Tag: Google Analytics

My AI Content Gap Workflow for Smarter SEO Priorities
I can publish consistently, follow SEO best practices, and still watch competitors outrank me. When that happens, I usually find that the issue is not content quality alone. It is content coverage. Competitors are answering questions my audience is already asking, while my site is not fully part of that conversation yet.

That is where I use content gap analysis. It helps me identify the topics competitors rank for that I do not, then decide which opportunities are actually worth pursuing.

Finding gaps is rarely the hard part. SEO tools make that fairly easy. The real challenge is making sense of thousands of keywords across several reports and deciding what deserves attention first.

My workflow combines competitor data, first-party search data, and AI so I can prioritize content opportunities around business impact instead of search volume alone.

I bring my SEO data together before analyzing it

In this workflow, I use Semrush to identify competitive opportunities, Google Search Console to validate where my site already shows signs of authority, and Google Analytics to add business context. Then I use Claude to bring those datasets together, group related opportunities, identify patterns, and help me decide what belongs on the content roadmap.

I follow this process in one of two ways.
- I export reports directly from the platforms and upload them to Claude.
- If I have connected those platforms through MCP (Model Context Protocol, a standard that allows AI models to connect securely to data sources), I let Claude pull the data directly without manual exports. The workflow changes, but the analysis does not.
Here is the process I use to turn a pile of SEO data into a prioritized content plan.

Step 1: I choose the right competitors

A content gap analysis is only as useful as the competitors I compare myself against. That sounds obvious, but it is one of the easiest places to go wrong.

If I compare my site to Amazon, Reddit, or Wikipedia, I will end up with thousands of keyword “opportunities” that were never realistic in the first place. My goal is not to find every site ranking for my target keywords. My goal is to find businesses competing for the same audience.

I usually start with Semrush’s Organic Competitors report. Instead of relying only on a list of known competitors, I use this report to find domains that compete across many of the same keywords. From there, I narrow the list to three to five sites that closely match the business and target audience I am analyzing.

I do not worry if a few familiar names do not make the cut. Business competitors and organic search competitors are not always the same.

I also filter out sites that can distort the analysis, including large marketplaces like Amazon, community-driven sites like Reddit or Quora, reference sites like Wikipedia, local directories, review sites, and publishers that do not directly compete with the business.

There are exceptions. If I am analyzing a publisher, comparing against other editorial sites makes sense. The key is choosing competitors that create the type of content I am realistically trying to outperform.

A Semrush competitor analysis view turns organic search data into a clear map of rival domains, traffic potential, keyword overlap, and content gap opportunities.

Before I move forward, I sanity-check the competitor list with stakeholders. Sales or product teams may know about newer competitors or strategically important niches that do not yet show up clearly in Semrush.

Once I have settled on the right competitors, I am ready to find the gaps that matter most.

Step 2: I gather and prepare the data

With the competitor list finalized, I collect the data Claude will analyze. Whether I upload exports or connect through MCP, the goal is the same: bring together competitive rankings, my site’s search performance, and engagement data so I can separate meaningful opportunities from noisy keyword lists.

I like to pull data from three core sources.

Semrush: I find the gaps

I start with Semrush’s Keyword Gap tool using the competitors selected in Step 1.

From there, I pay close attention to three buckets: keywords competitors rank for and I do not, keywords where I rank but competitors rank higher, and keywords where I rank but competitors do not.

The first bucket often points to missing topics or content hubs. The second bucket can reveal quicker wins, especially when my site already appears on Page 1 or Page 2. The third bucket shows existing strengths that I should protect and continue building around.

Google Search Console: I validate the opportunity

Next, I check Google Search Console before assuming every missing keyword deserves a new page.

For example, Semrush may show that I do not rank for a keyword, but GSC might reveal that I already receive impressions for closely related queries. That tells me Google has started associating my site with the topic, even if rankings have not caught up yet.

Those “almost there” topics often deserve a higher priority than topics where I would be starting from scratch.

In GSC, I look for queries with high impressions and average positions between 8 and 20, existing pages ranking for related terms, and long-tail queries that reveal additional search intent.

