I see marketing mix modeling (MMM) becoming easier to access, but I do not think it has become easy to get right.
After several conversations about MMM adoption, I keep hearing the same concern: “We believe in MMM, but we do not know how to get started.”
My answer is that open-source platforms have lowered the barrier to entry in a meaningful way. What they have not lowered is the level of expertise required to produce results that are trustworthy, explainable, and useful for decision-making.
Open-source MMM has changed the starting point
I am seeing MMM adoption accelerate because marketers need more durable measurement methods. Almost half of U.S. marketers expect to invest more in MMM over the next year, and many now rank it as one of the most reliable measurement approaches available.
The open-source shift is real. Three production-grade libraries now give teams a practical way to approach MMM across a wide methodological spectrum.
Robyn (Meta, R): I see this as the most approachable starting point because it includes automated hyperparameter search through Nevergrad, Pareto frontier model selection, decomposition, and response curve plots. It is also the one I use most often because it is highly customizable.
Meridian (Google, Python/TensorFlow): I view Meridian as a more rigorous option, especially because it uses Bayesian inference, geo-level priors, and principled uncertainty quantification. The tradeoff is a steeper learning curve.
PyMC-Marketing (PyMC Labs, Python): I consider this the most flexible path. It offers a full probabilistic model that comes closest to academic-grade Bayesian MMM, but it also demands the most statistical fluency.
This generation of tools has removed the old $150,000 to $500,000 consulting gate that used to be the primary path into MMM. A team with R or Python expertise and reasonably clean historical data can now run a model in-house.
Open-source R and Python tools have lowered the cost of starting with marketing mix modeling, but the expertise needed to produce trustworthy, actionable MMM remains the real ceiling.
The caveat I always make explicit is this: “free tool” does not mean “free model.” The software may be free, but the domain expertise needed to configure it correctly is not. That expertise is a major part of the value.
The vendor landscape is crowded and complicated
I also see a fast-growing SaaS layer built on top of open-source MMM. To evaluate it clearly, I find it helpful to separate vendors into a few practical groups.
Data-layer-first vendors
Platforms like Rockerbox and Northbeam started with attribution and data collection, then added MMM. Their advantage is usually pipeline speed and data access, not deep modeling flexibility or customization.
Measurement-first vendors
Platforms such as Measured, Analytic Partners, Ekimetrics, and Nielsen Gracenote tend to offer more rigorous modeling and enterprise-grade capabilities, usually at a higher price point.
Google Meridian and GA360
I think Google’s decision to open-source Meridian is both a generous contribution to the field and a strategic move. When a walled garden funds and packages a measurement methodology that can be used to evaluate its own channels, I believe it is worth maintaining healthy skepticism about priors, defaults, and assumptions, even when the code is transparent.
Open-source MMM tools now span a clear trade-off: Robyn offers the most approachable starting point, Meridian adds Bayesian rigor, and PyMC-Marketing pushes deepest into statistical flexibility.
The practical vendor question I keep coming back to is simple: who owns the data layer, and does that ownership create conflicts in the modeling layer?
Challenge 1: Data access can quietly break MMM
I think data access is the most underappreciated MMM implementation blocker. A well-specified model needs more than a quick export from a dashboard.
I usually want two to three years of weekly data as a baseline, so the model can capture at least two full seasonality cycles and enough spend variation to learn from.
I need consistent channel-level spend granularity, not just a broad “digital” bucket. Search, social, display, video, and other channels need to be separated.
I need offline channels such as TV, OOH, radio, events, and direct mail, even though they often live in different systems, belong to different teams, and use incompatible time periods.
I need external covariates, including macro indicators, competitor activity, pricing data, and product launch calendars.
For B2B, I often need even more history because longer sales cycles and lower conversion volumes make the data requirements more demanding.
In practice, I often find that the real blocker is the six-week data archaeology project that happens before modeling begins. Finance owns revenue. The brand team owns TV. The agency owns digital spend. A spreadsheet from 2021 may be the only record of trade promotions.
The model is only as good as the data archaeology behind it, and that is rarely the part anyone highlights in a vendor demo.
Challenge 2: I still have to roll up my sleeves
AI assistants have lowered the syntax barrier. They can scaffold a Robyn run, generate a Meridian configuration, or help debug a PyMC model. What they cannot reliably do yet is make the judgment calls that determine whether an MMM is credible.
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 still have to decide where to land on a Pareto frontier across hundreds of model solutions, balancing NRMSE against DECOMP.RSSD tradeoffs.
I still have to know whether Nevergrad’s optimizer has meaningfully converged or simply landed in a local minimum.
I still have to configure adstock transformation parameters, including Weibull shape and scale or geometric decay, so they reflect realistic channel behavior.
I still have to diagnose why a model gives a channel an implausible contribution and decide whether the fix is a prior, a data correction, or a variable exclusion.
In other words, if I try to vibe code my way into MMM, I may end up with a model that appears to work but is wrong in ways I will not catch. The scripting is not the hardest part. The real work is validating the output, including using channel-specific incrementality experiments to calibrate the model.
Challenge 3: Human expertise is not optional
Even if the tools mature enough for AI to run a competent default MMM, I still see human expertise as essential. The irreplaceable work is encoding business context that no model can infer from the data alone.
