Tag: Technical SEO

Why Technical SEO ROI Is So Hard to Prove and Fund
Six months ago, a core update could have crushed my website. But it did not.

It did not because my team had already fixed canonicals, redirect problems, duplication issues, and JavaScript rendering gaps eight months earlier. It was the kind of unglamorous technical work that often lands with an engineer or developer because the ticket has been sitting at the bottom of the list.

And I do not really have proof. What I have is experience from years in SEO and the ability to recognize that the site had the same warning signs I have seen on sites hit hard by similar updates.

Traffic could have been cut in half. It was not.

There is no parallel internet timeline where I skipped the work, so there is no clean way to confirm what would have happened. There is no record of the disaster that never arrived.

That is why technical SEO ROI is so hard to prove. I see it as an inference problem with no control group, even though the industry often treats it like a reporting problem we can solve with one more tool.

The internet doesn’t stop

When I work in digital, I am working inside at least two open systems: the internet and the market. I could add a third if I count the maturity and expectations of internet users. I could add a fourth if I count my own website infrastructure. In reality, there are even more moving parts than that.

The point is simple: the environment I am trying to measure is always shifting, expanding, shrinking, and changing shape. There is no fixed “before” state I can pin down, and there is no clean way to model what would have happened if I had done nothing. Bayesian forecasting and similar methods can help, but they are still educated guesses.

A technical change might improve visibility today. If I make that same change six months later, it might do very little. That could happen simply because Google changed its crawl budget behavior or adjusted how it reads websites.

Cause and effect do not always stay close together in SEO. Google recrawls and reindexes on its own schedule, so the impact of a technical fix may land long after the release. By then, the result is spread across a recrawl cycle and the clean before-and-after comparison I would want for a proper test has already blurred.

As with SEO overall, there is a lot I cannot control. If I tried to track every change across the web that might influence my site, I would end up with sleepless nights and a lot more gray hair.

Technical SEO adds another layer because these changes rarely ship in isolation. It is almost never, “I made one change to the website.” It is more often, “Thirty fixes from five teams are going live on Thursday so we still have people around on Friday if something breaks.” Please do not ship on Fridays.

A lot of technical SEO also keeps the site above water. I am managing technical debt, staying current with regulations, and adapting to new releases of codebases, platforms, and frameworks. True enhancements matter, but even those can be difficult to isolate.

Technical work is closer to insurance or public health than a standard growth campaign. I usually realize how important it was only when it stops working. Much of technical SEO is disaster prevention, not new-city construction. I cannot invoice for an earthquake that did not happen.

The control group was never there

Another reality is that many technical changes, whether SEO-led or not, are sitewide because they have to be. There is no control group. Render pipelines, crawl budget, and site speed touch everything at once, so there is no untouched slice of the site left to compare against.

Two examples make this clear.
- Sunsetting 301 redirects more than a year old: The server stops reading every redirect line on every page load. The benefit is crawl and resource efficiency, but that benefit is mostly invisible in analytics.
- A migration done right: The win condition is “we did not lose traffic.” Maybe the line stays flat. Maybe it ticks up slightly. Migration work usually becomes obvious only when it fails.
My only comparison is the past, and the past existed under different external conditions. Time becomes the problem. I can compare relative movement, incremental change, and long-term trends, but the outcome shifts based on which metrics I choose and which assumptions leadership brings into the conversation.

When I can, I want to run a proof of concept. In practice, that means something close to SEO A/B testing: choose a segment, make the change there and nowhere else, measure the result, and decide what to do next. But that is not always possible, and it requires a different kind of buy-in.

I am also working in a search environment where LLMs make more things probabilistic. Answers are personalized, discovery paths are less predictable, and many of the measurements I have relied on are less deterministic than they used to be.

So I keep it relative

There are two levels of relative thinking I come back to: how I prioritize technical work and how I measure its impact.

The way I prioritize the work helps determine the impact I am trying to create.

When I prioritize technical SEO, I start with impact. How much of the website does the issue affect? How much of that impact lands on priority sections or priority pages? After that, I move into the usual scoping and grooming conversations with development teams.

For me, impact is the anchor.

Measurement and reporting are harder. A lot of the SEO industry, myself included, is now rethinking how we measure almost everything, not just technical SEO. LLMs have accelerated that shift and left many of us in an uncomfortable middle ground.

I do not have a perfect “what would have happened if…” comparison for my own website. But I do have competitors. Watching how competitor sites respond to global events, especially Google updates, is probably the closest I can get to that missing counterfactual in technical SEO. It is ROI by proxy, sitting close to share of voice.

And the funding

Technical SEO is infrastructure. It is insurance. If I am struggling to get it done or funded, I need to look closely at how I am framing the work.

At its core, I see technical SEO as insurance against the shocks of an open system. I should treat it that way. It is not always a direct revenue driver.

Yes, technical SEO can produce meaningful improvements and help the line move up and to the right. But the workhorse, the 80%, the majority of the discipline, is keeping the engine running. The work does not always promise upside. It lowers the odds and the cost of getting hit. The core update that did not sink the site is the claim that paid out.

That is why I recommend talking to finance. I want to understand how finance teams quantify, value, and evaluate insurance, security, and infrastructure.

Then I can start looking at technical SEO that way. More importantly, I can start talking about it that way.

Technical SEO is growth resilience. It is the foundation my flywheel cannot move without, not an investment I should be apologizing for.

Inspired by this post on Search Engine Land.
July 3, 2026
My 120-Minute Weekly SEO Workflow That Drives Results

When one person is responsible for paid campaigns, landing pages, reporting, email, social posts, sales requests, and last-minute website updates, I know exactly what usually happens to SEO: it waits.

