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.
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.
I’ve always found the ability to share insights seamlessly to be crucial in our fast-paced digital world. One tool that I’ve come across is the generation of links to custom dashboards, which can be viewed by absolutely anyone.
Imagine the convenience of sending a link to your team or stakeholders, enabling them to access the dashboard data in real-time. This not only promotes transparency but also enhances collaboration by ensuring everyone has access to the same data, whenever they need it.
Through these easily shareable links, I’ve been able to bring a level of accessibility and efficiency to data sharing that seemed challenging before. It’s truly a game-changer, especially when managing multiple projects across different teams.
I’m excited to introduce you to a game-changing development in the world of research and data analysis. With Profound’s Prompt Research Reports, I have the power to pull insights from a staggering 1.5+ billion real user prompts. This transformative tool utilizes a proprietary ranking and clustering model, paving the way for data-driven decision making. Now, I no longer have to rely on guesswork when choosing prompts.
The system we use classifies and ranks user prompts, enabling me to access the most relevant data quickly and efficiently. This innovation not only optimizes my research process but also significantly enhances its accuracy and impact. By integrating such cutting-edge technology, I am able to stay ahead of the curve and meet my data needs with precision.
As I delve into the world of SEO reporting, I realize just how much we’ve outgrown platforms like Data Studio. Let me share what I’ve discovered and the exciting changes on the horizon that promise more efficient workflows powered by AI and APIs.
Imagine this scenario: Our team depends on Data Studio for delivering SEO reports. Just as we’re gearing up for a crucial meeting, Data Studio unexpectedly crashes, leaving us with nothing to showcase. It’s frustratingly common and incredibly embarrassing.
Just last year, I was praising Looker Studio (now Data Studio) for its advantages in SEO reporting. Fast forward, and it seems outdated compared to the dynamic coding tools I’m now utilizing. Here’s why rigid dashboards are holding us back and why transitioning to code-driven SEO reporting is essential.
Data Studio once reigned supreme for customizing SEO reports, but technology advanced, revealing its limitations. From dataset crashes to tedious manual interfaces, let me take you through some challenges I’ve faced with Data Studio.
We’re all familiar with the struggle: vast datasets in Data Studio are prone to breaking, often due to the low limits on rows and fields. Hasn’t it been just one too many times when a minor data addition causes everything to crash?
Manual updates in a slow interface make any iteration seem endless. Even the introduction of AI features addresses only a fraction of report-building issues.
Debugging Data Studio reports feels like a never-ending click maze. Unlike code-based systems where agents breeze through files, I’m often left clicking mindlessly within the interface.
Data Studio’s weak API is another stumbling block. It’s representative of Google’s missed opportunities for API-centric platforms. This flaw severely limits external management capabilities.
Despite recent rebranding efforts, these platforms lag behind modern SEO reporting technologies. Let me show you how everything is shifting with AI, APIs, and coding.
The evolution we’re witnessing is astounding. AI-driven coding tools like Claude Code and OpenAI Codex have changed the game. I describe my SEO reporting needs, and these tools take over, executing multi-step workflows efficiently.
Without needing deep coding expertise, I’m able to set up programmatic report workflows from beginning to end. Tools generate code that directly connects to data sources, eliminating reliance on cumbersome dashboard connectors.
Within minutes, comprehensive reports appear as I get accustomed to these tools. Each offers unique advantages, from reasoning to integration speed, transforming manual, rigid processes into infinitely flexible options.
AI coding tools usher in new possibilities for SEO teams by removing barriers between data management and reporting.
Speed is an unmistakable upside. Coding assistants enable SEOs to achieve in hours what once took days, and what took hours, now takes minutes.
Interacting with data directly through coding instead of dashboard interfaces drastically cuts down wait times for refreshes and modifications.
I’m no longer bound by rigid templates. Alongside on-demand data plotting and diverse frameworks, I can tailor reports to perfectly match needs and provide insightful visualizations.
Setting up these tools requires some initial effort but soon transforms the team’s efficiency, offering clearer data constraints and enhanced process transparency.
I’ve discovered how agentic coding assistants can revolutionize real-world SEO applications, from pre-meeting reports to ad hoc stakeholder requests, reducing late-night work and ensuring quick, reliable data access.
AI is reshaping the landscape for all professionals, not just us in SEO. As we adopt this technology, especially in SEO reporting, studies from Stanford and MIT show increased productivity. The shift isn’t optional; it’s imperative.
