Starting in June, Google Ads will implement a policy that deletes any reporting data older than 37 months, unless we take action to export and preserve it.
As someone who heavily relies on historical data for reporting and forecasting, I recognize the urgency to revamp my data management strategies before access to older records is lost.
What’s Changing. From June 1st, only data from periods shorter than a month—such as hourly, daily, and weekly reports—will be accessible for 37 months. For longer spans like monthly, quarterly, and annual reports, we will enjoy access for up to 11 years.
Once those retention periods lapse, the data will no longer be available in the Google Ads interface or through APIs.
Nitty-gritty Details. Metrics that measure reach and frequency will have even shorter retention limits, staying available for just three years. These metrics include:
unique users,
average impression frequency per user,
7-day and 30-day average impression frequency,
and frequency distribution metrics.
The Larger Impact. The policy change means I need to export and securely store historical Google Ads data soon, or it’ll become permanently inaccessible.
I acknowledge that long-term trend analysis and benchmarking depend heavily on years of granular data, which may no longer be directly accessible in Google Ads.
Looking Ahead. If I rely on external BI tools or customized reporting systems, I need to set up automated data export pipelines to maintain continuity before the new retention limits take effect in 2026.
For More Information. Read more about Google’s data retention changes on their official support page.
Today, I’m excited to share that Google is making Analytics 360 even more powerful by integrating the Meridian marketing mix modeling platform. They’ve also introduced a new predictive conversion metric that promises to enhance media mix decisions for advertisers.
I learned about these updates during the Google Marketing Live 2026 event, where Google unveiled several enhancements aimed at expanding measurement capabilities. The integration of Meridian, Google’s open-source marketing mix modeling tool, directly into Analytics 360 is a significant step forward.
Driving the news. With this integration, I’m able to unify first-party and cross-channel data, measure incremental performance, forecast campaign outcomes, and optimize media mix investments with greater ease.
Moreover, Google is rolling out Qualified Future Conversions (QFCs), a predictive reporting metric powered by Gemini. QFCs link current ad activity to future sales signals like branded search behavior, providing insights that were previously harder to visualize.
How it works. From my perspective, Meridian combines first-party data, media signals, and cross-channel performance metrics in Analytics 360. This helps to model incremental impact while Qualified Future Conversions use Gemini’s predictive signals to understand potential future purchasing behaviors.
In the long run, Google aims to integrate QFC insights into Meridian for more accurate predictive modeling. This is part of their broader effort to simplify measurement and refine ROI forecasting in today’s complex media landscape.
Why we care. As I’ve observed, measurement and attribution are becoming increasingly challenging with evolving customer journeys and the emphasis on privacy. These latest updates highlight Google’s commitment to helping advertisers like us better understand and plan for long-term performance.
The combination of Meridian and QFCs can empower marketers to make better budgeting decisions by accurately linking current campaign activity to future outcomes. It’s a tool we should all keep an eye on.
What to watch. Predictive measurement is becoming crucial. I’m looking forward to testing whether Meridian and QFCs can offer more actionable forecasting compared to existing solutions.
Availability. I found out that Meridian integrations are rolling out globally in Google Analytics 360, supporting all languages. QFCs are in a restricted global pilot phase, with wider beta access anticipated later this year.
Dig deeper. If you’re interested, there’s more news from Google Marketing Live 2026, including tests of new conversational ad formats and AI-powered tools in the Merchant Center, as well as expansions across various Google services.
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.
I’m thrilled to share that Google has just unveiled Ask Advisor, a new AI-driven tool designed to transform the way we approach campaign management, analytics, and optimization. Announced at Google Marketing Live 2026, this Gemini-powered AI is here to integrate seamlessly across Google Ads, Google Analytics, Merchant Center, and the Google Marketing Platform.
Making Waves. Ask Advisor is set to be a game-changer, acting as a unifying force that weaves together insights, workflows, and recommendations across Google’s vast marketing ecosystem.
For those of us in marketing, this means we can launch campaigns, analyze performance, and uncover optimization recommendations all without having to juggle between different tools.
Imagine asking Ask Advisor to “find new customers for my hair care products.” It would seamlessly pull details from the Merchant Center and assist in crafting a campaign right in Google Ads.