Google Analytics: I add business context

Search volume is only part of the story. Engagement metrics help me answer a more important question: if I improve visibility for this topic, is it likely to support business goals?

A Semrush content gap analysis view reveals where a competitor ranks and the analyzed site does not, turning keyword overlap data into a practical roadmap for SEO content opportunities.

I review metrics such as organic sessions, engagement rate, average engagement time, key events or conversions, and landing page performance.

If a related content hub already drives engaged visitors or conversions, expanding that topic may be a smarter investment than chasing a completely new keyword with higher search volume.

I clean the data before handing it to Claude

If I am manually downloading the data and uploading it to Claude, I clean it first. Claude is excellent at finding patterns, but it can only work with the data I provide. Cleaner data leads to cleaner topic clusters and better recommendations.

I remove duplicate keywords, competitor-branded terms, careers queries, login queries, support queries, locations or product lines outside the business, keywords with clearly different search intent, and high-intent commercial keywords that are too broad to compete for.

For a manual workflow, I export Keyword Gap data from Semrush, query data from Google Search Console, and landing page performance data from Google Analytics, then upload the files to Claude. For a connected MCP workflow, I ask Claude to retrieve the Keyword Gap report, GSC query data, and GA4 landing page metrics directly from connected accounts.

Step 3: I ask Claude to find the story in the data

At this point, I should have a clean dataset that combines competitive keyword gaps, Search Console performance, and Google Analytics data.

This is where the workflow becomes much more useful. Instead of scrolling through thousands of rows looking for patterns, I ask Claude to organize the data into something I can actually build a strategy around.

The mistake I see most often is asking AI to “cluster these keywords.” That usually produces clusters based on keyword similarity alone. That can be useful, but it does not tell me what to do next.

Instead, I ask Claude to think like an SEO strategist. I give it context about the business, including products or services, target audience, primary business goals, content priorities or constraints, and the exported or connected data from Semrush, GSC, and Google Analytics.

Then I ask Claude to organize opportunities by search intent, funnel stage, business relevance, existing authority signals from GSC, user engagement from GA4, recommended content format, and internal linking opportunities.

Rather than returning a spreadsheet of grouped keywords, I want Claude to produce topic clusters with a clear recommendation for each one.

For example, one cluster might be labeled Technical SEO Audits and include supporting keywords, estimated opportunity, existing pages that could be updated, whether a new page is needed, internal linking recommendations, a priority score, and the reasoning behind the recommendation.

A content gap workflow turns scattered SEO signals into topical clusters, showing where AI search visibility, privacy-first analytics, and technical SEO need deeper coverage.

Another cluster might reveal that several competitor keywords can be addressed by expanding an existing guide instead of publishing three separate articles. That is the kind of insight that is hard to spot manually but much easier for AI to surface.

I separate quick wins from long-term investments

Not every opportunity belongs on the same roadmap. As part of my prompt, I ask Claude to classify each cluster into quick wins, new content opportunities, and authority plays.

Quick wins are existing pages that can be refreshed, expanded, or better optimized. New content opportunities are topics that deserve dedicated content because the site has little or no visibility. Authority plays are larger subject areas that may require multiple pieces of content and ongoing investment to compete effectively.

This simple step helps me move from an overwhelming keyword list to a roadmap with both short-term wins and long-term initiatives.

I do not skip the human review

Claude can organize information remarkably well, but it does not know the business the way I do.

Before moving on, I ask whether the topic supports business goals, whether multiple search intents are being combined into one cluster, whether existing content could already satisfy the need, whether the opportunity is realistic given authority and resources, and whether I would actually assign the topic to a writer.

If the answer is no, I refine the cluster or remove it.

The goal is not to accept every AI recommendation. The goal is to spend less time organizing data and more time making strategic decisions.

The biggest prompt lesson is simple: I do not ask Claude to organize keywords. I ask it to recommend what my content strategy should be based on the data I have provided.

Step 4: I score and prioritize the opportunities

Once Claude has grouped the keywords into topic clusters, the next step is deciding what deserves attention first.

This is where many content gap analyses fall apart. Teams naturally gravitate toward the biggest search volumes, but volume is only one piece of the puzzle. A topic that attracts qualified visitors and supports business goals is often a better investment than a high-volume keyword that is difficult to rank for or unlikely to convert.

