How to Create a Wikipedia Page for Your Company

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. 

An example of a sandbox page on Wikipedia.

Source: https://courses.shroutdocs.org/tutorials/editing-your-wikipedia-sandbox/

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.

To register, use Wikipedia’s account creation form.

The Create Account Page on Wikipedia.

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.

An Autoconfirmed Wikipedia account.

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.”
  • Secondary sources (best for Wikipedia): Independent journalism, books, academic analysis, reputable profiles.
    • Upside: Shows the world noticed you. This is the backbone of the strongest pages.
    • Downside: Harder to earn, and fluff pieces don’t carry much weight.
  • Tertiary sources: Encyclopedias, databases, reputable directories.
    • 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:

  1. Scan the citations first. Are they mostly independent, secondary news coverage, press releases/company sites, or paid placements?
  2. 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.
  3. Look at the page history and Talk page. Lots of reverts, banners, or sourcing disputes usually mean the page is shaky.
  4. 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.

Start with the standard Wikipedia “shape”

Most company pages follow a formulaic layout:

  • 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).
An example company Wikipedia page.

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.​
  • Product-benefit phrasing stacked repeatedly (“includes tools for…,” “enables…,” “helps…”).​
  • 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:

  1. 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.​
  2. 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.​
  3. 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.​
  4. “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.

The Wikipedia Article Wizard confirming a page was uploaded.

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 looking to 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.

Read more at Read More

How to Leverage Google Natural Language to Boost Your ASO Efforts 

Over the past year, Google has significantly accelerated its investment in artificial intelligence and machine learning across its products and platforms. While most marketers are familiar with ChatGPT, Google has been advancing its own AI capabilities in parallel, including the relaunch of Bard as Gemini and the steady rollout of AI-assisted features across Google Play.

For app marketers and ASO specialists, these developments are not abstract. They represent a fundamental shift in how apps are understood, categorized, and surfaced to users. Google Play is no longer relying primarily on keyword matching. Instead, it is moving toward a deeper, semantic understanding of apps, their functionality, and the problems they solve.

This evolution raises an important question. If Google increasingly generates, interprets, and evaluates app metadata itself, how do ASO teams maintain control, differentiation, and long-term competitive advantage?

One underutilized answer lies in a tool that has existed for years but is rarely discussed in an ASO context: the Google Natural Language.

Key Takeaways

  • Google Play is moving away from keyword density and toward semantic understanding driven by machine learning and natural language processing.
  • The Google Natural Language provides valuable insight into how Google interprets app metadata, including entities, sentiment, and category relevance.
  • Optimizing for category confidence and entity relevance can improve keyword coverage and resilience during algorithm updates.
  • ASO teams that align metadata with user intent and natural language patterns are better positioned for long-term discovery performance.
  • Using tools like the Google Natural Language helps future-proof ASO strategies as automation and AI-driven ranking signals continue to expand.

Why Traditional ASO Signals Are Losing Impact

Before exploring how the Google Natural Language can support ASO, it is important to understand the broader shifts in Google Play’s ranking algorithms.

Over the past two years, Google Play has shifted away from frequent, visible algorithm swings towards a more continuous learning model. While ASO teams still see volatility, it is now driven less by discrete updates and more by ongoing recalibration as models ingest new behavioural, linguistic, and performance data. Reindexing events still occur, but they are increasingly tied to semantic reassessment rather than simple metadata changes.

At the same time, the effectiveness of traditional optimization levers such as keyword density, exact-match repetition, and rigid keyword placement has continued to erode. These tactics no longer align with how Google Play evaluates relevance.

Like Google Search, Google Play is now firmly optimized for meaning, not mechanics. Its systems are designed to understand intent, function, and audience context rather than rely on surface-level keyword signals. The algorithm is increasingly capable of identifying what an app does, who it serves, and the problems it solves, even when those ideas are expressed using varied, natural language.

This is where natural language processing becomes central to modern ASO tools and practices.

Explanation of Natural Language processing.

What is the Goal of the Google Natural Language

Google Natural Language is designed to help machines understand human language in a way that more closely mirrors human interpretation. It powers a wide range of Google products and capabilities, including sentiment analysis, entity recognition, content classification, and contextual understanding.

In practical terms, it analyzes a body of text and identifies:

  • The overall sentiment and tone.
  • Key entities and their relative importance.
  • The categories and subcategories that the content most strongly aligns with.

For ASO teams, this offers a rare opportunity. Instead of guessing how Google might interpret app metadata, it provides a proxy for understanding how Google’s machine learning systems read and categorise text.

Used correctly, it can help ASO specialists align metadata more closely with Google’s evolving ranking logic.

How Google Natural Language Applies to ASO

When applied to app metadata, Google Natural Language can reveal how Google is likely to associate an app with certain concepts, categories, and keyword themes. This insight is particularly valuable as keyword density becomes less influential and semantic relevance takes priority.

Below are the key components that matter most for ASO.

Sentiment Analysis

Sentiment analysis evaluates the emotional tone of a piece of text and categorises it as positive, negative, or neutral. While sentiment is not a primary ranking factor for app discovery, it does provide useful contextual information.

For example, overly promotional, aggressive, or unclear language can introduce noise into metadata. Reviewing sentiment outputs can help teams ensure that descriptions maintain a clear, neutral, and informative tone that supports both user trust and algorithmic interpretation.

Entity Recognition and Salience

Entity recognition identifies specific entities within a text and classifies them into predefined types such as company, product, feature, or concept. Each entity is assigned a salience score, which reflects how central that entity is to the overall content.

In an ASO context, entities might include:

  • Core app features
  • Functional use cases
  • Industry-specific terms
  • Recognisable product or service concepts

Salience scores range from 0 to 1.0. Higher scores indicate that an entity plays a more important role in defining the content.

From an optimization perspective, this is critical. If key features or use cases are not appearing as highly salient, it suggests Google may not be strongly associating the app with those concepts.

Strategically incorporating relevant entities into metadata in a natural, user-focused way can improve clarity and strengthen topical relevance. Placement also matters. Important entities that appear early in descriptions or are reinforced toward the end of the text tend to carry more weight.

Metadata entities.

Categories and Confidence Scores

Category classification is arguably the most impactful element of Google Natural Language for ASO.

When text is analyzed, it assigns it to one or more categories and subcategories, each with an associated confidence score. These scores indicate how strongly the content aligns with a given category.

For Google Play, this has major implications. Higher category confidence increases the likelihood that an app will be associated with a broader range of relevant search queries within that category. Rather than ranking for a narrow set of exact keywords, apps can gain visibility across an expanded semantic keyword space.

In practice, we have seen that improving category confidence can significantly enhance keyword coverage and ranking stability, particularly during periods of algorithm change.

To increase category confidence:

  • Use clear, natural language that reflects real user intent
  • Focus on describing functionality and value, not just features
  • Avoid keyword stuffing or forced phrasing
  • Reinforce category-relevant concepts consistently throughout metadata
Hinge's Dating App.

Applying GNL Insights to Metadata Strategy

The real value of Google Natural Language lies not in isolated analysis, but in iterative optimization. By repeatedly testing metadata drafts through the Google Natural Language, ASO teams can refine language until category confidence, entity salience, and overall clarity improve.

This approach aligns well with broader 2026 ASO best practices, which emphasize:

  • User intent over keyword lists
  • Semantic relevance over repetition
  • Long-term stability over short-term gains

Case Study Insights

We have applied GNL-driven optimisation techniques across multiple app categories. While results vary by vertical, the overall pattern has been consistent.

During periods of significant Google Play algorithm updates, apps optimized around category confidence and entity relevance showed greater resilience. In several cases, visibility improved despite widespread volatility elsewhere in the store.

In one example, keyword coverage expanded substantially following metadata updates that increased confidence across both a core category and secondary related categories. This translated into a more than fivefold increase in organic Explore installs over time.

A Yodel Mobile case study about keyword coverage.

These results reinforce an important principle. When ASO strategies align with how Google understands language, they are better positioned to benefit from algorithm evolution rather than being disrupted by it.

Connecting GNL to 2026 ASO Strategy

Looking ahead, the role of natural language processing in app discovery will only grow. As Google continues to automate metadata creation and interpretation, manual optimization will shift from mechanical execution to strategic guidance.

ASO teams that understand and leverage tools like Google Natural Language will be better equipped to:

  • Guide AI-generated content rather than react to it
  • Maintain differentiation in an increasingly automated ecosystem
  • Build metadata that supports both paid and organic discovery

This approach also complements broader trends such as AI-powered search, cross-platform discovery, and privacy-first measurement frameworks.

Conclusion

The rise of natural language processing does not signal the end of ASO. Instead, it marks a shift in how optimization should be approached.

