When you hear the term “contact page,” you probably think of a simple page containing contact info and maybe a form.
I’m here to tell you why that’s a big miss from a local SEO perspective and show you how to build a contact page that builds your prominence with Google and helps you convert more leads.
Google pays special attention to your contact page
The former head of Google Business Profile Support, Joel Headley, once told me that Google specifically crawls and parses your contact page to gather information about your business.
This led me to realize that most businesses have awful contact pages. They list their name, address, and phone number (NAP), embed a contact form, and call it a day.
Google is saying, “Give me data about your business,” and you’re saying, “No data for you.”
What you need to do instead is give your contact page the same level of care and attention as a multi-location landing page.
Here are the must-haves for a contact page that converts site visitors into paying customers:
Business identity.
Contact information.
Trust factors and social proof.
Location-specific content.
Amenities.
Call to action.
1. Business identity
Just like every other page on your site, your contact page should reflect your brand. This means you should include:
Your business logo (that matches all your other marketing materials and real-world signage).
Your slogan (bonus points if you can work some keywords into it for added SEO value).
A short introduction that explains what your business does, where it’s located, and what your unique value proposition (UVP) is.
A common mistake businesses make is embedding a map of their business address on Google Maps instead of their actual Google Business Profile.
Make sure you embed a map in your business listing on Maps so that whenever someone clicks it, they send engagement signals to your profile. Practically, this means:
Search for your business name on Google Maps.
Bring up your profile.
Click the Share button.
Click the Embed a map tab.
Copy and paste the code into your contact page.
A link to your Google Maps listing.
A few years ago, Holly Starks conducted a case study to test whether driving directions affected local rankings. She set up Google Maps driving directions on 100 cell phones, put them in her car, and drove to the business. The results were dramatic. The business’s rankings jumped from the 20s to number 1.
In the past, I recommended writing driving and walking directions on your contact page. Now, with Starks’ findings in mind, adding a link to your Google Maps listing with anchor text like “Get driving directions” is even better. It encourages people to use Google Maps driving directions and can increase engagement signals to your Business Profile.
Accepted payments.
Parking details.
Including detailed business information helps customers contact and visit you and signals to traditional search engines and AI search tools that your business is legitimate and credible.
Bonus tips for your contact form:
Add a compelling call to action (you can use the same CTA throughout your page).
Set up form conversion tracking.
Avoid spam by including reCAPTCHA, using a plugin, requiring double opt-in, and formatting your email address so bots can’t read it (e.g., hello (at) domain (dot) com).
Make sure your contact section matches your Google Business Profile as a signal of legitimacy.
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3. Trust factors and social proof
Your contact page shouldn’t just tell people how to reach you. It should prove they’re making the right decision before they ever click or call.
Clear expectations
Be clear about what a customer can expect once they reach out to you and confirm they’ve made the right choice in contacting you:
How long are response times? 24 hours? 2 business days?
What are the next steps? What can they expect from your team?
Is there any useful information you can give them about your team, your location, or anything else that sets you apart from your competitors?
Experience and credentials
Reinforce trust and increase your page’s conversion rate by listing any:
Industry associations you’re a member of (locally and nationally).
Local chamber of commerce groups.
Professional groups and associations.
Meetup groups.
Neighborhood associations.
Better Business Bureau rating.
Tip: Link each association name to your business’s profile on its website.
Include any awards your business has received or mentions in the press, and link each one to the relevant article or website. If you’ve been mentioned frequently in the press, you can create a dedicated media section on the page.
Reviews and testimonials
Embed reviews from other sites and include testimonials on your contact page to build trust. You can increase reviewers’ credibility by including their photos, names, cities, and a link to their websites or directly to the review platform they used.
Be sure to include your overall review rating and total number of reviews.
Remember, customers don’t expect your business to have a perfect 5-star rating. A rating around 4.7-4.9 signals you’re a real business, not one that’s purchased all its reviews.
Customer reviews not only build trust and increase conversions, they also add unique, locally relevant content to the page, which is great for traditional and AI search performance.
Tip: This section is also great for requesting reviews, since repeat customers might visit your contact page. Add a Google review request link with a call to action to generate more reviews for your Google Business Profile.
Create content that references local information and explains exactly what your business does, where it’s located, and why prospects should choose you.
Here are some ideas for local content:
Include photos and descriptions of your team members.
Tell visitors about the customers you serve and your areas of expertise.
If you’re located in a popular neighborhood or area, mention that in your content.
Highlight any customer satisfaction guarantees or price-match policies.
Mention any upcoming events, volunteer efforts, or relevant partnerships.
Start by reviewing your Google Business Profile’s attributes section and consider listing those attributes on your contact page, such as whether the business is family- or women-owned, neurodivergent-friendly, or offers outdoor seating or home delivery.
Then list any other attributes your business has that Google doesn’t provide as options. Detailed business attributes help search engines, LLMs, and customers understand that you meet specific needs.
This can be especially useful for AI search, where people use more conversational queries, such as “Give me a list of cafes in Seattle that are wheelchair accessible and have free WiFi.”
6. A clear CTA button
If you’re going to do all this work to make a killer contact page, don’t forget to put the cherry on top. Sprinkle strategically placed calls to action throughout the page to encourage visitors to contact you. Make them bright, animated, eye-catching, and convincing.
Treat your contact page like a local SEO asset
If you want a contact page that helps people reach out to you, informs search engines and LLMs about your business, and converts visitors into customers, treat it like a multi-location landing page. Save this list so you remember every section your contact page needs.
Do this, and your contact page will outperform 99% of your competitors’ contact pages, because most businesses do a terrible job with them.
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How often do you review your PPC ad copy? Not just analyzing the performance of each asset within the ad platform, but also reviewing your ads in the context of how they appear next to competitor ads?
Are you using the exact same messaging as your competitors? Does your offer stand out from theirs? Which ads are bland and generic, and which provide concrete calls to action and compelling selling points?
Let’s walk through several tips for writing paid search copy that stands out in search results and converts customers for your brand.
1. Think about how assets will appear together, not just individually
When you’re writing Responsive Search Ads, it’s easy to fall into the trap of simply filling in all 15 headline options and all four descriptions.
However, if each headline essentially says the same thing with slightly different wording, your ad copy will appear bland and repetitive in the SERP when two or three headlines are shown together.
For instance, if this example ad showed the following, it would be less helpful:
“Project Management Software – Trusted by 3 Million Users”
If you want to test multiple headlines with slightly different wording, pin them to the same position so the ad platform can rotate between them, but not show both at the same time. Zoho appears to be doing this by using both “Preferred by 3 Million Users” and “Trusted by 3 Million Users” as options.
The visibility of the ad strength rating looms over every Google Ads account. Don’t let chasing an Excellent score consume your focus.
Focus more on making sure each headline and description speaks accurately to your benefit points than on including the maximum number of each. Pinning may negatively impact ad strength, but as discussed above, it can help make your messaging cleaner.
3. Use AI as a partner, but don’t blindly outsource all your copy to AI
Google and Microsoft make ad writing easy, generating text for all your ad assets with a single click. Your LLM of choice can also spin out halfway acceptable copy with the right prompt.
These tools can provide a helpful starting point, but they shouldn’t be the final result you use without careful review. Don’t skip the human touch when reviewing the copy you get back.
Problems can range from copy that doesn’t reflect your brand voice to flat-out inaccuracies. In industries such as finance and healthcare, where legal guidelines matter, AI-generated copy may not be compliance-friendly.
It’s not enough to claim that you’re the “Best Local Contractor” in your area. Think of concrete ways to reinforce superlative statements like this.
For instance, “Voted Best Local Contractor by [News Outlet]” provides a tangible source for the claim. Mention awards or rankings from organizations your prospective customers are likely to recognize.
Incorporating numbers, where possible, also helps bring credibility to your messaging claims.
Years in business. If you’ve been around a long time, stating this positions you well against newer players in the market.
Number of customers served.
Number of locations for physical businesses.
Number of connectors for a software product.
Number of active users.
Number of trips booked.
Number of properties managed.
One word of caution: If you include numbers that are likely to change over time, such as how many customers you serve, revisit them periodically and update them for accuracy. Ranges are fine, too, for example, “Over 500 Locations.”
5. Highlight ease of effort
In today’s busy culture, saving time and hassle can be one of your biggest selling points. Think about where the product or service you’re promoting can reduce effort for your target audience.
Open an account in 10 minutes.
Complete your application online.
Schedule a same-day appointment.
Conduct your consultation remotely.
Repairs done while you wait.
Make sure you can back up what you promise here, and consider whether current customer reviews reflect the experience your claims describe.
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6. Offer a ‘free’ hook
Just like free samples at Trader Joe’s, mentions of “free” in ad copy immediately draw a user’s attention. What can you offer as a free entry point for potential customers?
Free demo.
Free trial.
Bonus for new customers.
Free college application.
Free quote.
Free content, such as ebooks, whitepapers, or webinars.
Whether it’s a trial of a software product or a free visit to your home to assess what’s needed for pest control, this type of offer can be what convinces prospects to fill out a form and enter your sales funnel.
For instance, Strayer University highlights, “Pass 3 Bachelor’s Courses, Earn 1 Tuition Free.” In an age of skyrocketing college costs, that’s an attractive reason to click and learn more.
7. Turn off automated assets
If you’re not careful with your account settings, Google and Microsoft can automatically generate assets, from ad copy to sitelinks, without your review. That can create concerns for compliance and for overall messaging accuracy.
Make sure you turn off this option at the account level to avoid issues with unwanted copy or unexpected links to irrelevant pages.
8. Highlight pricing where it makes sense for your brand
When people are comparison shopping, they usually want quick visibility into cost. Of course, providing pricing may be more or less straightforward depending on your business, and price isn’t always a primary selling point for every brand.
If you’re in an industry where showing a cost is simple, including it in your ad copy can help. When your pricing is competitive, mentioning it helps you stand out.
If your pricing is higher than most competitors, showing that cost may help filter out people you don’t want clicking your ads. For example, lower-priced competitors may cater to small businesses, while your company serves enterprise-level organizations that need more robust solutions.
If you offer multiple price tiers or clearly defined costs for different services, consider using price assets to highlight them. For example, you might break out cost by number of users for a SaaS product.
9. Mention locations in regional campaigns
If your business serves a particular region, mention locations in your ad copy to create a local connection.
For example, if you just opened a new store in Buckwheat County, including “Now Open in Buckwheat County” can help appeal to users in that area. Your ad will likely stand out against national brands running generic messaging.
You can set up ad groups based on regional keywords and tweak your headlines to reference those locations. Also consider using location insertion to dynamically include regions in your copy.
Now that we’ve covered ways to improve your paid search copy, take a moment to review your current ads.
Where can you better think through how assets combine?
What value propositions aren’t you mentioning yet?
How can you tailor your wording more directly to customers’ concerns, such as by highlighting pricing or regions?
Start creating new copy variants and testing them to improve your PPC performance.
Your ad doesn’t compete in isolation — it competes in the SERP
Paid search success isn’t about filling every field or chasing an Excellent ad strength score. It’s about how your messaging appears next to competitors in the SERP.
Review your ads in context. Look at how assets combine. Strengthen value propositions, highlight what makes you different, and test new variations.
If your ad sounds like everyone else’s, it won’t stand out. Make sure it does.
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Most SEO professionals give Google too much credit. We assume Google understands content the way we do — that it reads our pages, grasps nuance, evaluates expertise, and rewards quality in some deeply intelligent way. The DOJ antitrust trial told a different story.
Under oath, Google VP of Search Pandu Nayak described a first-stage retrieval system built on inverted indexes and postings lists, traditional information retrieval methods that predate modern AI by decades. Court exhibits from the remedies phase reference “Okapi BM25,” the canonical lexical retrieval algorithm that Google’s system evolved from. The first gate your content has to pass through isn’t a neural network. It’s word matching.
Google does deploy more advanced AI further down the pipeline, including BERT-based models, dense vector embeddings, and entity understanding systems. But those operate only on the much smaller candidate set traditional retrieval produces. We’ll walk through where each technology enters the process.
This matters for content optimization tools like Surfer SEO, Clearscope, and MarketMuse. Their core methodology — a mix of TF-IDF analysis, topic modeling, and entity evaluation — maps directly to how that first retrieval stage scores documents. The tools are built on the right foundation. The problem is that most people use them incorrectly, and the studies backing them have real limitations.
Below, I’ll explain how first-stage retrieval works and why it still matters, what the research on content scoring tools actually shows — and doesn’t show — and most importantly, how to use these tools to produce content that earns its way into the candidate set without wasting time chasing a perfect score.
How first-stage retrieval works and why content tools map to it
Best Matching 25 (BM25) is the retrieval function most commonly associated with Google’s first-stage system.
Nayak’s testimony described the mechanics it formalizes: an inverted index that walks postings lists and scores topicality across hundreds of billions of indexed pages, narrowing the field to tens of thousands of candidates in milliseconds.
Here’s what matters for content creators:
Term frequency with saturation: The first mention of a relevant term captures roughly 45% of the maximum possible score for that term. Three mentions get you to about 71%. Going from three to thirty adds almost nothing. Repetition has steep diminishing returns.
Inverse document frequency: Rare, specific terms carry more scoring weight than common ones. “Pronation” is worth roughly 2.5 times more than “shoes” in a running shoe query because fewer pages contain it.
Document length normalization: Longer documents get penalized for the same raw term count. All of these scoring algorithms are essentially looking at some degree of density relative to word count, which is why every content tool measures it.
The zero-score cliff: If a term doesn’t appear in your document at all, your score for that term is exactly zero. Not low. Zero. You’re invisible for every query containing it.
That last point is the single most important reason content optimization tools have value. If you write a comprehensive rhinoplasty article but never mention “recovery time,” you score zero for that entire cluster of queries, regardless of how good the rest of your content is.
Google has systems like synonym expansion and Neural Matching — RankEmbed — that can supplement lexical retrieval and surface additional documents. But counting on those systems to rescue a page with vocabulary gaps is a risky strategy when you can simply cover the term.
After first-stage retrieval, the pipeline gets progressively more expensive and more sophisticated. RankEmbed adds candidates keyword matching missed. Mustang applies roughly 100+ signals, including topicality, quality scores, and NavBoost — accumulated click data over 13 months, described by Nayak as “one of the strongest” ranking signals.
DeepRank applies BERT-based language understanding to only the final 20 to 30 results because these models are too expensive to run at scale. The practical implication is clear: no amount of authority or engagement signals helps if your page never passes the first gate. Content optimization tools help you get through it. What happens after is a different problem.
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What the research on content tools actually shows
Three major studies have examined whether content tool scores correlate with rankings: Ahrefs (20 keywords, May 2025), Originality.ai (~100 keywords, October 2025), and Surfer SEO (10,000 queries, July 2025). All found weak positive correlations in the 0.10 to 0.32 range.
A 0.24 to 0.28 correlation is actually meaningful in this context. But these numbers need serious qualification. Every study was conducted by a vendor, and in every case, the vendor’s own tool performed best.
No study controlled for confounding variables like backlinks, domain authority, or accumulated click data. The methodology is fundamentally circular: the tools generate recommendations by analyzing pages that already rank in the top 10 to 20, then the studies test whether pages in the top 10 to 20 score well on those same tools.