Adstock and carryover context: I need to know whether a TV buy carries over for four weeks, paid search carries over for three days, or a brand awareness campaign decays over months. That knowledge usually lives with channel experts, not inside the dataset.
Saturation curve shape: I need to recognize when a channel is probably approaching diminishing returns before the model says so, and I need to question the model when it suggests something implausible.
Guardrails and anomaly handling: I need to explicitly model or flag COVID troughs, product launches, pricing shifts, and macro disruptions as structural breaks. AI does not automatically know a client had a pricing crisis in Q3 2022.
Interpretation sanity checks: If a model assigns 40% of contribution to TV for a brand spending $2 million on TV, I need the experience to say, “That feels wrong,” and investigate. That intuition is earned, not computed.
Organizational translation: A technically correct model has little value if I cannot explain why it recommends moving 15% of search budget to CTV in language a CMO and CFO will act on.
I start with the groundwork before the model
The best place to begin is not the model itself. I start by understanding what data is needed, who owns it, and who can help interpret it in the context of real marketing decisions.
None of that is quick or easy, but it is essential if I want meaningful insight from MMM, whether I choose an open-source library or a subscription-based platform.
As a practical first step, I would download Robyn’s demo script and experiment with sample data before applying MMM to my own business data. That kind of hands-on testing makes the strengths, limits, and judgment calls much clearer.
Let me be blunt: SEO advice can sound completely made up to people who do not live in search every day.
When I say things like “change this canonical,”“don’t block that resource,” or “we need this content exposed in the rendered HTML,” I understand why someone outside SEO might hear it and wonder whether I am inventing rules on the spot.
That is one reason SEO still gets treated like black magic inside many organizations.
I have been pushing the idea of “un-nerding SEO” for years, but this is about something very practical: I use Google’s own documentation to earn approval, build trust, and help SEO work get prioritized.
Not because Google tells us everything. Not because every sentence in its documentation should be treated as gospel. I use it because documented evidence is much harder to dismiss than personal opinion.
When I need buy-in, the strongest argument is rarely “trust me.”
It is usually something closer to: “Google has already documented how this should be approached.”
The buy-in problem is usually not the recommendation itself
In my experience, most SEO recommendations do not die because they are wrong. They die because they are competing with everything else happening inside the business.
Dev sprints, product timelines, CMS limitations, legal concerns, brand standards, executive assumptions, and the classic “we’ve always done it this way” all have a seat at the table. SEO is rarely the only priority in the room, even when the recommendation is technically correct.
That is why I do not rely on “best practice says” or “from an SEO perspective” when I am trying to move work forward. Those phrases sound optional, especially to teams already balancing risk, deadlines, and competing requests.
But “Google has official documentation that supports this recommendation” lands differently.
It may not automatically win the argument, and it definitely does not mean the work will be prioritized tomorrow. But it changes the conversation from “the SEO person said so” to “we have official Google documentation explaining why this matters.”
Google documentation is not gospel
I know the objection already: “Are we really pretending Google tells us the full truth about how search works?”
Absolutely not.
Google’s documentation is not the complete truth of search. It has omissions. It simplifies complex systems. Sometimes it explains how Google wants site owners to behave, not every technical factor that influences organic visibility.
Google also writes for a broad audience, which means nuance gets smoothed out, edge cases get skipped, and the answer can be technically true without being the entire story.
So no, I am not treating every Google statement as if it were carved into stone and carried down from Mountain View.
But that does not make the documentation useless.
It makes it a starting point. A receipt. An official reference point.
It moves the discussion away from “I think this matters” and toward “Google has explicitly documented why this matters.” That distinction matters when I am asking someone else to approve and prioritize the work.
Documentation is especially useful with developers
This is where Google documentation often earns its keep the fastest. SEOs need developers, and I have learned that the quickest way to lose developer support is to treat every recommendation like a command instead of a requirement that needs to be implemented thoughtfully.
And yes, just in case it ever works, I still wish I could run this:
google.exe /disable-ai-overviews /please
Bummer. No dice.
Developers are not wrong just because they disagree with an SEO recommendation. Most of the time, they are optimizing for completely valid priorities: performance, code quality, technical debt, security, and avoiding the kind of production mistake that can take a whole site down.
But sometimes developers are wrong about how Google discovers, crawls, renders, indexes, or interprets content.
And telling a developer “you’re wrong” is a great way to make sure my ticket never sees the light of day.
This is where documentation helps. It removes some of the subjectivity and shifts the discussion toward how to implement the requirement inside the existing technical environment.
The point is that I now have an external source of truth to discuss. That is a much better conversation than two teams arguing from preference.
Documentation is also a client management tool
For client-facing SEO work, documentation helps me separate serious recommendations from “trust me, bro, I have a contact at Google” consulting.
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.
That matters even more when a client has been burned by bad SEO advice before.
Instead of saying, “We need to change this because it’s better for SEO,” I can frame the recommendation with evidence.
“Here’s what Google documents. Here’s where your current setup conflicts with that. Here’s the risk. Here’s the recommendation. Here is the estimated reward.”
That framing builds trust because it shows the recommendation is not relying on blind faith.
It also makes the SEO look less like a magician and more like an interpreter.
That is how I see the real role of SEO: translating Google’s documented needs into business and technical decisions that a team can actually act on.
Less black magic, more receipts
SEO has a reputation problem, and some of it is earned.
