How SEO changes by industry
Every industry I've worked in runs on the same system. Someone has a decision to make, and my job is to make sure they find the right page, understand it and choose it. Every industry searches differently. The same SEO system runs underneath all of them, but who is searching, what they're afraid of and what makes them choose changes everything about what I build first. Here's how I think about the ones I've worked in across my career, and how much thinking goes into each one before anything ships.
One system under every industry
Every industry I've worked in runs on the same system. Someone has a decision to make, and my job is to make sure they find the right page, understand it and choose it.
- Get found
Search engines and AI tools can reach the page, read it and show it for the right question.
- Get understood
The page answers the question in the reader's words, and machines can tell exactly what it's about.
- Get chosen
The reader trusts it enough to act, and the next step sits right where the decision is made.
What changes from one industry to the next is who that person is, what they're worried about and what makes them trust a page enough to act. So each industry below goes in the same order: the big idea, then the method, then the tools and the judgment calls behind them, then proof from my own sites, month over month.
E-commerce
The money is in category and product pages, and the questions shoppers ask before they buy decide whether those pages win.
- Who searches
- Shoppers comparing options, often on a phone, often with another tab open.
- What I build first
- The product and category templates, and the questions people ask right before buying: sizing, shipping, returns, which one to pick.
- The mistake I avoid
- Thousands of thin product pages that all say the same thing, so none of them stands out as the answer.
The method, step by step
Fix the template, not the page. One product page template carries thousands of products, so a change to what it shows (specs, sizing, shipping, real photos, reviews) improves every product at once.
Earn trust on the page itself: who is selling, the return policy, real reviews and the details a careful shopper checks before paying. Credibility is what turns a visit into a sale.
Build category pages that help someone choose, with a short answer to which one is right for them above the grid, so the category ranks for comparison searches and not only the product name.
Put the buy link next to the answer, never before it. The shopper who just settled the decision is the one ready to click.
Measure by page type, month over month, so I know which template change moved the numbers.
The tools, and how I use them here
- Screaming Frog
Crawl every product and category page and group the thin, duplicate and broken ones by template, so the fix lands once.
- Google Search Console
Export queries by page type and sort out the questions people ask before buying. AI speeds up grouping thousands of them into themes; I decide which themes deserve a page.
Proof from my own sites
My own reference sites follow the same rule: the product link sits beside the answer, after the decision is made, never in a popup or ahead of it.
Local and multi-location
Every location is its own search result, the map pack and reviews often matter more than the website, and a brand with many locations has to win city by city and place by place at the same time.
- Who searches
- Someone nearby who wants a place, a time or a phone number, usually right now.
- What I build first
- Two templates done right: a market page for each city and a location page for each place, with the same business details everywhere and the profile that shows in maps.
- The mistake I avoid
- Copy-paste city pages with the town name swapped in. Searchers and Google both see through them.
The method, step by step
Split the work into two templates: market pages that answer what's near me in this city, and location pages that answer what's at this one place, when, and how to get there.
Give every location page something only that location has: hours, parking, local details and real photos. A page with nothing unique doesn't get built.
Keep the name, address, phone and hours identical across the website, the map profiles and the directories, checked on a schedule instead of when someone notices.
Improve the template once and every market and location page gets better together, instead of fixing pages one at a time.
Measure by market and by location, so one strong city can't hide five weak ones.
The tools, and how I use them here
- Google Search Console
Filtered by the location folder and by city searches, so each market gets its own trend line.
- Screaming Frog
Every location page crawled for missing hours, duplicate titles and pages no menu links to.
Proof from my own sites
I don't run a local business site of my own, so the proof here is the method itself, written out: the three pillars a local plan stands on.
Legal
Accuracy and trust carry more weight than almost anywhere else, and every claim gets reviewed before it's published.
- Who searches
- Someone worried, sometimes in a hard moment, looking for a clear answer and someone to trust.
- What I build first
- Plain-language answers to the questions people actually ask, reviewed for accuracy, with the credentials behind them visible.
- The mistake I avoid
- Writing for other lawyers instead of for the person who's scared and searching at night.
The method, step by step
Collect the questions people really ask, in their own words, from search data, People Also Ask and the forums where they ask each other.
Answer each one in plain language in the first few sentences, then go deeper for the reader who needs more.
Give every claim a source and a review by a qualified professional before it ships, and show who reviewed it on the page.
Re-check pages on a schedule, because rules change and a stale answer is a wrong answer.
The tools, and how I use them here
- Google Search Console
Question-shaped searches grouped by topic. AI speeds up the grouping; I decide which questions deserve their own page.
- Peec AI
Which sources AI answers cite for these questions, and whether someone else's explainer is the one being quoted.
Proof from my own sites
WireRef answers electrical code questions, where a wrong number can hurt someone. Same rule: a primary source behind every value, checked before it ships.
Search clicks up 2% in September, a new best month, and the fifth month in a row it grew.
| Month | search clicks | Change from the month before |
|---|---|---|
| April | 655 | first month |
| May | 1.1K | +69% |
| June | 1.3K | +21% |
| July | 1.7K | +31% |
| August | 2.8K | +59% |
| September | 2.8K | +2% |
Real estate and housing
Demand splits into themes like affordability, location and quality, and each theme plays out differently in a single town, a region and the country as a whole.
- Who searches
- People making the biggest purchase of their lives, comparing places, prices and builders over months.
- What I build first
- A map of the themes people search, measured at each level, with branded and non-branded searches kept apart.
- The mistake I avoid
- Reporting one blended number. Branded searches hide how you're doing with everyone who hasn't heard of you yet.