The real question — whether following tool recommendations helps a new, unranked page climb — has never been rigorously tested. Clearscope’s Bernard Huang put it directly: “A 0.26 correlation is not the brag they think it is.”
He’s right. But a weak positive correlation is exactly what you’d expect if these tools solve the retrieval problem — getting into the candidate set — without solving the ranking problem — beating competitors once there. Understanding that distinction is what makes these tools useful rather than misleading.
Why not skip these tools altogether?
Expert writers are terrible at predicting how their audience actually searches. MIT Sloan’s Miro Kazakoff calls it the curse of knowledge. Once you know something, you forget what it was like before you knew it.
Clearscope’s case study with Algolia illustrates the problem precisely. Algolia’s writers were technical experts producing genuinely excellent content that sat on Page 9. The problem wasn’t quality. The team was using internal jargon instead of the language their audience actually typed into Google.
After adopting Clearscope, their SEO manager Vince Caruana said the tool helped the organization “start writing for our audience instead of ourselves” by breaking out of internal vocabulary. Blog posts moved from Page 9 to Page 1 within weeks. Not because the writing improved, but because the vocabulary finally matched search behavior.
Google’s own SEO Starter Guide acknowledges this dynamic, noting that users might search for “charcuterie” while others search for “cheese board.” Content optimization tools surface that gap by showing you the actual vocabulary of pages that have already demonstrated retrieval success.
You can do everything a tool does manually by reading top results and noting common themes, but the tools automate hours of SERP analysis into minutes. At $79 to $399 per month, the investment is justified when teams publish frequently in competitive niches or assign work to freelancers lacking domain expertise. For a solo blogger publishing once or twice a month, manual analysis works fine.
What about AI-powered retrieval?
Dense vector embeddings are the same core technology behind LLMs and AI-powered search features. They compress a document into a fixed-length numerical representation and can match semantically similar content even without shared keywords. Google uses them via RankEmbed, but they supplement lexical retrieval rather than replace it.
The reason is computational: A 768-dimensional embedding can preserve only so much information, and research from Google DeepMind’s 2025 LIMIT paper showed that single-vector models max out at roughly 1.7 million documents before relevance distinctions break down — a small fraction of Google’s index. Multiple studies, including findings on the BEIR benchmark, show hybrid approaches combining BM25 with dense retrieval outperform either method alone.
The bottom line for practitioners is clear: The AI layer matters, but it sits lower in the pipeline, and the traditional retrieval stage your content tools map to still does the heavy lifting at scale.
This is where most guidance on content tools falls short. The typical advice is “use Surfer/Clearscope, get a high score, rank better.”
That misses the point entirely. Here’s a framework built on how these tools actually intersect with Google’s retrieval mechanics.
Prioritize zero-usage terms over everything else
The highest-leverage action these tools identify is a term with zero mentions in your content. That’s a term where your retrieval score is literally zero, and you’re invisible for every query containing it. Going from zero to one mention is the single most impactful edit you can make. Going from four mentions to eight is nearly worthless because of the saturation curve.
When reviewing tool recommendations, filter for terms you haven’t used at all. Clearscope’s “Unused” filter does this explicitly.
Ask yourself: Does this missing term represent a subtopic my audience would expect me to cover? If yes, work it in naturally. If the tool suggests a term that doesn’t fit your angle — a beginner’s guide doesn’t need advanced technical terminology — skip it.
A high score achieved by forcing irrelevant terms into your content is worse than a moderate score with genuinely useful writing. As Ahrefs noted in its 2025 study, “you can literally copy-paste the entire keyword list, draft nothing else, and get a high score.” That tells you everything about the limits of chasing the number.
Be selective about which competitor pages you analyze
Default settings on most tools pull from the top 10 to 20 ranking pages, which frequently includes Wikipedia, major media outlets, and enterprise sites with overwhelming domain authority. These pages often rank despite their content, not because of it. Their term patterns reflect authority advantage, not content quality, and they’ll skew your recommendations.
A better approach: Look for pages that rank for a high number of organic keywords on mid-authority domains.
Ahrefs’ data shows the average page ranking No. 1 also ranks in the top 10 for nearly 1,000 other keywords. A page ranking for 500 keywords on a DR 35 site has demonstrated broad retrieval success through vocabulary and topical coverage, not just backlinks. Those pages contain term patterns proven effective across hundreds of separate retrieval events, not just one.
In most tools, you can manually exclude specific URLs from competitor analysis. Remove the Wikipedia pages, the Amazon listings, and any high-authority site where you know authority is doing the work. What’s left gives you a much cleaner picture of what content actually needs to include.
Use tools during research, not during writing
The worst workflow is writing with the scoring editor open, watching your number tick up in real time. That pulls your attention toward keyword insertion instead of communicating expertise. Practitioners reporting the worst experiences with these tools tend to be the ones writing to a live score.
The better workflow: Run the tool first. Review the term list. Identify gaps in your outline, especially terms with zero usage that represent subtopics you should cover. Then close the tool and write for your reader.
Run it again at the end as a sanity check. Did you miss any major subtopics? Add them. Is the score significantly lower than competitors? That’s information worth investigating. But your job is to build the best page on the internet for this topic, not to match a number.
Understand that content is one player in the game
NavBoost, RankEmbed, PageRank-derived quality scores, site authority, click data, and engagement signals all operate on the candidate set that first-stage retrieval produces. Content optimization gets you through the gate. It doesn’t win the race.
If you optimize a page, push the score to 90, and don’t see ranking improvements, that doesn’t mean the tool failed. It likely means the other ranking factors — backlinks, domain authority, and click signals — are doing more work for your competitors than content alone can overcome.
This is especially important when scoping on-page optimization projects. Be honest about what content changes can and can’t accomplish. If a page is on a DR 15 domain competing against DR 70+ sites, perfect content optimization is necessary but probably not sufficient.
When a client asks why they’re not ranking after you pushed their score to 95, the answer shouldn’t be “we need more content.” It should be a clear explanation of which part of the problem content solves — retrieval — which parts it doesn’t — authority, engagement, brand — and what the next strategic move actually is.
Focus on going beyond, not just matching
The philosophy behind these tools — structure your content after what top results cover — is sound. You need to demonstrate topical relevance to enter the candidate set. But the goal isn’t to produce another version of what already exists.
The pages that rank broadly, the ones that show up for hundreds or thousands of keywords, consistently do more than match the competitive baseline. They add original research, practitioner experience, specific examples, or angles the existing results don’t cover.
Surfer SEO’s December 2024 study supports this. It measured “facts coverage” across articles and found that top-performing content by keyword breadth had significantly higher coverage scores than bottom performers.
The content that ranks for the most queries doesn’t just include the right terms. It includes more information, more specifically. Use the tool to establish the floor of topical coverage. Then build the ceiling with value the tool can’t measure.
A note on entities
Google’s Knowledge Graph contains an estimated 54 billion entities. Entity understanding becomes most powerful in the later ranking stages where BERT and DeepRank process final candidates.
Some content tools are starting to incorporate entity analysis, but even the best versions present entities as flat keyword lists, missing the relationships between entities that Google’s systems actually evaluate.
Knowing that “Dr. Smith” and “rhinoplasty” appear on your page is different from understanding that Dr. Smith is a board-certified surgeon with published research at a specific institution. That relational depth is what Google processes, and no content scoring tool currently captures it.
Treat entity coverage as an additional layer beyond what keyword-focused tools measure, not a replacement for the fundamentals.
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Retrieval before ranking
Content optimization tools work because they’ve reverse-engineered the vocabulary of the retrieval stage. That’s a less exciting claim than “they’ve cracked Google’s algorithm,” but it’s the honest one, and it’s supported by what the DOJ trial revealed about Google’s infrastructure.
Use these tools to identify missing terms and subtopics. Be skeptical of exact frequency targets. Exclude high-authority outliers from your competitor analysis. Prioritize zero-usage terms over further optimization of terms you’ve already covered.
Understand that a perfect content score addresses one stage of a multi-stage pipeline and use the competitive baseline as your floor, not your ceiling. The content that ranks the broadest isn’t the content that best matches what already exists. It’s the content that covers what already exists and then goes further.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-02-23 15:00:002026-02-23 15:00:00Content scoring tools work, but only for the first gate in Google’s pipeline
Wikipedia is a fascinating experiment. It’s a community-built encyclopedia that’s always in motion. It runs on volunteer energy and openly shared infrastructure, and it’s closer to an open-source project in how it’s built than a traditional encyclopedia book. Anyone can write, edit, and debate what belongs on a page.
And that’s the twist. The “truth” on Wikipedia isn’t handed down by a single editor or community member. It’s negotiated in public, guided by community standards, citations, and a whole lot of conversation. Contributors don’t so much control a subject’s story as they continually test it. They’re constantly asking questions: What can we verify? What deserves weight? What’s missing?
When you read a Wikipedia article, you’re seeing a current snapshot of a living, evolving community decision.
This whole experiment has scale, too. As of February 6, 2026, the English Wikipedia had 7.13 million articles, and the project spanned more than 340 languages.
If you’re thinking about creating a Wikipedia page for your company, it helps to know what you’re signing up for. Wikipedia isn’t a marketing channel, and it isn’t designed for companies to shape their narrative.
It’s designed to summarize what independent, reliable sources have already said about a company, so not every organization qualifies for a stand-alone article. Wikipedia cautions that only a small percentage of organizations meet the requirements for an article in the first place.
The easiest way to orient yourself with the platform is to keep Wikipedia’s “five pillars” top of mind. Wikipedia is, first and foremost, an encyclopedia. It aims for a neutral point of view, the content is free for anyone to use and edit, editors are expected to be civil, and there are no hard-and-fast rules. It’s just policies and guidelines applied with unbiased judgment.
If your company is genuinely notable by Wikipedia’s standards and you’re willing to play by its guidelines, there’s a real visibility upside in a solid, well-sourced page that holds up over time.
Key Takeaways
Wikipedia isn’t for marketing. If a Wikipedia page reads like company positioning, a feature brochure, or a pricing page, it’ll get rejected, reverted, or flagged. Even if other company pages “get away with it,” you need to focus on creating a deeply researched, informative draft to give strong notability in Wikipedia’s eyes.
Notability = independent coverage. You need multiple strong secondary sources (real reporting with editorial standards). Press releases, paid placements, niche trade mentions, and contributor “interviews” don’t hold up.
Sources drive the outline (and the page). Build your outline from what your credible secondary sources already cover. Possible sections could include a lead, history, high-level operations, leadership, or controversies, if documented. Each company’s outline may look different depending on what information can be strongly sourced. If you can’t source a section cleanly, it doesn’t belong.
Use Wikipedia’s Articles for Creation (AfC) process to avoid conflict of interest (COI) roadblocks. If you’re connected to a company or paid to write a Wikipedia page for them, you must disclose it and lean on the AfC process instead of directly pushing a company page live.
Getting published isn’t the finish line. Volunteers continuously review pages. Expect ongoing edits, scrutiny, and occasional challenges, so monitor a live page and keep it updated with strong, independent citations.
What Are the Benefits of Creating a Wikipedia Page?
The most significant benefit of Wikipedia is its sheer size and reach. It is one of the most visited websites in the world, averaging more than 1.1 billion unique visitors per month.
In addition to the size of its audience, the platform offers other benefits to marketers and company owners:
Credibility via independent validation (earned, not claimed): A live Wikipedia page signals that reliable, third-party sources have covered your organization in a meaningful way. For journalists, partners, investors, and enterprise buyers, this can reduce skepticism during research.
Search and AI visibility (off-page, long-term): Wikipedia tends to surface prominently in search results and is commonly referenced by knowledge systems. A well-sourced page can support progress in how your company appears in search features, AI overviews (AIOs), and large language model (LLM) output, based on what independent sources say, not what a company wants to say.
A neutral orientation page for readers: Wikipedia’s format helps readers quickly understand a company’s basics, including history, products or services, leadership, milestones, and context. The tradeoff is accessible neutrality. Anything included needs support from reliable secondary sources, and promotional language rarely lasts.
Clarity and disambiguation: If your name overlaps with other companies, or your story includes mergers, rebrands, or multiple founders, Wikipedia can help people land on the right entity and timeline.
A durable reference hub: A good Wikipedia page often becomes a stable directory of the strongest independent sources about you, such as press, books, and other reputable coverage, so readers can verify details without relying on your website alone.
Consistency across the web (a quiet multiplier): Wikipedia and related knowledge sources are reused in many downstream places. When the facts are clean, cited, and consistent, it can improve how your company is represented across third-party profiles and information panels over time.
A Wikipedia page is rarely a conversion engine, and it isn’t a place to “own” your story. The value is credibility and discoverability that can compound, but benefits can vary based on the strength of independent coverage and ongoing community scrutiny.
Below, we’ll cover the 10 steps on how to create a Wikipedia page, as well as considerations to keep in mind.
1. Check to See If Your Company is a Good Fit for a Wikipedia Page
Before you think about how to create a Wikipedia page for your company, you need to answer one question:
Would Wikipedia editors consider your company “notable”?
On Wikipedia, “notability” has nothing to do with how compelling your company story is. It means there’s enough independent, reliable coverage about your company that an article can be written from what third parties have already published, without filling in gaps with interpretation, insider knowledge, or marketing claims.
This is also where a lot of brand teams get tripped up. Again, Wikipedia isn’t a marketing channel. It’s not a place to shape messaging or control a narrative. If the only story you can tell is the one you want to tell, the page will be declined during initial submission review or deleted later.
What Notability Actually Looks Like
A company is usually considered notable when it receives significant coverage in multiple reliable sources independent of the company. “Significant coverage” is the key phrase here. Editors are looking for articles that discuss your company in real depth, not quick mentions or short blurbs.
A helpful way to think about it is this: if you can’t outline a neutral article using independent secondary sources alone, you probably don’t have enough notability yet.
Editors typically want coverage that checks these boxes:
Independent: Truly third-party reporting. Not press releases, paid placements, sponsored posts, advertorials, partner blogs, or content your PR team arranged. If a piece exists because the company made it happen, editors tend to discount it.
Significant: More than a passing mention. A funding announcement, product launch blurb, or event listing can be real coverage and still not be enough. The strongest sources are the ones that explain context, impact, history, or controversy in detail.
Secondary: Sources that analyze, summarize, or report on the company from the outside. Primary sources like your website, blog, press page, or social channels can support basic facts in limited cases, but they do not establish notability.
Reliable: Publications with editorial oversight and a reputation for accuracy. Big-name outlets can help, but they are not the only option. Trade and industry publications can be excellent sources when they have real editorial standards and provide in-depth coverage, but you can rarely use them to establish notability.
Multiple and sustained: A single great source is rarely enough on its own. Editors want to see more than one strong source, ideally across time, so the page can hold up after more people review it.
Neutral tone: Even when a source is independent, it can still be weak if it reads like promotion. Glowing profiles, “thought leadership” posts, or contributor content that feels like marketing often carry less weight than staff-reported coverage.
One nuance that matters a lot in practice is that “lots of links” does not equal notability. Companies can appear all over the internet through routine announcements and PR-driven writeups and still fail Wikipedia’s notability test.
What matters is whether independent sources have treated the company as worthy of real, substantive coverage. This also means that magazines and trade publications can’t work as reliable coverage to establish notability. Many industry leaders also run trade organizations, creating a conflict of interest (COI, in Wikipedia’s terms) if their trade publication were to cover their own company or the companies of friends or contributors.