Understanding the Process. Ask Advisor connects the dots between Google Ads, Analytics, the Merchant Center, and the Marketing Platform via a Gemini-powered interface. This connectivity allows it to access a range of data to create recommendations, automate tasks, and offer insights that align with marketing goals.
It doesn’t stop there. The integration of insights from Google Ads and Google Analytics helps explain campaign performance and suggests subsequent steps.
The aim, Google states, is to democratize advanced campaign management, enabling even those without extensive technical expertise to make the most out of their advertising strategies.
This launch supports Google’s expanding lineup of AI-driven in-product agents, positioning Gemini as a fundamental layer in advertising and measurement tools.
Why This Matters to Us. Ask Advisor symbolizes one of Google’s most direct steps into agent-based advertising workflows.
Instead of interacting manually with separate reporting dashboards, campaign tools, and optimization settings, AI agents are being poised to handle operational tasks and present strategic insights.
The more substantial evolution is structural: Google is anchoring Gemini as the core across its advertising platform, potentially redefining how campaigns are developed, optimized, and evaluated.
Keep an Eye On. The biggest discussion point will be how much control advertisers are willing to cede to AI agents. Transparency over recommendations, automation choices, and reporting accuracy will be under scrutiny as Ask Advisor rolls out.
When You Can Get It. Currently in beta, Ask Advisor is available for English-language accounts, with more features anticipated later this year.
Want to Learn More? Here’s additional news from Google Marketing Live 2026:
I’ve often pondered the impact of AI on our work as SEO professionals. As AI takes over repetitive tasks, those of us who can strategically guide its use will find our skills even more valuable.
By now, you’ve likely heard the dire predictions:
Verizon’s CEO, Dan Schulman, cautioned that AI might push U.S. unemployment rates to 20%-30% in the next few years.
Anthropic’s CEO, Dario Amodei, warned of AI wiping out a significant portion of entry-level white-collar jobs.
According to Ford’s CEO, Jim Farley, AI could replace half of white-collar workers in the U.S.
SEO, a field I’ve been passionate about for years, is certainly in the crosshairs. But does this mean our careers are at risk? Not necessarily.
The landscape is evolving, yes. But if you’ve been in SEO as long as I have, you’re no stranger to adaptation.
Our roles have always demanded that we wear many hats, from being technical analysts to creative strategists. AI won’t replace this expertise—it’ll replace superficial approaches to SEO.
Success will belong to those who understand search behavior deeply, link it to business outcomes, and make insightful decisions.
The version of SEO many remember is already outdated. I’ve practiced SEO since before it even had a name, and every so often, someone claims that “SEO is dead.” While the field has changed drastically, it’s far from deceased.
SEO, as interpreted today, requires understanding how people search for your offerings and knowing how to meet their needs across various platforms. This journey is only just beginning for those of us in the know.
In a time where everyone can leverage AI tools, the real differentiator is how adeptly we employ these tools to achieve our visions.
Even now, some people believe that writing SEO prompts in AI means they can call themselves experts. But SEO isn’t just about title tags or decoding search engines; it’s about understanding user psychology and combining technical systems with strategic execution.
With AI, we’re entering a new phase requiring new skills. We’ll work more efficiently by incorporating AI into essential SEO tasks. The depth of our conversations with AI will be key to our differentiation.
Here’s a look at how I’ve begun integrating AI into my workflow for greater productivity and insight:
AI can help with the basics—like generating metadata—where precision takes precedence over creativity. We can use AI for better recommendations and design, allowing developers to work with better-prepared resources.
AI is also instrumental in drawing insights from GSC, GA4, and tools like Semrush to gather actionable user data and preferences.
Another frontier is using AI to prototype and improve upon web design layouts, thereby streamlining collaboration with designers and developers.
AI’s presence in analytics is similarly transformative. Though GA4 initially posed a setback for established workflows, AI allows us to develop new, more insightful reports.
Ultimately, my career’s foundation isn’t just in managing tasks that AI could handle. It’s in understanding customers, reading data for insights, and connecting these insights back to real-world results.
Like many others in our field, I’ve witnessed great companies start with grassroots efforts, which have only grown with time. As AI continues to evolve, its role isn’t one of replacement—but of empowerment.
SEO isn’t fading—it’s transforming, waiting for us to lead it into a new era.
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.