I have seen this play out on small marketing teams over and over. Everyone knows SEO can bring in qualified demand, reduce dependence on paid media, and support buyers long before they fill out a form. The problem is that SEO rarely feels urgent until traffic drops, rankings slide, or something breaks.

That is why I like a simple 120-minute weekly SEO workflow. It gives me a practical way to protect visibility, find opportunities, improve high-value pages, and turn search data into business impact without pretending I have unlimited time.

Why I keep SEO simple on lean teams

When SEO falls behind, I rarely see effort as the real problem. The bigger issue is usually competing priorities and a lack of clear prioritization.

On a lean team, SEO is one tab among 20. The person responsible for organic growth may also be sending newsletters, briefing designers, updating landing pages, and pulling the report leadership wants by Friday.

Then the advice starts piling up: fix technical issues, publish more, build topical authority, refresh old posts, add schema, improve Core Web Vitals, build links, optimize for AI search, and keep going. Most of that advice may be valid, but no small team can do all of it in one week.

The question I come back to is not, “What could I do?” It is, “What is the highest-leverage thing I can actually finish this week?”

I also try to avoid the reporting trap. It is easy to spend an entire SEO block looking at rankings, traffic, impressions, clicks, CTR, conversions, competitor movement, and keyword shifts. Then the hour ends and nothing ships.

For a small team, reporting has to be short enough to leave room for action. The goal is to decide what to fix next, not to build another dashboard.

Why 120 minutes can be enough

I do not try to run a lean team like an enterprise SEO department. If I audit everything, track everything, collect endless keywords, and ship nothing, I have not improved organic growth.

The point of time-boxing is to force a decision. Every weekly session should end with one or two changes that improve visibility, traffic quality, or conversion potential.

In my 120-minute workflow, I focus on four outcomes: finding what is already working, fixing what is blocking performance, improving the pages closest to revenue, and turning search data into next week’s actions.

I am not trying to “do SEO” for two hours. I am using two focused hours to make decisions and ship work that has a realistic chance of moving the business forward.

My 120-minute weekly SEO workflow

0-15 minutes: Check organic data

I start with a pulse check so I can catch problems before they turn into bigger performance drops.

I look at Google Search Console clicks, impressions, CTR, and average position. I also check organic conversions or assisted conversions in GA4, top landing pages gaining or losing traffic, branded versus non-branded movement, and any indexing, crawling, or manual action warnings.

What I do not do is turn this into a full reporting session. This is not a board deck. I only want to answer one question: is organic visibility moving in a direction that needs action?

My output is a short weekly note: the biggest organic win, the biggest organic concern, one page or query to investigate, and one action to take this week.

15-35 minutes: Find query opportunities

Next, I look for the easiest opportunities in Google Search Console. The richest ones are often queries ranking in positions 4-15 with real impressions. Those pages are already close, and a focused improvement can help them move.

I also watch for pages with strong impressions but weak CTR, queries climbing week over week, and rankings where the current page only partially matches search intent.

I resist the urge to build a long keyword list. Instead, I pick three things: one page to improve, one query to answer better, and one title or meta description to test.

For example, when I reviewed search data for a local accounting client, several queries kept appearing around tax help for freelancers, small-business tax mistakes, and the difference between an accountant and a bookkeeper.

The obvious reaction would have been to write three new articles. Instead, I rewrote one service page around freelancers, added a short FAQ based on those queries, and linked it to an existing bookkeeping article. One page served three search intents, which was far more useful than three unfinished drafts.

35-60 minutes: Improve one money page

This is the most important part of the workflow. I define a money page as any page close to revenue, pipeline, bookings, sales, demos, or consultations.

Money pages can include product pages, service pages, category pages, comparison pages, demo pages, consultation pages, pricing pages, and high-intent landing pages.

My weekly goal is not to optimize the entire website. It is to improve one important page in one meaningful way.

I ask what the buyer needs to believe before converting, what objection is missing, what proof would reduce hesitation, what comparison the buyer already has in mind, and what query the page almost satisfies but does not fully answer.

A meaningful update might be adding three FAQs based on real queries, improving the H1 and introduction, adding comparison language, including proof points, linking to a case study, clarifying who the offer is for, improving the CTA, or adding a short “how it works” section.

That is SEO work, but it is also conversion work. The best page improvements usually help both search engines and buyers understand the value faster.

60-80 minutes: Fix one technical or indexing issue

Technical SEO can take over the full two hours if I let it, so I stay focused on impact.

The question I ask is simple: what could stop an important page from being discovered, understood, indexed, or trusted?

That usually points me toward issues like priority pages not being indexed, broken internal links, redirect chains, duplicate or missing titles on key pages, incorrect canonicals, schema errors on important templates, or valuable pages buried too deep in the site.

I want one of three outcomes from this block: a fix shipped, an issue assigned, or a clear developer brief.

For example, if I find that ecommerce collection pages are not indexed because of incorrect canonical tags, documenting the affected URLs and writing a clear developer brief may be more valuable than publishing another generic article.

80-100 minutes: Improve internal links

Internal linking is one of the fastest SEO wins I can create because it does not require new content.

It helps search engines understand which pages matter, helps users continue their journey, and helps informational content support commercial outcomes.

Each week, I look for links from high-traffic articles to money pages, links from product or service pages to supporting guides, links from older articles to newer strategic content, and opportunities to use clearer anchor text.

If an article ranks for “how to choose accounting software,” I do not want it to be a dead end. I want it to guide readers toward a comparison guide, a relevant case study, and a demo or pricing page. The traffic is already there, so I try to make it more useful.

100-115 minutes: Turn one search insight into messaging

I do not want search data to stay trapped in an SEO silo. The best query I find each week is often a useful signal for the rest of marketing because it shows the language buyers actually use.

A query like “best CRM for small agencies” can become a comparison section on a landing page, a LinkedIn post, a sales email angle, and a paid search ad group.