If your company does not meet this bar yet, that’s not a judgment on it. It just means a Wikipedia article is likely premature, and the better move is to wait until there is enough independent coverage to support a neutral, well-sourced page.
A Note on Conflict of Interest (COI)
If you’re writing about your own company (or you’re paid to write for a company), Wikipedia considers that a conflict of interest (COI). That doesn’t automatically ban you from participating, but it does change how you should approach it.
When creating a new page, submit it to Articles for Creation (AfC) to ensure community editors review it properly.
When editing an existing page, you want to create your edits in a Sandbox draft (the Sandbox is a personal workspace where you can safely draft and refine changes to an article before submitting them for public review). Then, you submit that Sandbox draft onto the live Wikipedia page’s Talk page, along with a comment that asks community members to review and collaborate on the edits you suggested. Once a community consensus is reached, you can push those edits or additions live.
It’s also a good idea to disclose your COI connection. Your disclosure should be one of the following:
A statement on your User page.
A statement on the Talk page accompanying any paid contributions.
A statement in the edit summary accompanying any paid contributions.
Avoid directly creating or heavily editing an article and stick to Wikipedia’s COI process to request edits for independent editors to review.
Again, this is about expectations. If your team is hoping to just write a draft and hit “publish,” like you do with a blog, you’re going to have a bad time. But if you do have strong, independent coverage from credible outlets, you’ve got a real shot and can move to the next step.
2. Create a Wikipedia Account
Creating an account is a practical next step if you plan to contribute to Wikipedia. While you don’t need an account to read Wikipedia (or even to edit some pages), registering gives you features that make collaboration and transparency easier.
With an account, you can:
Create a User page (a simple profile and a place to draft in a Sandbox).
Use your Talk page to communicate with other editors.
Build an edit history tied to your username (helpful for credibility and continuity).
Work through article creation more smoothly, including drafting and submitting via AfC.
If you add images to your User page, make sure they’re properly licensed. Wikipedia generally accepts only freely licensed uploads.
After that, you’re set up to start editing, drafting, and participating in the community.
3. Contribute to Existing Pages
Quick reminder from earlier: If you’re connected to the company, you’re dealing with a COI. That’s why Wikipedia prefers that company pages undergo independent review before publication.
As a newbie, a good way to get comfortable on Wikipedia is to start by editing existing articles that have nothing to do with your organization. When you spend time improving clarity, tightening wording, and backing up facts with solid sources, you learn how Wikipedia works, and you build a history of helpful contributions.
As you do that, your account may become autoconfirmed. That usually happens automatically after your account has been around for more than four days and you’ve made at least 10 edits to Wikipedia pages that need them. Autoconfirmed status primarily grants a few basic permissions, such as creating pages and editing some semi-protected articles.
Here’s the key point, though: “Autoconfirmed” does not change your COI situation. Even if you can technically publish a page directly, a company-related article should still be written as a draft and submitted through AfC. This is the step that gets you the independent review Wikipedia expects, and it’s the safest, most appropriate route for a company page.
4. Conduct Research and Gather Sources
Before you write a single line of your Wikipedia draft, do the homework. Wikipedia doesn’t prioritize non-source-backed storytelling. The platform only cares about verifiability, meaning every meaningful claim must be backed by a reliable secondary source that an editor can check. Your company story could play well on Wikipedia, as long as there’s enough reliable evidence to back it up.
This is where most company pages fall apart. Not because the company isn’t real, but because the sources are thin, biased, or too “inside baseball.”
Why sources matter so much on Wikipedia
Wikipedia runs on two big rules:
No original research: You can’t “introduce” new facts, even if they’re true, without proper citation. Which leads to the next point…
Cite everything that matters: If it’s notable, controversial, or specific (revenue, awards, history, key dates, acquisitions), you need a secondary source to back it up.
Primary vs. secondary vs. tertiary sources (and how Wikipedia treats them)
Wikipedia breaks sources down into three categories: primary, secondary, and tertiary. Here is a look at each and how they play into the strength of your Wiki page:
Primary sources (you): Your website, press releases, investor decks, published reports, filings (e.g., Securities Exchange Commission (SEC), etc.).
Upside: Can work for basic, factual details (launch dates, historical milestones, etc.).
Downside: Biased by default. Editors won’t accept these for “notability” or big claims like “industry leader.”
Upside: Useful for quick confirmation and context.
Downside: Often too shallow to prove notability on their own.
Overall, secondary sources are the most important to your success. By their nature, these sources are pivotal in helping you summarize what experts think about a company or topic in Wikipedia’s voice. Relying heavily on these gives you a really strong case for notability in Wikipedia’s eyes.
What Makes a Good Wikipedia Source?
Good Wikipedia sources cover topics while maintaining editorial standards. Think major publications, local newspapers of record, respected business outlets, and independent industry analysis. If you’re short on that kind of coverage, that’s usually a PR problem, not a Wikipedia problem. Strengthening your digital PR (DPR) efforts can help you earn credible mentions that hold up under editor scrutiny.
But DPR for a Wikipedia use case must be handled carefully. What tends to work is focusing on independent coverage first. This looks like pitching credible story angles to journalists and outlets that genuinely cover your industry, and accepting that they may say no, or cover the story in a way you can’t control.
When an outlet does publish real, editorial reporting, that’s the kind of secondary source Wikipedia editors are more likely to accept.
Reliable Sources at a Glance
After seeing what Wiki editors consider reliable sources, you might be wondering where you even find sources that hit all their criteria. It helps to look at real-world use cases of which sources are best for your company. Here are some of the types of sites you can choose from.
For company pages, the sources that matter most are the ones that provide significant, independent coverage; the kind that demonstrates notability and gives editors something substantial to cite.
Major national/international newsrooms (strongest for notability + facts): Reuters, AP, BBC, Financial Times, The Wall Street Journal, Bloomberg, The New York Times, The Washington Post, NPR (news reporting over opinion).
Reputable business and investigative reporting: Deep dives and investigations from established outlets (e.g., ProPublica) can be highly valuable, especially for controversies, legal issues, and accountability reporting.
High-quality trade press with editorial oversight (context-dependent): Useful for industry coverage when it’s independent and more than a product announcement or reposted PR. You cannot use trade press as a primary indicator of notability, though.
Books from reputable publishers: Especially helpful for founders, company history, and industry impact when written by independent authors and published by established presses.
Government and major non-governmental organization (NGO) reports (within remit): Strong for regulatory actions, enforcement, public contracts, or formal assessments (but not a substitute for independent secondary coverage).
Medical/health claims (only when relevant): For biomedical statements, prioritize high-quality secondary sources like systematic reviews and authoritative guidelines (MEDRS standard), not individual studies or marketing claims.
Check out Wikipedia’s Perennial Sources list to see which sources have a good community track record because they all meet a high level of fact-checking and editorial standards. But remember, the sources featured in this list are still contextual; it’s not a whitelist.
Non-reliable Sources
To paint a clearer picture, here are some of the sources you should avoid:
Self-published/user-generated content (UGC): Personal blogs, Substack/Medium posts, self-hosted sites, most social media.
Press releases/advertorial: Company press rooms, PR wires; these are fine to state that an announcement occurred, not to establish third-party facts or notability.
Sensational/tabloid sources: Outlets known for gossip/sensationalism; poor for verifying facts.
Anonymous forums and crowdsourced threads: Message boards, comment sections, most Reddit/4chan/Discord posts.
Wikipedia views these types of sources as weaker because they aren’t research-backed, trustworthy, or credible. The common thread is that they undergo minimal editorial oversight (if any) or, in Reddit’s case, most of the content is UGC and self-published.
5. Research Your Competition
Like many things when it comes to Wikipedia, researching your competitors is fine if you do it the right way. As you start your research, view your competitors’ pages through the lens of what Wikipedia editors ultimately want.
The challenge here is that Wikipedia isn’t perfectly consistent. Some company pages are old, lightly monitored, or haven’t been updated to match today’s standards.
When someone says, “But other pages include feature lists and product tier breakdowns,” that doesn’t really matter. Editors don’t treat “other pages do it” as a justification. They judge your page on whether it reads like an encyclopedia entry and whether it’s backed by independent, reliable sources.
General Competitor Research Rules
Use competing Wiki pages to answer questions like:
What’s the typical structure for a company page in your category? Take note of the typical section titles. (We’ll dive into this next.)
What kind of claims survive without getting reverted? (Neutral, sourced, non-promotional.)
What sources are doing the heavy lifting on pages that stay live?
A “Wiki-safe” Research Method
Pick 3–5 competitors with live pages, then audit them like an editor would:
Scan the citations first. Are they mostly independent, secondary news coverage, press releases/company sites, or paid placements?
Check the tone. If it reads like a promotional brochure (feature-by-feature, pricing tiers, “best-in-class”), that’s a red flag, even if it hasn’t been removed yet.
Look at the page history and Talk page. Lots of reverts, banners, or sourcing disputes usually mean the page is shaky.
Note what’s missing. If competitors avoid detailed feature lists, that’s usually a sign that those details don’t belong on Wikipedia.
6. Create an Outline
Once you’ve got your sources, your outline has a starting point. The hard part is deciding what belongs.
On Wikipedia, an outline is not “everything you want to say.” It’s you making careful decisions about what independent, reliable sources have actually covered, what they have not covered, and what deserves space without turning the page into a brochure. That takes judgment, and it often takes multiple passes.
The mindset you want is simple: Wikipedia pages are built around what reliable secondary sources already said about the subject. Your outline is how you organize those sourced facts into a structure that editors recognize and are willing to review.
Infobox (quick facts): Founded, founders, headquarters, industry, key people, website, and similar basics. Only include items you can verify.
Lead (opening summary): 2–4 neutral sentences explaining what the company is, where it’s based, what it does at a high level, and why it’s notable. This is not a tagline.
History: Founding and major milestones, expansions, acquisitions, funding or IPO, only if independent sources cover them, and major pivots. Focus on events that third parties actually reported.
Operations/Business (optional, and only if sourced): What the company does at a high level and what markets it serves. Avoid feature-by-feature descriptions and pricing tiers.
Leadership/Ownership (optional): Only if reliable sources discuss executives, ownership changes, or governance in a meaningful way.
Reception/Controversies (only if they exist in sources): Reviews, notable criticism, legal issues, regulatory actions, all written neutrally and backed by sources.
See also / References / External links: References do the heavy lifting; external links are usually minimal (often just the official site).
Using Your Sources to Build the Outline
Start with your strongest independent secondary sources and work outward. As you read through them, you’re identifying what the coverage actually emphasizes.
As you review sources, pull out:
Events they cover (those become history sections)
Claims they support (those become lead and operations sections)
Any recurring themes across sources (those become section headings)
Each major section in your outline should be supported by multiple secondary sources, not a single mention. Also, keep an eye on the length as you draft. Wikipedia discourages overly long articles unless the amount of independent coverage truly warrants it. If a section or topic isn’t discussed in depth by reliable secondary sources, it usually doesn’t belong at length in the article.
If you focus on covering the topic from an encyclopedic angle and you leave out anything that feels like marketing, you will give your draft a much better chance of surviving review.
7. Write a Draft of Your Wikipedia Page
Take your time as you write a draft of your Wikipedia page from your outline. You want your content to be source-backed, thorough, thoughtful, and genuinely useful, giving readers the information they came for.
At this stage, it’s best to write your draft in a Wikipedia Sandbox. As mentioned earlier, this is a personal workspace where you can draft safely, revise freely, and share the link with others for informal feedback without accidentally publishing anything live.
While a Wikipedia page can support your broader visibility, the platform’s purpose is encyclopedic and impartial. Anything that reads as emotional, salesy, or promotional is likely to be flagged and can lead to rejection later in the process.
Aim for short, direct sentences that stick to verifiable facts. And those facts need strong secondary sources. For example, if you write, “Spot ran to the big oak tree yesterday,” that claim would need a source. Not just any source, but a credible, independent secondary source that Wikipedia considers reliable.
It’s also critical to remember you’re writing on behalf of Wikipedia. Aka, you’re writing in Wikipedia’s unbiased, impartial, and neutral voice.
Here are some examples to show what this looks like in practice:
Example 1: Product Description
Promotional: “XYZ Software is a revolutionary, industry-leading platform that empowers businesses to achieve unprecedented productivity gains. With its cutting-edge AI technology and intuitive interface, XYZ transforms the way teams collaborate, delivering exceptional results that exceed expectations.“
Neutral: “XYZ Software is a project management platform that combines task tracking, team messaging, and file sharing. The software is used by businesses to coordinate work across departments.[1][2]“
Example 2: Company History
Promotional: “Founded by visionary entrepreneur Jane Smith, the company quickly rose to prominence as a game-changer in the industry. Through relentless innovation and unwavering commitment to excellence, it has become the trusted choice for Fortune 500 companies worldwide.“
Neutral: “The company was founded in 2015 by Jane Smith in Seattle.[3] It launched its enterprise tier in 2019 and rebranded from “TaskFlow” to its current name in 2021.[4][5]“
Wikipedia also defines “promotional” language differently. It’s more than simply using words like “revolutionary” or “legendary.” Factually correct statements can still be considered “promotional” in a Wikipedia editor’s eyes if they meet certain structure and emphasis criteria:
Long, comprehensive feature inventories.
Plan/tier breakdowns that resemble packaging (“Free vs. Premium vs. Enterprise”).
Performance claims that read like sales positioning.
Details that feel like purchase guidance (pricing, quotas, storage limits, admin entitlements).
Let’s talk about specs and features for a second. If your company is well-known for a particular product or service, it can be tempting to include a specification or feature list on your Wikipedia page. Unfortunately, that can cause problems with Wikipedia for several reasons.
Here’s why:
Wikipedia isn’t a manual or catalog: Wikipedia tries to avoid becoming vendor documentation. Specs and feature matrices belong on the company site, in the documentation center, in release notes, or on third-party comparison sites, not in an encyclopedia.
Specs change constantly:Feature sets, tiers, storage limits, and admin/security capabilities change frequently. Wikipedia content must remain stable and verifiable over time. Highly granular spec content becomes outdated quickly and attracts disputes.
It’s hard to verify neutrally:If the only source for a feature or tier is the vendor’s own site or press release, Wikipedia considers that primary sourcing; useful for limited factual verification, but not ideal for describing capabilities in detail or making value claims.
“Undue weight” and imbalance:Even accurate feature lists can give a product more prominence than independent sources do. Wikipedia tries to reflect external coverage: if reliable third parties don’t treat a feature as notable, Wikipedia typically won’t either.
What a Company’s Wikipedia Draft Should Look Like
Much like sourcing, it’s hard to imagine what an acceptable draft should look like, given all of Wikipedia’s guidelines. Here’s a brief rundown of what a solid draft should look like when you’re done:
A clear, high-level description of what a company is (one paragraph, not a feature catalog).
A history/timeline of major milestones (launches, renames, major releases) backed by independent sources.
Widely covered integrations/partnerships only when reported by reliable third parties.
A short, selective “features” summary only for capabilities that independent sources treat as notable and cover in-depth.
8. Upload Your Page into the Article Wizard
Once your Sandbox draft is in good shape, move over to the Wikipedia Article Wizard. The Wizard is the guided tool that helps you move what you wrote from your Sandbox into Wikipedia’s Draft space, which is where new articles are typically prepared before they go live.
