volume as an AI visibility proxy, I can capture a more accurate view of my current impact.

Inspired by this post on Search Engine Land.

May 22, 2026
Boost Your Funnel: Tackle Signal Decay & Maximize Performance
Have you ever wondered why those campaigns designed to introduce customers to your brand seem to get the least credit when it comes to driving revenue? Let me walk you through how to reclaim those lost conversion signals.

In today’s digital world, conversion signals are fading from our marketing data. Personally, I’ve noticed it’s costing businesses money.

Factors like ad blockers, strict privacy laws, and the decline of cookies are hiding crucial conversion data. According to a Deloitte study, this can cost businesses as much as $203 million annually. That’s a staggering figure!

For most brands, the journey from discovery to purchase is obscured, and this isn’t just an irritating data issue. If left unaddressed, it can prevent new customers from discovering your brand.

It surprised me how many marketers don’t realize they’re basing decisions on incomplete data. They see top-of-funnel campaigns underperforming and shift budgets elsewhere, unaware that this could trigger a negative cycle.

When traffic diminishes further due to algorithmic reactions, ad investments dwindle, and new customer acquisition slows, it results in a downward spiral that’s tough to reverse.

To avoid this, rather than focusing solely on creative strategies or bigger budgets, I believe prioritizing data hygiene will offer a competitive edge by 2026. Feeding better data to Google’s algorithm can transform those top-of-funnel activities into effective customer acquisition channels.

Why Signal Loss Hurts Discovery Channels First

YouTube usually sits at the top of the funnel, where attribution is weakest. Unfortunately, this makes it an easy target for budget cuts because of incomplete performance data, despite its crucial role in product discovery and brand research.

According to Google research, “YouTube is the No. 1 platform viewers turn to for brand or product research.”
- “YouTube is the No. 1 platform viewers turn to when they want to research, vet, or make a decision about a brand or product.”
Yet, the decay of conversion signals detrimentally impacts YouTube’s performance as a marketing channel. It often acts as the initial touchpoint, with users making purchases off-platform, disrupting the signal flow.

Haus Research found that Google’s advertising tools underreport YouTube’s true impact by 70% or more. With improved measurement setups, advertisers can capture those missing signals, allowing for a more accurate assessment of YouTube and similar platforms.

Closing the Cross-Device Gap with Enhanced Conversions

Think about how often you watch TV while holding your phone. You might see a commercial, Google it on your phone, and complete the purchase on desktop days later. This cross-device journey complicates tracking with standard cookie-based tagging methods.

Enhanced conversions tackle this issue by adding a layer of hashed first-party data, like an email, which Google uses to connect conversions to ad interactions securely.

Incorporating enhanced conversions into analytics provides insights into purchase paths that begin on YouTube and conclude off-platform, highlighting YouTube’s effectiveness in driving conversions that might otherwise be missed.

Training the Algorithm with Offline Conversions

Consider viewing a YouTube ad for an expensive item—something you’re not comfortable purchasing online. You close the ad only to call the seller later. Cookie-based tagging often fails to track such valuable conversions back to their origin.

This tracking gap extends to lead generation campaigns too. Offline conversions connect CRM and call data back to Google, training the algorithm to follow which leads convert rather than just form completions, enabling smart bidding to optimize for actual revenue outcomes.

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Defining New Top-of-Funnel Signals with Micro Conversions

Enhanced conversions and offline tracking can retrieve lost signals, but sometimes, top-of-funnel campaigns like YouTube lack sufficient conversion data for the algorithm. That’s where micro conversions come in, feeding necessary data for ad optimization.

Micro conversions provide early signals—like video views, adding items to a cart, or time spent on a page—allowing campaigns that lack purchase-level data to still improve performance. Depending on the campaign’s position in the funnel, you might prioritize engagement signals or actions like cart additions.

Without these intermediate signals, distinguishing effective upper-funnel activities from wasted efforts becomes challenging. Micro conversions empower you to treat top-of-funnel actions like any other campaign, enabling data-driven decisions on what’s working.

