Tag: Data Analysis

How I Turn Search Console Data Into SEO Wins With AI

I rely on Google Search Console because it is excellent at collecting search data. The challenge is that it still does not make interpretation easy.

When I open almost any property, I usually find thousands of queries, landing pages, impressions, clicks, rankings, and click-through rates. That volume is useful, but it can quickly become overwhelming when I am trying to answer one simple question: what should I do next?

For years, my workflow was familiar: export the data into Excel or Google Sheets, build pivot tables, apply filters, and start digging for patterns. That approach works, but it is slow. More often than not, I am searching for insights without knowing exactly what I am looking for.

That is where AI makes the workflow more useful. I use it to speed up the hardest part of Search Console analysis: finding meaningful patterns hidden across thousands of rows of search data.

I think of Google Search Console as my source of truth and AI, whether ChatGPT or Claude, as the analyst sitting beside me. GSC shows me what happened. AI helps me explore why it happened, uncover opportunities I might miss, and organize messy data into decisions I can act on.

A quick note on regex

Most of the examples I use start in the same place inside Google Search Console: Performance → Queries → + Add Filter → Query → Custom (regex).

From there, I enter a regular expression to filter query data before exporting it for analysis.

The useful part is that I no longer need to memorize regex syntax. I can ask ChatGPT to write it for me. For example, I might prompt: Create a regex for Google Search Console that matches queries beginning with question words.

ChatGPT may return something like (?i)^(who|what|why|how|can|does|will|should)b.

If I need something more specific, I simply describe the pattern I want. I might ask for a regex that matches queries containing five or more words, identifies comparison searches, or finds branded queries that include product names.

The better I describe the pattern, the better the regex usually becomes.

Here are seven practical ways I combine Google Search Console with AI so I can spend less time sifting through data and more time making decisions.

1. I stop looking only at queries and start looking at intent

Most Search Console analysis still happens at the keyword level. The problem is that people do not really search by keyword. They search with intent.

Instead of reviewing thousands of individual queries one by one, I use regex to isolate investigation-focused searches before exporting the data.

One useful regex is (?i)^(best|top|vs|review|reviews|compare|comparison).

After exporting the filtered query data, I ask Claude or ChatGPT to classify intent. My prompt is usually something like: Categorize these queries into informational, navigational, investigation, transactional, and local intent. Return a CSV with classifications and confidence scores.

This helps me spot patterns that are difficult to see keyword by keyword. Informational traffic may be growing while commercial investigation queries are declining. Transactional queries may rank well but earn weak click-through rates. Comparison searches may be driving impressions without having dedicated content to support them.

When I segment by intent, the next steps become much clearer.

2. I discover questions my audience is already asking

Question-based keyword research is not new, but AI helps me identify themes across hundreds of question-oriented searches much faster.

I start with a regex like (?i)^(who|what|where|when|why|how|can|does|should|will)b.

Then I export the results and ask Claude or ChatGPT: Group these questions into common themes and identify unanswered topics.

A Google Search Console query filter highlights how regex can narrow SEO performance data, helping marketers turn thousands of search terms into focused insights.

Instead of manually reviewing hundreds of questions, I can quickly see broader patterns around pricing concerns, product comparisons, implementation challenges, and industry-specific use cases.

This becomes more than a content exercise. I can use these themes to improve FAQs, support resources, sales enablement materials, and AI Overview optimization.

The best opportunities are often not hidden in one query. They are hidden in clusters of related questions.

3. I find queries likely to trigger AI Overviews

Google does not give me a filter for queries likely to trigger AI Overviews, but I can build a useful approximation.

I start by isolating common informational and comparison patterns with a regex like (?i)^(what is|how to|best|vs|difference between|guide to).

Then I export the matching queries and ask Claude or ChatGPT: Review these queries and group them by the content format needed to answer them effectively.

The themes often fall into definitions, tutorials, comparisons, or expert recommendations.

This helps me see where my content may need to shift from simply ranking for keywords to becoming the best available answer. Increasingly, those are not always the same thing.

4. I track emerging trends earlier

Traditional keyword research can be reactive. By the time a trend is obvious in keyword tools, competitors may already be building content around it.

Google Search Console can help me identify shifts earlier, as long as I know how to look for them.

Instead of searching for individual keywords, I use ChatGPT to build regex around broader concepts. For example, I might prompt: Create a Google Search Console regex to identify searches related to AI agents, copilots, assistants, automation, and autonomous workflows.

The output may look like (?i)(ai agent|agentic|copilot|assistant|automation).

This same approach works for new technologies, product categories, competitors, industry buzzwords, and changing customer concerns.

Once I filter and export the data, I let AI look for emerging themes. A prompt I like is: Review these queries and identify emerging themes, new terminology, and shifts in search behavior. Highlight which topics appear to be gaining traction, recommend whether they deserve a new content asset or an update to an existing page, and identify any patterns that could influence our content strategy.