I score each opportunity across several criteria before I build a roadmap.

A prioritized content gap roadmap turns scattered SEO data into clear next moves, ranking quick wins by impact, effort and AI visibility.

Business relevance

I start with a simple question: if this content performs well, does it help the business?

Topics aligned with products, services, or the customer journey should carry more weight than informational topics with little commercial value.

Existing authority

Next, I look at signals from Google Search Console. If my site already earns impressions or ranks on the second page for related queries, Google has likely established some level of topical authority.

In those cases, improving an existing page or expanding a content hub may produce results much faster than starting from scratch.

Search demand

Search volume matters, but I do not let it dominate the scoring model.

A collection of related long-tail queries with moderate demand can sometimes generate more qualified traffic than one broad keyword.

Ranking difficulty

I review the current search results before committing to a topic. I look at whether authoritative brands dominate the first page, whether the intent is informational, commercial, or transactional, what types of content are ranking, and whether I can realistically create something more useful or complete.

This quick reality check keeps me from chasing opportunities that are not practical.

Estimated effort

Finally, I consider the work involved. Some opportunities require a light refresh of an existing article. Others call for a new content hub supported by multiple pages.

Both can be worthwhile, but they should not carry the same priority when resources are limited.

I let Claude apply the framework

Once I define the scoring criteria, Claude can evaluate every topic cluster consistently.

For example, I may ask Claude to score each opportunity on a five-point scale for business relevance, existing authority, search demand, ranking difficulty, and content effort. Then I ask it to calculate an overall priority score and explain why each recommendation received that score.

A tactical SEO refresh brief turns AI-assisted content gap analysis into page-level priorities, surfacing validation lessons, effort estimates, and the biggest opportunities.

The explanation is just as valuable as the number. If I disagree with a recommendation, I can adjust the weighting, add more business context, and ask Claude to score the opportunities again.

By the end of this step, I have more than a list of content ideas. I have a prioritized content strategy that shows what to tackle next, what can wait, and what is not worth pursuing.

Step 5: I turn priorities into page-level recommendations

Once I have prioritized the opportunities, the next step is figuring out exactly what to change.

Rather than handing a team a ranked list of topics, I ask Claude to generate page-level recommendations for the highest-priority opportunities. This is where connected data becomes especially valuable.

Because Claude has access to Semrush research, Google Search Console performance, Google Analytics metrics, and my prioritization framework, it can evaluate each page in context instead of treating every recommendation the same.

For each priority page, I ask Claude to produce a recommendation that explains why the page was selected, the primary keyword cluster, current rankings and impression data, supporting evidence from GSC and competitor research, recommended updates, estimated effort, expected impact, and priority level.

One of the biggest advantages of this approach is validation.

Before recommending a refresh, Claude can compare URL-level Search Console data against the original analysis. Sometimes what looks like a strong opportunity turns out to be misleading. A keyword may have inflated impression counts, a URL could have been mislabeled in an export, or the page may not be as close to ranking as it first appeared.

Catching those issues before assigning work can save hours of unnecessary effort.

The recommendations also make stakeholder conversations easier. Instead of saying, “I think we should update this page,” I can point to the supporting data, explain why it is a priority, estimate the effort involved, and tie the recommendation back to the larger content strategy.

I treat these recommendations as implementation plans rather than full content briefs. They help SEO and content teams understand what should change, why it matters, and where to focus first. Writers can then use those recommendations to create or update content with confidence.

Step 6: I measure whether the gap is closing

Publishing the content is not the finish line. It is the start of the next round of analysis.

Rows of illuminated data cabinets and paper files stretch into the distance, capturing the pressure on marketers to turn fragmented customer data into a smarter performance engine.

I begin with Google Search Console, tracking whether target queries are gaining impressions, improving in average position, and generating more clicks. When I refresh an existing page, I compare performance before and after the update to see whether the changes actually moved the needle.

Next, I look at Google Analytics. Better rankings do not always translate into better business outcomes, so I review organic traffic alongside engagement and conversion metrics. If an updated page attracts more visitors but fails to keep them engaged or contribute to conversions, I know it is time for another round of optimization.