For company-related pages, the key takeaway is that the Wizard is the structured path to getting your draft into the right place so it can be submitted for independent review.
9. Submit Your Article for Review
Now that your draft is in Draft space, you’re ready for the step that triggers formal evaluation by the community. Submit your draft through Articles for Creation by clicking “Submit for review.” This is when your draft enters the AfC queue, and a volunteer reviewer takes a look.
The timeline can range from a few weeks to a few months, depending on backlog and whether the reviewer requests changes. It’s also common for drafts to be declined at first, with feedback you’ll need to address before approval.
At NPD, we’ve found that sticking with AfC is the best practice for companies lookingto go live. Even though autoconfirmed accounts may have the technical ability to publish directly, that path often creates more friction for company-related topics. AfC sets expectations for independent review from the start and helps reduce avoidable issues related to COI and other Wikipedia guidelines.
10. Continue Making Improvements
Once your page is accepted, the work is not really over.
Wikipedia is editable by anyone, so changes can happen at any time. Some edits will be helpful, some will be mistaken, and some may reflect a negative point of view. The best approach is to keep an eye on the page so you can understand what is changing and respond appropriately, usually by suggesting improvements on the Talk page or updating the article with strong, independent sourcing.
As the page gets more visibility and gains traction on Google and LLMs, focus on accuracy and neutrality rather than “updating marketing messaging.” Wikipedia is not the place for routine product updates, but it is the right place to reflect significant, well-covered developments when reliable third-party sources have written about them.
You should also plan for the possibility that your draft will be declined. That is common, especially for company-related topics. If it happens, do not get discouraged. Read the reviewer’s comments carefully, make the requested changes, and resubmit when you have addressed the specific issues that kept the draft from being accepted.
FAQs
Should I build a Wikipedia page for my company?
A Wikipedia page can be a meaningful credibility asset, but it isn’t a fit for every company. The deciding factor is whether there’s enough independent, reliable secondary coverage to support a neutral article. If you can’t outline the page using third-party sources alone, it’s usually too early.
If your company does qualify, the value tends to be indirect: stronger brand legitimacy, clearer “who you are” context in search results, and more consistent entity information across the web. It’s less about immediate conversions and more about long-term visibility and trust signals that can compound.
Yes. Creating, publishing, and maintaining a company page is challenging because Wikipedia is community-reviewed and built around strict expectations: neutral tone, verifiable claims, and high-quality sourcing. You also have to plan for ongoing edits and scrutiny after the page goes live.
The opportunity is achievable if you have strong independent coverage and treat the process as encyclopedic documentation rather than company messaging.
How do I know if my Wikipedia page will be published?
There’s no guaranteed way to know. Even well-prepared drafts can be declined, revised, and resubmitted, especially for company topics.
Your best indicators are practical: you have multiple independent sources with significant coverage, your draft reads neutrally (not like marketing), and you submit through the Articles for Creation (AfC) process so reviewers can evaluate it in draft space.
How long will my Wikipedia article be under review before publication?
Review time varies widely. Some drafts are reviewed quickly, but it’s also common for company-related submissions to take weeks (or longer) depending on backlog and how many revisions are needed. A decline doesn’t mean “never”; it usually means “not yet” or “needs stronger sourcing and a more neutral rewrite.”
Conclusion
If you’re looking to increase traffic, improve your search everywhere visibility, or build credibility, Wikipedia can be part of the equation. But it’s not a marketing channel, and it isn’t built for companies to shape their narratives. It’s a community-edited encyclopedia that summarizes what independent, reliable sources have already said about you.
Where Wikipedia can help is in discovery and trust signals. A stable, well-sourced page often shows up prominently for company and topic queries, and it can reinforce consistent “entity facts” that search engines and other knowledge systems use to understand companies.
That’s also why Wikipedia often pairs well with entity SEO. When key details about your organization are documented consistently across reputable sources, your company is easier to interpret and surface accurately across platforms, including some LLM-style experiences. Results may vary based on implementation, the strength of independent coverage, and ongoing community review.
As you evaluate whether your company is a good fit for a Wikipedia page, keep in mind that the process is complicated, and it won’t be fully in your control. What matters most is having enough independent, reliable secondary coverage to justify a stand-alone article and being willing to follow Wikipedia’s COI expectations.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-02-20 20:00:002026-02-20 20:00:00How to Create a Wikipedia Page for Your Company
At just under 200 employees, Descript is not the biggest name in video editing software.
It’s not the most robust or the most popular, either.
But it’s punching way above its weight, competing with much bigger companies (like Adobe, and CapCut) in LLM search.
Using Semrush’s AI Visibility score, you can see that Descript is competing closely with giant brands like Adobe.
Descript found the way in.
And so can you.
In this SaaS LLM visibility case study, we’ll break down exactly how Descript is getting seen.
And more importantly, what you can copy to improve visibility for your own product.
Choosing Clear Niche Messaging
For years, Descript has been known as a podcast editing tool.
That matters.
Because when people talk about podcast editing, Descript comes up naturally.
In blog posts.
In forums.
And now, in AI answers.
This isn’t accidental. Descript is clear about who it’s for, and their content reflects that focus.
Their product pages and blog posts consistently speak to one core audience: people who want to edit podcasts easily.
Here’s why this matters:
When I asked Google’s AI Mode for the best software to edit podcasts — specifically as someone with no video editing skills — Descript was one of the first tools mentioned.
And what shows up second in the list of sources?
One of Descript’s own blog posts about podcast editing.
Across Descript’s own website and other third-party sources, this tool is regularly mentioned as ideal for podcasters.
This matters because of a key difference between AI search and traditional SEO.
LLMs don’t just surface pages. They based their answers on query fan-outs.
Here’s what that means: AI creates multiple searches after the original query, and tries to find an answer that is most directly matched to what was asked.
That’s why even articles and websites that aren’t ranking well in Google can still get cited by AI when they provide the most relevant, specific answer to what users are asking.
Because Descript’s content is tightly focused on one audience, one use case, one problem, it maps cleanly to those AI queries.
That doesn’t necessarily correlate to higher ranking in traditional search. In fact, Descript’s traffic from traditional SEO has been steadily decreasing since its peak in 2024:
But at the same time, branded traffic has increased.
So even while the brand isn’t succeeding in traditional search, more people are becoming aware of Descript and searching for the brand name specifically.
Why? In part, because the brand is known for exactly what it does: podcast editing.
AI knows that too. And I would bet that a higher amount of mentions in AI search is helping with brand recognition and influencing that increase in branded search traffic.
Here’s the point: Descript isn’t just checking off boxes of what to talk about.
The way they write — and the way they present their product — shows exactly who they’re speaking to. They match the way their audience talks.
Take the blog article on podcast editing that we mentioned above as an example.
The copy flows naturally, includes quotes from an internal expert in the way she describes the problem and solution, and speaks in an easy way that matches the tone of the audience.
As a byproduct of this natural way of writing and clear product position, their copy and content semantically matches what their audience is searching for.
And their AI mentions keep increasing.
Action Item: Identify and Focus on Your Niche Market
Effort vs. Impact: Medium effort. High impact.
If you’re trying to be all things to everyone, AI is less likely to recommend you for anything specific.
Instead, narrow your focus like Descript does:
Of course, you also want to find balance.
For example, “Podcast editing software for true crime hosts who only record on Thursdays,” may be a bit too niche.
To get the narrowest viable version of your core audience, look at your most successful customers.
Ask:
Who gets the most ROI from our product?
Who uses it weekly — or daily?
Which customers have become vocal advocates?
What do those users have in common? (Role, company size, industry, workflow)
That overlap is your niche.
Once that’s clear, your messaging gets easier.
You stop being an “All-in-one AI-powered platform for creators and teams.”
And start anchoring your product to a specific job: “Edit podcasts and spoken audio, without technical complexity.”
Then, your product becomes easier for AI systems to understand — and recommend — for specific use cases.
Once you’ve defined your niche, focus your content on what actually helps them.
Descript doesn’t target video editing professionals. So, they don’t show up in those searches.
They focus on content creators and podcasters. And their content reflects that.
To do the same:
Talk to people in your niche industry
Ask about their workflows, goals, and sticking points
Learn what slows them down
Pro tip: If you can’t speak directly to people in your audience or customer base, talk to your customer-facing teams. Customer success and sales teams have daily contact with your core audience. So, they’re in a better position to give you insights into what this audience cares about.
Online research also helps.
Find relevant subreddits to see what people are talking about. Check the comments section of relevant YouTube videos.
Look for recurring questions and complaints.
For example, the Descript team might peruse the r/podcasting subreddit to learn about their audience’s questions and opinions.
The goal: understanding.
When you deeply understand your audience’s day-to-day reality, creating helpful content becomes much easier.
And your content can become the source for AI answers.
Of course, getting citations back to your website isn’t the same as getting direct brand mentions. However, it’s still an opportunity to build awareness and authority.
Plus, building content around relevant core topics helps reinforce your niche messaging.
With image-processing models like contrastive language–image pre-training (CLIP,) AI systems can understand what’s happening inside screenshots and videos — not just the words around them.
And those visuals now show up directly in AI answers. Especially for SaaS product queries in tools like ChatGPT.
For example, when I search for “best CRM software for a small business,” the top AI result includes images of the actual product interface.
That’s a shift.
Highly polished mockups matter less. Real, in-product visuals matter more.
Which is why Descript shows up like this in ChatGPT:
Descript consistently shows real product images and videos across product pages, Help Center articles, and blog content.
These aren’t decorative.
They show:
What the product looks like
How features work
What users should expect when they log in
As a result, those same images and videos get pulled into AI answers — often with a link back to Descript’s site.
In this case, the link goes back to a very in-depth Help Center guide to getting started with podcast editing.
And most Interestingly, that’s a near-perfect semantic match to the original query.
Action Item: Include In-Product Images in Your Marketing Content
Effort vs. Impact: Low effort. Medium impact.
Start with the basics.
For every feature you highlight, ask one question: Can someone see this working?
Then act on it. Add real screenshots of your core product screens to key product pages. Replace abstract diagrams with in-product visuals where possible.
Next, expand beyond product pages.
Mention a feature in a blog post? Include a screenshot of it in use.
Explaining a workflow in a Help Center article? Show each step visually.
Teaching a process? Record a short screen capture instead of relying on text alone.
The goal is clarity.
Clear visuals help users understand your product faster. And they give AI systems concrete material to reuse in answers.
Which makes your product easier to recommend — and easier to recognize — inside AI search.
Creating Detailed MoFu/BoFu Content
Content mapped to different awareness levels performs especially well in AI search.
Descript understands this.
They don’t just publish top-of-funnel guides. They create content for product-aware and solution-aware searches, too.
When you search in ChatGPT for video creation or editing tools, Descript often appears in the results.
But more importantly, their own content is cited as a source.
In this example, the cited source is a Descript-owned “best of” article comparing video tools.
Instead of generic recommendations, the page:
Breaks tools down by specific use cases
Includes clear pros and cons
Explains who each option is best for
Descript follows this same pattern with multiple “best of” lists and comparison pages against their main competitors.
The payoff?
When I asked AI to compare podcast video editing tools, Descript appeared with clear labels explaining:
Who it’s best for
Key features
When it makes sense to choose it
That context helps AI recommend Descript to the right people (not everyone).
Action Item: Create Citable MoFu and BoFu Content
Effort vs. Impact: High effort. High impact.
Different awareness levels need different content.
To increase product-level AI visibility, focus on Product Aware and Solution Aware queries.
For Product Aware audiences, create:
Comparison pages
“Best alternative” posts
Owned “best of” lists
Want more ideas?
Talk to your sales team.
Ask them: What features are convincing people to buy? Which competitors are commonly brought up in sales conversations?
Those answers map directly to comparison content AI likes to cite.
For Solution Aware audiences, focus on how-to content that naturally features your product.
For example, when I asked Google’s AI Mode how to reduce background noise from a microphone, it referenced a Descript how-to article.
This same pattern repeats itself across many of Descript’s blog posts: Find a clear problem, give a clear solution, add product mentions naturally.
It’s all about finding the right questions to answer.
To find these opportunities faster, use Semrush’s AI Visibility Toolkit. This data is powered by Semrush’s AI prompt database and clickstream data, organized into meaningful topics.
Head to “Competitor Research” and review:
Shared topics where competitors appear
Prompts where they earn more AI visibility than you
Then, dig into the specific questions behind those prompts.
The goal isn’t simply “more content”.
It’s answering the right questions — at the right stage — with content AI can confidently cite.
Building Positive Sentiment With Digital PR and Affiliate Marketing
AI visibility isn’t earned on your website alone.
LLMs look for signals across the web.
This is what we call consensus. And it means that positive sentiment has to exist outside your owned channels.
Descript is doing this in two ways:
Digital PR on sites AI already trusts
A creator-friendly affiliate program that drives third-party mentions
Here’s how it works: Google’s AI Mode tends to favor certain websites to source when answering queries about software.
Semrush’s visibility research for AI in SaaS from December 2025 shows these sites dominate citations:
Zapier
PCMag
Gartner
LinkedIn
G2
Here’s what’s interesting.
Descript is mentioned in articles across nearly all of these top sources.
For example, in software listicles like this one on Zapier:
Or in real-world experience articles like this one on Medium:
Or in their clear listings on reviews sites like Gartner and G2:
When AI systems cite those favored sources, Descript comes along for the ride.
Not because it’s the biggest brand.
But because it’s present where AI is already looking.
The second lever is Descript’s affiliate program.
It’s simple:
$25 per new subscriber
30-day attribution window
Monthly payouts
No minimums
Those are solid incentives.
And they lead to more creator-driven content across the web.
For example, a YouTube walkthrough from VP Land explains how to use Descript and includes an affiliate link in the description.
When I later asked Google’s AI Mode how to use Descript, that exact video was cited as a source.
That’s the pattern.
Affiliate content creates citable, trusted references that AI systems reuse.
Action Item: Build a Strategy to Get More Mentions Online
Effort vs. Impact: High effort. High impact.
Getting third party mentions is all about building relationships.
First, build relationships with publishers, starting with the ones AI already trusts.
Even if you’re not an enterprise SaaS company with a full-sized PR team, this is still possible.
Granted, it’s not the easy route — but when you find the right websites and perform regular outreach to those teams, you can get your brand on these sites.
Before you start outreach, get your bearings.
Start by going back to Semrush’s AI Visibility Toolkit. Head to the “Competitor Research” tab and select “Sources.”
This shows you:
Which sites LLMs cite for your category
Where competitors are already getting mentioned
Gaps where your brand doesn’t show up (yet)
Those sites become your shortlist.
Outreach works better when you’re aiming at sources AI already relies on.
Second, build relationships with creators.
Affiliate programs work when creators want to talk about you.
So, build an affiliate program people actually want to be part of.
This means the program has to be easy to join, with clear terms that make it worth their time.
At a minimum, make sure you have:
A simple signup
Transparent tracking
Reliable payouts
Pro tip: Use a tool like PartnerStack to handle all of the details automatically. Better signups, better tracking, and automated payouts build trust with your affiliates.
If you need inspiration, research top affiliate programs to learn more about the conditions creators expect.