Instead of only confirming that a trend exists, AI helps me decide whether the trend is meaningful enough to act on and what the next move should be.

5. I surface conversion intent inside informational traffic

One of the most overlooked opportunities in Search Console is finding bottom-of-funnel signals inside queries that appear informational at first glance.

I might ask ChatGPT: Create a regex for searches that indicate evaluation, comparison, pricing, alternatives, migration, implementation, or vendor selection intent.

An example output is (?i)(cost|pricing|price|vs|alternative|compare|implementation|migration).

I apply that regex to the query report, export the filtered data, and then ask Claude or ChatGPT to analyze it.

My prompt usually looks like this: Review these Google Search Console queries and identify recurring buying signals. Group them into themes such as pricing, comparisons, implementation, and vendor evaluation. Recommend which existing pages should better address this intent, and identify opportunities to improve content through stronger CTAs, internal links, comparison tables, FAQs, or supporting resources.

A visual metaphor for AI turning messy Google Search Console queries into clear SEO decisions, separating qualified intent from irrelevant traffic signals.

I often find that pages created for top-of-funnel education are already attracting visitors who are evaluating solutions. In that case, the best opportunity may not be creating a new page. It may be improving the page that already earns the visit, so users can take the next step without breaking the informational experience.

Sometimes the biggest content opportunity is recognizing the conversion intent already reaching the pages I have.

6. I find audience-specific opportunities

One of my favorite ways to uncover new content opportunities is filtering queries by industry, audience, or customer segment. It quickly shows me whether my content is resonating with the audiences I intended to reach or revealing opportunities I had not considered.

I start by asking ChatGPT to create a regex based on the audience segments that matter most to the business.

For example, I might prompt: Create a Google Search Console regex that identifies queries related to healthcare, manufacturing, retail, education, financial services, government, and nonprofit organizations.

An example output is (?i)(healthcare|hospital|medical|manufacturing|factory|retail|education|school|financial|bank|government|public sector|nonprofit).

After applying the filter and exporting the results, I ask Claude or ChatGPT: Analyze these queries and group them by audience segment. Identify which industries show the strongest search demand, what recurring questions or pain points each audience has, and recommend opportunities for new content, landing pages, case studies, or internal linking that would better serve those audiences.

The differences can be valuable. Healthcare searches may consistently focus on compliance, while manufacturing queries may revolve around implementation. Retail searches may reveal entirely different use cases than financial services searches.

7. I uncover striking-distance opportunities at scale

Every SEO knows the classic advice: look at keywords ranking in positions 5-15 to identify opportunities within striking distance.

The challenge is doing that at scale. A report with hundreds of queries where a site is close to stronger rankings can become overwhelming fast.

I take the regex patterns above a step further. I apply the filters that match my goals, then narrow the report to positions 5-15 before exporting the queries.

Then I ask my AI analyst: Identify recurring themes across these queries and recommend page-level optimizations rather than keyword-level optimizations.

Instead of getting tiny recommendations for individual keywords, I often uncover larger opportunities. A page may be missing subtopics, comparison details, stronger internal links, or use cases that would make it more complete.

The result is usually fewer optimizations, but more meaningful ones.

Turning Search Console data into decisions

As an SEO, I do not have a data shortage. I have a prioritization problem.

Google Search Console remains one of the richest sources of insight into how people discover a business. The difficult part is turning thousands of rows into something actionable.

That is where AI fits into my workflow. It helps me uncover patterns, organize information, and surface opportunities I might otherwise miss. It is not a replacement for SEO strategy, experience, or critical thinking.

The real advantage is not writing better regex or exporting cleaner spreadsheets. It is spending less time searching for insights and more time acting on them.

Because data does not improve SEO. Better decisions do.

Inspired by this post on Search Engine Land.

July 9, 2026

Semantic PPC and SEO Tactics That Still Win With AI

Why advanced semantic techniques still matter in PPC and SEO

Now that I can use AI to generate keywords and launch a paid search campaign in minutes, it is tempting to think the hardest part of PPC and SEO work has already been handled.

But I still need more than fast keyword output if I want structured, scalable performance. I need to understand how search actually works, how people phrase intent, and how noisy search term data can distort a campaign if I do not organize it properly.

That is where semantic techniques such as n-grams, Levenshtein distance, and Jaccard similarity continue to matter. I use them to interpret messy data, apply real client context, and build frameworks that AI alone cannot reliably produce.

What I learn from n-grams in PPC and SEO analysis

I think of n-grams as the “n” words that make up a keyword. In the search term “private caregiver nearby,” I can break the phrase into smaller pieces that are easier to analyze.

3 unigrams (one word): “private,” “caregiver,” and “nearby”
2 bigrams (two consecutive words): “private caregiver” and “caregiver nearby”
1 trigram (three consecutive words): “private caregiver nearby”

I use n-grams because they simplify large keyword lists without stripping away the patterns that matter.