By moving beyond keyword density and embracing semantic relevance, ASO teams can align more closely with Google’s evolving algorithms. Google Natural Language offers a practical way to understand how app metadata is interpreted and how it can be improved to support discovery, conversion, and long-term stability.

As automation continues to expand across Google Play, the teams that succeed will be those who understand the systems behind it and adapt their strategies accordingly. Natural language optimization is no longer optional. It is becoming a core pillar of modern ASO.

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The Step-by-Step Guide to Designing Local Landing Pages That Convert

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.”

A local landing page.

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.

An AI overview of what are some of the best locksmiths in Chicago.

Bottom line: if local traffic matters to you, local landing pages need to be part of your SEO and conversion rate optimization (CRO) strategy.

A chart showing top ranking factors for the Local Pack.

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).
Demographics overview for Google Analytics 4.

Source

  • Google Search Console to see the “intent layer”—which local queries are driving clicks and impressions.
Google Search Console's intent layer.

Source

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.

A landing page for an Austin dentist.

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.

An infographic on how to create NAP data.

Source

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.

Source

Step 5: Add an effective CTA

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.

Google PageSpeed Insigihts.

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.

Schema markup in action.

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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.

However, it’s become a lot easier these days because of the plethora of landing page creators and landing page templates.

Read more at Read More

Why Entity-Based SEO is a New Way of Thinking About Optimization

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:

A Google search dropdown for the name “Malcolm,” showing a Knowledge Panel for author Malcolm Gladwell alongside various entity-based search suggestions like “Malcolm in the Middle” and “Malcolm X.”

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.

A Google search results page for "Malcolm Gladwell" showcasing a comprehensive Knowledge Panel. The layout displays the subject as a defined entity with categorized data points, including a photo gallery, biographical details (age, parents), linked YouTube videos, and a list of his published books, like "The Tipping Point" and "Revenge of the Tipping Point."

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:

  1. 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. 
  2. Better mobile capabilities: Entities allowed SEO to improve mobile results and improved mobile-first indexing
  3. 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:

A hypothetical entity record.

(Image Source)

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.

Google search results page for “how to do a smokey eye,” showing a video carousel with multiple YouTube makeup tutorials and a step-by-step blog result below.

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. 

A side-by-side comparison illustrating entity disambiguation. On the left is a realistic photo of a red apple fruit; on the right is the minimalist black logo of Apple Inc., the technology company.

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:  

A Google Search Results Page for "Eddie Aikau" featuring a Knowledge Panel highlighted in a red box.

(Image Source)

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.

Google search results for “jaguar animal,” showing an animal Knowledge Panel with images, facts, and Wikipedia information about the jaguar species.

Google search results for “jaguar car,” displaying a brand Knowledge Panel for Jaguar as a luxury vehicle manufacturer with models, company details, and images.

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.

NerdWallet credit cards hub page showing a central “Credit Cards” category with multiple subcategory links, including cash back, travel rewards, balance transfer, and business credit cards.

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.

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B2B Social Media Marketing: Build a Winning Strategy

While most direct-to-consumer brands are maximizing their social media presence with polished content and paid ads, many business-to-business companies (B2Bs) are still stuck in broadcast mode. They treat social like a checkbox or, worse, avoid it altogether. That’s a miss.

Your buyers are on these platforms every day, scrolling LinkedIn between meetings, watching YouTube explainers, and even picking up insights on TikTok.

The good news is that most of your competitors aren’t doing this well. And B2B social follows different rules. It’s less about selling, more about showing up with value and building trust over time.

This guide breaks down the platforms, strategy, and mistakes to avoid so you can stop blending in and start building something that drives real results.

Key Takeaways

  • Most B2B brands underperform on social because they focus on broadcasting, not solving problems or creating value.
  • LinkedIn leads for B2B, but platforms like YouTube, X, and even TikTok can work if you match the content to your audience.
  • B2B social content should educate, not sell. Use it to build trust and stay relevant throughout long sales cycles.
  • Build a strategy around real personas, funnel stages, and platform-specific content—not random posting or vanity metrics.
  • Avoid common mistakes like generic messaging and chasing impressions over actions like clicks or demo signups.

Why B2B Social Media Is (Still) Underrated

Many B2B companies still treat social media as an afterthought. They post a few updates, maybe recycle some blog content, and call it a day. But here’s the truth: social media isn’t just about brand awareness anymore. It plays a fundamental role in demand gen, and even sales.

Your buyers are on these platforms every day. LinkedIn? Still essential. YouTube? Massive for education. Twitter (X)? Great for thought leadership. Even TikTok is becoming a serious B2B player in some niches.

If you’re only thinking top-of-funnel, you’re missing the bigger picture. Social gives you direct access to influence buying decisions, build relationships, and stay top of mind during long sales cycles. It’s also a powerful signal for search. That’s why smart B2B brands treat social like a core channel, right alongside their email, paid, and B2B SEO strategies.

So yes, B2B social still flies under the radar but that’s your opportunity. While your competitors play it safe, you can build a strategy that actually drives the pipeline.

Top B2B Social Platforms

Not all platforms are worth your time, but these are a good starting point. Here’s a breakdown of the top B2B social channels and how to use each one to actually move the needle.

LinkedIn

B2B marketers love LinkedIn, with 97% of them using it for their content marketing strategy.

There’s a reason for this: LinkedIn is effective at securing leads.

The social goal of most B2Bs isn’t just traffic. It’s the right kind of traffic. More specifically, it’s leads from that traffic. That’s why LinkedIn has been the social media sweet spot of most B2Bs.

Social platformrs compared in a graphic.

LinkedIn does for B2Bs what Facebook, X, and Pinterest have all failed to do. It forms professional connections based on a single goal.

It’s not that Facebook, X, and all the rest are more personal and less professional than LinkedIn. LinkedIn brands itself as a professional networking site. On LinkedIn, you see fewer baby pictures, fewer cat videos, and nothing about “Dave just checked in at Downtown Bar.”

LinkedIn, devoid as it is of issues like “relationship status” and “favorite TV shows,” is much more appealing to the world of B2B exchanges.

A HubSpot Linkedin Post.

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X

X still punches above its weight for B2B if you use it right. It’s built for real-time conversations. That makes it great for PR moments and quick interactions with your audience or peers.

If you’re in tech or SaaS, this is where your buyers and early adopters are already talking. Threads and hot takes can build credibility fast, as long as you’re consistent and actually say something worth engaging with.

Just don’t expect conversions. X is a conversation starter, not a closer. Use it to build visibility, shape perception, and stay in the mix.

An Adobe X post.

Source

YouTube

YouTube is a goldmine for B2B content that keeps working long after you hit publish. Think product demos, how-to explainers, or customer stories, anything that helps prospects see your value in action.

It’s perfect for long-form content with high evergreen potential. A solid video can rank in search, appear in recommended feeds, and continue to drive traffic for months (or even years). And because Google owns YouTube, it plays nice with your overall SEO strategy.

Use it to educate, build trust, and answer the questions your audience is already Googling. Just keep the production clean and the content useful.

Image Source

Link to Video

TikTok + Instagram (Yes, Really)

These aren’t just playgrounds for influencers anymore. TikTok and Instagram can actually work for B2B if you play to their strengths. Short-form video is perfect for showing off your brand personality, simplifying complex ideas, or giving a behind-the-scenes look at your team.

They’re especially useful for building an audience that sees your brand as more than just a logo. Quick explainers and team moments go a long way here.

The key is to be intentional. You don’t need to chase every trend, but you do need to show up as a genuine person, not a corporate account.

A Zapier TikTok post.

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A Shopify Instagram post.

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How B2B Social Media Needs To Work Differently

Most B2B social strategies fall flat because they treat platforms like a digital brochure. Too much product pushing. Not enough problem-solving.

Your buyers don’t scroll through LinkedIn or YouTube looking for a sales pitch; they’re looking for answers. That’s your opportunity. When you lead with value, you earn attention. And in B2B, attention is the first step toward trust.

This isn’t about trying to “go viral.” It’s about consistently showing up with content that solves real problems. That might look like a short video explaining a common pain point or a post breaking down industry trends.

Educational content works because it positions you as a guide, not just a vendor. It says, “We get your world. Here’s how to navigate it better.” That’s way more powerful than just listing your features.

You also need to show up like a human. Buyers are smart. They can sniff out polished sales copy in seconds. What they actually want is an honest perspective, clear thinking, and content that feels like it came from someone who’s done the work. That’s how you build an audience that actually wants to hear from you, and buyers who remember your name when it’s time to act.

Building a B2B Social Media Strategy That Works

A solid B2B social strategy doesn’t mean posting constantly. It means making smarter posts. Here’s how to build a plan that actually drives results across the funnel.

Know Your Customer Profiles

Before planning content, you need to be clear about who you’re actually talking to. Who’s following you now, and who do you want to attract?