But most importantly: Treat affiliates as distribution partners, not just a side channel.
This means enabling them with clear positioning on your product, example use cases, demo workflows, screenshots they can reuse, and other resources.
The better you equip them, the stronger their recommendations will be.
Once you have this set up, track the results.
Use AI visibility data to see:
Which publisher relationships are turning into citations in AI search
Which creators show up in AI answers
Which formats perform best
Then, double down.
Now that we’ve discussed what Descript is doing well, let’s look at where there’s room for improvement.
Where Descript Could Improve: Reddit Marketing
Descript is doing a great job in many areas that are important for AI search visibility.
That said, there’s one area they’re missing out on: Reddit.
And yes, Reddit matters. A lot.
It’s still one of the most-cited sources in Google’s AI Mode.
And in almost all of the searches I tested above, Reddit was cited as a source (especially conversations in the r/podcasting subreddit).
Here’s the problem: right now, Reddit is not doing Descript any favors.
Here are a few thread titles I found just by searching for Descript in a podcasting subreddit:
When LLMs scan Reddit for sentiment, that unbalance matters.
AI wants to see consensus. So when Reddit skews negative, recommendations may weaken, and alternatives get surfaced instead.
Even when the product is strong.
That’s why, while Descript’s AI visibility is good, it’s still not as good as it could be. And that vulnerability could hurt them in the long run, even if they’re still doing everything else right.
Here are some ways that Descript (and you) could turn the tides on Reddit:
Avoid promoting and start participating: Reddit punishes marketing language. Helpful, honest comments perform better than posts.
Respond to criticism directly (when appropriate): Not defensively, but with clear explanations and fixes
Be present before there’s a problem: Accounts that only show up during damage control don’t build trust
Focus on comments, not posts: High-value comments in active threads outperform standalone branded posts
Monitor brand mention weekly: Focus especially on high-intent subreddits. In Descript’s case, that could be r/podcasting.
To be fair, it seems like Descript is taking steps in the right direction.
As of December 2025, the Descript team has taken control of a dedicated brand subreddit, with PMM Gabe at the helm.
And the team’s responses feel very Reddit-friendly, not using marketing jargon or being pushy.
But popular threads here still have very little interaction with the Descript team. And there seems to be very few (if any) comments from the Descript team outside of this branded subreddit.
It’s a step in the right direction, but there’s still a lot to work on.
Done right, Reddit becomes a sentiment stabilizer and a stronger input source for AI answers.
Ignore it, and Reddit can become a liability.
Remember: for AI visibility, silence isn’t neutral.
Further reading: If Reddit feels like a whole other world, we’ve got you covered. Read our full guide to Reddit Marketing.
What You Can Take Away from This SaaS LLM Visibility Case Study
Descript isn’t winning AI visibility because it’s the biggest brand.
It’s winning because it’s clear, focused, and consistently helpful.
None of that is accidental.
And none of it requires massive scale.
You can get started on this today by choosing one key action to work on.
Use the effort vs. impact lens from this article to choose where to start.
Add in-product screenshots and videos: Low effort, medium impact
Tighten your niche messaging: Medium effort, high impact
Build citable MoFu/BoFu content: High effort, medium impact
Invest in digital PR, affiliates, and community participation: High effort, high impact
Create seriously helpful content: High effort, high impact
Pick one, start there. AI search visibility tools for SaaS companies — like Semrush’s AI Visibility Toolkit — can help you see exactly where you stand today, and where you can improve.
Remember: LLM visibility isn’t about chasing algorithms.
It’s about making your product easier to understand, easier to trust, and easier to recommend.
Do that consistently — and AI search will follow.
Want to learn how it all works on a deeper level? Read our LLM visibility guide to discover even more ways to increase your brand mentions and citations in AI search.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-02-20 17:50:472026-02-20 17:50:47How a 200-Person Company Competes with a $160B Giant in AI Search
If you look at job postings on Indeed and LinkedIn, you’ll see a wave of acronyms added to the alphabet soup as companies try to hire people to boost visibility on large language models (LLMs).
Some people are calling it generative engine optimization (GEO). Others call it answer engine optimization (AEO). Still others call it artificial intelligence optimization (AIO). I prefer large model answer optimization (LMAO).
I find these new acronyms a bit ridiculous because while many like to think AI optimization is new, it isn’t. It’s just long-tailSEO — done the way it was always meant to be done.
Why LLMs still rely on search
Most LLMs (e.g., GPT-4o, Claude 4.5, Gemini 1.5, Grok-2) are transformers trained to do one thing: predict the next token given all previous tokens.
AI companies train them on massive datasets from public web crawls, such as:
Common Crawl.
Digitized books.
Wikipedia dumps.
Academic papers.
Code repositories.
News archives.
Forums.
The data is heavily filtered to remove spam, toxic content, and low-quality pages. Full pretraining is extremely expensive, so companies run major foundation training cycles only every few years and rely on lighter fine-tuning for more frequent updates.
So what happens when an LLM encounters a question it can’t answer with confidence, despite the massive amount of training data?
AI companies use real-time web search and retrieval-augmented generation (RAG) to keep responses fresh and accurate, bridging the limits of static training data. In other words, the LLM runs a web search.
To see this in real time, many LLMs let you click an icon or “Show details” to view the process. For example, when I use Grok to find highly rated domestically made space heaters, it converts my question into a standard search query.
Many of us long-time SEO practitioners have praised the value of long-tail SEO for years. But one main reason it never took off for many brands: Google.
As long as Google’s interface was a single text box, users were conditioned to search with one- and two-word queries. Most SEO revenue came from these head terms, so priorities focused on competing for the No. 1 spot for each industry’s top phrase.
Many brands treated long-tail SEO as a distraction. Some cut content production and community management because they couldn’t see the ROI. Most saw more value in protecting a handful of head terms than in creating content to capture the long tail of search.
Fast forward to 2026. People typing LLM prompts do so conversationally, adding far more detail and nuance than they would in a traditional search engine. LLMs take these prompts and turn them into search queries. They won’t stop at a few words. They’ll construct a query that reflects whatever detail their human was looking for in the prompt.
Suddenly, the fat head of the search curve is being replaced with a fat tail. While humans continue to go to search engines for head terms, LLMs are sending these long-tail search queries to search engines for answers.
While AI companies are coy about disclosing exactly who they partner with, most public information points to the following search engines as the ones their LLMs use most often:
ChatGPT – Bing Search.
Claude – Brave Search.
Gemini – Google Search.
Grok – X Search and its own internal web search tool.
Perplexity – Uses its own hybrid index.
Right now, humans conduct billions of searches each month on traditional search engines. As more people turn to LLMs for answers, we’ll see exponential growth in LLMs sending search queries on their behalf.
SEO is being reborn.
Your customers search everywhere. Make sure your brand shows up.
The SEO toolkit you know, plus the AI visibility data you need.
The principles of long-tail SEO haven’t changed much. It’s best summed up by Baseball Hall of Famer Wee Willie Keeler: “Keep your eye on the ball and hit ’em where they ain’t.”
Success has always depended on understanding your audience’s deepest needs, knowing what truly differentiates your brand, and creating content at the intersection of the two.
As straightforward as this strategy has been, few have executed it well, for understandable reasons.
Reading your customers’ minds is hard. Keyword research is tedious. Content creation is hard. It’s easy to get lost in the weeds.
Happily, there’s someone to help: your favorite LLM.
Here are a few best practices I’ve used to create strong long-tail content over the years, with a twist. What once took days, weeks, or even months, you can now do in minutes with AI.
1. Ask your LLM what people search when looking for your product or service
The first rule of long-tail SEO has always been to get into your audience’s heads and understand their needs. This once required commissioning surveys and hiring research firms to figure out.
But for most brands and industries, an LLM can handle at least the basics. Here’s a sample prompt you can use.
Act as an SEO strategist and customer research analyst. You're helping with long-tail keyword discovery by modeling real customer questions.
I want to discover long-tail search questions real people might ask about my business, products, and industry. I’m not looking for mere keyword lists. Generate realistic search questions that reflect how people research, compare options, solve problems, and make decisions.
Company name: [COMPANY NAME]
Industry: [INDUSTRY]
Primary product/service: [PRIMARY PRODUCT OR SERVICE]
Target customer: [TARGET AUDIENCE]
Geography (if relevant): [LOCATION OR MARKET]
Generate a list of 75 – 100 realistic, natural-language search queries grouped into the following categories:
AWARENESS
• Beginner questions about the category
• Problem-based questions (pain points, frustrations, confusion)
CONSIDERATION
• Comparison questions (alternatives, competitors, approaches)
• “Best for” and use-case questions
• Cost and pricing questions
DECISION
• Implementation or getting-started questions
• Trust, credibility, and risk questions
POST-PURCHASE
• Troubleshooting questions
• Optimization and advanced/expert questions
EDGE CASES
• Niche scenarios
• Uncommon but realistic situations
• Advanced or expert questions
Guidelines:
• Write queries the way real people search in Google or ask AI assistants.
• Prioritize specificity over generic keywords.
• Include question formats, “how to” queries, and scenario-based searches.
• Avoid marketing language.
• Include emotional, situational, and practical context where relevant.
• Don't repeat the same query structure with minor variations.
• Each query should suggest a clear content angle.
Output as a clean bullet list grouped by category.
You can tweak this prompt for your brand and industry. The key is to force the LLM (and yourself) to think like a customer and avoid the trap of generating keyword lists that are just head-term variations dressed up as long-tail queries.
With a prompt like this, you move away from churning out “keyword ideas” and toward understanding real customer needs you can build useful content around.
Most large brands and sites don’t realize they’ve been sitting on a treasure trove of user intelligence: on-site search data.
When customers type a query into your site’s search box, they’re looking for something they expect your brand to provide.
If you see the same searches repeatedly, it usually means one of two things:
You have the information, but users can’t find it.
You don’t have it at all.
In both cases, it’s a strong signal you need to improve your site’s UX, add meaningful content, or both.
There’s another advantage to mining on-site search data: it reveals the exact words your audience uses, not the terms your team assumes they use.
Historically, the challenge has been the time required to analyze it. I remember projects where I locked myself in a room for days, reviewing hundreds of thousands of queries line by line to find patterns — sorting, filtering, and clustering them by intent.
If you’ve done the same, you know the pattern. The first few dozen keywords represent unique concepts, but eventually you start seeing synonyms and variations.
All of this is buried treasure waiting to be explored. Your LLM can help. Here’s a sample prompt you can use:
You're an SEO strategist analyzing internal site search data.
My goal is to identify content opportunities from what users are searching for on my website – including both major themes and specific long-tail needs within those themes.
I have attached a list of site search queries exported from GA4. Please:
STEP 1 – Cluster by intent
Group the queries into logical intent-based themes.
STEP 2 – Identify long-tail signals inside each theme
Within each theme:
• Identify recurring modifiers (price, location, comparisons, troubleshooting, etc.)
• Identify specific entities mentioned (products, tools, features, audiences, problems)
• Call out rare but high-intent searches
• Highlight wording that suggests confusion or unmet expectations
STEP 3 – Generate content ideas
For each theme:
• Suggest 3 – 5 content ideas
• Include at least one long-tail content idea derived directly from the queries
• Include one “high-intent” content idea
• Include one “problem-solving” content idea
STEP 4 – Identify UX or navigation issues
Point out searches that suggest:
• Users cannot find existing content
• Misleading navigation labels
• Missing landing pages
Output format:
Theme:
Supporting queries:
Long-tail insights:
Content opportunities:
UX observations:
Again, customize this prompt based on what you know about your audience and how they search.
The detail matters. Many SEO practitioners stop at a prompt like “give me a list of topics for my clients,” but this pushes the LLM beyond simple clustering to understand the intent behind the searches.
I used on-site search data because it’s one of the richest, most transparent, and most actionable sources. But similar prompts can uncover hidden value in other keyword lists, such as “striking distance” terms from Google Search Console or competitive keywords from Semrush.
Even better, if your organization keeps detailed customer interaction records (e.g., sales call notes, support tickets, chat transcripts), those can be more valuable. Unlike keyword datasets, they capture problems in full sentences, in the customer’s own words, often revealing objections, confusion, and edge cases that never appear in traditional keyword research.
Your goal is to create content so strong and authoritative that it’s picked up by sources like Common Crawl and survives the intense filtering AI companies apply when building LLM training sets. Realistically, only pioneering brands and recognized authorities can expect to operate in this rarefied space.
For the rest of us, the opportunity is creating high-quality long-tail content that ranks at the top across search engines — not just Google, but Bing, Brave, and even X.
This is one area where I wouldn’t rely on LLMs, at least not to generate content from scratch.
Why?
LLMs are sophisticated pattern matchers. They surface and remix information from across the internet, even obscure material. But they don’t produce genuinely original thought.
At best, LLMs synthesize. At worst, they hallucinate.
Many worry AI will take their jobs. And it will — for anyone who thinks “great content” means paraphrasing existing authority sources and competing with Wikipedia-level sites for broad head terms. Most brands will never be the primary authority on those terms. That’s OK.
The real opportunity is becoming the authority on specific, detailed, often overlooked questions your audience actually has. The long tail is still wide open for brands willing to create thoughtful, experience-driven content that doesn’t already exist everywhere else.
We need to face facts. The fat head is shrinking. The land rush is now for the “fat tail.” Here’s what brands need to do to succeed:
Dominate searches for your brand
Search your brand name in a keyword tool like Semrush and review the long-tail variations people type into Google. You’ll likely find more than misspellings. You’ll see detailed queries about pricing, alternatives, complaints, comparisons, and troubleshooting.
If you don’t create content that addresses these topics directly — the good and the bad — someone else will. It might be a Reddit thread from someone who barely knows your product, a competitor attacking your site, a negative Google Business Profile review, or a complaint on Trustpilot.
When people search your brand, your site should be the best place for honest, complete answers — even and especially when they aren’t flattering. If you don’t own the conversation, others will define it for you.
The time for “frequently asked questions” is over. You need to answer every question about your brand—frequent, infrequent, and everything in between.
Go long
Head terms in your industry have likely been dominated by top brands for years. That doesn’t mean the opportunity is gone.
Beneath those competitive terms is a vast layer of unbranded, long-tail searches that have likely been ignored. Your data will reveal them.
Review on-site search, Google Search Console queries, customer support questions, and forums like Reddit. These are real people asking real questions in their own words.
The challenge isn’t finding questions to write about. It’s delivering the best answers — not one-line responses to check a box, but clear explanations, practical examples, and content grounded in real experience that reflects what sets your brand apart.
Expertise is now a commodity: Lean into experience, authority, and trust
Publishing expert content still matters, but its role has changed. Today, anyone can generate “expert-sounding” articles with an LLM.
Whether that content ranks in Google is increasingly beside the point, as many users go straight to AI tools for answers.
As the “expertise” in E-E-A-T becomes table stakes, differentiation comes from what AI and competitors can’t easily replicate: experience, authority, and trust.
That means publishing:
Original insights and genuine thought leadership from people inside your company.
Real customer stories with measurable outcomes.
Transparent reviews and testimonials.
Evidence that your brand delivers what it promises.
This isn’t just about blog content. These signals should appear across your site — from your About page to product pages to customer support content. Every page should reinforce why a real person should trust your brand.