For example, I recently restructured several campaigns that had more than 100,000 search terms. By using n-grams, I reduced those lists into much more workable sets.

~6,000 unigrams.
~23,000 bigrams.
~27,000 trigrams.

Once I have those smaller sets, I can spot patterns quickly. If every keyword containing the “free” unigram performs poorly, I can exclude “free” as a broad match negative.

On the other hand, if I see that “nearby” performs especially well, I may test more local variations, build location-specific landing pages, or adjust campaign structure around that intent.

I still have to respect the limits of this method.

I need a large volume of search terms, so this approach usually works best for accounts with bigger budgets.
As “n” gets larger, the output becomes less useful because the data expands again. At that point, I usually need more advanced methods such as Levenshtein distance or Jaccard similarity.

How I cluster keywords with n-grams

When I analyze SEO and PPC data, I often deal with huge volumes of long-tail search terms. Many appear only once and carry very little standalone data.

N-grams help me turn that chaotic long-tail data into clearer, more manageable intelligence.

That intelligence helps me reduce wasted spend, find new opportunities, and build a structure that can scale.

I start by exporting search term data. In PPC, that includes cost, impressions, clicks, conversions, and conversion value by search term.
For each n-gram, I sum cost, impressions, clicks, conversions, and conversion value.
Then I calculate CPA, ROAS, CTR, CVR, and any other metrics that matter for the account.

With a shorter and more digestible dataset, I can rank the top-spending n-grams that do not convert, which often become negatives, and the ones that do convert, which become positives.

From there, I build ad groups around recurring n-grams that consistently drive performance.

For example, I may find that emergency-related n-grams such as “24/7,” “same day,” or “urgent” deliver higher conversion rates. I would segment those terms so I can control budget, bidding, and messaging more precisely.

Bottom line: I use n-grams to isolate themes that deserve special attention.

Once I have identified those themes, it becomes much easier to build advanced paid search structures around high-impact n-grams and improve ROI.

Dig deeper: How to uncover hidden gems in your paid search accounts

How I use Levenshtein distance to improve keyword quality

Levenshtein distance measures the minimum number of single-symbol edits, including insertions, deletions, or substitutions, needed to turn one string into another.

That may sound complicated, but the idea is simple once I put it into practice.

The Levenshtein distance between “cat” and “cats” is 1 because I only need to add the “s.” Between “cat” and “dog,” the distance is 3.

One common PPC use case is finding brand and competitor misspellings inside search term reports.

For example, “uber” and “uver” have a Levenshtein distance of 1, so I would feel confident excluding the misspelled version from non-brand campaigns.

I can apply the same logic to keyword relevance.

If the distance between a keyword and the search terms it matches is too high, such as 10 or more, those terms probably have very little in common with the keyword and deserve review.

A low distance usually tells me those queries are close enough to be safe and do not need the same level of manual inspection.

How I consolidate PPC keywords with Levenshtein distance

After I use n-grams to create initial keyword clusters, I may still have thousands of search terms to organize into a practical campaign structure.

Manually sorting through 6,000 unigrams is not realistic. This is where Levenshtein distance becomes especially useful.

Venn diagram showing sets A and B with their overlapping intersection labeled A&B, illustrating Jaccard similarity for SEO and PPC keywords. — A simple Venn diagram visualizes how Jaccard similarity measures the shared overlap between keyword sets A and B in semantic PPC and SEO analysis.

My goal is to merge ad groups that target nearly identical keywords so I do not end up with an overly granular, SKAG-like structure.

Too much granularity makes reporting and account management harder. It can also create inefficient bidding and wasted spend.

Using the same dataset, I calculate the Levenshtein distance between queries across different ad groups.

Then I identify the closest keyword and ad group using a predefined threshold. A threshold of 3, for example, gives me a high degree of accuracy.

This helps me consolidate keywords and ad groups with confidence. If I use a looser threshold, such as 6, I can also group or name ad groups by broader similarity or intent.

Here is a simple example showing why these three keywords can be grouped together:

Levenshtein distance	24/7 plumber	24 7 plumber	247 plumber
24/7 plumber	0	1	1
24 7 plumber	1	0	1
247 plumber	1	1	0

Dig deeper: How to use negative keywords in PPC to maximize targeting and optimize ad spend

How I go further with Jaccard similarity

In PPC, I use Jaccard similarity as a practical proxy for understanding the overlap between two sets of n-grams.

The calculation is straightforward: I divide the number of shared unigrams between two sets by the total number of unique unigrams across both sets.

It sounds technical, but I visualize it simply:

Jaccard similarity = Red / Green

Here are a couple of concrete examples I use to explain the concept:

“new york plumber” and “plumber new york” = 1 because all three unigrams appear in both sets, just in a different order.
“new york plumber” and “NYC plumber” = 0.25 because only “plumber” is shared, and there are four unigrams in total.