If I am using Claude through MCP, I can also ask it to compare performance over time and summarize what changed. I might ask which refreshed pages improved the most, which content clusters gained the most visibility, which recommendations drove the strongest business results, and which opportunities still need attention.

Instead of comparing reports month after month, Claude can quickly surface significant changes and point me toward the pages that deserve attention.

I do not treat content gap analysis as a one-time exercise. Competitors publish new content, search behavior shifts, and my own site authority evolves. I like to repeat this workflow every quarter, or more often in fast-moving industries, so I can keep finding new opportunities and stay ahead of competitors.

The tools will continue to improve, but the repeatable workflow is what creates the advantage.

I build a repeatable content gap analysis process

A content gap analysis helps me prioritize opportunities worth pursuing instead of chasing every possible keyword.

Semrush helps me uncover competitive gaps. Google Search Console shows where I already have momentum. Google Analytics adds the business context that rankings alone cannot provide. Claude brings those datasets together, helping me identify patterns, prioritize opportunities, and create actionable recommendations in a fraction of the time it would take manually.

Whether I upload reports or connect my tools through MCP, the workflow stays the same. I gather the right data, validate the opportunities, let AI organize the information, and apply my own expertise to decide what comes next. That is the part AI cannot replace.

The biggest advantage is not simply having better prompts or faster analysis. It is having a repeatable process that helps a team make smarter content decisions every quarter.

Prompt template: My prioritized content gap roadmap

Here is the prompt I use after I have gathered the data, whether I have uploaded exports from Semrush, Google Search Console, and Google Analytics or connected those tools to Claude through MCP.

“You are an experienced SEO strategist helping me perform a content gap analysis.
I’ll either provide exported reports from Semrush, Google Search Console, and Google Analytics, or you’ll access those tools through connected MCP integrations.
My goal is to identify the highest-impact content opportunities based on competitor visibility, existing authority, business value, and implementation effort.
Here’s my business context:
– Company:
– Industry:
– Products/services:
– Target audience:
– Primary business goals:
– Geographic focus:
– Any strategic priorities or constraints:
– Tone of voice: [Insert brand voice adjectives here (e.g., authoritative, conversational, technical)].
Using the available data, complete the following tasks.
1. Identify content gaps
Organize keywords into these categories:
– Competitors rank and we don’t.
– We rank below competitors.
– We rank and competitors don’t.
Highlight any content gaps, opportunities to consolidate pages, or keyword cannibalization issues.
2. Validate the opportunities
Use Google Search Console data to determine:
– Which topics already receive impressions.
– Which pages rank between positions 8 and 20.
– Which existing URLs have the strongest chance of improving with optimization.
Use Google Analytics data to determine:
– Which pages drive meaningful engagement.
– Which pages contribute to conversions.
– Which content hubs are worth expanding.
3. Create strategic topic clusters
Group related opportunities by:
– Search intent
– Business relevance
– Funnel stage
– Recommended content type
– Internal linking opportunities
Don’t cluster based only on keyword similarity. Focus on topics that should become part of the same content strategy.
4. Prioritize every opportunity
Score each topic cluster using:
– Business relevance
– Existing authority
– Search demand
– Ranking difficulty
– Estimated effort
Assign each opportunity a priority (High, Medium, Low) and explain why.
Separate recommendations into:
– Quick wins
– New content opportunities
– Long-term authority investments
5. Recommend next steps
For every high-priority opportunity, recommend whether we should:
– Refresh an existing page
– Consolidate multiple pages
– Create a new page
– Build a pillar page with supporting content
Include supporting evidence for every recommendation.
6. Deliver the results
Create:
– An executive summary
– Prioritized topic clusters
– A scored opportunity table
– Page-level recommendations for the highest-priority URLs
– A phased implementation roadmap (30, 60, and 90+ days)
If you find conflicting data between Semrush, Google Search Console, and Google Analytics, explain the discrepancy and recommend which source should guide the decision. The output should both be HTML and a Google Sheet.
Before presenting your final recommendations, validate your own analysis. If reviewing Search Console or Analytics data changes your original recommendation, explain why and update your prioritization accordingly.”