Stop paywalling your best content
I’m seeing more brands put their strongest content behind logins or paywalls. I understand why. Many need to protect intellectual property and preserve monetization. But as a long-term strategy, this often backfires.
If your content is truly valuable, the ideas will spread anyway. A subscriber may paraphrase it. An AI system may summarize it. A crawler may access it through technical workarounds. In the end, your insights circulate without attribution or brand lift.
When your best content is publicly accessible, it can be cited, linked to, indexed, and discussed. That visibility builds authority and trust over time.
In a search- and AI-driven ecosystem, discoverability often outweighs modest direct content monetization.
This doesn’t mean content businesses can’t charge for anything. It means being strategic about what you charge for. A strong model is to make core knowledge and thought leadership open while monetizing things such as:
Tools.
Community access.
Premium analysis or data.
Courses or certifications.
Implementation support.
Early access or deeper insights.
In other words, let your ideas spread freely and monetize the experience, expertise, and outcomes around them.
Stop viewing content as a necessary evil
I still see brands hiding content behind CSS “read more” links or stuffing blocks of “SEO copy” at the bottom of pages, hoping users won’t notice but search engines will.
Spoiler alert: they see it. They just don’t care.
Content isn’t something you add to check an SEO box or please a robot. Every word on your site must serve your customers. When content genuinely helps users understand, compare, and decide, it becomes an asset that builds trust and drives conversions.
If you’d be embarrassed for users to read your content, you’re thinking about it the wrong way. There’s no such thing as content that’s “bad for users but good for search engines.” There never was.
Embrace user-generated content
No article on long-tail SEO is complete without discussing user-generated content. I covered forums and Q&A sites in a previous article (see: The reign of forums: How AI made conversation king), and they remain one of the most efficient ways to generate authentic, unique content.
The concept is simple. You have an audience that’s already passionate and knowledgeable. They likely have more hands-on experience with your brand and industry than many writers you hire. They may already be talking about your brand offline, in customer communities, or on forums like Reddit.
Your goal is to bring some of those conversations onto your site.
User-generated content naturally produces the long-tail language marketing teams rarely create on their own. Customers
Describe problems differently.
Ask unexpected questions.
Compare products in ways you didn’t anticipate.
Surface edge cases, troubleshooting scenarios, and real-world use cases that rarely appear in polished marketing copy.
This is exactly the kind of content long-tail SEO thrives on.
It’s also the kind of content AI systems and search engines increasingly recognize as credible because it reflects real experience rather than brand messaging many dismiss as inauthentic.
Brands that do this well don’t just capture long-tail traffic. They build trust, reduce support costs, and dominate long-tail searches and prompts.
In the age of AI-generated content, real human experience is one of the strongest differentiators.
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The new SEO playbook looks a lot like the old one
For years, SEO has been shaped by the limits of the search box. Short queries and head terms dominated strategy, and long-tail content was often treated as optional.
LLMs are changing that dynamic. AI is expanding search, not eliminating it.
AI systems encourage people to express what they actually want to know. Those detailed prompts still need answers, and those answers come from the web.
That means the SEO opportunity is shifting from competing over a small set of keywords to becoming the best source of answers to thousands of specific questions.
Brands that succeed will:
Deeply understand their audience.
Publish genuinely useful content.
Build trust through real engagement and experience.
That’s always been the recipe for SEO success. But our industry has a habit of inventing complex tactics to avoid doing the simple work well.
Most of us remember doorway pages, exact match domains, PageRank sculpting, LSI obsession, waves of auto-generated pages, and more. Each promised an edge. Few replaced the value of helping users.
We’re likely to see the same cycle repeat in the AI era.
The reality is simpler. AI systems aren’t the audience. They’re intermediaries helping humans find trustworthy answers.
If you focus on helping people understand, decide, and solve problems, you’re already optimizing for AI — whatever you call it.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/02/Grok-Highly-rated-space-heaters-made-domestically-bxRsJD.webp?fit=941%2C870&ssl=1870941http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-02-17 15:56:332026-02-17 15:56:33Why AI optimization is just long-tail SEO done right
While the growth of artificial intelligence (AI) and global conveniences like Amazon has been a great thing for society, there’s still an undercurrent of people returning to a local, more personal-feeling shopping experience.
But this “return to local” doesn’t change the fact that we still live in an internet age. Enter local search engine optimization (SEO) and landing pages.
Local SEO tends to work best for businesses with physical locations that require direct customer contact, but it can also work for virtual online businesses that don’t necessarily meet their customers before a business transaction takes place.
This is why local landing pages are so important. They can give customers the convenience of an online transaction while still providing the trust and personal feel of a local business—if your landing page is done right, of course.
Optimizing your landing page design with the proper elements can help you attract local customers to your business, increase lead generation, and boost conversion rates.
Key Takeaways
Local landing pages only work when they’re built for real locations and real intent. One page per city or service area, with localized keywords, metadata, and copy that matches how people actually search (“service + city” or “near me”).
Trust signals drive both rankings and conversions. Consistent NAP data, real reviews from nearby customers, local photos, and clear business details help you show up in map features and convince visitors to take action.
Content needs to feel local, not duplicated. Strong local landing pages include tailored copy, location-specific frequently asked questions (FAQs), social proof, and visuals that prove you serve that area, as opposed to generic pages with city names swapped in.
Mobile optimization is nonnegotiable for local SEO. Most local searches happen on mobile and convert fast. Pages must load quickly, display contact info above the fold, and make calling or getting directions effortless.
Schema markup and clear calls to action (CTAs) turn visibility into results. Structured data helps search engines and AI tools understand your business, while strong, localized CTAs guide users to call, book, or request a quote immediately.
Why Are Local Landing Pages Important?
Local landing pages help you show up when people search for services near them, and they’re key to winning conversions in your area.
Think about how people search: “best dentist in Austin,” “roof repair near me,” or “24/7 locksmith in Chicago.”
If you don’t have dedicated pages that target these local queries, you’re invisible in search engine results. In fact, recent stats show 80% of U.S. consumers surveyed search for local businesses online once a week, with about one-third (32%) searching for local businesses multiple times a day. Google’s local algorithm prioritizes relevance and proximity, and a well-optimized local page checks both boxes.
But optimizing your local SEO and landing pages is about more than appeasing Google’s algorithm. These pages can actually convert.
When someone lands on a page with your local address and glowing reviews from nearby customers, trust builds fast. In fact, according to Uberall.com, 85% of customers visit local businesses within a week of discovering them online. 17% of those visit the very next day. That’s why smart local businesses treat these like high-converting landing pages, not just generic content dumps.
With large language models (LLMs) and AI tools pulling content to answer local questions, the need for detailed, well-structured local pages becomes even more critical. These models lean on content that clearly signals relevance and authority, something a basic homepage or generic service page won’t do.
Bottom line: if local traffic matters to you, local landing pages need to be part of your SEO and conversion rate optimization (CRO) strategy.
Step 1: Identify where your customers are located.
Local landing pages only work when you know exactly which towns, neighborhoods, or service areas you’re trying to win. Otherwise, you can rack up traffic and still feel stuck because the visits come from places you can’t serve and don’t convert.
Start by answering two questions: Which locations do you want customers to come from? And which locations are they actually coming from today? Once you have both, planning local pages gets a lot easier.
Before you even open your reports, define your real-world service area. If you’re a storefront, your address needs to match how you operate in the real world (and be consistent everywhere it appears). If you’re a service-area business (such as a plumber, cleaner, or mobile vet), set a clear service area in your Google Business Profile so you don’t waste time targeting locations you can’t support.
Then, stop relying on a single data source. Use a few location signals together:
Google Analytics 4 (GA4) to spot city/region trends for session and key events (keep in mind location and demographics reporting is aggregated and can be limited by consent).
Google Business Profile to see who takes action: calls, direction requests, and website clicks.
Paid geo reporting in Google Ads to confirm what’s real versus inflated by “interest” targeting.
Finally, turn those insights into simple personas with local references, clear benefits, and social proof, so your page reads like it was made for that person in that place.
Step 2: Use localized keywords and metadata to create relevance.
Relevance still matters, but that doesn’t mean you can stuff a city name into every sentence and call it a day. Good local SEO matches what the searcher wants (intent) with what the page promises, starting right in the SERP.
Here’s the key difference: a local landing page usually targets transactional intent (“dentist in Austin,” “emergency plumber near me,” “book HVAC repair”), so your keyword + metadata strategy should read like a clear offer, not a watered-down blog headline.
Start with the basics that actually move the needle:
Title tag: Make a descriptive, concise, and unique title (Google can rewrite titles, but strong input helps). A simple formula works: Primary service + city + differentiator (and brand if it fits).
Meta description: Google primarily builds snippets from on-page content, but it may use your meta description when it better matches the query. Write unique descriptions per page, include the “what” + “where,” and add a reason to click (pricing, availability, social proof). Avoid long strings of keywords.
Meta keywords: Skip them. Google has said it ignores the keywords meta tag for web ranking.
Now, a quick warning: if you’re cranking out dozens of near-identical city pages that funnel to similar destinations, that’s exactly what Google calls doorway abuse. And lists of cities jammed onto a page can fall into keyword stuffing territory.
Step 3: Use consistent NAP data
NAP stands for name, address, and phone number, and it needs to be exactly the same everywhere your business appears online. That includes your local landing pages, your Google Business Profile, directories, and social platforms.
Why does this matter? Because Google (and users) rely on NAP consistency to trust your business is legit. Inconsistent info can hurt your rankings and knock you out of key local SERP features like the map pack.
Make sure your NAP is crawlable text, not embedded in an image. Add it in the footer or near your CTA, and match it letter-for-letter with your business listings. Even something small, like “Street” vs. “St.”, can throw off search engines.
If you serve multiple locations, each page should have its own unique NAP. No shortcuts here. Clean data builds trust, and trust drives clicks.
Step 4: Create and publish valuable content
Implementing local landing page design best practices in your content does two things: it helps you rank for location-specific searches and gives visitors a reason to trust you.
Start with copy that speaks directly to your audience in that area. Mention the city or neighborhood naturally, highlight the services you offer there, and include local differentiators like special hours or nearby service coverage. Make it feel personal.
Next, layer in content that builds credibility. Local reviews and case studies show real proof that your business delivers. Include names, star ratings, and even short quotes to make the social proof pop. Photos help, too. Real images of your team or completed projects add authenticity.
You should also include a brief FAQ section that answers questions specific to that location. Not only does this help your readers, but it also increases your chances of showing up in featured snippets or AI-generated results.
Every local landing page needs a clear call to action. Without it, you’re leaving conversions on the table.
The best CTAs guide visitors to take the next logical step, whether that’s calling your business, booking an appointment, or requesting a quote. To be effective, your CTA must feel local and relevant. “Get a Free Quote” is okay. “Get a Free Plumbing Quote in Phoenix” is better. It reinforces the location and makes the offer feel tailored.
Make sure your CTA stands out visually. Use buttons, bold text, and color contrast to grab attention. And don’t just put it at the bottom. Add it near the top of the page and repeat it throughout, especially after sections like testimonials or service descriptions.
If phone calls are your goal, use a click-to-call button—especially for mobile users. For forms, keep them short. Name, email, and one key question is usually enough.
Remember, your local landing page should do more than just inform, it should drive action. The CTA is where that happens.
Step 6: Optimize your local landing pages for mobile users
Mobile search isn’t just dominant, it drives action. In fact, 88% of mobile local business searches result in a call or visit within 24 hours, showing how urgent mobile intent has become.
Start with your page performance. Speed is critical. Slow mobile pages frustrate users and push them to competitors. Tools like Google PageSpeed Insights help identify bottlenecks, enabling you to improve load times by compressing images and deferring unused scripts. Fast pages mean better user experience (UX), which, in turn, leads to higher engagement.
Responsive design is nonnegotiable. Your layout must adapt to screens of all sizes with easily readable text and minimal pop-up interference. Prioritize large, clickable CTAs, and ensure your contact info is visible without scrolling.
Mobile users are often on the go. Clearly display your NAP details front and center, ideally above the fold. Clean navigation and quick access to key info make it easier for people to act immediately.
Step 7: Add schema markup
Schema markup helps search engines understand the context of your content, and that’s a big deal for local SEO.
When you add local business schema to your landing pages, you’re giving Google structured data that it can easily read. This increases the chances your business showing up in rich results like the map features or AI-generated summaries. It’s not just about visibility. It’s about making your information easier to find, trust, and act on.
At a minimum, include schema for your business name, address, phone number (NAP), hours of operation, and service area. This aligns perfectly with the on-page content you’ve already built. The more complete your schema, the more signals you’re sending to Google that your business is real, local, and helpful.
You can generate local business schema using tools like Google’s Structured Data Markup Helper or Schema.org. Then either embed it as JSON-LD in the <head> of your page or use a plugin if you’re on a platform like WordPress.
Don’t forget to test it. Use Google’s Rich Results Test to make sure your markup is working as intended.
It takes a few extra steps, but schema markup is one of the easiest technical wins you can add to a local landing page. It won’t guarantee rankings, but it gives your content a better shot at being seen and trusted.
FAQs
How do I create content for local landing pages for SEO?
Start with localized keywords (e.g., “[service] in [city]”) and ensure they appear naturally in your headlines and throughout the copy. Then, write content that actually helps local visitors: include location-specific details, highlight nearby landmarks, and speak directly to the needs of that community. Bonus points if you add customer reviews or links to local pages.
How to make local SEO landing pages
Structure each page around one location or service area with unique URLs (like /plumbing-los-angeles). Don’t forget your Google Business Profile and local schema markup. They help search engines match your page with nearby searchers.
How to optimize landing page for local SEO
Use consistent NAP (name, address, phone) info across the page and the web. Add a local map, embed reviews from customers in that area, and link internally to relevant services. Make sure your page loads fast and works well on mobile because that’s where most local searches happen.
Conclusion
To maximize your search results and lead generation, make sure that you design separate landing pages for each city that you’re targeting.
Above all, create unique, location-specific copy for your landing pages. Building a local landing page requires an investment. It could be the investment of your time, money, or both.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-02-11 20:00:002026-02-11 20:00:00The Step-by-Step Guide to Designing Local Landing Pages That Convert
Search engine optimization (SEO) was once defined by the number of keywords and synonyms scattered across your content. If you used the right word enough times, you’d rank.
Those days are long gone.
Since the launch of its Knowledge Graph in 2012, Google has been moving away from literal text matching toward deep semantic understanding.
Search engines no longer evaluate pages as collections of words. They evaluate meaning.
This goes beyond Google and search engine results pages (SERPs). Modern discovery operates on entities—distinct people, places, brands, and concepts connected through context and relationships. Search systems now interpret queries by mapping how these entities relate rather than counting keyword usage.
That’s where entity SEO comes in. Entity-based structures set the groundwork for the more intuitive search results we see today in AI platforms and large language models (LLMs). Grouping queries around one central “thing” gives these platforms a clear reference point they can connect to related concepts.
Ultimately, entity SEO helps these platforms research and provide information in a more human way. It gives us the answers we want quickly, and it powers Google’s more complex search features that take our query results beyond a simple list of blue links.
In this article, we’ll explain what entities are, how to use them, and how they’ll continue to shape the future of SEO.
Key Takeaways
Entity SEO focuses on clearly defined people, brands, products, and concepts and the relationships between them, rather than isolated keywords.