The method, step by step
Split demand into a few themes, affordability being the big one, and give each theme its own pages and its own measures.
Measure each theme at three levels, local, regional and national, because an affordable home means something different in each.
Separate branded from non-branded searches in every report. Non-branded is where new buyers find you, and where the growth is.
Write a set of prompts for each theme that a real buyer would ask an AI assistant, including ones that name competitors, to see who gets recommended and why.
Find the questions nobody answers well, and build content only we can write: real local costs, how payments compare, what's included.
The tools, and how I use them here
- Peec AI
Share of voice, citation share and sentiment for each theme's prompt set, with competitors side by side.
- Google Search Console
A branded and non-branded split with a filter, then each theme's searches grouped by market.
Proof from my own sites
On my own sites I never report one blended number either. Google and Bing are measured separately, site by site, every month.
Search clicks up 33% in September, a new best month, and the fifth month in a row it grew.
| Month | search clicks | Change from the month before |
|---|---|---|
| April | 1.2K | first month |
| May | 3K | +158% |
| June | 5.1K | +71% |
| July | 10.8K | +112% |
| August | 15K | +39% |
| September | 20K | +33% |
Publishing and reference sites
There's nothing to sell on the page itself; the whole value is being the most accurate and complete source.
- Who searches
- Fans and professionals who want one exact answer, fast, and will come back if it's right.
- What I build first
- The single question each page answers, kept current, with the sources visible.
- The mistake I avoid
- Publishing fast and fixing later. One wrong fact loses a reader for good.
The method, step by step
Pick the one question each page answers before a word gets written.
Use primary sources only, and show them on the page with their dates.
Build the page for a question before it peaks, like a release date nobody has announced yet.
Keep pages current on a schedule, and log every correction in public.
The tools, and how I use them here
- Google Search Console
Daily exports to spot a page climbing before it peaks.
- Bing Webmaster Tools
Bing clicks and AI citations, which often move first on a new reference site.
Proof from my own sites
The Crawler's Cookbook, my fan site, built a page for the next book before it had a release date.
Google clicks up 47% in September, a new best month, and the fifth month in a row it grew.
| Month | Google clicks | Change from the month before |
|---|---|---|
| April | 42 | first month |
| May | 491 | +1,069% |
| June | 1.2K | +153% |
| July | 5.7K | +357% |
| August | 9.3K | +64% |
| September | 13.8K | +47% |
The toolkit, and the call I make after each tool
No single tool gets the last word. Each one answers one question, and then I make the call.
Where each tool sits in my system: the outcome it serves (get found, get understood, get chosen) and the pillars of how I work. The full map is in my SEO system.
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Peec AI
How often do AI answers mention us, which pages do they cite, and how do they describe us?
A fixed set of prompts for each theme, run on a schedule: share of voice, citation share and sentiment, with competitors side by side.
My call: I read the actual answers, not just the scores, and look for questions where nobody gives a good answer. That's where original content wins.
Serves: Get found, Get understood. Pillars: Be the source worth citing, Measure, then double down.
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Ahrefs Brand Radar
Where does the brand get cited and mentioned across AI answers, compared with competitors?
Citation and share of voice trends across far more prompts than I'd write by hand.
My call: Its numbers come from sampled prompts, so I check them against first-party data before they go in a report.
Serves: Get found, Get chosen. Pillars: Be the source worth citing, Measure, then double down.
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Bing Webmaster Tools
What does first-party data say about AI citations and Bing search?
Bing's AI performance report counts real citations in Copilot answers, and Bing clicks often move before Google's on a new site.
My call: It's my check on the sampled tools. When the sources disagree, I find out why before I report either one.
Serves: Get found. Pillars: Never ship a wrong fact, Measure, then double down.
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Google Search Console
What do people actually search, and which pages are they finding?
Bulk exports with far more rows than the interface shows. AI helps me sort a mountain of queries into themes per pillar, which would take weeks by hand.
My call: I set the themes and decide which gaps are worth a page. A gap with no real person behind it doesn't get built.
Serves: Get found, Get understood. Pillars: Start from the person, Measure, then double down.
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Google Analytics 4
Did the visit turn into the action that matters?
Key events and revenue by landing page and channel, including visits from AI assistants.
My call: Every visibility report ties back to an outcome the business cares about, and I flag the channels I don't trust, like Direct traffic that looks like bots.
Serves: Get chosen. Pillars: Measure, then double down.
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Screaming Frog
Can a search engine reach and read every page?
Full crawls for broken links, redirect chains, missing tags, duplicate titles and orphan pages, grouped by template.
My call: I decide what's a real problem and what's noise, and the fix goes into the template so it stays fixed.
Serves: Get found. Pillars: Never ship a wrong fact, Turn every lesson into a system.
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Technical audits, by hand and with AI
What's actually broken, and what matters most?
A crawl, the indexing reports and a manual walk through the site on a phone, the way a reader sees it. AI speeds up sorting thousands of rows; the walk-through catches what no export shows.
My call: I rank fixes by what they unblock for the reader and the crawler, and every fix becomes a rule or a check so it doesn't come back.
Serves: Get found, Get understood. Pillars: Never ship a wrong fact, Turn every lesson into a system.
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Microsoft Clarity
What do people do once they're on the page?
Heatmaps and session recordings on the pages that matter most.
My call: I watch for where people stop or tap something that isn't a link, then change the page, not the report.
Serves: Get understood, Get chosen. Pillars: Start from the person, Tell it as a story.
What never changes
What stays the same in every industry: start from the person and the question they typed, build the page that answers it best, and measure whether they found it, understood it and chose it.
The same method on my own sites, month by month or back to how I work at my full-time job. I've also written about industry SEO.