Teams leveraging AI tools in SEO witness faster iterations and can tackle complex issues more robustly, transforming analysts into strategists with unprecedented capabilities.
Begin this transformation with a small, repeatable project, connect data sources, and slowly expand your use of code-driven reporting. Early adopters are set to lead in SEO efficiency and results.
Traditional SEO reporting tools no longer meet the fast-paced demands of today’s analytics and strategic needs. Through AI and coding, we can leap ahead in reporting accuracy and timeliness, securing a competitive edge.
I’ve often marveled at high ROAS numbers during my campaigns, thinking they spell success. But, is this performance truly driving growth?
High ROAS numbers can be misleading, often masking mere demand capture rather than creation. To accurately assess growth, I focus on incrementality and marginal ROAS to guide more effective spending strategies.
An ecommerce company once collaborated with my PPC agency, eager to delve into the world of paid search. We crafted a robust plan that quickly led to impressive results: high conversion figures and a commendable ROAS.
It seemed like a strategy success story at first glance. However, when I took a closer look, I noticed something crucial.
Some conversions might have transpired naturally through direct or organic search channels, suggesting our campaigns perhaps weren’t spurring actual growth. This is a vital aspect that often remains unexamined. To gain genuine insight into performance, I examine incremental lift alongside marginal ROAS.
The truth about ROAS
I recall hearing about eBay’s paid search experiment. They heavily invested in brand PPC ads, only to later conduct controlled tests by pausing these ads for certain users, measuring their impact.
Much of the conversion was absorbed by organic traffic, scarcely affecting revenue. Yet, intriguingly, eBay reactivated the branded ads. Whether this was driven by fear or wisdom, I ponder the implications.
As automated search and multi-touchpoint customer journeys evolve, accurately attributing conversions to their channels becomes increasingly complex. Advert platforms often claim the credit, but adopting a skeptical view towards these reports is invaluable.
I comprehend that what these platforms report as attributed return doesn’t necessarily equate to causal lift. While ROAS indicates platform-influenced revenue, it falls short in revealing how much revenue would have materialized regardless of the ads.
With tools like Performance Max and Advantage+, platforms excel in optimizing conversion avenues, often not discovering new clientele but instead marking the costliest touchpoints in pre-determined conversion paths.
In the absence of incrementality assessment, automation tends to amplify non-incremental signals: capturing existing demand through brand search campaigns, retargeting nearly-converting users, and creating falsely “safe” channel reports.
Incrementality tells you whether marketing created something extra
By analyzing incrementality, I can determine how the campaign wrought changes it wouldn’t have caused otherwise, typically through comparisons of exposed groups with control groups. This reveals the actual organizational impact of the campaign.
Recognizing this might feel uncomfortable, yet it serves as a more precise lens for budget allocations than superficial platform attributions.
Sometimes, even a seemingly successful channel in-platform ROI might not equate to impactful incremental growth. Often, it merely realizes existing demand rather than inventing it.
If I truly wish to ascertain if a campaign drives genuine growth, the incrementality factor must become my focal question.
Despite being vital, incrementality only provides part of the picture. The necessity for marginal ROAS to chart subsequent steps can’t be overstated.
An incremental channel alone doesn’t specify where the next budget investment should proceed. Understanding marginal ROAS is essential here.
The marginal ROAS examines the revenue from an additional unit of spend, surpassing the average ROI across all expenses. Often, initial budget allocations perform well but subsequently deliver diminishing results.
As investments continue, dollars spent towards the end become disproportionately less efficient. This principle also holds true for CPA metrics: a blended CPA might appear satisfactory while the terminal dollars spent demonstrate poor efficiency, luring advertisers beyond optimum bidding zones.
I consider an example where an initial $10,000 budget generates $50,000 in revenue (500% ROAS). Deciding to expand, I then invest an additional $5,000, only to generate an incremental $5,000 revenue.
Your new average ROAS: 366%
Your marginal ROAS: 100% (Essentially a $1-to-$1 trade.)
In such instances, the final $5,000 expenditure was ineffective, despite overall acceptable “average” performance on dashboards.
This highlights the folly of focusing solely on average ROAS. It can obscure the genuine scalability that might only be viable at lower spend levels, misleadingly disguising profitable demand capture as flawed incremental expansion.
Informed decision-making requires peering deeper: platform ROAS aids in optimizing in-platform efforts, incrementality assesses campaign-generated value, while marginal ROAS indicates where the ensuing budgets should be directed.