An ideal customer profile.

Source: The Smarketers

B2B audiences aren’t one-size-fits-all. A CMO wants high-level insights and strategic trends. A sales manager cares more about tactics and results. Founders might look for big-picture thinking or lessons from the trenches. If you post the same content to all of them, you’ll miss the mark every time.

Start by segmenting your audience. Review your analytics and consult with your sales team, then map out which personas matter most for your business and what they care about.

Also, know where they hang out. Your audience might be active on LinkedIn and totally absent on Instagram. Or maybe they’re watching explainers on YouTube but ignoring X. Match your platform and content format to what your ideal customer actually uses and engages with. That’s how you create content that lands.

Set The Right Goals/KPIs

If you don’t know what you’re aiming for, it’s easy to waste time chasing the wrong metrics. Start by defining what success actually looks like for your brand.

Is your focus on awareness? Then you’re tracking reach, impressions, and follower growth. Want to drive engagement? Look at comments, shares, and saves, not just likes. If lead gen is the goal, prioritize CTRs or traffic to high-intent landing pages.

You might also be building community or educating users on your product. In those cases, qualitative feedback can be a stronger signal than raw numbers.

The key is to tie your content back to goals that matter for the business—and track the right KPIs for each. Don’t get distracted by vanity metrics that look good but don’t move the needle. Set benchmarks, track consistently, and optimize based on what’s actually working.

Build A Content Marketing Calendar

An effective content calendar maps content to each stage of the funnel, so you’re guiding prospects from awareness to action and making the most of your b2b content strategy.

At the top of the funnel (TOFU), focus on educational content. Think industry stats and quick tips that stop the scroll and add value fast. For the middle (MOFU), shift to case studies and testimonials that build trust and show proof. Bottom-of-funnel (BOFU) content should drive action—think offers and clear (call to actions) CTAs.

A Linkedin Post from Neil Patel with a graphic.

A well-planned calendar also helps you stay consistent without burning out your team. You can batch content and avoid that last-minute “what do we post today?” panic.

Turn Employees and Executives Into Advocates

People trust people, not brands. That’s why employee advocacy is one of the most powerful (and underused) tools in B2B social.

When your team shares content, adds their take, or shows up in the comments, it expands your reach and adds credibility. Their networks are often full of the exact decision-makers you’re trying to reach. And posts from real people perform better than anything coming from a company page.

The same goes for your leadership team. Help your CEO or founder post in their own voice, not just polished PR copy. A short LinkedIn post sharing a real insight or lesson learned often lands better than a glossy video.

A Linkedin Post from an NP Digital employee.

Make it easy for your team to participate. Share post templates, content ideas, or just ask them to weigh in on relevant threads. The goal isn’t to turn everyone into a creator—it’s to activate your people as trusted voices for your brand. The image above shows how to do this versus something to the effect of “helping your CEO or founder.”

Measure, Learn, Optimize

If you’re not measuring, you’re just guessing. The best B2B social strategies are built on real data, not hunches.

Start with the basics: engagement rate, impressions, and click-throughs. Track how often people interact with your content and where they go next. Are they hitting your demo page? Signing up for a webinar? Those are signals your content is working.

Use tools like GA4 and each social platform’s native analytics to connect the dots. Don’t just track what performs best. Look at why. Was it the topic? The format? The tone?

Speaking of format, test everything. Short videos. Carousels. Polls. First-person posts. What works on LinkedIn might fall flat on X. What drives DMs might not drive clicks. The only way to know is to try.

Then optimize. Double down on what works. Cut what doesn’t. Keep tweaking until your content not only earns attention but drives action.

Additional Strategies For B2B Social Media

Once your core strategy’s in place, these advanced plays can help you scale faster, get more mileage from your content, and squeeze more value out of every post.

Figure Out a Non-boring Angle

A lot of B2Bs feel like they’re boring, and this perception of being a boring company becomes a self-fulfilling prophecy. Because they think they are boring, they write boring articles and make boring social media posts.

Let’s look at a company that sells project management software. On the surface, nothing is exciting about that product or industry, but when you start to look at how the product can help your customer, things become unboring very quickly.

A new project management platform can include cool features for collaboration. It could also increase productivity or help business teams achieve goals that previously seemed out of reach.

Your job is to “sell the sizzle.” Put yourself in your customer’s shoes and brainstorm the solutions your product or service can provide their business that will get them excited!

Each B2B with an unintelligible product or service needs to develop an angle that is both understandable and appealing to a broader audience. This will allow them to create an initiative or idea that can gain traction on social media.

You can find an unboring angle. Once you do, you’re ready to roll forward with your social media efforts.

Feature A Human Aspect

One of the major shortcomings of many B2Bs, is the lack of a genuine human backing their efforts.

The lack of real people makes the B2B company seem so distant and unreal. It’s like talking to a robot. It just doesn’t feel right.

Every B2B needs to make an intense effort to humanize their brand tone and voice on social media and content marketing. Here’s what this looks like in practice:

  • Using first-person voice when writing updates and articles
  • Using a brand front person to tweet, post updates, and write articles
  • Using real people with their names in customer service
  • Initiating engagement and outreach from a real person
A Hubspot Linkedin Post.

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Hire The Right Person

B2Bs are often challenged in social media because they don’t hire the right person to manage their social media efforts.

Here are a few tips to help a B2B hire the right person for social media:

  • Hire an expert in social media. Look for someone who has social media success in a similar niche, but not necessarily in your own niche.
  • Hire a social media consulting company or agency, not just an individual. Companies often have more resources at their disposal. For a lower price, they can help you engage on multiple levels, such as creating social media graphics and writing content.

Anyone leading a social media initiative must have familiarity with the industry. But B2Bs also need someone who is a social media ninja. Why? Because B2B social media is a hard nut to crack. It’s not inherently sexy or awesome. It doesn’t automatically generate buzz. It takes a social media expert to really unleash the hidden power in B2B social media.

Brands need someone who can develop a social media movement, shaping the brand’s voice and expanding its reach. It’s not just status updates. It’s an entire identity creation.

If the first objective of social media is leads, then things have gotten off on the wrong foot. Leads don’t come first. Engagement and presence come first. Leads are a byproduct. This goes back to the “unboring angle” I mentioned above.

Back Your Social Media With Content Marketing

There is no such thing as a successful social media campaign without a successful content marketing campaign. They’re like two links in an indestructible chain.

Fortunately, about half of B2B companies understand the importance of content marketing, according to Statista. They realize it’s essential for customers to trust their brand, and they know how far content marketing can go in solidifying that trust. 

I’m convinced that the better a B2B company is at content marketing, the more effective they will be at social media.

This article is not the place to discuss the ins and outs of B2B content marketing. Instead, I’ll point out that the company should find the most engaging form of content and share it on social media.

Common B2B Social Mistakes

Most B2B social feeds feel like a wall of noise. Why? Because too many brands treat social like a megaphone instead of a conversation. Here are some of the biggest mistakes I see with B2B social accounts:

  • Constantly pushing products and making salesy updates, treating your account like a billboard. If your posts aren’t solving a problem or offering insight, don’t expect engagement.
  • Posting just to stay “active.” If your content calendar is driven by days of the week instead of strategy, your audience will feel it. Every post should aim to educate, engage, or move someone closer to buying.
  • The platform dilemma. What works on LinkedIn won’t work on TikTok. You need to adapt your message, tone, and format based on where you’re showing up and who you’re trying to reach.
  • Tracking the wrong metrics. Chasing impressions or vanity metrics won’t tell you what’s driving value. Prioritize metrics like click-throughs and demo page visits—things that tie back to real business outcomes.

Avoid these traps, and you’ll be in much better shape than most of your competition.

FAQs

Which social media platform is best for B2B marketing?

LinkedIn is the go-to platform for most B2B brands. It’s built for professional networking and decision-maker engagement, making it ideal for thought leadership and brand awareness. But depending on your audience, YouTube, X (Twitter), and even TikTok can play a role too.

How to use social media for B2B marketing?

Start by sharing content that solves real problems—think educational posts, customer stories, and product demos. Focus on building trust and staying visible across the buyer journey, not just selling. Then measure what works and keep improving.

What is B2B social media marketing?

B2B social media marketing uses platforms like LinkedIn, YouTube, and X to connect with business buyers. It’s about building relationships and sharing valuable insights as you guide potential customers through the sales funnel.

Conclusion

In the next few years, I predict that we’ll see more and more B2B markets focus more time and energy on their social media skills. Already, there are a few bright spots in the B2B social horizon. 

Using these tips are a great way to optimize your cross-channel marketing efforts. Becoming a platform ninja who understands social media trends, and can incorporate them into the B2B marketing sales funnel, is the clear path forward for today’s marketers.