A query like “is [product] worth it” can become a proof section, a pricing explainer, a “who this is not for” paragraph, or a ready-made answer to a sales objection.

When I share one search insight each week, SEO becomes more than a channel. It becomes a source of customer intelligence.

115-120 minutes: Choose next week’s priority

I end with a decision, not a long list. I choose one clear priority for next week based on business impact, search demand, ease of execution, current performance gap, and proximity to revenue.

The template I use is: “Next week, my highest-leverage SEO action is [X] because [Y].”

For example: “Next week, my highest-leverage SEO action is updating the pricing page because it gets non-branded traffic, supports demo requests, and does not answer implementation cost questions.”

That is how I make SEO operational. The work becomes specific, owned, and easier to repeat.

A sample month for the workflow

To keep the workflow balanced, I like rotating the emphasis each week.

In week one, I focus on a revenue page. I update copy, add FAQs, improve internal links, check indexing and schema, and sharpen the CTA.

In week two, I refresh existing content. I choose one article with impressions but weak clicks or rankings, improve the title, add missing sections, update examples, link to money pages, and better match search intent.

In week three, I handle technical cleanup. I focus on one crawl, indexing, or template issue, such as broken links, duplicate titles, sitemap problems, or a developer brief for a higher-impact fix.

In week four, I turn SEO data into broader marketing assets. That may mean one landing page insight, one sales objection, one content brief, one paid or social angle, or one FAQ or comparison section.

This rotation keeps me from spending every week in dashboards, technical audits, or new content production while ignoring the pages that already have potential.

What I stop doing

Most small teams do not have a doing problem. They have a stopping problem.

I stop chasing every low-impact technical warning. I stop creating content just because a tool found a keyword. I stop publishing AI-assisted articles at scale without a strategy. I stop rewriting pages without a hypothesis. I stop optimizing low-value pages before revenue pages. And I stop treating rankings as the only score that matters.

Before I create new content, I review the pages I already have. The highest returns often come from pages that already rank on Page 2, already get impressions, sit close to revenue, and are one focused update away from doing more.

My test for any task is simple: if I cannot connect it to qualified traffic, conversions, discoverability, buyer education, or trust, it does not belong in the 120 minutes.

How I make it work without a dedicated SEO person

This workflow does not require a full SEO department. It requires one owner, a weekly rhythm, and a bias toward shipping.

A marketing manager can own prioritization and the weekly SEO note. A content marketer can update copy, FAQs, and page sections. A developer or web support partner can handle technical fixes. A paid search manager can share query and conversion insights. A founder or sales team can contribute objections and buyer language.

The owner matters most. Someone has to protect the 120 minutes, choose the priority, and make sure the session ends with an action.

Without ownership, SEO becomes everyone’s job and nobody’s job.

How I use AI to save time

I use AI to shorten repetitive SEO work, not to hand over strategy.

That might mean using a focused workflow to identify queries in positions 4-15, pages with high impressions and low CTR, search queries that should become FAQs, internal linking opportunities, or technical issues that should become developer briefs.

For agencies, client-specific assistants can reduce context switching by remembering each client’s services, priority pages, competitors, and customer objections.

The most useful AI workflows are narrow: a GSC opportunity analyzer, a money page refresh assistant, an internal linking assistant, a technical SEO brief generator, or an SEO reporting summarizer.

I do not want one generic SEO assistant trying to do everything. I want small workflows that help me move faster from data to decisions.

Consistency is the advantage

Small teams win SEO by doing the highest-leverage things repeatedly.

A 120-minute weekly SEO workflow will not replace a full strategy. It will not solve every technical issue, build every content asset, or uncover every opportunity.

But it gives me a practical way to protect visibility, learn from search data, improve revenue pages, and keep organic growth moving.

The mindset is simple: less auditing, more shipping, more buyer intent, less busywork, and more business impact.

Inspired by this post on Search Engine Land.

July 3, 2026
Google Search Console Indexing Delay: What I Am Watching

I am seeing Google Search Console’s page indexing report running more than two weeks behind, with the latest visible timestamp still showing June 11, 2026. That means I cannot get a fresh view of page indexing data for the pages on my site right now.

When I check the Google Search Console page indexing report, I would expect to see that June 11 date instead of a more recent update. The delay is inconvenient, especially when I am trying to understand whether Google has recently found, crawled, or indexed important pages.

This report matters because it shows me which pages Google can find and index on a website. It also helps me spot indexing problems Google may have encountered while crawling the site.

I can access the report in Search Console over here, or I can open Search Console, go to the Indexing section, and then select Pages.

Inside the report, I usually see a chart with indexed pages in green and not indexed pages in gray. I can also overlay impressions on the chart, which makes it easier to connect indexing patterns with search visibility.

Below that chart, Google lists the reasons pages on the website are not being indexed. That section is often where I look first when I need to understand whether the issue is related to crawling, duplication, redirects, noindex signals, canonical choices, or another indexing reason.

For more details about how the page indexing report works, I can refer to Google’s help document.

Why I care: if I am trying to debug why Google has not indexed specific pages over the past couple of weeks, this delay leaves me with limited visibility. Until Google updates the report again, I would need to rely on my own SEO analysis or use the URL inspection tool to investigate indexing issues one page at a time.

The delay is frustrating, but I do not see it as especially uncommon. Search Console reports can lag from time to time, so for now I would treat the page indexing report as stale and avoid making major conclusions from that delayed data alone.

Inspired by this post on Search Engine Land.

July 3, 2026
Does llms.txt Matter for AI SEO? What My Data Shows
I have watched the debate around llms.txt become one of the most polarized conversations in web optimization.

Some people treat llms.txt as essential infrastructure for AI discovery. Others, especially longtime SEO practitioners, see it as speculative theater. Platform tools are starting to flag missing llms.txt files as site issues, yet server logs still show that AI crawlers rarely request them.