Too much SEO work is still explained with vague phrases and shaky confidence. I hear people say things like “Google likes this” or “this needs to exist for the bots” when the stronger version is: “Google documents this behavior here, and here is how it applies to our situation.”
That does not mean documentation alone creates buy-in.
Dropping a Google link into a ticket or Slack thread is not a strategy. I still have to translate what it means, explain the risk, connect it to business outcomes, and help the team understand why the recommendation deserves attention.
Google documentation will never replace experience, testing, or judgment. It will not tell me everything, and I should not treat it like the final answer to every SEO debate.
But it can make SEO easier to defend, easier to prioritize, and much harder for leaders to dismiss.
The best SEOs are not just the ones who know what to recommend. They are the ones who can prove why the recommendation deserves to be taken seriously.
Less black magic. More receipts. More results.
Google documentation may not be the whole truth, but I would rather show up to a buy-in conversation with official references than with “my buddy from Google told me.” Suuuure they did.
This post first appeared on the author’s website and is republished here with permission.
I’m adjusting how I refer to Google’s shopping platform now that Google has dropped “Next” from Merchant Center Next. Going forward, the product is simply called Google Merchant Center.
Google made the change official in a Merchant Center announcement, saying, “The platform you use today will simply be referred to as Google Merchant Center.” For anyone managing product feeds, shopping campaigns, or merchant accounts, this is mainly a naming update rather than a product change.
I remember when Google Merchant Center Next was introduced in 2023 as the newer version of the old Google Merchant Center. Over the past few years, more merchants, site owners, and advertisers moved into that updated experience.
At this point, it appears that Merchant Center Next has effectively become the standard experience. So Google is removing the “Next” branding and returning to the simpler name: Google Merchant Center.
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.
Google said users will start seeing the “Next” branding removed from Help Center articles, email communications, and the Merchant Center interface.
Google also clarified that no action is required and that the name change does not affect existing accounts. In other words, I do not need to update settings, migrate anything, or make account-level changes because of this rebrand.
Why does this matter? When I talk about Google’s merchant tools now, I can leave off “Next” and just call the platform Google Merchant Center. Honestly, that is what many of us were already calling it anyway.
I am introducing support for Grok 4.5 in Profound, bringing SpaceXAI’s newest flagship model into workflows built for deeper, more capable AI analysis.
Grok 4.5 is designed for agentic workflows and knowledge work, which makes it a strong fit for teams and operators who need AI systems that can reason, assist, and move complex tasks forward with more context.
With this support now available in Profound, I can use Grok 4.5 as part of a broader AI workflow and explore how its capabilities help with research, strategy, automation, and day-to-day knowledge work.
I see these two new analyses as an important reminder that ChatGPT citations are not as fixed or transparent as they may look. The sources shown in an answer can change when ChatGPT routes search traffic through different hidden retrieval pipelines.
Research from Chris Green and Suganthan Mohanadasan adds a new wrinkle to AI visibility tracking: the final answer does not reveal how ChatGPT selected its sources. Both researchers found internal source-selection labels, including Labrador, Bright, Oxylabs, and SERP, but those labels sit behind the answer rather than inside the citation cards users see.
Green tested 1,000 prompts up to 10 times each and captured 9,946 completed search runs. In most cases, prompts stayed on one retrieval source. Labrador accounted for 88.1% of primary search sources in his dataset, followed by Bright at 9.9%, Oxylabs at 1.7%, and SERP at 0.3%.
What stands out to me is that 11.6% of prompts changed their primary search source across repeated runs. When that happened, URL overlap dropped from 0.273 to 0.149, and domain overlap fell from 0.265 to 0.155. Green calculated that as roughly 45% lower URL overlap and 42% lower domain overlap.
Mohanadasan looked at the issue from another angle. He inspected two days of raw ChatGPT network traffic from one logged-in Pro account and logged about 1,240 source records across a few dozen searches. He found a result_source field attached to web results, with four observed values: SERP, Labrador, Bright, and Oxylabs.
He described Labrador as including established publishers and reference sites, Bright as tied to Bright Data, Oxylabs as tied to Oxylabs, and SERP as an open-web baseline that appeared mostly in news-style results. While Green’s repeated-prompt test found Labrador dominating his dataset, Mohanadasan saw Bright play a larger role in his sample, especially for commercial, shopping, finance, weather, and local queries.
I also think the skipped-search finding matters. Mohanadasan found that ChatGPT classified some queries before searching, using a turn_use_case field. Some prompts were filed as text and skipped web search entirely, even when they sounded current. In those cases, no page could be fetched, cited, or used as evidence.
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.
More complex “thinking” queries behaved differently. Mohanadasan found that ChatGPT could branch into many searches, including site: probes, pricing checks, and searches for unnamed competitors. That changes which pages can enter the answer process because ChatGPT may search rewritten queries, direct site probes, or follow-up checks instead of the exact phrase a user typed.
Another useful distinction is that fetched does not always mean cited. Mohanadasan separated three outcomes: fetched, cited, and mentioned. A page can be pulled into ChatGPT’s context without being shown to users, cited as support for a specific sentence, or skipped as a source even when a brand is mentioned in the answer.