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AI Content Generation for SEO: Pros, Cons & How to Use It

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)

A ChatGPT answer about passive income.

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.

We even won an award for this work at the Drum Awards.

Conclusion

AI can be used to help you, the issue is most marketers are relying on it to fully create their content for them.

AI is great, but it’s not there yet to just do everything for you.

And even if AI was perfect, if it doesn’t talk about something new that people haven’t seen before it won’t produce the results you are looking for.

So, are you using AI to create your content?

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Digital Marketing Trends & Predictions 2026

If 2025 taught us anything, it’s that AI is no longer just a side tool. It’s the engine running campaigns and reshaping how people discover brands.  

At the same time, platforms have declared war on the “click.” We’re seeing an aggressive push for native conversions, where the goal isn’t to drive traffic to the website but to close the deal right in the feed. 

That shift toward “frictionless” experiences, combined with the saturation of AI-generated noise, has forced another major change. Content with deep educational value is starting to outperform the high-volume, “101-level” content that simply fills space. 

As we get deeper into the new year, those shifts are accelerating. 

The top digital marketing trends for 2026 reflect this reality: Automation handles execution, while human elements like strategy and storytelling set the winners apart.  

If you want to stay relevant, abandon the old metrics of “rankings” and “reach.” They no longer guarantee relevance. Here’s what’s actually moving the needle in 2026 (and how the best digital marketers are keeping up). 

Key Takeaways

  • With the rise of agentic AI, machines can now handle the lifecycle and campaigns, but human oversight is essential. 
  • User discovery spans platforms like TikTok, Reddit, YouTube, and Meta. Each one requires unique formats, signals, and intent-based optimization. 
  • Funnels are no longer static. AI personalizes journeys in real time based on user behavior, replacing manual segmentation and drip campaigns. 
  • Chat assistants recommend brands based on trust and content relevance. Consistency and large language model optimization (LLMO) are key to inclusion. 
  • Google’s traditional and AI systems (PMax, AI Overviews, Demand Gen, and Search) now operate as one. Aligning creative and goals across all touchpoints boosts results. 

AI Agents Take Over Execution

We’re already seeing AI streamline much of a marketing team’s content production. But the new flex is agentic AI. We’re talking about autonomous “team members” that can now handle your entire campaign workflow.  

According to PwC, nearly 80 percent of organizations have already adopted AI agents to some degree. And most plan to expand use as these systems move from experimentation into day-to-day operations. 

 AI agent adoption levels across organizations, with most reporting broad or limited adoption. 

This goes far beyond production and publishing. Large language models (LLMs) have advanced to the point that they can manage the full lifecycle. We’re talking about agents embedded into tools that can help: 

  • Manage your customer relationship management (CRM) data 
  • Analyze data performance 
  • Provide campaign insights 
  • Adjust ad bids for paid campaigns in real time 

This year, AI is going from writing your content to autonomous operations. It handles the execution while you focus on strategy and oversight. 

Search Everywhere Optimization Becomes Mandatory

For the last few years, “search everywhere” has been a catchy conference buzzword. In 2026, it’s a baseline for survival. 

The era of the “Google-default” mindset is over. Discovery now happens across platforms, feeds, and AI systems. Today’s SEO is drifting more and more toward search everywhere optimization and less search engine optimization. 

Your audience isn’t just “Googling it” anymore. They’re asking questions and validating purchases on the platforms they trust most. And each has its own algorithm, formats, and user behavior.  

For example: 

  • TikTok viewer wants quick, visual tips.  
  • Reddit user wants deep, authentic discussion.  
  • Pinterest needs eye-catching visuals with keyword-rich descriptions.  
  • YouTube demands longer, high-value content with tight intros and strong engagement. 

The most disruptive shift, however, is happening outside traditional feeds. Voice assistants like Alexa and Siri, and generative chat tools like ChatGPT, Gemini, or Claude are increasingly acting as answer engines.  

The numbers show where we’re headed. Nearly 1 in 5 people use voice search, and Statista predicts 36 percent of the global population will be searching via AI by 2028.  

Example of an AI chat assistant returning a summarized product recommendation list, showing how search increasingly happens inside answer engines.

Prompt-Driven Campaigns and Product Development

Digital marketers no longer need full engineering cycles to test new ideas.  

Prompt-driven tools now make it possible to prototype calculators, quizzes, internal tools, and campaign utilities in hours instead of weeks. 

Tools like Cursor and Replit let marketers translate plain-language instructions into working interfaces, lowering the barrier to experimentation. You still need engineering for production-scale products, but prompts now handle much of the early build and validation work. 

Base44 is another example of a “vibe coding” platform that can turn your detailed descriptions into functional tools, reinforcing the same idea: Prompts are becoming a new control layer.  

Everyone’s an engineer now. Look out, Silicon Valley!  

The game has changed. You can now test fast, learn faster, and skip the bottlenecks that used to slow everything down. 

Funnels Become Dynamic and Self-Optimizing

Static funnels are out. In 2026, customer journeys are becoming shorter and increasingly influenced in real time by AI systems. 

It may seem shocking at first, but it makes sense when you zoom out and think about it. We are no longer pushing users through a pre-set funnel. We’re letting AI agents build the funnel around the user in real time. 

In the early days of Google (and online shopping), a customer would have to visit several sites to research and read reviews—and, eventually, make a purchase. This is the classic marketing funnel we’re all familiar with. There’s a clearly defined top-of-funnel, mid-funnel, and bottom-of-funnel. 

With generative AI tools now offering in-platform purchases, that funnel shrinks significantly. Your typical user can now research, build trust, and make a purchase all within an LLM like ChatGPT.  

We’ve even begun to see major retailers like Walmart and Amazon move toward this model.  

Walmart Sparky can answer user queries and pull in product recommendations to answer deeper questions. It even leads you to check out when you’re ready to purchase.  

Walmart interface showing its AI shopping assistant answering product questions, comparing options, summarizing reviews, and guiding users toward checkout within a single on-platform experience. 

(Image Source) 

The same setup applies to Amazon Rufus, enabling customers to get details, get suggestions, get help, and get inspiration (and ultimately get stuff) all within one platform.  

Amazon’s Rufus AI assistant helping users research products, get recommendations, and shop without leaving Amazon

(Image Source

The result is higher engagement and faster conversions with way less manual work. These tools provide a hyper-personalized shopping experience faster than ever before. Platforms like Shopify and Etsy have also partnered with ChatGPT to purchase products directly in the LLM. 

AI Attribution Connects Content to Revenue

Attribution isn’t new, but it’s getting more accurate. AI-powered attribution now connects every touchpoint—from the first video view to the final click—with real revenue outcomes. 

Platforms like Wicked Reports are enabling marketers to tie initial ad clicks to lifetime purchases and provide “first click” and “time decay” tools to help you pinpoint the most successful starting point for your customers’ buying journeys. This app also provides revenue forecasting to help B2C and e-commerce businesses reliably predict and scale their growth. 

Marketing analytics dashboard showing AI-driven measurement, signal correction, and performance insights used to connect campaigns to real revenue outcomes. 

(Image Source

Your latest blog post may not have converted immediately, but it made the visitor trust you enough to subscribe for email updates. That email is the next stop in their journey, pushing them to check out your pricing page. AI sees it all and assigns value accordingly. 

With these new insights, you finally know which content moves the needle.  

And it’s having a real financial impact. Teams using AI-driven marketing analytics report return on investment (ROI) improvements of roughly 300 percent and customer acquisition costs dropping by more than 30 percent. 

Chat Assistants Reshape Discovery

We mentioned earlier how people’s search has evolved into asking AI chat tools like ChatGPT, Gemini, and Perplexity to answer their product questions. These platforms now include brand recommendations built right into the response, as well as the ability to shop for Shopify and Etsy products. 

This is the same dynamic powering tools like Walmart Sparky and Amazon Rufus, where research and recommendations happen within a single AI experience.  

These assistants don’t list 10 “sponsored” links, a la Google. They summarize what they trust. If they don’t mention your brand, you’re invisible in this new layer of discovery. 

AI answer engine Perplexity showing summarized recommendations for ‘best email marketing tools for SaaS,’ with brands cited directly in the response instead of traditional search links. 

It takes more than gaming keywords to show up on these platforms. It’s all about relevance and consistency.  

The more helpful, high-quality content you create around a topic, the more citations you’ll receive from users sharing it across the internet. Signals like structured content, schema markup, and consistent third-party validation help AI systems interpret your authority and decide when your brand is worth referencing. 

This shift has given rise to large language model optimization (LLMO), a new branch of SEO focused on training AI to recognize and recommend your brand. If you’re not already thinking about LLMO, it’s time to get caught up. 

The big takeaway here is that usefulness matters more than volume as discovery moves into AI systems. Provide enough high-quality answers to your audience’s questions, and the bots will start to bring your name up first. 