Recovering Lost Signals with Google Tag Gateway

The final piece in maintaining data hygiene is recovering blocked conversion signals before they reach Google. Browsers like Safari and Firefox restrict third-party tracking, contributing to massive signal decay during online purchases.

Google introduced Google Tag Gateway (GTG) to help reclaim lost data. GTG uses server-side technology to load tracking tags from your site’s domain instead of Google’s, bypassing some blockers.

Google reports an 11% signal uplift for GTG users compared to advertisers not using the tech. GTG also benefits advertisers with faster page speeds, enhancing Google’s landing page experience score and reducing click costs.

Setting up GTG is straightforward, especially if you’re on a content delivery network like Cloudflare, and it can significantly enhance your data infrastructure.

Your Data Infrastructure is Your Competitive Advantage

Conversion signal decay affects every brand selling online, but recognizing the real underlying problem is crucial: signal distortion from cross-device behavior, offline conversions, ad blockers, and low top-of-funnel signal volume distorts actual purchase behavior.

Armed with inaccurate data, many opt to tweak creatives, cut budgets, or inadvertently drop channels like YouTube, which secretly contribute to discovery. This leads to a detrimental downward spiral.

In 2026, those excelling won’t merely skirt around issues but will implement advanced data hygiene methods to feed lost data back into Google’s algorithm, gaining an edge over competitors.

To run more successful ads, prioritizing data improvements is key. Everything else tends to fall into place thereafter.

Inspired by this post on Search Engine Land.
May 21, 2026
Boost Team Efficiency: Overcome GTM Barriers with Storyblok

I’ve recently stumbled upon some fascinating global research data that highlights a tech gap silently draining team speed, revenues, and competitive edge. The Storyblok Global Speed-to-Market Benchmark Report explores these issues comprehensively.

This rapidly evolving world demands a new pace, driven by cutting-edge AI and technology, and constant shifts in digital trends have redefined how we handle go-to-market (GTM) strategies.

In today’s marketplace, everyone, from customers to organizations, expects top-notch deliveries with speed. Unfortunately, only 22.5% of teams consistently meet these soaring speed-to-market expectations, revealing a disconcerting gap between ambition and actualization.

One might ask, what’s holding us back?

The Global Speed-to-Market Benchmark survey involved several GTM teams who shared insights on where processes are stalling or facing delays and what steps would truly improve speed-to-market in today’s fast-paced business environment.

The survey uncovered four significant bottlenecks largely tied back to technological hiccups or dependencies. The approval process, for instance, emerged as the most substantial bottleneck, with over 50% of teams identifying it as a major hurdle. This includes enduring multiple rounds of content revisions largely driven by disorganized feedback systems, exacerbating inefficiencies.

The practical solution? A well-configured CMS, particularly a headless one, allows for an organized and efficient content review process by decoupling content from presentation. This ensures stakeholders have access to a central content repository, thereby minimizing review confusion and delays.

Equally problematic is the overreliance on developers, where 38% of teams require developer input for most GTM operations. This not only slows marketers but also distracts developers from more critical tasks. A modern tech stack enabling team autonomy can mitigate this issue, allowing each team to concentrate on their core functions.

Moreover, compounding tech limitations, including complex deployment and outdated systems, further warrant an overhaul. Tech bottlenecks often operate silently, but they demand attention and timely solutions for improved GTM cycles.

I also noticed how post-launch firefighting issues are rampant, affecting 79% of teams. This inefficiency stems from fragmented systems, where constant developer intervention is necessary, further delaying launch processes.

Addressing these challenges involves refining the tech stack, especially choosing a CMS that aligns with modern delivery needs. This results in smoother launches, improved efficiency, and fewer post-launch issues.

The cost of slow GTM delivery is undeniable, leading to lost revenue and missed market opportunities, while also impacting team morale and increasing turnover risks. Interestingly, there’s a visible discrepancy between executive priorities and the requisite support for improved speed-to-market capabilities.

Armed with data, teams can make a compelling business case for change, drawing attention to specific bottlenecks and their ramifications, thus bridging the leadership alignment gap.