In his small commercial-query sample, Reddit and YouTube were both fetched often, but Reddit was cited and YouTube was not. He attributed that gap to text availability: Reddit threads expose text, while YouTube search results often provide metadata rather than full video transcripts. Vendor pages were cited for their own facts, such as prices and specs, while third-party pages were more likely to support broader recommendation claims.
The practical takeaway for me is that there is no single ChatGPT visibility result to measure. A page may never be considered if ChatGPT skips search, uses another retrieval source, or finds a clearer third-party page to support the claim.
Both analyses also point back to readability. ChatGPT’s source selection depends partly on what it can retrieve and understand. Mohanadasan found cases where ChatGPT appeared to prefer official pricing pages, then fell back to third-party sources when prices were hidden behind JavaScript or otherwise hard to parse.
Green’s results showed that source routing can change which URLs and domains enter the answer set. That makes plain HTML, crawlable facts, clear pricing and specs, strong third-party coverage, and text-heavy pages more important when source selection depends on retrieval and readability.
In Part 1, I looked at the third-party citation signals that matter so much for AI visibility. In Part 2, I made the case for publishing original data, because it is the strongest single predictor of page originality, and the barrier to earning visibility and authority through this approach is still surprisingly low.
Now I have more evidence for why proprietary data should be part of content creation.
Publishing a number matters, but the number itself is not always what gets cited. I looked at Gauge’s citation data to understand what AI systems actually reward when brands publish first-party data. The answer is narrower, sharper, and more useful than simply saying, “original data wins.” Original data does win, but only when it is packaged in the right way.
The format AI rewards most is the benchmark that answers a clear commercial question: which option is best?
First-party research is scarce and punches above its weight
I worked from Gauge’s cited-URL set: 301 live pages cited by AI systems across 316 unique prompts and 7 verticals. Together, those pages carried 1,075 citations.
After auditing the URLs, I found that only 8 of the 301 pages qualified as primary research. To count, the page had to include the original source of the data and its methodology, rather than simply writing about someone else’s numbers.
That means primary research made up just 2.7% of the cited set. But those same 8 pages earned 90 of the 1,075 citations, or 8.4% of the total citation volume. In other words, first-party research appeared rarely, but when it appeared, it over-indexed by roughly 3x on citation share.
The cleaner way I see this is citation density.
Primary research averaged 11.3 citations per page. Everything else averaged 3.4 citations per page. A primary-research page was 3.3x as citation-dense as a non-primary page.
Primary research is rare, but this Gauge analysis shows it punches above its weight: cited pages with original research averaged 3.3x more citations than everything else.
That is the compounding effect of primary research.
There, original data correlated with page originality more strongly than any other trait. Here, original data correlates with citation density. Both findings point in the same direction: the number only you can produce is the lever.
Original research wins when the question has a benchmark
This is where the “original data wins” idea needs more precision.
The 90 primary-research citations were not distributed evenly across the 8 pages. They were not distributed evenly across topics either.
Of those 90 citations, 75 came from one cluster: cloud data warehouse benchmarks. Fivetran’s warehouse benchmark alone earned 44 citations, which was just under half of every primary-research citation in the set.
Once I strip out the benchmark cluster, first-party research barely registers in the citation set. The win is not simply, “I published original data.”
The real win is, “I published a benchmark that answers a buying comparison,” and almost nobody builds those well. By benchmark, I mean a page that measures a set of named things against each other on a specific yardstick and publishes the results as numbers.
A striking citation split: cloud data warehouse benchmarks dominated AI-cited primary research, with Fivetran’s benchmark alone pulling 44 citations from the 90-citation set.
Original research is most powerful when it directly answers commercial comparison queries.
This is also what Google is pushing toward with non-commodity content: new, helpful information that is hard to get elsewhere.
The primary-research citations clustered where prompts asked AI to compare options on measurable specs such as speed, cost, latency, yield, or performance.
That explains the warehouse benchmark spike. The “HR Tech / Compensation” label was noisy, but the citations inside that bucket mostly came from cloud data warehouse benchmark prompts. Fivetran, Estuary, and ClickHouse had numbers AI could use.
Crypto / Solana showed the same pattern at a smaller scale. Marinade and Helius earned citations because staking and MEV questions need firsthand ecosystem data, not generic explainers.
The pattern disappeared in topics without a clear benchmark. B2B SaaS / CRM, Education / TEFL, and Product Analytics returned listicles, product pages, explainers, and case studies. After cleaning the data, I found no cited primary-research page in those topics.
A closer look at the content that held 44 citations
Fivetran’s warehouse benchmark took 44 citations from this dataset on its own. Fivetran’s 2 benchmark pages together took 58 of the 90 primary-research citations. So I wanted to understand why.
The page was published in 2022, but when I examine it, it is easy to see why LLMs still prefer it.
Primary-source visibility is highly concentrated: benchmark-driven topics like HR tech and crypto attract far more AI citations than explainers or listicles.
It answers a measurable comparison head-on. The page names BigQuery, Redshift, Snowflake, and Databricks, then ranks them on speed and cost. It is entity-rich and willing to name the major players directly.
It runs on real first-party data. Fivetran tested against actual customer usage rather than relying on synthetic assumptions, and the page calls that choice out clearly.
It shows the method step by step. The page walks through what data was queried, which queries were used, and how each warehouse was configured and tuned. A reader, or a model, can see how the numbers were produced.