Google even appeared to adopt it. Sort of. In December, Google added llms.txt files across many developer and documentation sites.

At first, the signal looked obvious to me: if the company behind the sitemap standard was implementing llms.txt, maybe the file really mattered.

Then Google removed it from its Search developer docs within 24 hours.

Google’s John Mueller said the change came from a sitewide CMS update that many content teams didn’t realize was happening. When asked why the files still exist on other Google properties, Mueller said they aren’t “findable by default because they’re not at the top-level” and “it’s safe to assume they’re there for other purposes,” not discovery.

The llms.txt research

I wanted data, not another debate.

So I tracked llms.txt adoption across 10 sites in finance, B2B SaaS, ecommerce, insurance, and pet care. I looked at the 90 days before implementation and the 90 days after.

I measured AI crawl frequency, traffic from ChatGPT, Claude, Perplexity, and Gemini, and the other changes each site made during the same window.

Here is what I found:
- Two of the 10 sites saw AI traffic increases of 12.5% and 25%, but llms.txt was not the cause.
- Eight sites saw no measurable change.
- One site declined by 19.7%.
The 2 ‘success’ stories weren’t about the file

The Neobank: 25% growth

One digital banking platform implemented llms.txt early in Q3 2025. Ninety days later, its AI traffic was up 25%.

That sounds compelling until I looked at what else happened during the same period.
- The company ran a PR campaign around its banking license and earned coverage in major national publications.
- It restructured product pages with extractable comparison tables for interest rates, fees, and minimums.
- It published 12 new FAQ pages optimized for extraction.
- It rebuilt its resource center with new banking information and concepts.
- It fixed technical SEO issues, including header structure problems.
When a company earns Bloomberg coverage in the same month it launches optimized content and fixes crawl errors, I cannot isolate llms.txt as the growth driver.

The B2B SaaS platform: 12.5% growth

A workflow automation company saw AI traffic jump 12.5% two weeks after implementing llms.txt.

The timing looked perfect. It would be easy to call the case closed. But the surrounding context told a different story.

Three weeks earlier, the company had published 27 downloadable AI templates covering project management frameworks, financial models, and workflow planners. These were functional tools, not ordinary content marketing assets, and they drove the engagement behind the spike.

Google organic traffic to those templates rose 18% during the same period and kept climbing throughout the 90 days I measured.

Search engines and AI models surfaced the templates because they solved real problems and created an entirely new site section. They did not surface them simply because the URLs appeared in an llms.txt file.

The 8 sites where nothing happened after uploading llms.txt

Eight sites saw no measurable change after adding llms.txt. One of them declined by 19.7%.

The decline came from an insurance site that implemented llms.txt in early September. Based on the data, the drop likely had nothing to do with the file.

The same pattern appeared across all traffic channels. Llms.txt did not prevent the decline, and it did not create any visible advantage.

The other seven sites, which included ecommerce brands in pet supplies, home goods, and fashion, plus B2B SaaS, finance, and pet care sites, used llms.txt to document their best existing content. That content included product pages, case studies, API docs, and buying guides.

Ninety days later, nothing changed. Traffic stayed flat. Crawl frequency was identical. The content was already indexed and discoverable, and the file did not change that.

The pattern was clear: sites that launched new, functional content saw gains. Sites that only documented existing content saw no gains.

Why the disconnect?

No major LLM provider has officially committed to parsing llms.txt. Not OpenAI. Not Anthropic. Not Google. Not Meta.

Google’s Mueller put it plainly:
- “None of the AI services have said they’re using llms.txt, and you can tell when you look at your server logs that they don’t even check for it.”
That is the reality I saw in the data. The file exists. The advocacy exists. But platform adoption does not show meaningful use yet.

The token efficiency argument and its limits

The strongest case for llms.txt is efficiency. Markdown can save time and tokens when AI agents parse documentation. It gives agents clean structure instead of forcing them through complex HTML, navigation, ads, and JavaScript.

Vercel says 10% of its signups come from ChatGPT. Its llms.txt includes contextual API descriptions that help agents decide what to fetch.

That matters, but mostly for developer tools and API documentation. If your audience uses AI coding assistants like Cursor or GitHub Copilot to interact with your product, token efficiency can improve integration.

For ecommerce brands selling pet supplies, insurance companies explaining coverage, or B2B SaaS companies targeting nontechnical buyers, token efficiency does not automatically translate into traffic.

llms.txt is a sitemap, not a strategy

The closest comparison I can make is a sitemap.

Sitemaps are useful infrastructure. They help search engines discover and index content more efficiently. But I would not credit traffic growth to simply adding a sitemap. The sitemap documents what exists; the content drives discovery.

Llms.txt works in a similar way. It may help AI models parse a site more efficiently if they choose to use it, but it does not make the content more useful, authoritative, or likely to answer user queries.

In my analysis, the sites that grew did so because they:
- Created functional assets such as downloadable templates, comparison tables, and structured data.
- Earned external visibility through press and backlinks.
- Fixed technical barriers such as crawl and indexing issues.
- Published content optimized for extraction, including FAQs and structured comparisons.
Llms.txt documented those efforts. It did not drive them.

What actually works

The two successful sites showed me what actually matters.
- Create functional, extractable assets. The SaaS platform built 27 downloadable templates that users could deploy immediately. AI models surfaced them because they solved real problems, not because they appeared in a markdown file.
- Structure content for extraction. The neobank rebuilt product pages with comparison tables for interest rates, fees, and account minimums. That is data AI models can pull directly into answers without heavy interpretation.
- Fix technical barriers first. The neobank fixed crawl errors that had blocked content for months. If AI models cannot access your content, no amount of documentation will help.
- Earn external validation. Coverage from Bloomberg and other major publications drove referral traffic, branded searches, and likely influenced how AI models assessed authority.
- Optimize for user intent. Both sites answered specific queries, such as “best project management templates” and “how do [brand] interest rates compare?” Models surface content that maps to what users ask, not content that is merely well documented.
None of this requires llms.txt. All of it can drive results.