This prompt is only a starting point. I add business context, editorial guidelines, and scoring criteria that are unique to the organization I am analyzing. The more context I give Claude, the more useful and actionable its recommendations become.

Inspired by this post on Search Engine Land.
July 8, 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
Enhance AEO with Must-Have Tools for Today
I recently found myself attempting to map out a Lumascape of answer engine optimization (AEO) tools. It’s a daunting task, and my computer simply doesn’t have the bandwidth for that!

Instead, I pivoted to focus on a select few tools I’ve been using effectively to boost my clients’ visibility in AI search results.

Here, I’m sharing a concise list: four tools that I consistently rely on, alongside three others I’m currently evaluating for potential integration into my workflow.

1. AI Assistants: ChatGPT, Claude, Perplexity

These AI assistants have proven invaluable. When used with intentionality, they serve as powerful tools for research and analysis in AEO.

For AEO, they assist in several key areas:
- Competitive landscape research.
- Content gap analysis.
- Prompt testing.
- Entity and topical coverage audits.
- Structured content drafting.
The difference from casual usage lies in applying a specific AEO research methodology.

Why They’re Essential

Understanding AI systems processing is key to AEO, and regularly engaging with these tools analytically is the most direct way to gain that knowledge.

By querying AI with your audience’s prompts, you glean insightful data on sources, entities, and answer structures.

Competitive Strengths

These platforms each offer unique advantages:
- ChatGPT is well-known for its broad synthesis of general knowledge.
- Claude provides nuanced, analytical responses.
- Perplexity excels with its clear citation methods, beneficial for AEO research.
What You Can’t Do Without Them

They are crucial for firsthand AEO status assessment, including:
- Manual prompt testing: Assess your brand representation.
- Competitive research: Use category-level queries to analyze competitor presentation.
- Topical gap analysis: Identify missed opportunities.
- Structural content analysis: Understand preferred AI answer formats.
Caveats

AI outputs are variable, influenced by many factors. These tools help build intuition and hypotheses that should be validated with quantitative data.

Beware of the time-consuming nature of manual testing. Establish a framework and stick to it.

2. Profound

Profound specializes in AEO intelligence, tracking how AI platforms interact with and cite your content. It also measures brand mention frequency, sentiment, and competitor visibility.

Why It’s Essential

Profound provides direct insights into your brand’s presence in the AI answer ecosystem, shifting the focus from rankings to visibility in AI responses.

Competitive Strengths

Its cross-platform view offers comparative insights, allowing you to see how your citation share compares to competitors.

What You Can’t Do Without It

Without it, quantifying your brand’s presence in AI-generated answers becomes difficult. It also tracks citation shares and identifies content driving AI mentions.

It’s a costly tool, but valuable for identifying areas where your brand is losing ground to competitors.

Caveats

As the tool evolves rapidly, the data remains a timely reflection of AI outputs. Remember, these metrics are signals, not precise rankings.

3. Google Trends and Google Keyword Planner

Google Trends shows search interest trends, while Keyword Planner gives search volume estimates, both critical for AEO strategy.

Why They’re Essential

Understanding demand is crucial for content optimization in AI answers. These tools provide reliable data on trending topics and search volume.

Competitive Strengths

While Google Trends offers momentum analysis, Keyword Planner’s forecasting can prioritize content based on future demand.

What You Can’t Do Without Them

Build a dynamic AEO strategy by monitoring demand trends and identifying emerging topics and seasonal patterns.

Caveats

These tools reflect traditional search behavior, not AI-acre queries, and Keyword Planner requires an active Google Ads account.

Always use them as a guide, not a complete picture, of AI demand.

4. Google Search Console and Google Analytics

These are essential for tracking search performance and on-site behavior, revealing insights into AI platform traffic and content effectiveness.

Why They’re Essential

They help diagnose whether AI-cited content is also visible in traditional search and track AI-driven visits and engagement.

Competitive Strengths

GSC offers unmatched query data, while GA4’s cross-channel tracking reveals AI platform engagement.

What You Can’t Do Without Them

Understanding AEO’s business impact and addressing indexing issues rely on these insights.

They illuminate high-impression, low-CTR content, indicating potential AI Overview cannibalization.