Content Structure Becomes Even More Important

Old-school SEO was all about keywords. In 2026, performance increasingly comes from covering topics in depth and structuring content so both people and machines can understand it. 

As we mentioned in the last section, search engines and AI assistants care more about how well you answer a question than how many times you use a keyword. That means your content needs to be thorough and easy to interpret at a glance, no matter who (or what) is doing the glancing. 

NerdWallet does this well by organizing credit card content into a clear hub, then breaking it into tightly related subtopics that cover a ton of topical ground. It’s no longer a game of relying on individual keyword pages. Notably, Nerdwallet is one of the most frequently cited websites in LLMs. 

NerdWallet credit cards hub showing a structured topic cluster with subcategories like travel, cash back, balance transfer, and student cards organized under a single pillar. 

So, switch your strategy mindset from pages to topic clusters. Cover a topic from every angle across multiple assets. Use headers, FAQs, schema markup, and internal links to connect the dots.  

The better you structure your content, the easier it is for AI to find and promote it. 

Your target audience is searching across multiple channels in today’s environment. Focusing on individual keywords leaves a lot of opportunity on the table.  

Today’s rising search platforms, like social media apps and LLMs, revolve around semantic queries. 

People talk to these tools naturally and conversationally (some of them even use ChatGPT’s voice functionality). This means you can’t hone in on a specific keyword. Using a keyword cluster that covers the most popular phrasings customers may use is a much better way to make sure you’re covering what people are asking, increasing your probability of being found.  

This query within Perplexity demonstrates how people interact with search tools. They’re not always typing keywords. They’re asking full, conversational questions and expecting a clear answer. 

AI answer engine responding to a conversational question, ‘Which is better for a headache, Tylenol or ibuprofen?,’ with a summarized comparison pulled from multiple medical sources. 

You also have to consider that many users never click through to your site. Zero-click searches are growing fast, which means your content needs to deliver value right in the SERP—or immediately on platforms like social, LLMs, and voice. 

If you’re still chasing individual keywords, you’re missing the bigger opportunity: becoming the trusted source on your topic. 

Brand Trust Is Measured in Citations and Sentiment

AI doesn’t care how loud you are. It cares how often others talk about you, and what they say when they do. 

Large language models prioritize brands with consistent, credible citations across the web. That includes mentions in blog posts, news articles, podcasts, reviews, and Reddit threads. The more quality signals you earn, the more likely AI is to recommend you.  

But the mentions are just the beginning. Your performance in 2026 really boils down to your audience’s perception of you. Sentiment analysis now plays a big role in ranking. Positive discussions boost your chances of surfacing in AI results, while negativity can drag you down. 

Until recently, this layer of discovery was almost impossible to measure. Traditional analytics don’t show when your brand is cited inside AI-generated answers. But a new class of AI visibility tools now tracks where and how often brands appear across platforms like ChatGPT, Perplexity, Claude, and Google’s AI Overviews (along with the surrounding context). But what types of brands are succeeding using this strategy? 

Brands like Patagonia and TOMS are shining examples of this. These companies leverage philanthropy to increase their goodwill and, in turn, their customers’ positive sentiment toward them.  

Leveraging elements like philanthropy the right way switches these brands’ audiences from customers to loyal supporters. 

Patagonia webpage outlining causes the company funds and does not fund, illustrating clear brand values and consistent public positioning. 

This shift rewards brands that build goodwill rather than just backlinks. If your strategy still centers on shouting the loudest, you’ll get buried by brands that are being talked about, and for the right reasons. 

A ChatGPT result talking about TOMS philanthropy efforts.

Trust is now your most important ranking factor. Earn it or fade out. 

Blogs Influence AI Models, Not Just Traffic

If you think blogs don’t “work” like they used to, you’re missing the bigger picture. They still do heavy lifting behind the scenes to shape AI output and position your brand as a go-to source. 

In modern search, everything you publish helps shape how AI models understand your brand. When you consistently cover a topic with depth and clarity, models start to associate your name with that subject.  

This new reality turns your blogs from content assets into signals of authority. 

Even if search traffic dips due to zero-click results or AI summaries, the long-term payoff is still there. The more high-quality content you create, the more likely your brand is to be cited by the higher-profile AI channels and included in trusted content roundups. 

Social Platforms Function as Search Engines 

As the search everywhere trend shows us, search behavior is spreading. And, according to Statista, nearly a quarter of U.S. adults treat social media as their starting point for search. 

People are searching TikTok to see how something works or whether a restaurant’s worth trying.  

TikTok search results for ‘best places to eat in Las Vegas,’ showing short videos answering a local restaurant query instead of traditional search links. 

They’re using YouTube to learn how to install software or compare skincare brands. Considering that this is the largest search engine after Google, it’s a great platform to focus efforts on. 

This matters because social search runs on a different logic than traditional SEO or AI answer engines. These platforms reward relevance through engagement. 

Each platform has its own discovery logic. TikTok rewards watch time and velocity. YouTube favors relevance and retention. Instagram leans on recency and interaction. 

Without optimizing for these platforms, you’re missing a huge part of the search pie. You should be treating social platforms like search engines, because your audience already does. 

This is where more traditional on-page SEO comes into play. That means digging into the types of questions your audience is asking and focusing on tried-and-true tactics like using clear, searchable titles and engaging hooks to “stop the scroll” and get your viewers’ attention in the first three seconds. 

Content Quality Outperforms Quantity Across Channels

Publishing more content won’t save you in 2026. 

Social platforms are flooded, and search is competitive. On top of that, AI is getting better every day at filtering out thin, repetitive, or regurgitated content.  

Consequently, original insights and pieces that actually teach something are rising to the top. 

We see this in emerging trends. For starters, the average number of posts per day among brands has decreased to 9.5. Engagement is moving in the opposite direction, with inbound interactions increasing by roughly 20 percent year over year.  

Instead of posting five times a day, focus on publishing things worth reading and sharing, even if it’s only one well-structured piece of content per week.  

A thoughtful video or long-form LinkedIn breakdown that sparks conversation will do much better than 100 pieces of AI-generated blogs that barely scratch the surface of a topic. 

Take National Geographic, for example. Rather than posting constantly, it focuses on educational storytelling. Check out its TikTok grid

National Geographic’s TikTok profile showcasing educational, documentary-style videos that prioritize learning and storytelling over high-volume posting. 

Content creators are experiencing the benefits of this strategy in real time.  

recent survey finds that 35 percent of creators say they’re seeing higher potential ROI from longer-form content formats, with 39 percent saying they’re seeing better engagement. And almost half (49 percent) say that the choice to produce longer-form content is helping them reach a wider audience.  

If your strategy is still built around churning out content to “stay active,” it’s time to shift. Fewer pieces. Bigger impact. Better outcomes. 

That’s what wins in 2026. 

Conversion Happens On-Platform, Not On-Site 

The platforms people use every day are getting very good at keeping them there.  

Think about it: Nearly every social platform has lead forms and lets you shop inside the app. The goal of these features is to help you convert without ever leaving their platform. 

Instagram and TikTok, for example, have fully integrated shopping experiences. And it’s working. Sales through social media channels are forecasted to reach nearly 21 percent in 2026. 

Google’s even testing AI-generated product recommendations with built-in checkout links, like Etsy and ChatGPT. The whole point is to remove friction and keep the experience seamless. 

That shift changes what a “landing page” even means. In many cases, it’s a native form, a product card, or an in-app checkout flow that closes the deal on the spot. 

Your website still matters, but forcing every conversion to happen there can introduce unnecessary drop-off. When users are ready to act, the simplest path usually wins. 

This shift is giving rise to what some teams now call checkout optimization, and it’s getting some pretty serious results. E-commerce brands with 1,000 to 2,000 orders per month are implementing checkout optimization and seeing measurable gains in shipping revenue and order total.  

Comparison of e-commerce checkout flows before and after optimization, showing fewer steps, clearer shipping options, and reduced friction at checkout. 

(Image Source) 

When you meet users where they are, you lower the barrier to action. No load times. No messy redirects. Just a quick tap or swipe to buy, book, or sign up. 

Video Becomes a Primary Search and AI Input 

Video is increasingly becoming more than just a distribution format. It’s now a primary way people search—and a growing input for AI systems. 

Search engines and AI platforms now index video much like they do written content, pulling from structural signals to generate results. If those signals aren’t there, the video might as well not exist. 

ChatGPT interface responding to the prompt ‘Hit me with some funny cat videos’ by embedding a YouTube video thumbnail of a cat sitting in a plastic container in water. 

What do those signals look like in practice? 

Well, because search engines and AI platforms can’t watch your videos, they instead rely on clean transcripts, keyword-rich titles and descriptions, and clear segmentation. Think chapters, not rambles. Structure is what makes video searchable. 