Should you implement an llms.txt file?

If you run a developer tool and AI coding assistants are a primary distribution channel, I would implement llms.txt. In that context, token efficiency matters because your audience is already using agents to work with documentation.

For everyone else, I would treat llms.txt like a sitemap: useful infrastructure, not a growth lever.

It is good practice to have. It likely will not hurt. But the hour spent implementing llms.txt is often better spent restructuring product pages with extractable data, publishing functional assets, fixing technical SEO issues, creating FAQ content, or earning press coverage.

Those tactics have shown real ROI in AI discovery. Llms.txt has not, at least not yet.

The lesson I take from this is not that llms.txt is bad. It is that we are reaching for control in a system where the rules are still being written. Llms.txt offers comfort because it is concrete, actionable, and familiar. It looks like the web standards we already understand.

But looking like infrastructure is not the same as functioning like infrastructure.

My focus would stay on what is already working:
- Create useful content.
- Structure it for extraction.
- Make it technically accessible.
- Earn external validation.
Platforms and formats will change. The fundamentals will not.

Inspired by this post on Search Engine Land.
July 3, 2026
How AI Revolutionized My Hreflang XML Sitemap Creation
I’ve witnessed AI tools become indispensable in automating complex processes that traditionally demanded a lot of manual effort. However, I’ve also seen them used without any real benefit just because they are available.

That’s why I prefer focusing on AI applications that save time and address genuine challenges.

Recently, I was tasked with aligning the SEO architecture for over a dozen websites across three separate businesses, eight regional domains, and numerous languages, including three English dialects, Italian, Japanese, Spanish, Thai, French, and Korean.

Mapping thousands of URLs to create seamless hreflang XML sitemaps traditionally required specialized software or extensive spreadsheet work. Instead, I used Google Gemini to develop a custom Python script to handle the heavy lifting.

Here’s how an initial prompt evolved into a fully customized automation tool and what it taught me about utilizing AI for technical SEO.

Where AI Delivers the Most Value

I leverage AI primarily for practical, time-saving tasks, including:
- Generating regex patterns when I need quick solutions without researching syntax from scratch.
- Creating complex spreadsheet formulas for reporting workflows that depend on manual data exports.
- Speeding up research and planning for projects requiring competitive analysis across business lines.
- Building custom automation tools for recurring SEO and data-processing tasks.
The hreflang project I discuss here fits perfectly into the last category.

Mapping hreflang at Scale

The challenge was straightforward: accurately map thousands of URLs across multiple multilingual websites into cohesive hreflang XML sitemaps.

I chose not to tackle this manually. Instead, Google Gemini helped me build a custom Python solution.

Here’s a walkthrough of how the process unfolded.

Phase 1: Asking for an Approach, Not Just a Script

One common pitfall of using generative AI for coding is asking it to sprint before understanding the course. Typing, “Write a Python script to create an hreflang sitemap,” will yield generic code prone to breaking with real-world data.

Instead, I started by asking for an approach. I detailed the scenario: multiple regional domains, organic growth over several years leading to mismatched URL slugs, translated subfolders, and appended revision years.

Gemini suggested a multi-step, data-driven approach:
- Crawl the websites to collect live URLs and their metadata.
- Use Python in Google Colab to process the raw data.
- Run an exact match cluster to group identical slugs.
- Use an advanced semantic AI model (like SentenceTransformers) to fuzzy match translated pages based on their titles and normalized URLs.
Phase 2: Crawling and Data Collection

Following the recommended strategy, I used a crawler to spider all regional websites to generate a unified CSV file with live URLs, status codes, title tags, and H1s. Screaming Frog proved ideal for this task.

The quality of AI output relates directly to the quality of your crawl data, so make sure it’s robust.

An AI script can miss an obvious “exact match” if a target URL is a 404 or a 301 redirect. Before feeding data into the script, filter your CSV to include only indexable content.

Dig deeper: International SEO in 2026: What still works, what no longer does, and why

Phase 3: The Google Colab Sandbox

Google Colab offers a free, cloud-based Jupyter notebook environment for coding, bypassing local installations or environment variable issues. I used Google Drive to access it. The free version sufficed for this project.

After uploading the CSV to Colab, Gemini provided an initial Python script that utilized a domain-mapping routine to assign language codes, clean the URLs, and generate an XML tree. The initial results required refinement.

Phase 4: The Iteration (Where the Real Work Happens)

If you expect AI to produce a flawless script on the first try, you’ll be disappointed. Like an intern, AI requires oversight. The true value lies in iteration.

After running the initial script, several unmatched URLs left orphaned pages rather than grouping them with international counterparts. Here’s how I iteratively guided AI through the complexities of human-managed websites.

The Directory Flattening Problem

The U.S. site had recently reorganized its blog into topical folders, unlike the Mexican and Italian sites. I presented these mismatches to Gemini, leading to a script adjustment that flattened directories, allowing slugs to align.

The Aggressive Semantic Trap

Concept traps we implemented were initially strict. A UK article about manufacturing wouldn’t match its Italian counterpart due to a slightly different title. By loosening these traps for general industries and enforcing them for critical terms, the AI became adept at delivering better matches.

The Translated Slug Epiphany

The pivotal insight arrived when examining Mexican blog orphans. A Spanish URL /detras-de-escenas-historias... matched the English /behind-the-scenes-stories..., which I pointed out to Gemini. As a result, Gemini updated the script to create a “Combined Semantic Signature,” dynamically translating slugs and efficiently bridging language gaps.