A robust ROAS can reflect true efficiency or merely illustrate a platform ensnaring already-converting demand. Hence, incrementality tests form the cornerstone of my analysis.
My critical inquiry is not whether a channel is efficient per se, but whether subsequent dollars are sufficiently efficient. This understanding is essential for prudent scaling.
Embarking on incrementality testing doesn’t require a flawless measurement lab. Utilizing geo tests, holdouts, audience exclusions, and controlled spending reduction can enhance understanding far beyond another month spent in attribution debates.
Geo-split testing: Organize markets into dual comparable geographic groups, maintaining ad runs in a “test” grouping while halting them in a “control” group. Revenue disparities between these regions unveil the genuine incremental lift of your ads.
Search lift tests (holdouts): Leverage platform tools to generate holdout groups, excluding a small user fraction from ad exposure. The behavioral contrasts between them and exposed groups unveil Search or YouTube campaign direct impacts.
Furthermore, investigating remarketing, branding, awareness campaigns, or supplementary social channels can reveal additional insights.
The real shift: From reporting performance to allocating capital
For too long, marketing teams have restricted measurement to explaining past events. The optimal application lies in shaping future endeavors effectively.
Incrementality helps me discern value creation within a channel, while marginal ROAS justifies additional investments. Together, they elevate marketing measurement from mere reporting to informed capital allocation.
ROAS demonstrates credit allocation, incrementality pinpoints actual transactional changes, and marginal ROAS guides subsequent budgeting. It’s crucial to remember that incrementality differs from attribution. While attribution awards channel credit, incrementality evaluates whether this pursuit justified itself.
I recently discovered that Google is making significant updates to Analytics and Ads consent rules, which are set to take effect this June. This change will prioritize user permission as the key factor in how ads collect and utilize data.
Starting June 15th, the process of data collection in Google Ads will now rely exclusively on the ad_storage consent setting. This alteration removes the previous layer of complexity that came from linked Google Analytics configurations.
Previously, the flow of ad data between Analytics and Ads was governed by both Consent Mode and Google Signals settings within Google Analytics. This often led to confusion among marketers like myself, as many controls were hidden deep within the Analytics settings, rather than clearly visible in consent banners or tag implementations.
Moving forward, Google is streamlining the process. While Google Analytics data collection will still use Google Signals, Google Ads will now focus solely on whether users have consented to ad_storage.
This means that a linked Google Analytics tag will no longer influence Google’s ability to collect or use advertising identifiers.
The new update offers a cleaner, albeit more rigid, consent framework. If ad_storage consent is given, Google Ads can use all available advertising signals, including linking activity to a user’s signed-in Google account when feasible. If denied, Google will only utilize less persistent signals such as URL parameters like gclid.
This change substantially reduces ambiguity—marketers will have a clearer understanding of what drives ads data collection, with fewer options to customize what gets shared.
The primary concern here is that this adjustment makes consent settings more significant for measurement, attribution, and audience targeting. From June, whether Google Ads can leverage identifiers will depend largely on the ad_storage signal, highlighting the importance of correct consent mode setup for optimal campaign performance data.
The update simplifies some of the complexity hidden in linked Google Analytics settings, providing advertisers with more defined rules but less flexibility.
This move by Google underscores a broader strategy to enhance the understanding of consent systems for both advertisers and regulators. Having a single source of truth for ad consent could minimize implementation errors and simplify compliance explanations, but it also demands that brands ensure their Consent Mode is accurately configured.
Should consent updates be delayed or improperly configured, marketers might face gaps in measurement, attribution, and audience targeting.
Marketing teams need to take action before the June deadline by auditing their consent implementation. We should verify that Consent Mode update calls are firing correctly, and that ad_storage settings reflect users’ choices precisely. Brands with Google Signals disabled should be especially vigilant, as they could witness more Ads-linked data under the new setup if users allow ad consent.
The takeaway is clear: streamlined rules are on their way, but getting consent right will be more critical than ever.
I’m excited to share that Google is bringing back Data Studio as a streamlined platform for analyzing marketing and business data across its ecosystem. It’s aimed at helping us easily delve into and act on the data that powers our daily decisions.
Why the switch back? The new Data Studio will serve as our go-to central hub, encompassing a wide range of assets—from traditional reports and dashboards to advanced data applications created in Colab and BigQuery conversational agents. This single platform will enable us to access all the tools and insights essential for shaping our businesses.