The structure is easy to lift. Descriptive headings such as “Results,” “How much did performance improve?,” and “Why are our results different from previous benchmarks?” help AI map a question to the exact passage that answers it.
It links to raw data and sources. The page footnotes references, including the C-Store paper, and points to the underlying data. That makes the claims verifiable. Few brands put that much work into a data-backed content piece, and even fewer share the full dataset for transparency.
It shows its limits. Dated correction notes from December 2022, named qualitative limitations, and an honest “performance floor” caveat make the claims more credible, not less. The corrections also show care.
The URL never moved. A page from 2022 is still earning citations in 2026 because it stayed live at one canonical address.
The data behind a page like this is easier to pull and analyze than it has ever been. The hard part is everything around the data: the clean method, linked sources, corrections, navigable structure, and willingness to say what the numbers do not prove. That is the craft, and that is the moat.
Fivetran's 2022 benchmark page shows why clear, comparison-led research can become a lasting citation source for AI and search visibility.
This kind of first-party data content is not a thin press release with a few loosely pulled numbers. It requires real work, and it can hold authority for years. My takeaway is simple: AI does not reward “original data” by default. It rewards first-party research when the page gives a clear answer to a measurable comparison and signals depth, expertise, and trust.
The opportunity is to publish a retrievable dataset for a buyer question where AI does not yet have a clean benchmark source. That connects directly to the unanswered-questions finding from Part 2. The opening exists, but in many verticals, nobody has walked through it with a real dataset.
Original data needs a citation-ready package
Original data gives a page something AI cannot get from another explainer. But AI still has to retrieve it, parse it, and map it to the user’s question.
That is where many brands lose the citation. They publish proprietary numbers, but bury them in narrative, gate them behind forms, move the URL, or skip the methodology. The data exists, but the citation never happens.
The pages that won in this dataset had both ingredients: original numbers and a clean citation shape. They had stable URLs, clear methods, named comparisons, and results that answered buyer questions directly.
Who wins: brands with proprietary product, usage, or pricing data that package it into a comparison a buyer can act on, especially one that can inform LLM-generated recommendations.
Who loses: brands that publish original numbers inside dense narratives, on slow or unstable pages, with no clear comparison frame for AI to retrieve and reuse.
When I think about a citation-ready research page, I look for four parts.
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.
Lead with the comparison result. The headline finding, such as “X is fastest” or “Y is cheapest at scale,” should appear in the first 30% of the page. Lead with the result, then explain the method and nuance.
Box the methodology. Show the sample, time window, what was measured, and how the measurement happened. Attribution confidence is part of what makes a number citable, so the method needs to be clear on the page.
Frame the comparison explicitly. AI reaches for benchmarks when prompts ask “which is best.” A table comparing named options on named specs is the format most likely to be lifted.
Keep the URL stable. Use one canonical page and keep it live. Do not migrate it or rename it every redesign. The citation earned this quarter only compounds if the page is still there next quarter. In this dataset, 64 of 365 cited URLs were dead, redirected, or otherwise broken, taking 203 citations down with them.
This is the work behind a citable benchmark, and it is more involved than it looks.
HockeyStack documented its own version in a playbook on launching research reports. The company published 18 original reports built entirely on anonymized first-party customer data, the kind of data no competitor could replicate.
Its process includes the same steps the Fivetran page demonstrates: list the data points needed, have a teammate pull them with SQL, define and document the method so the numbers can withstand scrutiny, and structure the report around a real ICP question. HockeyStack calls methodology non-negotiable because without it, someone will always dispute the data.
With AI analysis, pulling the data is often the easier part now. Building the content into something citable, trustworthy, and durable enough to keep earning visibility for commercial queries years later is where the harder work sits.
What sites are already trusted for your topic? When a benchmark you did not publish is earning the citations in your category, the Citation Source Mapper can map that trusted set into a ranked, pitchable target list. It is available in the premium library.
This post first appeared on the author’s website and is republished here with permission.
I have watched the SEO industry become exceptionally strong at its technical craft. We have made real progress in crawl architecture, Core Web Vitals, content frameworks, entity optimization, and link acquisition at scale.
Where I still see a gap is in how SEO connects that craft to the financial realities of the businesses it supports. Too often, SEO struggles to speak the language that gets budgets approved and strategies prioritized.
If I want more funding and a stronger seat at the table, I have to change how I define what SEO is trying to achieve. That means moving beyond visibility alone and tying organic search to commercial outcomes.
Here is how I make an SEO strategy more commercially aware.
Why paid search often gets more funding
Paid search usually frames its goals around clear commercial inputs and outputs. Money goes in, revenue comes out, and the difference helps determine whether investment should increase, decrease, or shift. Every campaign sits inside a financial framework.
Even when paid search is expensive or inefficient, leadership can still see the goals, the numbers, and the tradeoffs. That makes resource decisions easier.
SEO teams often present rankings as the final goal rather than a route to revenue. They report traffic without connecting it to transactions, or highlight technical improvements that matter to SEO but do not translate clearly into business value.
When organic search does not get enough funding, it is easy to say leadership does not understand SEO. I think the more useful explanation is that SEO has not always made its commercial case clearly enough. Leadership needs to see organic search measured in sales, margins, and channel ROI.
What commercial awareness requires
Before I plan SEO work, I try to change the questions I ask.