When I search for products on Google, I’ve noticed significant changes to the results page. Now, product packs and scrollable carousels appear multiple times within a single results page, reshaping my shopping experience.
As part of my ongoing journey to boost ecommerce visibility, I constantly analyze data. Recently, I’ve tracked searches presenting up to 60 individual organic product listings on one page. These premium placements increasingly mark the beginning of the purchase journey for many users.
This transformation is gradual, and interestingly, I see many brands still adjusting their strategies. It’s crucial to revisit these changes because the opportunity for traffic through product packs is immense, with fierce competition. Today’s leading brands approach this differently.
Thanks to Nozzle, I’ve delved into data from over 63,000 merchants across a wide array of ecommerce keywords from January 2025 to January 2026. Here’s what I discovered that really caught my attention.
Defining Success: Appearances vs. Actual Traffic
I found that just appearing in product packs and actually capturing traffic are two distinct achievements, and the difference between them can be substantial as the data shows.
For instance, in this dataset:
eBay appears in product results for 874,621 keywords.
Home Depot has a similar presence, appearing for 831,699 keywords.
However, the estimated traffic paints a contrasting picture:
eBay garners about 3.2 million visits from these pack appearances.
Home Depot, meanwhile, generates nearly 28.8 million visits from a slightly smaller keyword range.
The secret? Quality position within the pack. Home Depot’s products consistently snag prime, visible, above-the-fold spots that attract shoppers’ clicks.
For eBay, many keywords involve long-tail marketplace terms that dilute overall impact. Understanding Google’s use of product packs to drive purchase decisions for common goods is crucial for brands aiming to compete effectively in this space.
For marketers: Dissecting product pack performance means wisely segmenting data, focusing on categories with significant search volumes to optimize visibility within the packs. That’s how to pinpoint where the genuine opportunities lie.
The Critical Gap: Distinguishing Product Pack Visibility
Product carousels scroll horizontally, increasing exposure for the first few slots, while listings tucked further back remain unseen. This distinction is crucial for assessing true reach.
Disparities among major retailers further illustrate this point:
REI has a massive catalog of 3.8 million products, yet 1.52 million of these require scrolling before they are visible.
Walmart finds itself in a similar spot, with 1.29 million of its 3.5 million unique products are relegated to non-visible placements.
Even industry titans often miss out on optimal visibility, skewing the perceived benefits of their presence. Analyzing visible versus non-visible appearances is essential for identifying where optimizing product data and feeds can yield substantial returns.
For CMOs: When using total product pack appearances as a metric, it’s wise to ask how many of those appearances are truly visible. Understanding this ratio better reflects the channel’s contribution to the business.
Does Discounting Drive Product Pack Visibility?
It’s a common belief that discounted items might secure better placement in Google’s product packs. However, data from the top 10 merchants doesn’t necessarily support this notion.
Amazon.com leads the pack with 49% of its catalog discounted, achieving a 72% visibility rate, placing it squarely mid-tier.
eBay, on the other hand, discounts only 8% of its products yet matches the highest visibility rate in the dataset at 81%.
Walmart Seller discounts 24% of its items, reaching 81% visibility, while Walmart itself discounts 27% but ranks lower at 62% visibility.
This irregularity indicates that discounting is just one of many factors. It doesn’t solely determine a product’s chance of securing a prominent spot. Feed quality, category relevance, reviews, and image standards wield greater influence.
For retail teams: If your strategy for product packs relies heavily on promotions, you might need to pivot. The current landscape favors strategies aligned with where purchasing decisions occur over sheer pricing tactics.
Specialist Brands Competing with Giants and Winning
A refreshing realization from this data is that product pack success isn’t exclusive to the retail giants. Specialist brands, leveraging focused expertise, compete exceptionally well against far larger competitors.
Camp Chef, for instance, appears in results for 155,299 keywords—just a small fraction of Walmart or eBay’s footprint—yet it pulls in an estimated 2.6 million visits, thanks to advantageous product placements.
Brands like Fellow, expanding into niches such as high-end coffee makers, find opportunities for growth through strong organic channels.
These brands achieve impressive product pack traffic against much larger rivals because they prioritize category relevance and high-quality product feeds over sheer scale.
For brands traditionally overshadowed in traditional SEO, product packs present a chance to compete on a more level field. Detailed product data, competitive prices, quality imagery, and favorable reviews can supersede a larger competitor for crucial category keywords.