Overall, overcoming GTM challenges requires adopting adaptive technology stacks that align with today’s fast-paced demands. By doing so, we not only keep up with competition but also foster a resilient, engaged team poised for success.

For the complete analysis and strategies, the full Storyblok Global Speed-to-Market Benchmark Report is an invaluable resource.

Inspired by this post on Search Engine Land.

May 20, 2026
Create Custom SEO Reports with Ease Using Claude Code & GSC

I’ve always found SEO reporting to be a bit of a hassle. It used to mean spending hours exporting data from Google Search Console (GSC), tidying it up in spreadsheets, and then trying to make sense of it all in Data Studio.

Now, with AI tools like Claude Code, my workflow has completely changed. I can instantly create customized data visuals and reports in a fraction of the time it used to take.

Let me walk you through the journey of transforming GSC data into tailored reports, streamlining the entire process.

Using Claude Code is different from the standard Claude experience. While the regular Claude.ai acts like a chatbot, Claude Code functions as an AI coding assistant right on my computer. It’s capable of reading GSC CSV files, analyzing large datasets, and transforming raw data into clear, visual reports.

Initially, setting up Claude Code can be daunting, especially if you aren’t familiar with technical tasks. But don’t worry, the setup is a one-time effort. Once it’s up and running, generating reports takes just minutes.

The real magic happens after you connect Claude to GSC. Whether you’re in an enterprise environment or you’re an independent SEO consultant, having Claude Code set up is invaluable.

Starting your journey with Claude Code begins by creating an account on Claude.ai. Even without a paid subscription, I find the platform extremely helpful for generating SEO reports.

A crucial step in using Claude Code is installing Node.js on your machine. For this tutorial, I used a Mac, but it’s compatible with other operating systems too. Once Node.js is installed, I am able to install Claude Code and verify my setup through simple terminal commands.

After setting everything up, I navigated a series of prompts in Claude, choosing how to access GSC data and defining key parameters for my reporting.

Connecting Claude to GSC involves interacting with the Search Console API, albeit a bit technical. But Claude guides me through each step, ensuring a smooth setup.

The exciting part comes after the connection is established. I can now rapidly create focused reports, such as identifying top-performing pages or tracking keyword trends over time, tailor-made for my needs.

Overall, Claude Code redefines how I manage SEO reporting. It offers the perfect balance of speed, flexibility, and control. Once the groundwork is laid, it makes my reporting both dynamic and precise, adapting to the demands of my stakeholders with ease.

Inspired by this post on Search Engine Land.

May 19, 2026
Navigating Marketing’s AI Era: The Air Traffic Control Approach

As I dive into the ever-evolving world of marketing, I can’t help but notice a profound shift. We’re no longer just performing for an audience; we’re adapting to customer journeys that mirror advanced AI systems. These systems interpret trust, risk, intent, and identity in real-time, and it feels like a whole new era.

For much of marketing’s history, the game plan was almost theatrical. Brands performed while consumers watched, and marketing channels existed primarily to broadcast these performances efficiently. Even as performance marketing gained popularity, it was still fundamentally based on the idea that a real person was sitting on the other side of the screen making straightforward decisions.

But now, that model is shattering. It’s not that consumers have disappeared; it’s that software is now an integral part of decision-making, demanding marketers’ attention.

Recommendation engines, fraud models, identity systems, and inbox providers have taken the reins more forcefully than creative campaigns ever did. Algorithms are shaping where attention goes long before consumers consciously choose anything.

I find myself contemplating the implications of layering autonomous agents into this complex environment. We often talk about AI as if it’s just another tool to enhance productivity—helping us segment faster, generate content quicker, and optimize swifter. This framing is comforting because it implies humans are still the pilots, with AI acting as copilots.

But this perspective will likely become outdated.

We are witnessing the rise of machine coordination. What is unfolding is less about workflow automation and more about distributed machine coordination. Here, marketing becomes an orchestration layer, interacting with thousands of semi-independent systems that interpret intent, trust, risk, relevance, identity, and value simultaneously.

Marketing is beginning to resemble air traffic control more than broadcasting.