This video from Neil Patel uses chapters, summaries, and clear topic segmentation, making it easier for search engines and AI systems to interpret and reference specific sections. 

The more structured and searchable your video content, the more likely it is to be cited by AI assistants. 

Text still matters. But if video isn’t part of your SEO and discovery strategy, you’re leaving serious visibility on the table. 

Paid Media Shifts to AI-Led Campaigns

We’ve seen AI-driven paid media campaigns for some time now, but platforms like Google’s Performance Max and Meta’s Advantage+ are refining and elevating how it’s done. We’re seeing these platforms automatically testing creative and placements to hit performance goals, and even testing the benefits of AI-powered segmentation or ad bidding. 

The result is less manual control and more system-led optimization, which is a benefit for many marketers. Retail marketers, for example, have seen a 10 percent to 25 percent lift in their return on ad spend (ROAS) by implementing AI-powered campaign elements.  

But “hands-off” doesn’t mean “set it and forget it.” 

In this model, your role shifts from managing campaigns to training the system. The better your inputs—creative variety, first-party data, and clear conversion signals—the better your results.  

Lazy targeting and generic ads just get ignored. 

Want to lower customer acquisition cost (CAC) or increase return on ad spend (ROAS)? Focus on refining your creative and uploading strong first-party data. AI will handle testing and optimization, but it can’t fix bad inputs. 

Savvy marketers are shifting their roles from campaign operators to strategy leads. They’re spending less time on dashboards and more time building assets that actually convert, such as a robust content library or unique, impactful insights from proprietary data. 

It all comes down to this: AI runs the ads, but you train it. If you’re not giving the algorithm something great to work with, you’re not going to like what it gives back. 

FAQs

What are the digital marketing trends for 2026?

In 2026, AI is running full campaigns, dynamic funnels are replacing traditional static ones, and users are increasingly discovering brands across platforms. Chat assistants like ChatGPT now also recommend brands, and SEO is more about structured topics than keywords. Quality content outperforms quantity, and conversion often happens off your site. 

How can businesses stay updated on marketing trends?

Follow trusted industry blogs (like NeilPatel.com), subscribe to marketing newsletters, and keep an eye on platform updates from the big players (Google, Meta, and TikTok). Tools like Ubersuggest can also help spot shifts in search behavior. But more than anything, continue testing and tracking, and stay close to what your audience responds to. 

Conclusion

Many experts say that marketing is changing, but the fact is that it’s already changed.  

AI now drives the full spectrum of content marketing. Platforms prioritize native conversion. Content shapes how machines and people see your brand. If you’re still playing by old rules—keyword-centric strategy, manual funnels, or high-volume posting—you’re going to get left behind. 

Winning in 2026 means adapting quickly to emerging digital marketing trends by thinking strategically and building trust across every touchpoint. 

If you’re not sure where to start, check out my guide on search engine trends to see how modern discovery actually works today. 

The marketers who move first always get the advantage. So, make your move. 

Read more at Read More

The Future of Content Marketing: A 2026 Guide

Following last year’s content marketing playbook won’t cut it in 2026.

AI is evolving how we create, but human connection still drives what performs. Search behavior is splintering across platforms, and brands are being judged not just on what they publish but also on how it shows up.

To win this year, marketing pros need to be smarter about what they’re doing. That means your content must be rooted in real insights about how people actually buy from your brand.

This complete guide to content marketing breaks down what’s changing, what’s working, and where to focus your time and budget. If you’re serious about growing through content in 2026, you need to understand the shifts shaping the space.

Key Takeaways

  • AI should speed up execution, not replace strategy. Use it to draft and repurpose content, but rely on human perspective and editing to determine performance.
  • Content that feels human outperforms content that feels polished. Audiences respond to authentic opinions and usefulness versus brand-safe, committee-written copy.
  • Thought leadership now requires original insight. Repackaging what already ranks won’t build authority; bold takes, experienced authors, and first party data will. First party/proprietary data also helps your content be more original and unique.
  • Distribution is half the strategy. Content needs a plan for where and how it gets discovered across platforms, not just published on a blog.
  • Measure influence, not output. The content that matters most is what changes thinking and earns trust, not what fills a calendar.

AI Can Help You Scale but It Can’t Think for You

Your competitors may already be creating AI content, but I want you to understand how to create better AI content.

AI is great for maximizing your efficiency. It can drastically cut the time you spend on mundane content marketing tasks like researching and building outlines. It can even save you time by helping you repurpose old content into new formats. That kind of scale used to take teams. Now, all it takes is prompts.

But don’t mistake speed for strategy.

AI doesn’t know your customer. It doesn’t understand your brand’s voice or point of view—the elements of your brand or product that actually matter to people. Introducing those elements and ensuring they stay intact is on you.

Use AI to take the grunt work off your plate (i.e., building drafts or summarizing competitor content). But when it comes to telling your story, positioning your offer, or crafting something people want to read and share, human judgment still wins.

You can feel the difference between templated, AI-written content and something with a real perspective. So can your audience. If your content feels robotic or generic, they’re gone. No one shares or converts from content that reads like it came off an assembly line.

So yes, use AI. Just don’t hand it the keys to your content strategy. If you’re not putting in the human effort to edit and elevate what comes out, you’re just publishing noise.

Content Must Feel More Human, Not More Polished

People are tired of content that sounds like it was written by a committee.

You know the type: no strong opinions and so sanitized it could’ve come from any brand in your space. It’s forgettable. This year, forgettable doesn’t work.

Your audience doesn’t want another corporate how-to. They want to hear from someone who gets it. Someone who’s been in their shoes and isn’t afraid to say what actually works—and what doesn’t.

That doesn’t mean being sloppy. It means being real.

Ditch the fluff. Cut the clichés. Talk like a smart peer, not a brand trying to tick SEO boxes. Share what you’ve learned and what surprised you. That’s what builds trust. That’s what gets people to read and come back.

An article from Slite.

Slite’s piece on “people first” only going so far for parents is a great example. The post comes from a parent on their team, immediately creating expertise on the subject. There’s no promotional content anywhere in the blog, and the focus is on entertaining and educating the readers.

Let me pause a second here and make something clear: authentic doesn’t mean unedited. It means intentional. Sure, edit your pieces for grammar and structure, but don’t sand down the voice. Let the human fingerprints show.

In a world of AI-generated everything, human content stands out, not by being perfect, but by being authentically helpful and worth someone’s time.

Thought Leadership Requires Saying Something New

You don’t become a thought leader by echoing what everyone else is already saying.

Too many blogs and LinkedIn posts are just rewrites of what’s already ranking. They quote the same stats and land on the same safe conclusions. That’s not thought leadership, that’s content recycling.

You need to bring something new to the table if you want people to see you as an authority. That could mean sharing a bold opinion others won’t say out loud. It could be a unique framework you’ve developed through real experience. Or it might be calling out what isn’t working anymore, even if it used to.

Google’s EEAT principles—experience, expertise, authority, and trustworthiness—favor exactly this kind of content. You’re not just writing for algorithms anymore. You’re writing to earn trust from real people and search engines. Along with this, the style of throwing anonymous blog posts out into the aether isn’t going to cut it either. Adding named authors with credentials to your blog posts is essential for EEAT.

Brand Voice Is a Strategic Asset

AI can write. So can your competitors. Sticking to your unique voice is what’s going to set you apart.

In a sea of content that all sounds the same, brand voice is what makes people recognize you, even without seeing your logo. It’s more than tone or personality. It’s how you show up. And in 2026, it’s one of your biggest strategic advantages.

The best brands sound human. Clear and consistent across platforms—no matter if it’s a blog post, a LinkedIn comment, or a product page. That consistency builds trust and, over time, creates familiarity, which then leads to trust and loyalty—two essential elements of marketing to customers on the modern playing field. 

Innocent Drinks is a British brand that’s a great example of a unique tone and voice. They keep customer interactions fun with cheeky British humor and self-deprecating jokes. The laid-back, conversational tone presents their smoothie drinks amidst daily jokes, weather updates, and more that keep their customers coming back.

Innocent Drinks.

Source: https://iconicfox.com.au/brand-voice-examples/

But the catch is: you have to guard your brand voice with your life.

Well, maybe it’s not that drastic. But you at least have to define it, teach your writers how to use it, and maybe most importantly, defend it—especially when AI starts diluting it with generic phrasing or over-polished outputs.

Think of brand voice like a design system. It should guide every piece of content you publish. The goal isn’t to sound perfect. The goal is to sound unmistakably like you across formats, channels, and teams.

When everything else feels copy-pasted, your voice is what makes people stop scrolling and actually listen.

Video Isn’t Optional in a Content Strategy

If video still feels like “bonus content” to your team, you’re already behind.