When Google understands the primary entity behind a page, it can rank that page across a broader range of relevant queries without exact-match targeting.
Site structure communicates meaning. Topic clusters, internal links, and consistent terminology help search engines map how content fits together.
AI-driven search relies on entity context to disambiguate terms and interpret intent, not keyword strings alone.
Maintaining consistent signals across pages and trusted third-party profiles strengthens entity recognition and long-term visibility.
What Is Entity-Based SEO?
Entity-based SEO uses context (not just keywords) to help users find exactly what they’re looking for.
You can see this shift in action every time you type a query. For example, when you type a common name like “Malcolm” into a search bar, Google doesn’t just look for those seven letters. It tries to determine which entity you’re looking for:
Google offers suggestions to searchers to provide immediate context. It speeds up the search for those looking for popular figures like Malcolm Gladwell or Malcolm X, and it prompts others to add more specific details if their intended “thing” isn’t listed.
Once you select a specific entity, the search engine stops scanning for keywords and starts delivering a comprehensive Knowledge Panel.
This layout displays the subject as a defined entity, grouping biographical details, books, and videos into a single source. While this shift makes search more intuitive for users, it makes things slightly more complicated for content creators.
Here are three ways entity-based SEO has changed the landscape:
AI visibility: Entity SEO revolves around an entity record. These records parse dozens of data points about a particular search query, making all information easy for AI platforms to access. Brands that structure their data properly make themselves much more visible in LLM search.
Better mobile capabilities: Entities allowed SEO to improve mobile results and improved mobile-first indexing.
Translation improvements: Entities can be found regardless of homonyms, synonyms, and foreign language use, thanks to context clues. For instance, a search for “red” will include results for “rouge” or “rojo” if the searcher’s settings allow it.
Let’s dig a little deeper into entity records to understand how they connect to LLMs and search engines like Google.
To start, let’s look at a hypothetical entity record about Taylor Swift:
This makes it clear how entity SEO works in practice. Search engines don’t rely on a single page or keyword to understand a brand. They aggregate structured signals across the web to build a unified view of the entity.
The reason behind this is that search systems and LLMs don’t read content the way humans do. They extract discrete facts, attributes, and relationships, then assemble them into a coherent understanding.
The example above illustrates how an entity can be broken into clear, machine-readable components.
Keywords vs. Entities: What’s the Difference
Entities might sound similar to keywords, but they’re actually quite different. Here’s how they differ and why those differences are so important.
Keywords
Keywords are words or phrases people use to express intent in search. They take many forms, including questions, sentences, or single words.
For example, users looking for makeup tutorials might search for “makeup tutorial,” “smokey eye,” “how to do a smokey eye,” or something similar.
Today, keywords tend to work best as demand signals rather than quotas to be filled. They show how users frame their intent, whether they want to learn, compare, buy, or solve a problem, and give you language to match your content to that intent.
That’s why long-tail queries and modifiers (“best,” “near me,” “for beginners,” “price,” “vs.”) are still gold.
These modifiers provide the intent that tells a search engine how to connect a user to your brand. Your goal is to rank for these high-intent terms to drive organic traffic and establish your site as the definitive source of truth for your niche.
Long-tail and informational (what, how, why) keywords also help you line up your content with where search is heading.
Data shows that about 90 percent of influential SERP features, like AI summaries and “People also ask,” come from queries like these, making them useful inputs for LLM-powered workflows like content production plans based on real query language.
If your page answers the query fully and clearly, you’re using keywords the modern way.
Entities
Google defines an entity as “a thing or concept that is singular, unique, well-defined, and distinguishable.” They can be people, places, products, companies, or abstract concepts.
What makes entities powerful is not just what they are, but how they connect. They are defined by their relationships to other entities, which helps search engines and LLMs understand how each concept fits into the “big picture.”
Once Google is confident about what your page is about, it can rank you for searches you never explicitly targeted. That happens because entities carry built-in relationships, including attributes, categories, synonyms, and commonly associated concepts.
This is where entity SEO really starts to differ from keyword-based optimization. Essentially, entity SEO prioritizes mentions and human discussion over keywords.
For example, a search for the word “apple” could result in pages about the fruit or pages about the company. As interesting as both topics are, reading about iPhones probably won’t be too helpful if you’re trying to figure out whether apple seeds are indeed poisonous.
You need to add some keywords or modifiers to give crawlers and LLMs context.
This is also why pages sometimes rank for “weird” keywords. If your content clearly describes the entity—what it is or related terms—Google can connect you to unexpected queries that share the same underlying intent. This concept is known as latent semantic intent (LSI).
That’s not magic. It’s entity understanding plus context signals.
For entities to be useful, search engines map them into knowledge graphs, which are structured systems that connect related information across the web and make retrieval more reliable.
As of May 2024, Google’s Knowledge Graph contains 1.6 trillion facts about 54 billion entities, and about 1.6 trillion facts about them. Not only do these data points help answer complex informational or long-tail queries, but they also power Google’s Knowledge Panel. Here’s an example:
To help search engines or LLMs make sense of which entity fits your query, you want the pages of your website to behave like solid references. Spell out defining details (names, dates, specs, locations), connect related subtopics, and use consistent terminology.
Add supporting cues like internal links to your own deeper pages and clear headings that map to common questions. Structured data is also key here, making it easier for engines to see specific information that you deem to be important on a given page, like product information, locations, or other items.
How Do Entities and Keywords Work Together?
An effective SEO strategy recognizes that keywords are the signals, but entities are the destination. On-page, you can treat your website as a mini knowledge graph that uses keywords to link to different pages on your site.
You can further validate your brand by connecting your content to established knowledge graphs like Wikipedia or LinkedIn, which are high in experience, expertise, authoritativeness, and trust (E-E-A-T). While this won’t directly affect your page rank, it can improve your page’s authority in search results.
Practically, this means your keywords should map to specific entity details (features, use cases, comparisons, FAQs, structured data). The clearer those entity connections are, the easier it is for search engines to match your page to related searches. That’s especially the case for those long-tail ones where intent is clear, but the wording is inconsistent.
How To Start Building Up Your Entity-Based SEO
The biggest upside of entity clarity is that it helps your whole site act like a connected knowledge hub. When search systems recognize your brand, products, services, locations, and experts as distinct entities, they can more accurately map your content to complex user intent.
Content Depth and Topical Relevance
Entity-based SEO nudges you away from thin, keyword-targeted pages toward deep, comprehensive content. Instead of fragmented articles, build authoritative topic clusters that cover definitions, use cases, and FAQs.
This depth reinforces the “identity” of your subject matter, signaling to search engines that your site is the definitive source for that specific entity across all related queries.
Strengthening Relationships via Internal Linking
Internal linking is the connective tissue of your entity strategy.
Consistently linking supporting content to a central entity page explicitly defines relationships for search engines. That can be as simple as connecting which services belong to which categories or which authors are connected to which brands.
This internal relationship graph is essential for earning broader semantic visibility and is a core component of reputation management, as it ensures search engines never lose the thread of who you are.
Consistency as a Signal of Authority
Your entity becomes much more powerful when your brand and authors remain consistent across the web. Using the same naming conventions, professional bios, and expertise signals makes it easier for search systems to verify your “identity.”
Consistency cuts through ambiguity to make sure your authority is attributed to the correct entity. And that goes a long way in preventing your brand from being confused with unrelated concepts.
Trust Signals and Entity Clarity
Trust signals like reviews and citations match up perfectly with entity clarity. Clear, consistent data—like name, address, phone number (NAP) details—help search engines attach your content to the right real-world entity for local SEO.
Modern algorithms prioritize clear signals like these when deciding which brands to feature in high-stakes search results and AI-generated overviews.
The Role of AI in Entity SEO
AI-driven search doesn’t “read” the web like a human. It builds a model of the world.
That model is made of entities (people, brands, products, places, concepts) and the connections between them.
That’s why entities are foundational. A keyword is just a string of text. An entity has a unique identity.
When Google sees “Jaguar,” it has to decide between the animal, the car brand, or the NFL team? AI makes that call by looking at entity context—nearby terms, linked pages, structured data, and known relationships in systems like the Knowledge Graph.
The screenshots below show how that entity resolution plays out in real search results. The same keyword produces entirely different SERPs based on which entity Google identifies as the best match.
This is also how AI gets better at interpreting intent.
Someone searching “best running shoes for flat feet” isn’t asking for a dictionary definition of shoes. They’re signaling a problem, a use case, a set of constraints.
Entity relationships help AI connect that query to brands, product categories, medical concepts, reviews, and comparisons before picking results that match the implied goal.
You can see the shift in your data. In Google Search Console, queries often widen into themes, with multiple variations driving impressions to the same page.
In the SERPs, features like Knowledge Panels, AI Overviews, and “People also ask” reflect entity understanding, not exact-match phrasing. Content performance aligns better with topic clusters and user journeys than with single keywords.
Entity SEO future-proofs your content by aligning with how AI systems learn.
If your pages clearly define the entities you cover, connect them with strong internal linking, and stay consistent in terminology and positioning, they’re easier to interpret, categorize, and reuse as search evolves.
How to Shift Your Strategy to Entity-Based SEO
Understanding entity SEO is only useful if it changes how you work. Here are the concrete changes that move a keyword-first strategy toward an entity-based one.
Identify Core Entities Tied to the Business
A core entity is a small, intentional set of “things” that you want Google to associate with your brand. It goes beyond what you want to rank for.
Start by pressure testing your site against three questions:
Who is this? (the brand/author entity)
What do they do? (the offering entity)
Who do they serve? (the audience/market entity)
If the answer to any of these feels fuzzy, your entities are too broad or buried within your content.
Keep core entities limited and intentional. Pick the ones that define your positioning, then give each one a clear home on the site.
An example structure might be: a homepage for the brand, service pages for offerings, an about page for brand/author credibility, and supporting content that links back to those pillars.
Build Topic Clusters Around Those Entities
One page can define the entity, but topic clusters give it depth and context. The goal is coverage, not volume.
For each core entity, build one primary page that acts as the hub (your “entity’s home”). Then publish supporting pages that answer related questions, common use cases, comparisons, and next-step topics that your audience actually searches for. This is known as the hub and spoke model.
Your supporting content should do three things:
Answer real follow-up questions.
Reinforce the same entity from different angles.
Link back to the hub page with clear, consistent anchor text.
That internal structure is what helps search engines connect the dots.
Reinforce Entities Through Internal Links and Content Structure
Internal links are how you “wire” entities together across your site. Structure matters as much as the words on the page.
Link pages with related topics, not whatever feels convenient in the moment. If two articles support the same entity, connect them. If a page is a subtopic, point it to the hub and to other closely related subtopics.
NerdWallet’s credit cards hub shows how internal linking reinforces entities, with a single category page connecting related subtopics like cash back, travel rewards, and balance transfers under one clear concept.
Keep your anchor text consistent and descriptive. And use the entity name (or a tight variation) instead of vague links like “click here” or “learn more.”
Make sure your cluster works both ways. In other words, supporting pages should link up to the main entity page, and related supporting pages should link to each other where it genuinely helps the reader move to the next logical question.
Maintain Entity Consistency Across the Site and Beyond
One way to leverage entity-based SEO is to list your business on directories across the internet. These directory sites are a popular data source for search engine crawlers and LLMs. Your Google Business Profile, for example, is used as a data source for the Google Knowledge Graph.
Other listing services, such as Yelp, can also help create strong, authoritative backlinks for your brand and define a well-known entity.
Listing sites may vary by location, so do your research when deciding where to list. Additionally, be sure to choose sites with high domain authority to improve your search engine standing.
Ultimately, consistency is key. Listing your business in multiple locations across the internet eventually turns entity signals into trust signals, but it’s important to list your business carefully.
Avoid using multiple names for the same entity and conflicting descriptions from page to page. Also, make sure your listings stay focused on topics related to entities in your industry. Don’t lose focus or drift to unrelated topics.
Prioritize Brand Building
Brand building is another essential tactic in entity-based SEO. Offline brand signals should be mirrored online wherever search engines and AI systems look for training data.
This includes your about page, author bios, case studies, podcast/webinar pages, and third-party profiles (Crunchbase, G2, LinkedIn, industry directories, etc.). For LLM optimization, you want consistent, crawlable signals in the places models and search engines pull from.
Use the same brand description, key services, and leadership names everywhere. That consistency makes it easier for systems to connect the dots.
Common Entity SEO Mistakes
Entity SEO fails when you treat it like a checklist instead of a system. These are some of the mistakes that do the most damage:
Treating schema as a shortcut. Markup helps Google label what’s on the page. It doesn’t create authority. If the content is thin or unclear, schema just highlights that faster.
Publishing thin entity pages. A quick definition page won’t earn trust. Weak entity pages struggle to rank, and they don’t attract links or support clusters.
Chasing unrelated entities. Dropping in trendy topics or random brands dilutes relevance. It can also confuse search engines about what you actually do.
Ignoring internal linking and structure. Entities need connections. If supporting pages don’t link to the hub (and to each other where it makes sense), Google can’t map the relationship.
Sending inconsistent signals. Mixed terminology, shifting positioning, and conflicting service descriptions make your entity harder to identify.
FAQs
What are entities in SEO?
Entities are the “things” search engines recognize—people, places, brands, concepts, and more. Unlike keywords, entities have context and relationships. Google uses them to understand meaning and intent. For example, “Amazon” as a company is an entity, and it’s different from the Amazon rainforest.
How do you find SEO entities?
Start with your main topic and use tools like Google’s Knowledge Graph, Wikipedia, and Ubersuggest to identify related entities. Look for people, brands, terms, and categories commonly associated with your topic. Also, check competitor content. What entities are they connecting to? Use this to build a structured, semantically rich content plan.
What is entity SEO?
Entity SEO is the practice of optimizing content around recognizable concepts, not just keywords, so search engines better understand and rank your site.
Conclusion
Entity SEO isn’t some advanced trick. It’s how modern search actually works.
Search engines no longer rely on traditional keyword research alone. They map concepts, understand relationships, and evaluate authority across connected topics.
If you want to stay visible long term, your content needs more than keywords.
Clarity and a strong topical focus are the way to go. That’s how you build trust with Google and future-proof your branding strategy as AI continues to reshape the search landscape.
Leaning into entity-focused optimization builds a durable presence that lines up with how users search and how Google works.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-02-10 20:00:002026-02-10 20:00:00Why Entity-Based SEO is a New Way of Thinking About Optimization
AI content generation for SEO can be a game-changer if you use it the right way.
AI tools help increase the speed of your content production, from brainstorming to drafting. And yes, we’ve built our own AI writer into Ubersuggest to make that process easier.
But here’s the thing: AI isn’t a shortcut to rankings. Without the right prompts and a human touch, AI content can actually hurt your traffic. Google’s recent updates and the rise of AI Overviews in search show just how important quality and clarity are.
So no, AI-generated content isn’t bad, but you need a strategy. Otherwise, it’s just more noise.
Key Takeaways
LLMs won’t cite your content unless it’s structured, trustworthy, and answers real user questions.
AI content generation for SEO works, but only with the right strategy and human oversight.
AI can speed up all stages of content production, but publishing without reviewing will tank your results.
Prompts matter. Clear direction on content structure and audience and strong keyword targeting separate ranking content from noise.