Jaccard similarity is a helpful first step for deduplicating similar keywords. I see it as a bridge between old phrase match logic and broad match modified logic.

But it has an important limitation: it does not understand meaning.

In the example above, “new york” and “NYC” should be treated as equivalent, but the Jaccard calculation sees them as different.

To handle that kind of nuance, I need more advanced techniques, which I would treat as the next layer of analysis.

How I combine Jaccard similarity and Levenshtein distance

Consider a cybersecurity course campaign with the following top 10 keywords:

Keyword	Semrush average monthly searches in the U.S.
cybersecurity courses	5,400
cybersecurity online course	1,900
free cybersecurity courses	1,300
online cybersecurity courses	1,300
cybersecurity course	1,000
cybersecurity courses online	880
google cybersecurity course	880
cybersecurity courses free	720
cybersecurity free courses	590
cybersecurity online courses	480

By combining singular and plural versions, along with reordered versions of the same idea, I can reduce that top 10 into a more actionable top four.

“Cybersecurity courses.”
“Cybersecurity courses online.”
“Free cybersecurity courses.”
“Google cybersecurity course.”

I could use n-grams to do this, but scaling n-gram analysis across thousands of keywords can quickly become overwhelming.

A more efficient approach is to use both similarity metrics in sequence.

First, I apply Levenshtein distance to consolidate very similar queries.
Then I use Jaccard similarity to deduplicate reordered variants.
At each step, I sum the usual KPIs, including cost, conversions, and other performance metrics, so the analysis stays actionable.

The result is a clear, compressed structure that can hold up even as search term volume grows.

How I restructure paid search campaigns with semantic techniques

With the right semantic techniques, I can restructure massive keyword sets quickly and still produce consistent, high-quality results.

AI can absolutely help me create an initial summary, but I do not rely on it entirely.

Otherwise, I run into the classic problem of “garbage in, garbage out.”

Broad match can be powerful, but it also introduces more noise. These techniques help me verify that the queries I am matching stay aligned with campaign goals.

I use n-grams, Levenshtein distance, and Jaccard similarity to apply client context to raw search data and build a stable structure around real intent.

If the process feels overwhelming at first, I use this summary to decide which technique fits the job:

Scenario	Best technique	Why
Identify high-intent patterns in huge search-term exports	n-grams	Surfaces themes fast; reduces dimensionality
Clean duplicate / near-duplicate keywords at scale	Levenshtein distance	Captures spelling + structural similarity
Deduplicate reordered or slightly varied keyword strings	Jaccard similarity	Order-insensitive token-based comparison
Create scalable clusters for campaign rebuilds	Combo: Levenshtein → Jaccard → n-gram	Sequence gives accuracy + compression

For me, the main lesson is simple: AI can accelerate PPC and SEO work, but semantic analysis gives that work structure, signal quality, and strategic control.

Inspired by this post on Search Engine Land.

July 3, 2026

Why Reddit’s Conversation Data Matters for AI Search

I am paying close attention to how Reddit conversations are shaping AI search, especially after Profound collaborated with Reddit to analyze how conversational data informs AI-generated answers.

What stands out to me is how much value AI systems can draw from real discussions, lived experiences, and community-driven context. Reddit’s conversational data helps reveal the kinds of answers people are looking for, the language they use, and the perspectives that can influence how AI-generated responses are formed.

Inspired by this post on Try Profound Blog.

July 3, 2026
Boost Your Funnel: Tackle Signal Decay & Maximize Performance
Have you ever wondered why those campaigns designed to introduce customers to your brand seem to get the least credit when it comes to driving revenue? Let me walk you through how to reclaim those lost conversion signals.

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

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

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

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

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

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

Why Signal Loss Hurts Discovery Channels First

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

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

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

Closing the Cross-Device Gap with Enhanced Conversions

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

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

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

Training the Algorithm with Offline Conversions

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

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

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Defining New Top-of-Funnel Signals with Micro Conversions

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

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

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

Recovering Lost Signals with Google Tag Gateway

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

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

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

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

Your Data Infrastructure is Your Competitive Advantage

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

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

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

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

Inspired by this post on Search Engine Land.
May 21, 2026
Unleash Real-Time Insights with Google Ads’ AI Dashboards

I’ve always found it exciting when Google Ads updates its features. Now, they’ve integrated Gemini into Ads dashboards, transforming data analysis into an engaging, interactive experience.

What’s happening. Google Ads is introducing a new Dashboards feature, designed to provide advertisers with performance data through visually appealing charts, graphs, and tables, all powered by Gemini.

What makes this even more fascinating is how users can effortlessly customize their views by typing prompts. The dashboard dynamically updates in real-time based on these input queries.

Why we care. Traditionally, data analysis in Google Ads required manual setups and navigating countless reports. This update shifts towards a more intuitive approach, letting advertisers ask questions and receive immediate visual feedback.