Video—short and long-form—is now a core part of how people discover and share information. It’s not just for YouTube or TikTok anymore. It belongs in your blog strategy, your LinkedIn posts, your email sequences, and even your whitepapers. YouTube has risen to be the #2 largest search engine as well as a top source for Google Gemini.

The smartest content teams don’t treat video as a separate effort. They treat it as an extension of what they’re already creating.

Wrote a blog post that’s performing well? Turn it into a 60-second explainer for Instagram. Got a data-packed whitepaper? Break it into a mini-series of clips or animated infographics. Publishing a thought leadership piece? Record a quick POV video that puts a face (and voice) to the ideas.

Here’s an example from my Instagram:’

An Instagram post from Neil Patel.

This isn’t about adding more work. It’s about getting more mileage from what you’ve already built.

People scroll past walls of text. But they’ll pause for a story or a strong hook in motion. Video improves retention and makes your message stick.

In 2026, you should be including video in your strategy from the start.

Distribution Is Just as Important as Creation

If you’re not planning for distribution, you may just be publishing into the void.

Too many marketers hit “publish” and hope for traffic. But in 2026, the real game happens after the content goes live. Distribution is half the strategy.

Every piece you create should have a plan for where it lives and how it spreads. That could mean breaking your blog post into a X thread or syndicating it as a native article on LinkedIn or Medium.

And don’t underestimate the power of partnerships. Influencers, creators, and subject matter experts can extend your reach with the right angle and format.

This is where Search Everywhere Optimization becomes essential. People aren’t just searching on Google anymore. They’re searching across all the major social media platforms, LLMs like Chat GPT and Perplexity, and e-commerce sites like Amazon. You need to meet them where they are, in the format they prefer.

Good content doesn’t go viral by luck. It travels because someone planned the route.

So before you write your next post, ask yourself: how will people actually find this? If you don’t have a solid answer, you’re not done yet.

Content Must Map to the Buyer Journey, Not Just the Funnel

A customer’s actual buying journey is moving away from the classic funnel we all know and love. The funnel itself isn’t changing, but where people go along the funnel is changing. People can shop in ChatGPT, meaning they can follow the entire funnel without ever leaving an LLM.

The marketing funnel.

Real decision-making is messy. People bounce between tabs, skim reviews, watch videos, compare products, and ask peers for input—all before ever booking a demo or hitting “buy.” If your content only speaks to top-of-funnel traffic, you’re leaving serious revenue on the table.

Modern content strategy needs to follow the buyer wherever they go, not just the funnel stages.

That means going beyond how-to posts and SEO guides. You need content that helps buyers decide. Product comparisons. Honest breakdowns of pricing and features. Content that tackles objections head-on. Even onboarding previews and post-purchase FAQs count. They reduce friction and increase trust. This method is useful for appearing in LLMs as we.

Not only does this kind of content help convert, but it also ranks. Buyers search for “[Product A] vs [Product B],” “Is [Brand] worth it?”, and “How hard is it to implement [Tool]?” If you’re not showing up there, your competitor will.

So build for real behavior, not static funnels. Meet your buyer where they are—digging deep into research and looking for clarity before they buy.

Refreshing Existing Content Beats Churning Out New Posts

More content doesn’t always mean more results.

If you’re constantly creating from scratch but ignoring your old posts, you’re missing one of the easiest wins in content marketing: updates.

Refreshing content isn’t just minor changes; it’s making your best-performing or best-potential content even stronger. That could mean improving the structure, adding internal links, expanding thin sections, or aligning it with new search intent.

And it works. Updated content often ranks faster and converts better because it already has history and positive social signals, like backlinks. Google rewards freshness, but it also loves authority.

Instead of publishing five new blog posts next month, what if you refreshed five that are slipping in rankings? Or turned an old listicle into a detailed comparison guide? That’s not less work, it’s smarter work.

Start by running a quick content audit. Identify top traffic drivers and declining or outdated post topics. From there, prioritize updates that align with current search demand and business goals.

New content still matters, but refreshing what you already built often delivers a faster, more predictable ROI. Don’t start from zero when there’s gold in your archives.

User-Generated Content Builds Credibility and Community

Positive product recommendations, reviews, and stories from your customer base are gold for social proof. 

That’s the power of user-generated content (UGC). Testimonials and stories or spotlights can build trust faster than anything you write yourself. They’re authentic social proof, and one of the most underused levers in content strategy.

Coca-Cola’s Share a Coke campaign is a great example of this. The company rolled out product with some of the most popular first names on each can or small bottle. Store displays encouraged shoppers to find a can with their name on it, take a picture, and post to social media with the caption “#shareacoke”. The result was social media feeds flooded with posts just like this:

Share a Coke examples.

UGC is such a powerful strategy because it reduces your content lift while increasing credibility. Instead of creating everything from scratch, you’re curating voices from your community. A five-minute video from a happy customer or a LinkedIn post from an employee can do more than a polished landing page.

So how do you get it? Ask. Prompt your audience to share their experiences. Feature real users in your blog posts or newsletters. Turn customer feedback into quote graphics or build case studies around standout use cases.

This strategy really gets powerful when you turn it into a system. Create UGC submission forms. Add review prompts to your post-purchase emails. Encourage your team to share behind-the-scenes stories on social.

The more people see themselves in your brand, the more they want to be part of it. UGC turns customers into advocates, and that’s content you can’t fake.

Measurement Is Moving to Influence, Not Just Output

Content volume used to be the metric. How many blogs did we publish? How many posts went live?

Now, it’s about impact.

Smart teams aren’t asking, “How much did we ship?” They’re asking, “What moved the needle?” That means tracking content that supports real business goals, not just filling up a calendar.

Engagement quality matters more than vanity metrics. Are people sharing or talking about it? Did it convert to revenue or reduce friction in the sales process? That’s the kind of content worth doubling down on.

Brand lift and even SEO performance are shifting, too. With AI Overviews reshaping how content appears in search, your content marketing performance hinges on owning the conversation through citations that strengthen brand trust.

An AI Overview.

Writers play a huge role here. When your content solves a real problem or answers a specific question better than anyone else, it sticks. That influence compounds.

So shift your mindset. Don’t just create content that gets clicks. Create content that changes people’s thinking or provides new insights. That’s the metric that matters now.

FAQs

What is the future of content marketing?

Content in 2026 is shifting toward authenticity. Brands are focusing on aspects like voice and distribution over volume. Repurposing across channels, creating content with real perspective, and measuring influence over output are now core strategies. The new goal is content that actually earns trust and drives action.

Conclusion

Creating a winning content marketing strategy in 2026 will certainly look different. The smart use of AI to scale instead of substitute, while still leaning on human editing and content elevation, will be a huge brand separator. You and your team can do that effectively by building a voice people recognize, creating content that feels human, and aligning it all to authentic buyer journeys and behaviors.

You don’t need to chase every trend. Focus on strategy, quality, and distribution that drives results.

Because in a world full of content, the only stuff that stands out is the kind that actually matters.

Read more at Read More

January 2026 Digital Marketing Roundup: What Changed and What You Should Do About It

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

The Performance Max Channel Performance Report keeps evolving. You can now see performance broken down across Search, YouTube, Display, Discover, Gmail, and Maps.

The PMAX Channel Performance Report.

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

A Bing Ad Example.

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.

Read more at Read More

AI Hallucinations, Errors, and Accuracy: What the Data Shows

AI hallucinations became a headline story when Google’s AI Overviews told people that cats can teleport and suggested eating rocks for health.

Those bizarre moments spread fast because they’re easy to point at and laugh about.

But that’s not the kind of AI hallucination most marketers deal with. The tools you probably use, like ChatGPT or Claude, likely won’t produce anything that bizarre. Their misses are sneakier, like outdated numbers or confident explanations that fall apart once you start looking under the hood.

In a fast-moving industry like digital marketing, it’s easy to miss those subtle errors. 

This made us curious: How often is AI actually getting it wrong? What types of questions trip it up? And how are marketers handling the fallout?

To find out, we tested 600 prompts across major large language model (LLM) platforms and surveyed 565 marketers to understand how often AI gets things wrong. You’ll see how these mistakes show up in real workflows and what you can do to catch hallucinations before they hurt your work.

Key Takeaways

  • Nearly half of marketers (47.1 percent) encounter AI inaccuracies several times a week, and over 70 percent spend hours fact-checking each week.
  • More than a third (36.5 percent) say hallucinated or incorrect AI content has gone live publicly, most often due to false facts, broken source links, or inappropriate language.
  • In our LLM test, ChatGPT had the highest accuracy (59.7 percent), but even the best models made errors, especially on multi-part reasoning, niche topics, or real-time questions.
  • The most common hallucination types were fabrication, omission, outdated info, and misclassification—often delivered with confident language.
  • Despite knowledge of hallucinations, 23 percent of marketers feel confident using AI outputs without review. Most teams add extra approval layers or assign dedicated fact-checkers to their processes.