Marketers aren’t gaining more control; they’re becoming like air traffic controllers. We govern dynamic systems we can’t fully see, predict, or command. Our value lies in maintaining harmony under challenging conditions of limited visibility and escalating complexity.

Today’s customer journey feels like a negotiation between competing models. One predicts purchase intent, while another assesses fraud risk or alters outreach frequency. These competing systems aren’t sequential but simultaneous, often adversarial.

Many organizations are already embroiled in this machine ecosystem, making contradictory decisions about customers simultaneously. One system may label a user as high value while another suppresses them as suspicious.

AI merely speeds up the revelation of these inconsistencies.

This scenario partly explains why identity infrastructure is moving back to the forefront. Over years spent focusing on activation, we’ve neglected signal integrity. This was manageable when humans were dominant interpreters. But autonomous systems operationalize ambiguity instead of compensating for it.

Having an inaccurate identity layer in a partially automated environment resembles corrupted air traffic telemetry. Small inconsistencies compound, leading to multiplied routing errors and deteriorating trust.

For marketing leaders, creativity is more important than ever, but at an architectural rather than asset level. The strategic advantage might lie with those who design stable coordination systems between machine intelligence layers.

This shift changes the strategic role of signal networks, once seen as supporting functions, to central components of a successful marketing strategy.

In this landscape driven by autonomous decision-making, orchestration quality is inseparable from identity confidence quality. If systems can’t differentiate between signal and noise or real activity and mimicry, they can’t coordinate effectively.

Companies might soon realize they can’t discern how much of their performance is actual human value versus synthetic behavior. AI systems optimize for measurable success rather than truth, occasionally rewarding synthetic engagement until financial or legal consequences arise.

This evolving environment makes personalization less about predicting customer desires and more about maintaining stable trust frameworks across intricate systems of human, AI, and synthetic interactions.

Today’s competitive advantage hinges on creating resilient signal infrastructures rather than stockpiling data. More information doesn’t always yield clarity and can sometimes create interference instead.

Activity-based intelligence is becoming crucial beyond traditional campaign optimization. Identity confidence and cross-channel trust are now vital components of autonomous ecosystems.

The shift favors organizations maintaining operational trust while scaling automation, moving away from systems built on static assumptions to those grounded in ongoing real-world activity.

This juxtaposition reveals the irony of years-long advice for marketing teams to become more scientific and data-driven. Scaling intelligence without scaling signal integrity equates to advancing aircraft technology while ignoring radar calibration.

Visibility, rather than data abundance, is about to become the defining constraint.

But not just visibility into consumers—visibility into the systems acting on their behalf.

Inspired by this post on Search Engine Land.

May 18, 2026
Unlocking Insights: Microsoft Clarity’s New Citations Dashboard

I’m thrilled to share that Microsoft has unveiled the Citations dashboard within Microsoft Clarity, their powerful analytics tool. This exciting update means you can now see how your content is being referenced in AI-generated responses across various AI platforms.

The introduction of this feature moves Citations in Microsoft Clarity into general availability, complete with all the refinements users have come to expect. With this, you’ll have clearer visibility into how your pages are performing in AI-driven experiences.

Citations Dashboard. With the Citations dashboard, I can monitor how my content is referenced in AI-generated answers by summarizing and aggregating citation activities. This is crucial because it covers essential areas such as:

Page Citations: This displays the frequency of page references from my domain in AI-generated answers during a specified period, even if multiple citations occur within the same answer.

Share of Authority: Here’s where I get a competitive view of how many citations my domain receives compared to others during the same set of queries.

AI Referral Traffic: This metric shows the percentage of my site’s sessions that originated from AI assistants in the chosen timeframe, calculated by dividing AI-referred sessions by total sessions.

Queries: Understanding the queries AI systems use to evaluate and retrieve my content gives me insight into AI’s interpretation of user intent.

My Cited Pages: I can view which URLs from my domain AI systems often cite, complete with citation counts and corresponding grounding queries.

Trendlines: These help me track changes in citation activity over time as content and AI query patterns evolve.

Microsoft also improved Clarity by enhancing the reporting model, query views, filtering, and pagination, making it more robust and efficient for analyzing larger datasets over extended periods.