Zoom in. These new dashboards will showcase crucial metrics such as impressions, clicks, video views, and costs. You’ll also find them breaking down performance data across various dimensions like devices, audiences, and campaign types.

The main goal is to empower advertisers with a clearer and faster way to understand what’s happening within their accounts.

What to watch. I’m curious to see how broadly this prompt-driven reporting will be adopted and if it will lessen the need for custom reports and additional analytics tools.

What’s next. Google has promised to reveal more details at Google Marketing Live.

Bottom line. Google is reshaping reporting into a conversation — using AI to accelerate how quickly advertisers receive data-driven answers, enabling swift actions.

Inspired by this post on Search Engine Land.

May 12, 2026
Unlock More with Microsoft’s Customizable Conversion Metrics

As someone exploring the ins and outs of Microsoft Advertising, I’ve discovered an update that’s sure to enhance our campaign analysis. Microsoft is now allowing us to customize columns with all conversion metrics, providing us with deeper insights and aligning reports with our unique business goals.

What does this mean for us? Well, according to Navah Hopkins, our go-to expert at Microsoft, we can now build custom metrics by leveraging the full spectrum of conversion data available in the platform. This means we can track all conversions and primary conversions, enabling us to tailor our reporting to meet our specific objectives more closely.

Please note the new image showcasing Microsoft’s enhanced custom columns feature. It’s a visual reminder of how these updates can transform our analytical capabilities.

Why am I excited about this? Because the standard reporting often doesn’t mirror how we truly measure success. By giving us the tools to expand custom columns, Microsoft allows us to define metrics that truly matter—be they lead quality, revenue, or a combination of conversion actions.

This flexibility is crucial for managing a variety of conversion types or navigating complex marketing funnels. Now, I can create custom columns, using ratios and metric combinations such as cost per qualified lead or conversion rates focused on primary goals.

Moreover, I appreciate that the revenue and ROAS calculations will now reflect the values that align with my conversion goals, providing more accurate insights directly linked to business outcomes.

What does this change imply for us in a broader sense? It represents a shift toward a more flexible and advertiser-defined measurement approach, instead of relying solely on standardized platform metrics.

This update highlights the ongoing demand for improved reporting customization as campaigns become increasingly automated and intricate.

So, what should we keep an eye on? I’ll be observing how advertisers like us utilize these custom metrics to guide optimization decisions, whether consistency in reporting improves across teams, and if similar flexibilities will roll out in other areas of the platform.

Bottom line? With Microsoft giving us more control over how we measure success, custom columns are evolving into a vital asset for campaign analysis. Read more about this update here.

Inspired by this post on Search Engine Land.

May 7, 2026
Unleashing Data-Driven Insights with Profound’s Prompt Research Reports

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.

Inspired by this post on Try Profound Blog.

May 1, 2026
Mastering the Art of Delivering Tough SEO News to Executives

I’ve learned that executives don’t crave SEO jargon. What they need is clarity, honesty, and a clear path forward. Here’s how I deliver just that when faced with disappointing results.

Traditional SEO metrics haven’t been reassuring, and while more studies could affirm this, the existing data already does. Organic traffic is dropping for many of my clients, with studies like Seer Interactive’s showing a 61% drop in CTR for queries with AI Overviews. Executives notice these downward trends on their dashboards, often for extended periods.

Many consultants I’ve spoken with find themselves unprepared for these tough conversations. Diagnosing the traffic drop is one thing, but sitting across from a CMO and explaining not just what’s happened, but why it happened and what you propose to do about it, requires a whole different skill set. This skill is crucial, yet often overlooked.

Having spent 13 years in SEO and the last six managing an agency, where I personally lead strategy and present to senior executives, I’ve discovered five key lessons on breaking bad news in what I consider one of the most challenging times to be an SEO consultant.

1. Executives are more predictable than you think

A while back, a client expressed concerns after isolating our SEO work from the rest of their site’s organic traffic. Our reported overall numbers looked fine, but the performance of our specific work hadn’t improved over eight months.

Upon reviewing, I found my team indeed avoided acknowledging the underperformance, opting instead to present only the numbers that looked good. No one wants to admit failure in a meeting, yet concealing it often proves more damaging.

The client eventually finds out, and it’s not the underperformance that breaches trust, but the omission. Revealing issues early allows us to address what executives value most: problem-solving capability, diagnosis, and a strategic plan for recovery.

This experience transformed our client engagement approach. We now rigorously separate and analyze our work’s performance, ensuring any underperformance is flagged early along with a proposed solution. Every executive I’ve met has been burned by vendors hiding results; they value the rare consultant who promptly addresses and plans for solutions.

2. Diagnose before you communicate

A prospect once approached me about a traffic decline, assuming AI Overviews were to blame. Instead of presuming, I thoroughly diagnosed the issue. My investigation revealed a PR-induced traffic spike had skewed the comparison, and once adjusted, current performance was actually solid growth.