Instead of asking which topics have the highest search volume, I ask which categories and product lines carry the strongest margins. Then I evaluate search demand within those areas.
Instead of asking where I should create new content, I ask which existing pages would generate meaningful revenue if they ranked better. From there, I work backward into the SEO plan.
Instead of measuring success only in organic sessions, I measure it in organic profit. To do that, I need to know what the channel costs and what it returns.
Financial metrics I use for commercial SEO
When I run organic search as an acquisition channel, I pay close attention to these metrics:
Organic sales.
Organic revenue.
Organic profit.
Average order value from organic traffic.
Average margin per organic sale.
Channel ROI.
These metrics are not exotic or especially difficult to calculate. They usually require connecting analytics data to backend transactional data, which most organizations can do with a modest investment in reporting infrastructure.
One metric I keep returning to is organic profit per sale. I calculate it by dividing organic profit by organic sales.
This turns organic search into a customer acquisition channel with a measurable cost per outcome. It also gives me a concrete benchmark I can compare against other channels.
When I break that metric down by category, subcategory, and page, I can make strategic decisions using commercial data first, then layer SEO execution on top.
Focus on value-side metrics
Most SEO strategies lean heavily on demand-side metrics such as:
Search volume.
Keyword difficulty.
Current ranking positions.
Traffic estimates.
I still need those inputs, but they only show half of the picture. They tell me where demand exists, not where value is strongest.
To make better commercial decisions, I layer value-side metrics on top of demand data, including:
Categories with strong margins.
Pages that drive high transaction values.
Customer segments that stay profitable over time.
From a revenue and profit perspective, a category with modest search volume can outperform a higher-traffic segment if it has stronger margins or a higher average order value.
SEO tactics that move the commercial needle
When I take a commercially aware approach, I evaluate strategic decisions against business outcomes rather than traffic projections alone. That includes decisions about informational content, authority building, and brand visibility.
Informational content and topical authority still matter. A channel that only chases transactional queries will eventually hit a ceiling. The difference is that I want every major SEO initiative to have a clear commercial role.
Score demand and business value together
I apply a second filter that considers business value alongside search demand.
That means I look at margin potential, average sale value by category, and current organic performance compared with where it needs to be. Then I weigh those signals against demand.
The highest-priority work usually sits where meaningful demand and strong commercial signals overlap. In practice, that often produces a different priority list than traditional keyword research alone.
Update commercial pages before creating more content
Commercial pages naturally decay over time. Competitors improve their pages, SERPs change, and freshness signals fade. That decay can turn directly into lost revenue from pages that used to perform well.
When I update commercial pages, I focus on a few practical moves:
I use keyword and competitor research to find content gaps.
I restructure information into formats that search engines and AI interfaces can easily extract, especially tables where they make sense.
I use a large language model to review first drafts and stress-test the content against competing pages.
I strengthen internal links to the pages that have revenue and margin potential.
Increase internal linking
Internal links from strong informational assets and high-authority pages to commercial pages can create direct business value when those destination pages have revenue and margin potential.
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 spend significant time building internal links into commercial page clusters, especially when supporting content has authority but the connected commercial pages are underperforming in search.
Borrow conversion intelligence from paid search
SEO usually cannot see exactly which organic keywords drive conversions. I may have page-level conversion data, but the specific queries that create visits and purchases are often hidden.
The best workaround I have found is to review recent PPC campaign data, usually from the last 30 to 90 days, and adjust for seasonality. This helps me identify keyword patterns that generate sales and high-value customers in paid search.
I can then use those insights to prioritize organic landing pages, update commercial content, and decide where conversion optimization is most likely to pay off.
Recover transactional terms just outside Page 1
A valuable group of transactional keywords often sits in positions 10 through 20. These are commercial-intent terms where I am already in the conversation, but not yet visible enough to convert meaningful traffic.
I identify these opportunities by filtering for commercial intent and business potential. Then I apply targeted improvements such as content updates, internal links, and relevant authority building.
Build digital PR with commercial architecture
Digital PR campaigns that exist only to acquire links rarely create meaningful commercial impact. I prefer to build a linking environment that supports the product categories I care about most.
That means I structure campaigns around a few principles:
I focus on topics that are thematically relevant to important product categories.
I create an on-site asset that acts as the campaign destination and links back to relevant commercial pages.
I build the asset with internal links to the commercial page clusters it is designed to support.
Treat branded search protection as a profit issue
When affiliates rank for discount and voucher terms and capture that traffic, I may end up paying commission on customers who were already in the funnel and likely would have converted directly.
The fix is straightforward. I improve on-site pages that target branded intent, strengthen internal signals, monitor branded click share, and enforce affiliate program terms around branded bidding.
That can improve margins as well as revenue because it removes acquisition costs from conversions that should have been organic in the first place.
Choose an attribution model
Attribution is rarely clean. Organic sessions may appear as direct traffic, GA4 and backend systems may report different numbers, and multi-touch journeys can resist neat channel assignment.
These problems are not unique to organic search. As AI-mediated search complicates referral paths further, attribution will become even harder.
I choose an attribution model the organization can agree on, stay transparent about its limitations, and focus on growing the revenue attributed to organic search under that model.
When leadership consistently sees organic search contributing meaningful and growing revenue, the finer attribution nuances become less important.