To check out Citations, navigate to Dashboards, then select AI Visibility, and finally Citations. For additional details, you can visit this help document.

What it looks like. Here’s a glimpse of the Citations dashboard in Microsoft Clarity:

Why we care. As AI search continues to gain traction, understanding how users discover our content and websites through AI is invaluable. Clarity’s new Citations report equips us with the necessary tools to navigate this landscape effectively.

Similarly, Google Analytics has also introduced AI assistant traffic reporting to enhance our understanding of AI-driven traffic.

Expect these reporting tools to evolve and improve over time, providing even more robust insights.

Inspired by this post on Search Engine Land.

May 15, 2026
Unlock AI Insights: Google Analytics Adds AI Traffic Tracking

I’m excited to share that Google Analytics has introduced a new feature that allows me to track traffic from AI assistants, such as ChatGPT, Claude, and Gemini. This update gives me the ability to see which AI tools drive visits to my website and analyze user behavior more effectively.
With this new AI Assistant channel, I can now easily measure visits from these AI-powered chatbots without needing to apply custom filters or workarounds. The convenience of having this data readily available in Google Analytics is a game-changer for my analysis and reporting.
What’s New. Google Analytics now automatically labels traffic from supported AI assistants. Whenever a user visits my site through a supported AI chatbot, the visit is categorized under this new channel, which uses specific traffic source values such as Medium: ai-assistant, Channel Group: “AI Assistant,” and Campaign: (ai-assistant).
Why This Matters. This update is incredibly important to me because it provides a cleaner and more straightforward way to monitor AI traffic directly within standard GA4 reports. I can now track which AI assistants send the most traffic, gauge whether AI traffic is on the rise, and compare it to organic search and other channels. Moreover, it gives me insights into whether users from AI tools exhibit different conversion behaviors.
The Announcement. For more details on the new AI Assistant traffic measurement, I can refer to the official announcement.

Inspired by this post on Search Engine Land.

May 14, 2026
Build Trust in Your Marketing Data to Eliminate Skepticism
As a marketer, I know how it feels to operate with a hidden skepticism tax. Trusting marketing data can be a challenge, often leading to countless hours spent cleaning spreadsheets and reconciling conflicting reports. And let’s not forget second-guessing those attribution models and AI outputs.

This lack of trust slows down execution, weakens team alignment, and results in decisions built on shaky foundations. A prime example is branded search, which often undeservedly takes credit for conversions that were likely to happen anyway. It’s like crediting a revolving door for everyone who enters a building. This gap between correlation and causation highlights a broader issue in modern marketing—a reliance on fragmented or low-confidence data.

The key isn’t just collecting more data, but building a foundation of data we can actually rely on—through verified identities, unified reporting, cleaner pipelines, and a robust measurement framework designed to distinguish true signals from noise.

Let’s break down some core concepts behind building this foundation and the types of data environments they foster.

Probabilistic vs. Deterministic

Consider a coffee shop loyalty app to explain probabilistic vs. deterministic data: When a customer logs in and orders, you know it’s Sarah. That’s deterministic. Conversely, if someone on the same Wi-Fi browses your menu without logging in, you might assume it’s Sarah based on the device and location signals—it’s probabilistic. Both have their uses, but assumptions can lead to inaccurate messages, like sending a “Happy Birthday, Sarah!” notification without certainty.

Using a data-to-confidence mapping, like the identity confidence thermometer, can help explain this concept effectively to clients.

Deterministic data sits at the top of the thermometer (100% confidence), with various probabilistic confidence levels descending down to the bottom.

Siloed vs. Holistic

Imagine the old tale of blind folks describing an elephant: Marketing describes the trunk as a hose, Sales sees the leg as a tree, and Finance calls the tail a rope. This illustrates the pitfalls of siloed data in ROI reporting. A holistic approach ensures everyone sees the whole elephant.

In a more practical example, a B2B SaaS company runs LinkedIn ads. Marketing registers 5,000 form fills, Sales finds only 2,000 worthy leads in the CRM, and Finance reports 1,200 closed deals attributed to organic traffic due to broken UTMs. Different teams, different truths, zero confidence.