This diagnosis turned the discussion from a crisis into a positive affirmation of growth—within minutes. Conversely, I’ve also encountered genuine issues. For example, technical errors causing crawl waste impacted a client’s performance. Recognizing the pattern from past experience, I proposed a tried-and-tested solution.

Executives don’t need to understand every technical detail; they need assurance of a diagnosed problem and a plan. Confidence stems from the quality of diagnosis and the specificity of the corrective plan, not the delivery itself.

3. Surprise bad news and failed experiments are different conversations

Surprises

The worst kind of bad news, surprises arise from work done without strategic anchoring. Without a defined plan, diagnosing a traffic dip becomes impossible, as no hypotheses were being tested—only tasks executed.

Failed experiments

In contrast, a failed experiment implies a deliberate strategy and defined expectations. While outcomes may disappoint, assessing performance and proposing informed next steps provides clarity and direction.

Organizing work into structured cycles with specific bets and outcomes avoids surprise dips, fostering a culture of planned experiments. Clients are then prepared for any result, seeing it as a learning opportunity rather than an unexpected issue.

4. Never arrive without a recommendation

When clients ask, “What’s next?” after receiving bad news, an immediate, concrete recommendation is crucial. Lack of one heightens the perceived severity of issues. A seamless answer shows preparedness and instills confidence.

I ensure thorough diagnostic and recommend two realistic paths, helping clients choose solutions rather than dwell on problems. This proactive approach shifts focus to resolution rather than dissatisfaction with setbacks.

5. The tough conversation builds the relationship

Strong client relationships often stem from overcoming difficulties, demonstrating capability under pressure. Being upfront and strategic when challenges arise consolidates trust more than perpetual smooth sailing.

Clients appreciate honest, smart communication over avoiding tough topics. I’ve found that taking ownership of mistakes, providing diagnoses, and recommending solutions earns more respect and confidence.

The conversation is part of the work

As SEO becomes more challenging and results fluctuate, my conversations with clients have grown in importance. Providing clear diagnoses, backed by actionable plans, ensures we manage setbacks effectively.

Clients now assess not just outcomes, but how I handle them, emphasizing the role of strategic, honest communication as an indispensable element of effective SEO consultancy.

Inspired by this post on Search Engine Land.

April 23, 2026
Transforming PPC: From Tactics to Strategic Profit Engineering
Roll back the clock by five, 10, or even 15 years, and I can tell you that a PPC specialist’s value was primarily based on tactical skills. That’s all changed.

Nowadays, platforms like Google and Microsoft have automated much of the tactical work. Machine learning and AI now handle bid management, creative testing, and audience targeting far more efficiently than any human could hope to.

This shift has left many experienced practitioners grappling with a mid-career identity crisis. If the algorithms are doing the heavy lifting, what role do I play, and how do I continue to add sustainable value to the business?

Let’s explore what this evolution means in practice and how it has transformed the critical skills within my PPC toolbox.

From Tactical Execution to Strategic System Design

Having spent 24 years in the paid search trenches, I’ve seen everything from the wild early days of Overture to the advent of Google AdWords and the mobile shift, and now, the complete domination of algorithms over ad platforms.

In the past, my value came from painstakingly researching keywords, micromanaging bids, split-testing every piece of ad copy, and crafting a meticulous exact-match account structure. I was a lean, mean PPC machine.

If I rely solely on tactical execution, I risk becoming obsolete, merely a behind-the-scenes lever-puller. Today’s top practitioners are not just media buyers; they’re architects of revenue and profit.

Rather than blindly manipulating levers, I design systems. The true value I offer is in configuring the system to guide the machine effectively. To become an engineer of revenue and profit, I need to:
- Master data analysis and signaling.
- Develop a deep understanding of how my company or clients generate income.
- Enhance my presence in the executive landscape to confidently convey strategies to the C-suite.
This confluence is my career’s golden ticket. Here’s a roadmap to achieving just that.

Dig deeper: 10 keys to a successful PPC career in the AI age

1. Linking the Account to Profit & Loss

Entering an interview, client pitch, or meeting with simply, “I’ll re-examine your metrics,” makes me sound like any other media buyer. It’s essential to stand out.

Instead, imagine saying, “I’ll align your paid search campaign directly with your profit and loss statement. Each dollar spent is maximized for optimal margin.” That sets me apart as the most valuable person in the room, shifting focus from selling clicks to selling a business advantage.

Traditional PPC accounts often mimic a website’s navigation—with separate campaigns for shoes, shirts, etc. While not wrong, it shows limited thinking. I aim to create a nuanced account structure that aligns with what impacts the P&L, moves inventory, or generates the highest-value leads.