Dig deeper: Cultural SEO: A practical framework for Spanish markets in AI search

Lessons from Building an AI-Assisted SEO Tool

This project reinforced a simple truth: AI excels as a collaborator rather than a shortcut.
- Be the strategist, let AI be the coder: Rather than demanding a finished product, discuss architecture and logic first, treating AI as a junior developer needing guidance.
- Provide concrete examples: Don’t simply state, “It’s broken.” Give specific failed URL examples or mismatches to help AI refine its logic.
- Embrace the iterative loop: Run the code, identify issues, and iterate. Each iteration enhances the tool’s intelligence.
- Leverage Google Colab: You don’t need to be a Python guru to apply Python in SEO. Colab bridges the gap, providing access to complex data science libraries in your browser.
In the end, I had a fully customized Python script capable of processing a massive CSV to generate a cross-referenced hreflang XML sitemap in minutes.

Though AI isn’t replacing technical SEOs, those who collaborate with AI to build scalable tools will have a significant edge.

Dig deeper: How AI search defines market relevance beyond hreflang

Inspired by this post on Search Engine Land.
June 16, 2026
Discover the Leading Veterinary SEO Agencies of 2026
Last updated: June 12, 2026

I’ve recently delved into the world of veterinary SEO agencies and analyzed a whopping 73 companies. With a robust scoring system, I’ve ranked each based on eight criteria to ensure the firms making the list are truly top-notch.

The criteria include average review scores, leadership experience, being founder-led, notable clients, years established, average client tenure, and media references. Extra emphasis was placed on reviews from veterinary clientele, signaling relevance and client satisfaction.

After rigorous analysis, I’ve narrowed it down to the top 6 companies, and here’s the detailed ranking:

The Top Veterinary SEO Companies of 2026

1. First Page Sage: Leading the chart with an impressive blend of local SEO and GEO targeting.

2. Beyond Indigo Pets: Known for their holistic digital marketing strategies tailored for vet clinics.

3. LifeLearn: Offers an integrated platform that blends SEO with practice management.

4. True North Social: Focuses on SEO and social media to engage and convert pet owners.

5. Veterinary Marketing: Ideal for budget-conscious practices, offering essential digital marketing packages.

6. UppercutSEO: Renowned for their technical SEO expertise and local search improvements.

Insights on First Page Sage

Ranked first, First Page Sage utilizes a comprehensive thought-leadership SEO strategy. I found their approach to blend SEO with geo-targeting, engaging qualified veterinary leads. Their techniques help transform veterinary practices into authoritative local resources, driving meaningful traffic poised for conversion.

With AI becoming more prevalent in decision-making, they’ve innovated through generative engine optimization, giving clients a visible edge in AI-generated search results.

Highlights:
- Average Review Score: 4.9
- Leadership Experience Score: 4.9
- Founder Led: Yes
- Notable Clients: San Francisco SPCA, Blue Cross Pet Hospital, Lakeview Veterinary Hospital
- Year Established: 2009
- Average Client Tenure: 3.2 years
- Media References: ~820
- Approach to SEO: Local SEO and GEO targeting
Beyond Indigo Pets: A Closer Look

Beyond Indigo Pets tailors marketing strategies for veterinary practices, focusing on seasonal needs and competitive dynamics. While their services cover a wide array of digital marketing aspects, they do not specialize solely in SEO, which may be a consideration for practices in hyper-competitive areas.
Attributes:
- Average Review Score: 4.6
- Leadership Experience Score: 4.5
- Founder Led: Yes
- Notable Clients: Dutt Veterinary Hospital, Switzer Veterinary Clinic
- Year Established: 1997
- Average Client Tenure: 1.9 years
- Media References: ~210
- Approach to SEO: Digital marketing for vet clinics
Exploring LifeLearn

LifeLearn offers a comprehensive suite integrating SEO with practice management, making it an appealing choice for those desiring a one-stop solution. However, if dedicated SEO specialization is your focus, you might explore other firms on this list.

Details:
- Average Review Score: 4.6
- Leadership Experience Score: 4.4
- Founder Led: No
- Notable Clients: N/A
- Year Established: 1994
- Average Client Tenure: 3.0 years
- Media References: ~75
- Approach to SEO: Integrated platform with SEO
Diving into True North Social

True North Social curates content that strikes an emotional chord with pet owners, transforming them into clients through strategic SEO and advertising. They prioritize intimate client engagement, which might limit their capacity for larger veterinary organizations.
- Average Review Score: 4.4
- Leadership Experience Score: 4.5
- Founder Led: Yes
- Notable Clients: N/A
- Year Established: 2016
- Average Client Tenure: 2.4 years
- Media References: ~70
- Approach to SEO: SEO, social media marketing, PPC
Understanding Veterinary Marketing

If your practice operates on a tighter budget, Veterinary Marketing offers essential services to get you started with online growth. While their packages are budget-friendly, you might need additional expertise for advanced SEO strategies.
- Average Review Score: 4.3
- Leadership Experience Score: 4.5
- Founder Led: Yes
- Notable Clients: Ocean Animal Hospital, Garbizo Animal Clinic, CityVAX
- Year Established: 2020
- Average Client Tenure: 2.0 years
- Media References: ~10
- Approach to SEO: Veterinary-specific SEO, PPC, social media
Delving into UppercutSEO

UppercutSEO focuses on technical SEO fundamentals, beneficial for practices needing foundational web optimization. They may not cover veterinary-specific insights that others on this list specialize in, so keep that in mind.
- Average Review Score: 4.4
- Leadership Experience Score: 4.4
- Founder Led: Yes
- Notable Clients: N/A
- Year Established: 2020
- Average Client Tenure: 1.8 years
- Media References: ~95
- Approach to SEO: Technical SEO and local search
The Best Veterinary SEO Companies by Specialty

Our in-depth analysis also classified top veterinary SEO agencies into three key specialties reflecting unique client needs: content marketing, local search optimization, and technical implementation.
Top Companies for Content Marketing
1. First Page Sage
2. Beyond Indigo Pets
3. Veterinary Marketing
4. LifeLearn
5. True North Social
Leading Firms for Local Search Optimization
1. First Page Sage
2. UppercutSEO
3. LifeLearn
4. True North Social
5. Beyond Indigo Pets
Top Choices for Technical SEO
1. UppercutSEO
2. First Page Sage
3. Beyond Indigo Pets
4. LifeLearn
5. Veterinary Marketing
For more details, visit our source.