Caveats

GSC data is Google-centric and has some limitations, while GA4 requires precise configuration for accurate tracking.

Rapid-Fire Roundup

With numerous tools still to explore, consider testing these emerging options to assess their AEO value:

5. AI Trust Signals

This tool evaluates credibility signals influencing AI citation decisions. It’s a new dimension worth exploring as AI citation mechanics advance.

6. Ahrefs

Ahrefs shines with backlink analysis and content gap insights, indirectly supporting AEO by building authority signals.

Its Content Explorer helps identify high-performing content likely to be referenced by AI.

7. Roadway AI

This AI-native platform focuses on marketing growth activities, including attributing AEO signals to revenue.

Keep an eye on this developing option as it may gain importance quickly.

The Reality of AEO Tools: Fast-Moving and Imperfect

The AEO landscape is evolving, with tools still catching up. Prioritize consistent measurement, analysis, and testing to extract actionable insights.

Aiming for perfect setup may be unrealistic, but if a tool shows how it enhances your AEO efforts, that’s a positive start.

Consult industry colleagues with firsthand tool experience before committing, as better or cheaper alternatives may emerge soon.

Inspired by this post on Search Engine Land.
May 4, 2026
Boost Your Data Insights with Google Analytics Task Assistant

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.

Inspired by this post on Search Engine Land.

April 30, 2026
Google’s New Consent Update: A Simplified Guide for Marketers

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.

Inspired by this post on Search Engine Land.

April 13, 2026
Google Revives Data Studio: A Central Hub for Data Analysis
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.

Inspired by this post on Search Engine Land.
April 13, 2026
Transform Your SEO Workflow with Claude Code
Recently, I’ve found myself immersed in Claude Code, especially within Cursor. I’m not a coder by trade; I run a digital marketing agency. But using Claude Code through Cursor has dramatically sped up how I handle critical tasks such as data extraction and analysis from Google Search Console, GA4, and Google Ads.

Setting up this system takes about an hour, but once it’s done, asking questions like “Which keywords am I overpaying for that I already rank for organically?” becomes a breeze. It provides answers in seconds, eliminating the need for tedious hours spent on spreadsheets.

Let me share the step-by-step process I developed for our agency clients. If any of this seems too intricate, simply paste this article’s URL into Claude, and ask it to guide you through the steps.

Ultimately, you’ll build a project directory where Claude Code can access Python scripts that pull live data from your Google APIs. The data is fetched, stored in JSON files, and you’re free to interact with it without the need for dashboards or complex templates.
```
 
seo-project/
├── config.json               # Client details + API property IDs
├── fetchers/
│   ├── fetch_gsc.py         # Google Search Console
│   ├── fetch_ga4.py         # Google Analytics 4
│   ├── fetch_ads.py         # Google Ads search terms
│   └── fetch_ai_visibility.py  # AI Search data 
├── data/
│   ├── gsc/                 # Query + page performance
│   ├── ga4/                 # Traffic by channel, top pages
│   ├── ads/                 # Search terms, spend, conversions
│   └── ai-visibility/       # AI citation data
└── reports/                 # Generated analysis
```
Begin by setting up Google API authentication. This step requires a Google Cloud service account, which covers GSC and GA4. Google Ads, however, requires its own OAuth setup.

Next, you’ll move on to building the data fetchers. Each fetcher is a Python script that authenticates, pulls data, and saves it in JSON format. You won’t need to dive into API documentation either; Claude Code can write the scripts based on simple descriptions of what you want to achieve.

Once you’ve got your data, Claude Code can answer cross-source questions, such as spotting keywords with paid and organic gaps, or analyzing content performance across platforms.

For AI visibility tracking, consider tools like Scrunch or Semrush. Export your data as CSV or JSON to further enhance your insights through Claude Code.

Overall, this workflow takes about thirty-five minutes for a new client and reduces monthly refresh times to about twenty minutes. It saves you from the hassle of manually managing and deciphering data across multiple platforms.

Claude Code enhances your data analysis capabilities, but it’s not a replacement for strategic insight. Remember to verify results just as you would scrutinize work from a new team member.

Inspired by this post on Search Engine Land.
March 4, 2026