How to Implement This

Each business has unique needs, but the process to achieve this follows a typical framework.
- Margin Interrogation: Collaborate with clients or finance teams to understand profit margins on core products. It’s often revealed that the high-volume product has the lowest margin, while niche services may yield greater profitability.
- Architectural Shift: Update campaigns by margin tier and business value rather than by product category alone. This may mean setting different target ROAS (tROAS) or target CPA (tCPA) based on financial capacity to acquire a specific customer.
Equating a low-margin conversion with a high-margin one in account structures results in revenue and profit leaks, regardless of stellar in-platform metrics.

Segregating Metrics for Different Audiences

Once mapped, it’s crucial to separate metrics accordingly.
- In the “engine room” (daily platform optimizations), I still consider click-through rates (CTR) and costs per click (CPC), crucial indicators for navigating campaigns.
- However, when in the “boardroom” (leadership reporting), I lead with insights into outcomes: “We reallocated budget to high-margin tiers, maintaining our $150 CPA target and safeguarding overall profitability.”
Dig deeper: Why PPC teams are becoming data teams

2. Mastering Signal Engineering

This is the most pivotal skill for a modern PPC profit engineer like myself. Algorithms need input but inherently lack intelligence and judgment. They understand only what I tell them.

In our automated bidding era, appropriately “feeding the machine” delineates experts from the obsolete. If I supply Google Ads only with data on who filled out a form, the algorithm will pursue more form-loving but non-converting leads.

Today, a significant part of my role involves understanding and using first-party backend data to inform machine learning for superior outcomes. I am now an optimizer of signals, not just bids.

How to Implement This

It’s time to move beyond basic pixel tracking by employing robust offline conversion tracking (OCT) or direct CRM integrations like HubSpot or Salesforce into Google Ads.

In managing larger programs, tools like Search Ads 360 (SA360) present enormous advantages for signal engineering, enabling seamless data management across search engines.

For Lead Generation

It’s time to stop optimizing for generic leads. Instead, map client sales stages into ad platforms, assigning monetary values to stages based on historical closure rates.

For instance, consider a raw lead worth $10, a marketing-qualified lead (MQL) worth $50, and a closed/won deal worth $500, then switch bidding strategies to value-based bidding (Target ROAS). This programs AI to focus on lead quality and revenue, not just form completion.

For Ecommerce

Ecommerce stands apart with unique complexities. Tracking revenue to meet basic ROAS is foundational. For true profit engineering, I work with signals about inventory, margins, and lifetime value.
- Feed Engineering: The modern e-commerce specialist doesn’t just upload a product feed; they methodically engineer it. Using Custom Labels, I segment products based on business concerns like inventory status or return rates. A product with a 40% return rate, if pushed hard, destroys profitability despite impressive ROAS data.
- Profit Margin Bidding: Tracking gross revenue alone isn’t enough. Integrating profit margin data via custom conversion variables reshapes bidding strategies. Algorithms bid differently in auction when differentiating a $100 sale with varied margins.
- New Customer Acquisition (NCA): Algorithms often take the easiest path—crediting returning loyalists. First-party customer lists differentiate new buyers from repeat customers, allowing aggressive market share bids for the former while protecting margins for the latter.
Dig deeper: Why better signals drive paid search performance

The journey continues as I enhance my career by focusing on creating profitable business solutions beyond mere clicks.

Inspired by this post on Search Engine Land.
April 15, 2026
PPC Teams Evolving into Data-Driven Powerhouses
Automation and AI are revolutionizing the PPC landscape. Now, PPC teams are transforming into data teams, mastering data infrastructure, measurement, analysis, and experimentation.

Like many people, I worry about AI taking over jobs. Where do my ‘old school’ PPC skills fit in an AI-dominated landscape?

Relax. It’s not a binary situation. The shift is towards data and strategy. Media buying might look automated from the outside, but don’t be misled. The role is simply evolving once more.

Having been in PPC for over 15 years, I’ve learned that there’s nothing to fear. The real question is: am I riding the wave or getting left behind?

Let’s explore what the current PPC landscape looks like with ad network automation, and more importantly, where today’s PPC teams truly add value.

The Return of the Technical PPC Team

A decade ago, technical PPC agencies distinguished themselves by developing scripts, managing data on a large scale, and overseeing complex structures. As automation matured, many teams pivoted towards strategy and creativity.

Now, with AI’s help, producing quality creatives or analyzing massive datasets to create strategies is easier than ever. However, these outputs aren’t flawless.

From a client’s perspective, the typical creative-centric or strategy-focused agency might be out of the game. Therefore, rejoice, PPC folks: the technical edge is back, albeit in a different form. It’s time to bring back the spreadsheet enthusiasts from the 2010s who can now drive the PPC industry forward.

Still skeptical? Let’s rewind and get a clearer view of the necessary skill sets.

The PPC Edge: From Spreadsheet Skills to Data Nerds

Today, successful PPC agencies sell something vastly different than a decade ago, though the core mindset remains the same.