Inspired by this post on First Page Sage Blog.
June 12, 2026
Mastering SEO Fixes: Predict Traffic Impact with Confidence

Hey there! If you’re anything like me, your backlog is overflowing, your developer is eager to know what to tackle first, and your boss is questioning why months of SEO work haven’t shown results. I’ve been stuck defending my roadmap with gut feelings, and it’s tough.

Without estimating the traffic impact of a fix before it’s live, it’s just a guess—and we both know guesses don’t cut it in budget meetings.

Let me share a framework I use to transform messy data into reliable estimates. It’s not perfect, but it’s solid enough to prioritize with confidence and explain my strategy in any meeting.

Why every recommendation can’t be high priority

I’ve seen teams spend sprints on minor schema issues, ignoring a bigger problem—like a title tag bug affecting thousands of pages. Both were marked as “high priority,” but the traffic impact of one was negligible compared to the other.

Traffic guides true priority. While we can’t neglect brand visibility or UX, traffic offers a universal measure to compare efforts. Without quantified impact, you’re letting the loudest voice, or the most tempting technical puzzle, dictate your roadmap instead of focusing on what truly drives business value.

Plus, SERP landscapes have changed drastically. According to SparkToro, 68% of U.S. Google searches this year ended without a click, up significantly since just two years ago.

With AI Overviews intercepting traffic, the impact of a ranking improvement can vary wildly by SERP layout. Jumping to position three on a commercial keyword might be gold, but on an informational query dominated by AI? Not necessarily.

Your forecasts should account for these dynamics to avoid overpromising.

Step 1: Define the scope

Before making any estimates, I always define the scope. Is the adjustment sitewide, a template fix, or a single-page optimization? Each scenario changes the math.

Sitewide technical fixes

These encompass site speed, mobile usability, HTTPS migrations, and Core Web Vitals. They influence every page, but not uniformly. Address areas with pages on the borderline of failing tests first.

Template-level changes

Fixes like rewriting title tags can have a major impact, but it’s vital to focus where traffic truly exists. Product templates might garner the majority of clicks, while blogs might trail behind.

Individual page optimizations

Actions like updating meta descriptions can provide quick wins, but their small scale might not significantly impact the business. Focus on these without losing sight of larger opportunities.

Step 2: Calculate your current traffic exposure

To gauge traffic exposure, I turn to Google Search Console to pull essential data.

Organic clicks serve as a baseline. By filtering affected URLs and reviewing trends, I assess urgency and context.

Impressions and near-win rankings pinpoint real potential. Pages ranked 8-15 are ripe for improvements—push them higher for a CTR boost.

SERP features can greatly influence CTR. Using Search Console’s AI Mode data, I check for AI Overview dominance and adjust expectations.

Step 3: Estimate potential lift

Now, it’s time for educated estimation.

Your own history

When I’ve optimized similar pages before, I use those outcomes as future baselines. Keeping track of past projects builds a valuable benchmarking library.

Competitor benchmarks and SERP analysis

Review competitors and pinpoint their advantages, whether it’s content depth, UX, or backlinks. Aiming to close these gaps can justify a ranking gain.

AI-influenced CTR assumptions

Forecasting can falter without updated CTR assumptions. Seer’s research shows drastic CTR changes due to AI integration. Staying aware of these shifts is essential.

Step 4: Build three scenarios, not one number

One definitive forecast can be deceptive. I prefer building three—conservative, expected, and aggressive—to provide a range that reflects real possibilities.

In the conservative model, expect partial implementations and competition improvements. With the expected model, rely on solid historical benchmarks. The aggressive model accounts for perfect execution and fast indexing.

This comprehensive view guides stakeholders through potential outcomes, ensuring transparency and credibility.

Step 5: Use the forecast to build your roadmap

After forecasting, I compare traffic impact predictions to effort levels using frameworks like RICE. This demonstrates which initiatives offer the most value for the effort and helps align priorities with business goals.

A well-organized roadmap doesn’t just appeal to me but speaks clearly to everyone involved, highlighting efficiency and business impact.

Inspired by this post on Search Engine Land.

June 11, 2026
Unlocking Hidden SEO Insights Through Server Logs

I’ve discovered that server logs hold a treasure trove of information for large websites, often uncovering technical SEO issues before they impact rankings. They offer insights into how search engines interact with our site, where we might be wasting crawl budget, server response times, and the accessibility of critical pages.

Unlike Google Search Console or third-party SEO tools, server logs capture every single request made by search engines to our infrastructure. It’s surprising how many organizations overlook analyzing them, thus missing out on valuable technical SEO data.

SEO teams often place their trust in tools like Google Search Console, Bing Webmaster Tools, and various third-party crawlers, which rely on data samples, delayed reporting, or simulated crawls. Server logs, however, document direct interactions between crawlers and our infrastructure, which is crucial for websites with a vast number of URLs.

Logs record every server request, and when used for SEO purposes, the most revealing entries come from search engine bots like Googlebot and Bingbot. These records create a detailed history of how our site gets crawled over time.