For agencies: This channel awards dedication and quality over brute scale. Brands with depth in a category can translate that expertise into superior product pack performance, outpacing broader competitors.
Staying Informed on Product Pack Visibility Shifts
Examining the entire dataset, I noticed a consistent pattern: nearly all merchants experience shifts in product pack visibility throughout the year.
Brands holding strong positions during parts of the year sometimes see fluctuations as Google adjusts how it surfaces product results. Some grew steadily midyear only to recede in Q4, while others surged during promotions before reverting to previous levels.
This fluidity is typical of the channel. Google regularly updates its criteria for product pack placements, influenced by factors like feed quality, product availability, review counts, pricing, and images.
The brands thriving are those with sustained visibility into performance, staying agile and responsive to changes before they impact revenue.
With Google’s future announcements and AI integration like Gemini 3 looming, the foundational structure of product packs will shift, influenced by agentic commerce and the Universal Commerce Protocol.
As Google navigates balancing paid and organic visibility, a two-tiered search economy emerges. Securing AI Overview citations becomes vital for brand recognition, impacting both organic and paid product pack performances.
The Bigger Picture
Google’s product packs have morphed from merely supplementary to pivotal touchpoints in commercial searches.
The extensive Nozzle data analysis of over 63,000 merchants reveals that competition is already fierce in this domain. Leaders are distancing themselves, and the gap between attentive and indifferent brands manifests tangibly in traffic and revenue disparities.
The silver lining is that the essentials for success in this space are accessible to most brands: robust product data, strategic pricing, high-quality creative, and vigilant monitoring.
These require not a colossal budget but focus, the right tools, and asking the right strategic questions within the right organizational levels.
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.
In my latest venture into Google Analytics, I’ve discovered exciting news. Google is enhancing its Analytics Data API by adding cross-channel conversion reporting. Although it’s still in the alpha phase, developers like myself now have programmatic access to both paid and organic conversion data in a unified view.
What’s happening. Currently in alpha, this new feature lets users pull conversion data across various channels through the API, mirroring data from the Conversion performance report in the Analytics interface.
For developers, this means we can now capture the same insights without the need for manual reporting, making the process smoother and more efficient.
Why it matters. In a world where digital measurement is increasingly complex, having a unified view of performance across both paid and organic channels is crucial. This feature empowers teams to automate their reporting processes, seamlessly integrate data into existing systems, and build advanced analysis workflows.
It’s a game-changer for businesses juggling multiple platforms, helping to centralize performance data for better strategic decisions.
The caveat. Not every Google Analytics property has access to this feature yet. Google is actively working to broaden availability, so it’s wise to connect with support teams to verify eligibility.
What to watch:
The transition from alpha to wide availability of the feature.
How advertisers leverage this API access to create customized attribution models.
Potential addition of more reporting capabilities to the Data API.
Bottom line. Google’s integration of cross-channel conversion data into the API equips advertisers and developers like me with more control over how we access, analyze, and act on performance data. You can find more information about this update here.
When I first heard about Google Analytics introducing their new Task Assistant, I was intrigued. This tool promises to be a game-changer for those of us who want to maximize our use of Google Analytics without needing deep technical know-how.
It’s exciting to see Google simplify such a complex product. Task Assistant is designed to help advertisers and analysts like me gain more value from our data effortlessly.
What’s New. With the rollout of Task Assistant, Google Analytics offers a guided workflow tool that surfaces tailored recommendations. This means improving property setup, data collection, and reporting is easier than ever.
How It Works. Located in the left-hand navigation, Task Assistant organizes recommendations into clear categories like connecting accounts and enhancing reporting. I can mark tasks as complete or skip items not aligning with my goals, making the setup more flexible.
Why We Care. Identifying gaps in tracking quickly helps ensure I’m working with reliable data. Task Assistant minimizes the risk of missed insights or inaccurate reporting, allowing for confident optimization of campaigns and budgets.
Between the Lines. Analytics platforms, as powerful as they are, can be underutilized due to poor configuration. I’m glad Google is turning setup into a step-by-step process rather than leaving it as a daunting manual audit.
The Bottom Line.Task Assistant is all about making Google Analytics more actionable. It guides users toward better data quality and effective measurement, all with less guesswork.