Treat budget as a lever, not a constraint
I view an SEO budget as a variable that can be adjusted based on commercial KPIs.
The model is simple: SEO profit equals the business margin generated from organic search minus the cost of running the channel.
When revenue growth is the priority, I can invest more aggressively in link acquisition, digital PR, and content production to expand visibility and capture incremental demand.
When channel profitability matters more, especially during a business cycle where margin preservation is more important than top-line growth, I can reduce spending to improve short-term profit. I just need to be clear about the competitive risk of sustaining those reductions for too long.
How I secure internal alignment
Commercial SEO depends on cross-functional cooperation. To build alignment, I focus on the conversations that help other teams see SEO as part of the business growth engine.
Speak the language of decision-makers
Commercial and finance leaders care about growth, margins, and competitive position. I frame SEO in those terms, with revenue and margin projections tied to specific strategic initiatives.
Generate proof before asking for major investment
SEO takes time to show results, so I prefer to earn buy-in with a contained test before asking for a larger investment. That test might involve updating a group of commercial pages, completing a targeted internal linking project, or launching a branded search protection initiative.
Use competitive visibility strategically
I show leadership where competitors outrank us for high-value commercial terms, then quantify what that could mean in lost market share and revenue. Concrete numbers make the opportunity easier to understand.
Build relationships that make execution faster
When SEO is positioned as part of an integrated commercial growth engine, with shared data and coordinated prioritization, it becomes much easier to get work shipped. SEO touches paid search, content, product, and PR, so I treat those teams as allies rather than separate workstreams.
Why commercial awareness should shape SEO strategy
SEO has become technically sophisticated, but technical sophistication alone does not secure budget or influence priorities. I need to connect SEO work to the outcomes commercial leaders care about.
I believe SEO should be held to the same standards of commercial accountability as other marketing investments. When that happens, organic search can become a cost-effective driver of growth and profitability.
Commercial awareness does not require abandoning SEO fundamentals. It requires redefining success and having the discipline to organize strategy around revenue, profitability, and return on investment.
AI now shows up in nearly every corner of marketing, and for every useful initiative I see, it feels like 10 vendors appear with a tool that claims to solve it.
When this wave first started, I took more vendor calls and answered more outreach than I do now. Over time, I noticed I was asking the same core questions again and again to decide whether an AI tool was actually worth deploying.
If I feel overwhelmed by AI vendor pitches, these are the five questions I use to separate useful solutions from noise. They help me understand what the tool does, whether it solves a real business problem, and whether the vendor is the kind of partner I would trust with my budget, data, and team’s time.
1. What problem does your tool solve?
I start here because I want to understand the purpose of the tool and, more importantly, whether the value it creates connects to real business outcomes.
If a vendor cannot clearly explain the challenges or use cases the tool addresses, I assume it was not purpose-built for a real problem my team faces. That applies whether I am evaluating it from an in-house perspective or on behalf of an agency. I am cautious when vendors lead with feature-heavy language but cannot explain the business benefits those features are supposed to deliver.
If a vendor can identify at least one existing team problem and explain how the tool improves business outcomes, I keep the conversation going. My next question is usually for a case study that shows how the tool was used and what results it delivered for an organization similar to mine in size, market, or vertical.
I look for benefits such as increasing output or identifying tracking gaps that speed up troubleshooting. I do not rush to buy a tool simply because it promises to save time, even if that promise is true. I need to know how I will use that extra time before I can decide whether the savings are meaningful.
2. What expertise do you have in the space where this tool solves a problem?
This answer tells me whether the vendor built the tool for advertisers or merely at advertisers.
Technical skill matters, but so does understanding how a media buyer actually spends the day. If the vendor does not have direct experience in media buying, I want to hear how the team researched the market and how those insights shaped the product.
A shallow understanding of the problem is a red flag for me. I do not expect every sales rep to have deep domain expertise, but someone on the team should. If I am seriously considering the tool, I want access to that person early in the process.
When a vendor has a credible story about identifying a problem I recognize firsthand and building a solution around it, I find that compelling. A founding mission tied to my actual challenges gives me more confidence that the tool can make a real difference in performance.
3. What case studies, real use cases, and results can you share?
In a fast-moving AI market, I treat case studies as essential. I want to know whether the vendor has a strong track record with customers like me or whether I would be one of the first teams testing the product in my space.
If I would be an early adopter, I weigh the tradeoffs carefully. I might gain an advantage by finding a growth accelerator before competitors do. I might also spend time working through bugs, giving detailed feedback, or discovering that the tool does not deliver what was promised.
If I cannot trust the tool, or if I will need to provide a lot of feedback just to make it useful, I have to decide whether the potential payoff is big enough to justify the time and money. In most cases, that bar should be high.
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.
If I am clearly going to be an early adopter and the vendor will not offer flexible contract terms that reduce my risk, I consider that a nonstarter. Established tools may be less flexible on pricing because they can already prove consistent value. Newer tools that take a hard line on price and contract terms are much less likely to become strong long-term partners.
For established vendors, I want specific and relevant case studies with real numbers from advertisers in a similar space, at a similar size, or with a similar use case.
For early-stage companies, the best answer is honesty. If a vendor says, “You’d be one of our first clients in this vertical. Here’s what we’ve seen elsewhere, and here’s what that partnership would look like,” I see that transparency as a positive sign.