Why? Let’s consider the key performance drivers nowadays:
- Integrating down-funnel data into strategy.
- Building a data infrastructure to support strategy.
- Providing accurate signals to ad algorithms.
- Building systems to scale operations, including creative tasks.
See the pattern? A broken data model can’t be solved just by prompts. This is your advantage, what clients value most. Automation enhances the value of technical literacy rather than diminishing it.

Who do you turn to for technical literacy? The seasoned PPC marketers who thrived on manipulating paid search ads using custom Excel macros or managing extensive product feed items. They have the mindset: a love for automation, data, and math.

1. Data Engineer

The data engineer builds and maintains the infrastructure. Although they might come after the tracking specialist in the data chain, they are central, which is why we mention them first.

In today’s multi-platform world, think of CRM integration with Google Ads or blending online and offline data sets to strategize effectively.

Without a comprehensive data model, strategies become vague gut feelings needing constant reality checks. The data engineer’s role is to set a strong foundation to prevent such situations.

Without this role, you face repetitive manual exports and inconsistent numbers across teams, leading to sluggish decision-making.

What is the Data Engineer’s Scope?

Building a data infrastructure follows an ETL process: extract data, manipulate it, and make it usable in tools like Looker Studio, Power BI, or Tableau.
- Build data pipelines from ad platforms, analytics, or CRM tools into the warehouse for data like spend and revenue.
- Structure tables for these sources and merge them for specific use cases.
- Maintain datasets and perform automated QA, including refresh schedules.
What Skill Sets and Tools Does the Data Engineer Use?

In a Google-centric world, we often hear about BigQuery, but there are alternatives like Microsoft Azure. The essential skills are coding, particularly SQL and Python.

These languages are used to structure tables within the data warehouse (using SQL) and to create data pipelines (using Python).

2. Tracking and Measurement Architect

Some might think this role overlaps with data engineers, but I strongly disagree. This person focuses solely on maintaining signal quality within tight deadlines when issues arise.

Tracking failures mean lost conversion data, impacting ad platforms’ performance because they’re built on conversion data insights.

Notice this when CPAs fluctuate unexpectedly or in-platform data varies drastically from your ‘source of truth’ (GA, CRM, others). These architects help stabilize bidding and improve event match quality for better data in Google Ads.

What is the Tracking Architect’s Scope?

They design comprehensive, regulation-compliant data collection mechanisms, making sure everything is aligned with privacy compliance.
- Align tracking with privacy regulations.
- Design client- and server-side tracking.
- Implement GTM and server containers.
- Co-manage Conversions API integrations with the data engineer.
- Co-ensure deduplication logic with the media buyer.
What Skill Sets and Tools Does the Tracking Architect Use?

While many PPCs have used Google Tag Manager, few have set up server-side tagging. This role needs a deep understanding of Consent Mode frameworks, CAPI, among other tools.

3. Data Analyst

If data engineers build the pipes and tracking architects secure the signals, data analysts interpret what the data implies. It’s a role quite affected by AI, yet crucial due to the risk of misinterpretation.

Wrong interpretations can lead to costly errors. Fully relying on AI over data analysts could be a grave mistake, as misinterpreted metrics like ROAS versus actual contribution margins or CPA disparities can derail strategies.

What is the Data Analyst’s Scope?

While outsiders might think they only build dashboards, data analysts handle much more, like designing models aligned with KPIs and rigorous analysis, all while questioning platform narratives.
- Align data models with business KPIs.
- Analyze performance cohorts, churn rates, and profitability.
- Challenge existing platform narratives critically.
What Skill Sets and Tools Does the Data Analyst Use?

Think of data analysts as translators; understanding numbers doesn’t mean you’re ready to interpret them correctly. They need SQL for warehouse queries and modeling skills for strategic planning, along with strong statistical reasoning.

4. CRO and Experimentation Lead

Once data is cleaned and analyzed, CROs leverage insights to enhance visitor economics. A low conversion rate can mean higher CPA, which no one wants. Their expertise helps scale operations efficiently rather than throwing money at inefficient processes.

What is the CRO’s Scope?

CRO roles are not just about landing pages but full-funnel optimizations, identifying friction points, structuring tests, and working with creative teams to position offers effectively.
- Navigate from impression to revenue.
- Utilize heat maps to locate friction points.
- Use proper methodologies instead of random experiments.
- Coordinate with creative and product teams for best offer placements.
What Skill Sets and Tools Does the CRO Lead Use?

Core tools include GA4 and heat mapping software, with options to scale based on needs. Critical skills involve a firm grasp of statistical reasoning and translation of business metrics into actionable insights.

From Media Buyers to Data Teams

Today’s PPC teams resemble hybrids of marketing, data, and product roles rather than mere media buyers. Successful teams deliberately build capabilities around understanding algorithms, data dynamics, and economics, enabling AI to become a strategic asset rather than a threat.

Inspired by this post on Search Engine Land.
March 9, 2026