Here’s what these inconsistencies look like, contrasted with a unified data spine approach.

Third, First, and Zero-Party Data

Think about buying a house:
- Third-party data: a nosy neighbor speculating about a move—it’s just hearsay.
- First-party data: a realtor who sees them attending open houses—observed behavior.
- Zero-party data: the buyer expressing intent on a form—it’s direct communication.
As cookies fade away, marketers will shift from widespread hearsay to less frequent but more valuable direct interactions.

Visualize this as a pyramid: third-party data at the base (widest, lowest trust), first-party in the middle, and zero-party at the top (narrowest, highest trust).

Big Data vs. Correct Data

Picture a cluttered kitchen where nothing is ever discarded. The fridge is full, but half the contents have expired, forcing you to sift through it all for a single fresh ingredient. Occasionally, you use something spoiled—this is ‘big data’ for you.

By contrast, ‘correct data’ is a well-organized pantry: fewer items, all fresh, accurately labeled, and easily accessible. Consider feeding an AI model a massive data set with duplicates and errors—it might mislead rather than help you make informed decisions.

Here’s a visual metaphor of raw data flowing into a ‘swamp’ versus passing through a filter into a clean, reliable reservoir.

Correlation vs. Causation

You’ve probably encountered this concept before. In marketing, branded search often seems like a high performer because it records conversions right before purchases, similar to a revolving door taking credit for everyone entering a building.

Correlation identifies that those walking through the door became customers, while causation asks whether they’d have entered regardless of the door. Incrementality testing is key here.

In this test, you hold out a group from seeing ads and compare conversion rates to the exposed group. If both groups convert similarly, ads may be taking credit rather than creating demand.

An example might show branded search with inflated ROAS compared to a more accurate, incrementality-adjusted view emphasizing prospecting channels.

Building a Stronger Marketing Confidence Layer

To establish cross-team confidence, consider these data foundation tools:
- Identity confidence thermometer: Go from probabilistic data (low confidence) to deterministic data (high confidence).
- Siloed vs. holistic: Transition from siloed data to a holistic view for greater confidence.
- Data trust pyramid: Move from third-party (low confidence) to first- and zero-party data (high confidence).
- Big data vs. correct data pipeline: Filter raw data to reliable outputs, moving away from a ‘confidently wrong’ AI.
- Correlation vs. causation ROAS: Shift from identifying correlations to proving causation with a scientific approach.
While AI can automate countless tasks, effective decision-making must be upheld by experienced marketers applying good judgment. These data foundations help us move closer to achieving that.

Inspired by this post on Search Engine Land.
May 13, 2026
Explore Agent Analytics: Unleashing Insights with AI Nodes

I’m thrilled to introduce two groundbreaking Agent Analytics nodes from Profound: Bot Visits and Human Referrals. These innovative additions bring AI bot and Answer Engine referral data directly into Profound Agents, revolutionizing how we understand and optimize interactions.
Imagine the possibilities with Bot Visits, where you can track and analyze the influx of AI bot visits to your platform. This insight helps us optimize how we engage with automated traffic, ensuring that our systems are always at peak performance.
On the flip side, the Human Referrals node provides in-depth data about Answer Engine referrals. It’s like having a direct line to understanding how humans are being referred to our platforms, enabling us to craft more targeted strategies that resonate with real users.
With these nodes in place, we’re not just collecting data; we’re gaining a strategic advantage, allowing for more informed decisions and streamlined processes. Let’s embrace this new era of analytics that’s powered by AI intelligence.

Inspired by this post on Try Profound Blog.

May 12, 2026
Discover Your AI Rankings with Profound’s Agent Analytics

As a Profound customer, I’m excited to share that I can now clearly see where my site and pages stand in terms of AI citations compared to other peers in the Profound Agent Analytics Network.

This feature empowers me with detailed insights, allowing for a competitive analysis that helps in enhancing my digital strategy and boosting my AI visibility effectively.

Inspired by this post on Try Profound Blog.

May 12, 2026

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