4. Who owns my data, and how is it being used to train models?
I am still surprised by how quickly people share data with AI tools in the rush to find a competitive edge. Before I sign anything, I take data ownership and model training terms seriously.
I watch for any answer suggesting that my data could be used to train shared or third-party models without my explicit consent. I also treat vague answers, deflections, or terms of service that conflict with the salesperson’s verbal explanation as major warning signs.
I own my data, full stop.
The vendor should be able to clearly explain where my data is stored, how long it is retained, whether it is used for model training, and what happens to it if I stop using the tool. If model training is involved, I want that training limited to refining my own instance. Most importantly, I want those commitments in the contract, not just in a conversation. If the language is missing, I insist that it be added before I sign.
5. What does implementation actually look like, and what does success require from our team?
Before I commit budget, I need to understand the real cost of adopting the tool. That cost is not just the subscription price. It includes the time, internal lift, integration work, training, QA, and possible disruption to the existing martech stack.
If the tool requires resources my team does not have, or if I cannot realistically dedicate the time needed to use it well, I do not consider it a smart investment yet. A lot of wasted martech spend could be avoided by asking this question and taking the answer seriously.
I do not expect every tool to fit every organization, but I do expect implementation to be clear and the product to be intuitive enough for the team to adopt. If people cannot understand it, trust it, or fit it into their workflow, it will not create the value the vendor promised.
I do not let AI hype rush my decision
I know firsthand that many AI tools sound too good to be true, and often they are. I still want to stay curious and ambitious, but I balance that with caution.
I also remind myself that AI adoption is still early. If a tool feels too expensive, too difficult to onboard, or too rigid in its contract terms compared with its track record, I am willing to wait. A better option may appear in the next few months.
When I am unsure, I ask for a free trial. If integrating the tool will not create too much work for the team, a trial can be the best way to decide whether I have found a real competitive advantage or just another AI pitch dressed up as one.
When I’m running Google Ads in 2026, one setting I always check carefully is “Search Partners.” It often appears in campaign settings as a simple way to extend reach beyond Google Search, and on the surface, that sounds useful.
But more reach does not automatically mean better reach. In my experience, Search Partners can bring traffic, but the quality of that traffic is usually the problem.
For most advertisers, I would not leave Search Partners enabled by default. I’d rather start with the main Google Search results page, prove the campaign can convert, and only then consider whether extra volume is worth testing.
What are Google Search Partners?
Google Search Partners are third-party sites that use Google-powered search results. When someone searches on those sites, your ad may be eligible to show there. This network can include YouTube, directories, other search experiences, and even parked domains.
That sounds like a broader opportunity, but I usually see a familiar pattern: lots of impressions, plenty of clicks, and cheaper CPCs than Google Search. The issue is that cheaper clicks are not always useful clicks. Real conversions and meaningful business value from these placements are often limited.
If I’m using conversion-focused Smart Bidding, I often see Search Partner spend fall naturally over time. The bidding system eventually learns that those placements are not producing the conversions it wants, so it stops pushing budget there.
How Search Partners differ from the Google Display Network
I see advertisers confuse Search Partners with the Google Display Network all the time. Some websites can be involved in both, but the intent and placement logic are different.
The Google Display Network is made up of websites and apps that use AdSense, where ads can appear while people browse content. It can show up as a placement option in Demand Gen, Video campaigns where it is called “Video Partners,” and Performance Max campaigns.
Search Partners are tied to search-based activity. That is why they apply to Search, Shopping, and Performance Max campaigns rather than standard Display placements.
How I audit Search Partner performance
I do not recommend taking anyone’s word for it, including mine. The better move is to check what Search Partners are actually doing inside your own Google Ads account.
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.
For Search or Shopping campaigns
In Google Ads, I go to the campaign view, select Segment, and choose Network (with search partners). This splits performance into separate rows for Google Search and Search Partners, which makes the difference much easier to see.
What I usually find is a lot of Search Partner impressions and clicks, often at lower CPCs than Google Search. But when I look for true conversions, the results are usually weak unless the account is tracking something shallow or easy to manipulate, such as a page view or a low-friction form fill.
For Performance Max campaigns
Performance Max works differently. Search Partners are required for this campaign type, so I cannot simply opt out. What I can do is monitor the activity through the Channel Performance report.
If I see heavy Search Partner spend in a Performance Max campaign, I treat it as a signal to review conversion tracking, bid strategy settings, and the quality of the conversion actions being used for optimization.
Check the Content Suitability report
For more transparency, I also check the Content Suitability report under Insights and reports. This report can show the actual websites or YouTube channels where ads appeared on the Search Partner network.
That list is often enough to make the decision clear. Once I see where the ads have been running, I usually find many placements that look low quality, irrelevant, or simply not worth the spend.
In Google Ads, many decisions really do depend on the account, the market, and the goal. This is one of the few areas where my starting recommendation is straightforward.
If I’m building a new Search or Shopping campaign, I leave Search Partners unchecked. After the campaign is performing well and has strong conversion data, I may test Search Partners for added volume. Until then, I keep the budget focused on the main Google SERP.
This article is part of the ongoing Search Engine Land series, Everything you need to know about Google Ads in less than 3 minutes. In each edition, Jyll highlights a different Google Ads feature and explains what advertisers need to know to get better results in a quick 3-minute read.
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.