Human elements like originality, firsthand insights, and strong E-E-A-T signals are still non-negotiable.
AI VS Humans: Pros & Cons
With AI, we found that you can’t just publish the content it generates and go off to the races.
It still takes time to use AI.
From modifying the content to putting it in your CMS to adjusting the format, creating content takes time whether you use AI or not.
Here’s how long it takes to create content using AI versus a human.
When using AI we found that you can write content, post it into a CMS, and publish it all within 16 minutes.
Humans on the other hand took an average of 69 minutes.
But there are some issues that most people don’t talk about.
The first is AI takes what’s on the web and “regurgitates” the same old info.
People want to read something new…
The second is we found that 94.12% of the time human written content outranked AI-created content.
With that said, there is still a role for AI-generated content in an SEO strategy.
<h2>Does AI-Generated Content Support SEO? </h2>
Our findings aren’t all “doom and gloom” for AI, especially as platforms and LLMs evolve. It can absolutely support your SEO strategy, especially when it comes to scaling content or repurposing existing assets, but AI needs direction. If you feed it a vague prompt like “write a blog post about SEO,” you’ll get generic, surface-level content that won’t rank or convert.
Your prompt is essential in making AI-generated content SEO-friendly. You need to tell the tool exactly what keywords to target, what questions to answer, what structure to follow, and who the audience is. Doing that requires real marketing experience.
This is where human input and oversight still matter. You need to choose the right keywords and guide the AI to meet quality standards. AI is just guessing without that input, and that rarely ends well for SEO.
It’s also worth noting that while AI can help draft content, it won’t replace human editing. You still need someone to review for tone and voice accuracy, and depth.
<h3>Does AI-Generated Content Help with LLM Presence? </h3>
AI content won’t magically get picked up by LLMs. But with smart prompting and a clear optimization strategy, it can absolutely improve your chances.
Large language models (LLMs) like ChatGPT and Gemini pull from indexed content to generate answers. This process is known as retrieval augmented generation (RAG)
If your content is well-structured and authoritative, it has a better shot of getting cited or referenced in those answers, but generic content won’t cut it. These models are picky.
To actually earn LLM visibility, you need to create content that matches how LLMs surface information. That means answering specific questions, using structured data where it makes sense, and writing in a way that’s clear, concise, and trustworthy.
AI tools can help here, but again, prompting is key. If your AI-generated content isn’t shaped around real user questions or lacks structure that aligns with LLM output patterns, it’s unlikely to perform.
Digging deeper and learning more about LLM SEO and LLM optimization is a great way to improve your skills in this area. By understanding these concepts, you’ll learn exactly what to include in your content and how to use AI to get there.
Integrating AI Into Your Content Approach (The Right Way)
Used well, AI can help you move faster but it’s the human touches that drive results. You need to start thinking of AI as a starting point, not the whole process.
We ran an experiment across 68 sites, publishing 744 articles—half written by humans, half by AI. Five months in, the average AI article brought in 52 visitors a month. Human-written articles? 283.
Now, sure, you could scale faster with AI, but pumping out a ton of mediocre content does more harm than good. In fact, when we pruned low-quality posts, we saw an 11 to 12 percent traffic lift.
If you’re going to use a GenAI tool to do your writing, do it with intention:
Start with smart prompts. Include keyword targets and content goals.
Feed the tool solid references like existing content, credible sources, or structured outlines.
Don’t just hit publish. Run a full human review: fact-check, rewrite weak sections, fix tone issues, and make sure it aligns with your brand.
And here’s the secret sauce: add manual value. Include firsthand insights via screenshots or updated data. Layer in trust-building elements like personal experience or expert sourcing. That’s how you build E-E-A-T—Google’s framework for judging helpful, credible content.
FAQs
Is AI-generated content good for SEO?
It can be, if you do it right. AI can help you scale content creation, but you still need a human touch to make sure it’s high-quality and helpful. Google rewards useful content, not mass-produced fluff.
Does AI-generated content affect SEO?
Yes, but how it affects your SEO depends on what you publish. If your AI content adds value and matches search intent, it can help you rank. If it’s generic or purely written for keywords, it’ll likely hurt you.
Will Google penalize SEO content generated by AI?
Google will not penalize you for using AI alone. Google doesn’t care how content is made as long as it’s useful and trustworthy. But if the content is spammy or misleading, that’s where penalties come in.
Case Study: How We Use AI
AI’s biggest impact on our content writing process isn’t even the writing part.
It’s the research part.
For example, at NP Digital, we used AI to help UTI boost its traffic.
Instead of relying on AI to write extensive content, we leveraged it to create select drafts (which then undergo our human editing process) and assist us in conducting research for all the cities in which UTI has campuses.
This allowed us to scale the creation of their local pages and ensure high quality by leveraging our human content staff to incorporate other elements that would be useful for someone performing a local search.
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January didn’t bring flashy product launches. It brought something more valuable: clarity.
Platforms spent the month explaining how their systems actually work. Google detailed JavaScript indexing rules that matter for modern sites. Reddit opened up automation insights most platforms keep hidden. Amazon positioned itself as a legitimate cross-screen player with first-party data advantages traditional TV can’t match.
Automation kept expanding, but with firmer guardrails. AI continued to compress discovery. Zero-click experiences grew. Brands without clear expertise signals or off-site authority started disappearing from AI-generated answers.
For digital marketers, January reinforced one reality: performance in 2026 depends less on clever tactics and more on getting fundamentals right across channels.
Key Takeaways
Indexing logic must live in base HTML, not JavaScript. Google may skip rendering pages with noindex directives in initial HTML, leaving valuable content invisible even if JavaScript removes the tag later.
Performance Max channel reporting is now essential, not optional. Budget pressure is currently your sharpest lever for managing underperforming surfaces like Display or Discover.
Share of search is becoming a better demand signal than traffic alone. As AI reduces click-through rates, measuring how often people search for your brand versus competitors reveals momentum better than vanishing clicks.
Digital PR now directly impacts AI visibility. Authoritative mentions and credible coverage determine whether AI systems recognize and recommend your brand in zero-click answers.
Influencer marketing reached enterprise maturity in January. Unilever’s 20x creator expansion and 50% social budget shift prove influence at scale is baseline strategy, not experimentation.
Review monitoring must track losses, not just gains. Google’s AI is deleting legitimate reviews without notice, affecting rankings and trust faster than new reviews can rebuild them.
Search, SEO, and Indexing Reality Checks
Search teams started 2026 with clearer rules, not more flexibility. Google spent January confirming how it treats indexing signals on JavaScript-heavy sites.
Google Clarifies Noindex and JavaScript Behavior
Google confirmed that pages with a noindex directive in their initial HTML may not get rendered at all. Any JavaScript meant to remove or modify that directive might never execute.
Indexing intent belongs in base HTML. JavaScript should enhance experiences, not define crawl behavior. For headless stacks and dynamic frameworks, search engines respond to what they see first, not what you hope they’ll see after rendering.
If your site uses React, Next.js, Angular, or Vue with client-side rendering, audit how noindex tags are implemented. Server-side rendering or static generation solves most of these issues.
Google Clarifies JavaScript Canonical Rules
Google detailed how canonical tags work on JavaScript-driven pages. Canonicals can be evaluated twice: once in raw HTML and again after rendering. Conflicts between the two create real indexing problems.
Server-rendered HTML pointing to one canonical while client-side JavaScript points to another forces Google to pick. That choice often hurts rankings quietly, without throwing obvious errors in Search Console.
Teams need to decide where canonicals live and enforce consistency. One canonical after rendering. No ambiguity between server and client.
December Core Algorithm Update Wraps
Google’s December 2025 core update finished after roughly 18 days of volatility. Sites with stale content, weak expertise signals, or unclear intent lost ground. Others gained visibility by being more useful and better aligned with user needs.
Core updates no longer feel disruptive because they’re frequent. Three broad core updates rolled out in 2025 alone. The advantage now comes from consistent execution, not post-update recovery tactics.
Paid Search, Automation, and Audience Control
Paid media keeps moving toward automation. January showed where control still exists and where it doesn’t.
Using Google’s PMax Channel Report More Strategically
You still can’t control bids or exclusions at a granular level. What you can control is budget pressure. One surface consistently underperforming? Budget becomes your corrective lever. Pull back overall spend and PMax reallocates to better-performing channels automatically.
Teams that review this report monthly make better creative and investment decisions. Track this data over time. Patterns emerge. You start understanding which channels deliver at which funnel stages, even inside automation.
Google Drops Audience Size Minimums
Google lowered minimum audience size thresholds to 100 users across Search, Display, and YouTube. Previous minimums ranged from 1,000 users down to a few hundred depending on network and list type.
This opens doors for smaller advertisers and niche segments. Remarketing lists, CRM uploads, and custom audiences that previously failed minimums now become usable.
Smart teams will use this to test tighter segmentation strategies. But don’t chase volume that isn’t there. A 100-user audience won’t scale into a growth channel overnight.
Bing Tests Google-Style Ad Grouping
Bing briefly tested a sponsored results format similar to Google’s recent changes. Multiple ads grouped under a single label, with only the first result carrying an ad marker.
The test ended quickly, but the signal matters. Search platforms are converging on similar layouts. How ads appear now affects click quality and intent, not just click-through rate.
Social Platforms and Performance Content
Social platforms spent January rewarding clarity while punishing shortcuts.
Reddit Launches Max Campaigns
Reddit introduced Max Campaigns, an automated ad product handling targeting, placements, creative, and budget allocation in real-time.
What stands out is visibility. Reddit surfaces audience personas and engagement insights that most automated systems hide. Early testers report 27% more conversions and 17% lower CPA on average.
Testing works best when anchored to existing campaigns. Replicate your best-performing Reddit campaign as a Max Campaign. Let automation prove efficiency gains with known benchmarks.
Instagram Caps Hashtags
Instagram rolled out a five-hashtag limit across posts and reels. This confirms discovery on Instagram is driven by AI-based content understanding, not hashtag volume.
Hashtags now function like keywords. They clarify intent and help Instagram’s systems categorize content. They don’t manufacture reach.
Captions, on-screen text, subtitles, and visuals do the heavy lifting. Choose five hashtags that directly describe your content. Mix specificity levels: one broad category tag, two niche topic tags, one community hashtag, one branded hashtag.
LinkedIn Shares Performance Guidance for 2026
LinkedIn reiterated that human perspective drives performance. Video continues outperforming other formats. Hashtags do not impact distribution. Automated engagement and content pods face increased scrutiny.
Posting two to five times per week remains effective. AI can support thinking, but content still needs lived experience and clear points of view.
Brand Visibility, Authority, and Demand Measurement in an AI Era
AI-driven discovery is reshaping how brands get surfaced and evaluated.
What AI Search Means for Your Business
AI-generated summaries and zero-click experiences shape early discovery now. Users often form opinions before visiting a site. Google’s AI Overviews, ChatGPT’s SearchGPT, and Perplexity answer questions directly, compressing or eliminating the need to click through.
AI favors brands with clear expertise, structured content, and external validation. Generic explanations get compressed into summaries that strip away brand identity. Thin content disappears entirely.
Optimization now includes being understandable and credible to machines, not just persuasive to human readers. That means structured data markup, clear content hierarchy, author credentials, and topical authority signals.
Share of Search Becomes a Core KPI
As AI reduces click-through rates, traffic becomes a weaker signal of demand. Share of search fills that gap.
It measures how often people look for your brand compared to competitors. That correlates strongly with market share and future growth. Brands with rising share of search typically see revenue growth follow within quarters, even if organic traffic stays flat.
Calculate share of search by tracking branded search volume for your brand and key competitors over time. Tools like Google Trends, Semrush, or Ahrefs make this accessible.
Digital PR Matters More Than Ever
AI systems recommend brands they recognize and trust. That trust is built off-site, not through on-page optimization.
Authoritative mentions, expert commentary, and credible coverage now influence visibility across AI-driven experiences. Links still matter, but reputation matters more.
PR, SEO, and content strategy can no longer operate independently. Authority compounds when they align. If you’re not investing in Digital PR alongside traditional SEO, you’re optimizing for a search ecosystem that’s rapidly shrinking.
Video, CTV, and Cross-Screen Media Strategy
Video buying is consolidating across screens.
Amazon Emerges as a Cross-Screen Advertising Player
Amazon is positioning itself as a unified advertising ecosystem across Prime Video, live sports, audio, and programmatic inventory. Layered with first-party shopper data, this creates a powerful performance and measurement advantage traditional TV buyers can’t match.
Amazon now competes higher in the funnel through premium video and live sports while retaining lower-funnel accountability through its commerce data. Interactive features let you add “add to cart” overlays directly in OTT video ads.
CTV Breaks the 30-Second Format
Streaming dominates TV consumption. Ad formats are finally catching up. Interactive and nontraditional CTV units are gaining traction, supported by early standardization efforts from IAB Tech Lab.
Traditional :15 and :30 second spots still work, but they blend into an increasingly crowded environment. Emerging formats offer differentiation in lower-clutter streaming contexts.
Brands that test early build creative and performance advantages before these formats normalize and competition increases.
Pinterest Acquires tvScientific
Pinterest’s acquisition of tvScientific connects intent-driven discovery with CTV buying. This closes a long-standing measurement gap between inspiration and awareness channels.
For brands rooted in discovery—home decor, fashion, food, travel, DIY, beauty—this creates a clearer path from interest to action.
Brand-Led Attention and Influence at Scale
Attention increasingly flows through people, communities, and culture-driven media.
Unilever’s Influencer Expansion
Unilever announced plans to work with 20 times more influencers and shift half its ad budget to social. This isn’t a test. It’s a structural reallocation signaling influencer marketing has reached enterprise maturity.
Unilever’s SASSY framework now activates nearly 300,000 creators. The company reported category-wide outperformance, attributing significant gains to influencer-driven campaigns.
Brands still treating creators as side projects will struggle to compete against organizations running influencer programs with the same rigor and budget as paid search or programmatic display.
Google’s AI Is Deleting Reviews
Google’s AI moderation is removing reviews at scale, including legitimate ones, often without notice. Business owners report hundreds of reviews disappearing overnight.
That affects rankings, conversion rates, and consumer trust. Reputation strategy now includes monitoring review loss, not just tracking new reviews.
Check your Google Business Profile weekly. Document total review count and average rating. When drops occur, investigate patterns. Better yet, diversify review platforms beyond Google.
Experimentation and Growth Discipline
Sustainable growth depends on knowing why a test exists before judging its outcome.
Growth vs Optimization: Drawing the Line
Growth experiments explore new opportunities. Optimization improves what already works. Blurring the two creates misaligned expectations and poor decision-making.
Clear intent leads to clearer measurement and stronger buy-in. Teams that label tests correctly scale with more confidence.
What Digital Marketers Should Take Forward
Platforms are clarifying rules. AI rewards authority and consistency. Measurement is shifting away from clicks alone.
The advantage in 2026 comes from alignment across teams and channels. Durable signals outperform clever workarounds.
Indexing logic must live in base HTML. Performance Max channel reporting is essential. Share of search reveals momentum. Digital PR impacts AI visibility. Influencer marketing reached enterprise maturity. Review monitoring must track losses.
This is the work we focus on every day at NP Digital.
If you want help aligning fundamentals across SEO, paid media, content, and PR in a way that compounds over time, let’s talk.
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