Looking back. Three years ago, Data Studio was merged into Google’s analytics efforts with a rebranding as Looker Studio. Now, Google’s responding to evolving customer needs by separating these products again.
Two versions available. Google is introducing two variations of Data Studio:
Data Studio remains free for individuals and small teams seeking quick analysis and visualization capabilities.
Data Studio Pro is designed for larger organizations, providing enhanced security, compliance, management controls, and AI features. Licenses can be purchased through Google Cloud and Workspace admin consoles.
Why it matters to us. This revamped Data Studio can significantly ease the process of gathering campaign, audience, and performance data from Google’s ecosystem into one place. This means quicker reporting, more straightforward analysis, and faster responses—often eliminating the need for analysts or engineering support for everyday tasks.
Integrating Looker. Under the new setup, Looker will continue to be Google Cloud’s enterprise-level business intelligence platform, focusing on managed data, semantic modeling, and large-scale analytics. In contrast, Data Studio is geared towards more flexible personal exploration, ad hoc reporting, and accessible dashboards via services like BigQuery, Google Sheets, and Ads.
What’s on the horizon. For those of us already using Data Studio, the transition should be seamless. Reports, data sources, and assets will automatically transfer without requiring any action on our part.
Google plans to reveal more details about the relaunch and its expansive analytics strategy at Google Cloud Next ’26 later this month. I’m looking forward to discovering what’s next!
Dig deeper. For more in-depth information, check out this article on the new Data Studio.
I’ve discovered that measurement is truly the cornerstone for all we achieve in performance marketing. Without precise measurement, everything I recommend, implement, and optimize becomes mere speculation. Today, maintaining accurate measurement is more challenging than ever—and it’s only getting more difficult.
With regulatory crackdowns and growing privacy concerns, paired with elongated multi-touch journeys, we face a measurement crisis. Brands that still rely on outdated tactics are missing the mark when it comes to modern measurement challenges.
If your brand falls into this category, it’s time I help you rebuild your measurement foundation—from integrating first-party data (crawl), to creating cross-channel reporting for actionable insights (walk), to advanced media mix modeling (MMM) and incrementality testing for true media lift (run).
The crawl: Building a first-party data foundation
By integrating first-party data into our performance marketing channels, I can move beyond reliance on third-party signals. While those metrics offer surface-level insights, they don’t reveal how channels impact our business goals.
Audience integration
The first step involves integrating CRM data into our paid media platforms. This includes:
Remarketing to abandoners.
Creating exclusion lists for current subscribers or recent purchasers.
Compiling priority contact lists.
I might be uploading lists today, but integration enhances targeting by connecting to up-to-date audience lists for media platform targeting.
Offline-conversion tracking
For lead-gen businesses like ours, setting up offline conversion tracking (OCT) is crucial. It reveals the bottom-line impact of our media on sales, passing sales data back to platforms for campaign attribution.
Once OCT is in place, we can optimize for lower-funnel, higher-quality conversion steps in the sales cycle or even begin optimizing toward revenue to enhance our return on ad spend.
Server-side tracking and consent mode
To progress from crawl to walk, I need to move from client-side to server-side tracking.
By adopting server-side tracking, we bypass browser-based tracking and instead rely on our first-party data. This approach ensures data accuracy and resilience as privacy restrictions increase and cookies become obsolete.
Partner integration uses pre-built connectors for setup through platforms like Shopify or Google Tag Manager.
Direct API requires a development team to handle complex data or custom backends.
The walk: Cross-channel reporting integration
With a robust measurement foundation, my next step is breaking down platform silos to understand the full ecosystem.
Going beyond last click
After implementing server-side tracking, I created a clean data pipeline. Yet, traditional attribution models neglect the full-funnel customer journey.
To address this, I recommend using data warehousing solutions like BigQuery to centralize your data and apply custom logic, thereby gaining insights across the ecosystem.
Unified reporting dashboards
Integrating evolved attribution with unified reporting dashboards, like Looker Studio, allows me to visualize data across the funnel and obtain actionable insights into what platforms are truly driving volume and conversions.
The run: Media mix modeling and incrementality testing
With a comprehensive, everyday view of performance, significant questions persist about growth potential and offline performance measurement.
By employing media mix modeling and incrementality testing, I can discern the full impact of media investments at a macro level to make informed decisions.