Most technical SEO problems start as crawl inefficiencies. I’ve seen scenarios where search engines request a page but receive unexpected responses, or they follow complex redirect chains, contributing to delays and inefficiencies.

Server logs clearly expose these inefficiencies. For instance, on large ecommerce platforms, logs might show that crawl resources are wasted on parameterized URLs, while important product pages are overlooked.

Retaining logs over time provides historical visibility into trends related to migrations, infrastructure changes, and platform redesigns. This ongoing visibility is something Google Search Console does not offer.

For instance, large sites often compete internally for crawl attention, and search engines don’t treat all pages equally. Logs can reveal if our valuable category pages are getting the right amount of attention or if outdated URL structures are still consuming resources.

Without these logs, many crawl inefficiencies might remain hidden. The crawl data in logs also assists us in understanding which sections of our site need optimization for better crawl efficiency and response timing, influencing SEO and even our infrastructure.

It’s amazing how log file analysis can differentiate between temporary issues and persistent infrastructure problems, helping us focus our efforts where it truly matters.

Having extensive log data enables us to monitor site migrations effectively, understanding crawler behavior pre- and post-deployment to ensure a smooth transition.

Operating without retaining server logs is like flying blind. Logs bridge the gap that many SEO tools cannot fill, providing a comprehensive view of crawler behavior and interactions with our web infrastructure.

Inspired by this post on Search Engine Land.

June 9, 2026
AI Adventures: When Confidence Meets Costly Errors

Have you ever found yourself immersed in the SEO world, only to be told by an AI that everything you know is wrong? That’s exactly what happened to me, and not just once, but three times in a single week with Gemini.

It’s not the mistakes that rattled me—it was how credible they sounded. The answers from Gemini were polished and convincing, enough so that most would accept them without question.

When it comes to topics you’re not deeply versed in, how do you even begin to challenge such confident wrongness?

Laughably, I caught two, but the third one hit me where it hurts—my wallet. All this unfolded within a week.

Here’s a closer look at what went down.

In one scenario, Gemini misguidedly walked me through technical SEO for a client. During a site migration task on Shopify, where canonical tags were misbehaving, I turned to Gemini for solutions.

The advice was not just misleading but used terms that would raise red flags with leadership—talk about penalties!

Semantic clarity is crucial here; an internal misstep with jargon can make stakeholders halt essential projects.

Gemini further compounded the issue with incorrect guidance on URL parameters hosting.

The experience echoes another incident where Gemini’s mechanical advice almost led me to make a $3,000 error on my Jeep SRT. The AI’s confident proclamation of a rear differential issue had me nearly misappropriating my resources.

After sharing more data, Gemini pivoted, claiming it had leapt to conclusions without sufficient evidence.

In yet another amusing episode, my Madden game finance strategy, courtesy of Gemini, resulted in a fictional $20 million oversight. Although the stakes were virtual, it was a stark reminder of why critical thinking remains indispensable.

These anecdotes underline that it’s not AI replacing experts but rather pushing out those who stop questioning.

The real skill remains in smelling the bull and asking deeper, more insightful questions.

Inspired by this post on Search Engine Land.

June 4, 2026
Boost Your SEO: Harness Schema Markup for the Agentic Web

I’ve discovered that AI agents heavily rely on structured data to understand and interact with my content. Embracing schema markup is essential to thriving in the emerging agentic web.

Schema markup has become pivotal in SEO and Generative Engine Optimization (GEO) conversations. I learned that both Google and Bing utilize structured data to fuel AI overviews, and platforms like ChatGPT incorporate it for product suggestions.

The evolution towards the agentic web means AI systems interact directly with websites on our behalf. It’s not just about understanding content; they need schema markup to interpret and act on it. This makes it clear why schema is becoming an integral part of the agentic web’s infrastructure.

In the traditional search landscape, schema markup enhances visibility by making my content eligible for search engine results page (SERP) features. It aids search engines in understanding entities better, thereby influencing how results are presented to users.

AI agents go beyond by leveraging schema markup to understand relationships and relevance. They assess if content is actionable enough to be recommended or used for task completion. This knowledge helps them determine if my content is trustworthy.

With structured data, my website becomes easier and cheaper for AI systems to process. Parsing unstructured HTML is more costly compared to clean, structured data, especially as large language models (LLMs) work within finite context windows and escalating inference costs.

Sites that simplify content interpretation are more attractive to AI agents as these systems expand. This simplification becomes critical for ensuring my content is accessed and utilized effectively.

I understand that NLWeb, built on schema markup, plays a vital role in the agentic web’s infrastructure. Microsoft’s open-source initiative, NLWeb, enables websites to integrate AI-powered conversational interfaces, transforming them into AI apps for natural language queries.

Developed by R.V. Guha, NLWeb connects with my existing schema markup, leveraging structured formats like Schema.org. This allows both humans and AI agents to interact seamlessly with the web.

Incorporating structured data like RSS with NLWeb ensures a real-time, interactive experience for AI agents, making my site truly ‘agentic’. The transition from humans browsing to AI agents querying underlines the significance of these initiatives.

For someone like me aiming to optimize for the agentic web, schema markup is a game-changer. It enables my site to be more than just readable, allowing for direct, real-time interactions through NLWeb’s capabilities.

NLWeb uses AI tools to create natural language interfaces, enhancing how my content can be queried and interacted with. It doesn’t require a complete rebuild of my existing content structure, just good order in my schema markup.

By prioritizing completeness, automating processes where possible, and utilizing JSON-LD, I can make steady progress in schema optimization. It’s crucial that I view schema as a comprehensive graph across my site, improving reliability and trust for AI agents.

Ultimately, adopting schema markup and understanding its evolving role in the agentic web is vital. As AI systems evolve, content that aligns with their preferences will reap ongoing benefits.

Inspired by this post on Search Engine Land.

June 1, 2026