The holistic view through MMM
I view MMM as my compass, providing a holistic, quantitative guide for paid media investments, helping me analyze the relationship between inputs and business outcomes.
Pulse checks with incrementality testing
Incrementality testing offers validation for MMM and helps evaluate if specific tactics or channels are driving true incremental lift by comparing test and control groups.
The sprint: Clean, integrated, and validated first-party data
With first-party data integrated through server-side tracking and cross-channel reporting, I’ve built a robust measurement foundation. Guided by MMM and validated by incrementality testing, I’m now ready to sprint towards a more informed and successful marketing strategy.
I’ve spent a decade delving into PPC strategies and what I’ve learned is that chasing ‘best practices’ often limits true performance potential. Real growth stems from daring to deviate and experiment with new methods.
PPC conversations frequently revolve around sticking to best practices. These mandates include maintaining clean account structures, controlling match types, scaling budgets incrementally, ensuring campaigns don’t overlap, and keeping everything logical and easy to explain.
While these fundamentals do promote consistency and prevent inefficiencies, they are not the secret to achieving significant gains.
Looking back, many of the most impactful improvements came from testing unorthodox ideas that didn’t neatly fit into the established frameworks, but instead aligned with how platforms like Google Ads and Meta actually operate. These platforms don’t optimize for best practices, but rather for signals, prompting a rethink in approach to performance.
Control Still Matters: Revisiting SKAGs
In several accounts, reintroducing Single Keyword Ad Groups (SKAGs) for high-intent, high-revenue keywords led to improved performance. Ad relevance shot up, conversions grew, and query matching became more precise. It’s not about reverting to old structures, but recognizing where control adds value.
The narrative that machine learning abolishes the need for such control is overly simplistic. My experience shows that precision matters, but only in contexts where the intent justifies it.
Harnessing Broad Match with Control
Historically, broad match has been met with skepticism due to its expansive nature. However, combining broad match with aggressive negative keyword management allows Google to explore broadly while you shape the output through strategic query mining.
By continuously refining query inputs, broad match can expand reach without compromising relevance, redefining how control is applied.
When Visibility Trumps Efficiency: Target Impression Share
Target Impression Share often supports defensive strategies, but applying it to high-value, non-branded terms can boost SERP dominance even at the cost of efficiency. In such cases, ensuring visibility can outweigh concerns over cost efficiency, especially when aiming for market dominance rather than mere competition.
Focusing on Conversion Quality: Weighting Over Tracking
Most lead generation accounts capture multiple conversion actions, but treating them equally can lead to suboptimal interpretations. In one instance, assigning different values based on conversion likelihood—like prioritizing phone calls—shifted optimization to improve conversion quality rather than volume.
This approach emphasizes what’s truly valuable, ensuring platforms optimize effectively based on input.
Competitor Bidding: Leveraging Existing Intent
Despite their reputation for inefficiency, competitor campaigns succeed by capturing existing intent. Users searching for competitor brands often convert thanks to their advanced position in the decision process, proving crucial when strategically managed with clear positioning and relevant landing pages.
Rethinking Top-of-Funnel Keywords
Although often removed for low conversion rates, top-of-funnel keywords can indirectly enhance account performance by strengthening remarketing pools and audience signals, thus supporting high-intent campaign efficiency.
These queries play an unseen but vital role in driving conversions across the account.
Trusting the Data Over Assumptions
Initial audience hypotheses frequently miss the mark, whereas data often pinpoints the most efficient converters. By trusting data and adjusting strategies accordingly, accounts can improve performance by aligning with audience realities.
Revisiting Account Structure’s Role
While clean setups simplify management, they’re not always the most effective. Controlled overlaps between campaigns can leverage shared signals for better auction outcomes, challenging the notion that rigid structures lead to optimal performance.
Treating Product Feeds as Dynamic
In Shopping campaigns, product feeds are often overlooked. Yet, revisiting and adjusting feed details—like product titles and attributes—can significantly enhance product visibility and click-through rates, underscoring their strategic importance.
Retargeting: A Hub for Testing Strategy
Retargeting is not just about conversions; it’s ideal for testing variations in messaging and creative content due to its high-intent audience. Successful test results can then be confidently scaled, reframing retargeting as a strategic testing ground.
The Real Secret Behind Top Account Success
Over the years, I’ve realized that outperformance doesn’t stem from strictly adhering to playbooks, but from understanding and influencing platform signals and stepping beyond conventional boundaries to outperform beyond expectations.