What Do We Know About AI Hallucinations and Errors?

An AI hallucination happens when a model gives you an answer that sounds correct but isn’t. We’re talking about made-up facts or claims that don’t stand up to fact-checking or a quick Google search.

And they’re not rare.

In our research, over 43% of marketers say hallucinated or false information has slipped past review and gone public. These errors come in a few common forms:

  • Fabrication: The AI simply makes something up.
  • Omission: It skips critical context or details.
  • Outdated info: It shares data that’s no longer accurate.
  • Misclassification: It answers the wrong question, or only part of it.
A graphic showing common AI Hallucination Types

Hallucinations tend to happen when prompts are too vague or require multi-step reasoning. Sometimes the AI model tries to fill the gaps with whatever seems plausible.

AI hallucinations aren’t new, but our dependence on these tools is. As they become part of everyday workflows, the cost of a single incorrect answer increases.

Once you recognize the patterns behind these mistakes, you can catch them early and keep them out of your content.

AI Hallucination Examples

AI hallucinations can be ridiculous or dangerously subtle. These real AI hallucination examples give you a sense of the range:

  • Fabricated legal citations: Recent reporting shows a growing number of lawyers relying on AI-generated filings, only to learn that the cases or citations don’t exist. Courts are now flagging these hallucinations at an alarming rate.
  • Health misinformation: Revisiting our example from earlier, Google’s AI Overviews once claimed eating rocks had health benefits in an error that briefly went viral.
  • Fake academic references: Some LLMs will list fake studies or broken source links if asked for citations. A peer-reviewed Nature study found that ChatGPT frequently produced academic citations that look legitimate but reference papers that don’t exist.
  • Factual contradictions: Some tools have answered simple yes/no questions with completely contradictory statements in the same paragraph.
  • Outdated or misattributed data: Models can pull statistics from the wrong year or tie them to the wrong sources. And that creates problems once those numbers sneak into presentations or content.

Our Surveys/Methodology

To get a clear picture of how AI hallucinations show up in real-world marketing work, we pulled data from two original sources:

  1. Marketers survey: We surveyed 565 U.S.-based digital marketers using AI in their workflows. The questions covered how often they spot errors, what kinds of mistakes they see, and how their teams are adjusting to AI-assisted content. We also asked about public slip-ups, trust in AI, and whether they want clearer industry standards.
  1. LLM accuracy test: We built a set of 600 prompts across five categories: SEO/marketing, general business, industry-specific verticals, consumer queries, and control questions with a known correct answer. We then tested them across six major AI platforms: ChatGPT, Gemini, Claude, Perplexity, Grok, and Copilot. Humans graded each output, classifying them as fully correct, partially correct, or incorrect. For partially correct or incorrect outputs, we also logged the error type (omission, outdated info, fabrication, or misclassification).

For this report, we focused only on text-based hallucinations and content errors, not visual or video generation. The insights that follow combine both data sets to show how hallucinations happen and what marketers should watch for across tools and task types.

How AI Hallucinations and Errors Impact Digital Marketers

A graphic that shows how often Marketers Encounter AI Errors.

We asked marketers how AI errors show up in their work, and the results were clear: Hallucinations are far from a rarity.

Nearly half of marketers (47.1 percent) encounter AI inaccuracies multiple times a week. And more than 70 percent say they spend one to five hours each week just fact-checking AI-generated output. That’s a lot of time spent fixing “helpful” content.

Those misses don’t always stay hidden. 

More than a third (36.5 percent) say hallucinated content has made it all the way to the public. Another 39.8 percent have had close calls where bad AI info almost went live. 

And it’s not just teams spotting the problems. More than half of marketers (57.7 percent) say clients or stakeholders have questioned the quality of AI-assisted outputs.

These aren’t minor formatting issues, either. When mistakes make it through, the most common offenders are:

  • Inappropriate or brand-unsafe content (53.9 percent)
  • Completely false or hallucinated information (43.5 percent)
  • Formatting glitches that break the user experience (42.5 percent)

So where does it break down?

AI errors are most common in tasks that require structure or precision. Here are the daily error rates by task:

  • HTML or schema creation: 46.2 percent
  • Full content writing: 42.7 percent
  • Reporting and analytics: 34.2 percent

Brainstorming or idea generation had far fewer issues, with each landing at right about 25 percent.

A graphic showing where marketers encounter AI errors most often.

When we looked at confidence levels, only 23 percent of marketers felt fully comfortable using AI output without review. The rest? They were either cautious or not confident at all.

Teams hit hardest by public-facing AI mistakes include:

  • Digital PR (33.3 percent)
  • Content marketing (20.8 percent)
  • Paid media (17.8 percent)
A graphic showing teams most affected by public AI mistakes.

These are the same departments most likely to face direct brand damage when AI gets it wrong.

AI can save you time, but it also creates a lot of cleanup without checks in place. And most marketers feel the pressure to catch hallucinations before clients or customers do.

AI Hallucinations and Errors: How Do the Top LLMs Stack Up?

To figure out how often leading AI platforms hallucinate, we tested 600 prompts across six major models: ChatGPT, Claude, Gemini, Perplexity, Grok, and Copilot.

Each model received the same set of queries across five categories: marketing/SEO, general business, industry-specific use cases, consumer questions, and fact-checkable control prompts. Human reviewers graded each response for accuracy and completeness.

Here’s how they performed:

  • ChatGPT delivered the highest percentage of fully correct answers at 59.7 percent, with the lowest rate of serious hallucinations. Most of its mistakes were subtle, like misinterpreting the question rather than fabricating facts.
  • Claude was the most consistent. While it scored slightly lower on fully correct responses (55.1 percent), it had the lowest overall error rate at just 6.2 percent. When it missed, it usually left something out rather than getting it wrong.
  • Gemini performed well on simple prompts (51.3 percent fully correct) but tended to skip over complex or multi-step answers. Its most common error was omission.
  • Perplexity showed strength in fast-moving fields like crypto and AI, thanks to its strong real-time retrieval features. But that speed came with risk: 12.2 percent of responses were incorrect, often due to misclassifications or minor fabrications.
  • Copilot sat in the middle of the pack. It gave safe, brief answers. While that’s good for overviews, it often misses the deeper context.
  • Grok struggled across the board. It had the highest error rate at 21.8 percent and the lowest percentage of fully correct answers (39.6 percent). Hallucinations, contradictions, and vague outputs were common.
A graphic showing how major LLMs performed in our 600-prompt accuracy test.
A graphich showing most common error types across models.

So, what does this mean for marketers?

Well, most teams aren’t expecting perfection. According to our survey, 77.7 percent of marketers will accept some level of AI inaccuracy, likely because the speed and efficiency gains still outweigh the cleanup.

The takeaway isn’t that one model is flawless. It’s that every tool has its strengths and weaknesses. Knowing each platform’s tendencies helps you know when (and how) to pull a human into the loop and what to be on guard against.

What Question Types Gave LLMs The Most Trouble

Some questions are harder for AI to handle than others. In our testing, three prompt types consistently tripped up all the models, regardless of how accurate they were overall:

  • Multi-part prompts: When asked to explain a concept and give an example, many tools did only half the job. They either defined the term or gave an example, but not both. This was a common source of partial answers and context gaps.
  • Recently updated or real-time topics: If the ask was about something that changed in the last few months (like a Google algorithm update or an AI model release), responses were often inaccurate or completely fabricated. Some tools made confident claims using outdated info that sounded fresh.
  • Niche or domain-specific questions: Verticals like crypto, legal, SaaS, or even SEO created problems for most LLMs. In these cases, tools either made up terminology or gave vague responses that missed key industry context.

Even models like Claude and ChatGPT, which scored relatively high for accuracy, showed cracks when asked to handle layered prompts that required nuance or specialized knowledge.

Knowing which types of prompts increase the risk of hallucination is the first step in writing better ones and catching issues before they cost you.

AI Hallucination Tells to Look Out For

AI hallucinations don’t always scream “wrong.” In fact, the most dangerous ones sound reasonable (at least until you check the details). Still, there are patterns worth watching for:

Here are the red flags that showed up most often across the models we tested:

  • No source, or a broken one: If an AI gives you a link, check it. A lot of hallucinated answers include made-up or outdated citations that don’t exist when you click.
  • Answers to the wrong questions: Some models misinterpret the prompt and go off in a related (but incorrect) direction. If the response feels slightly off topic, dig deeper.
  • Big claims with no specifics: Watch for sweeping statements without specific stats or dates. That’s often a sign it’s filling in blanks with plausible-sounding fluff.
  • Stats with no attribution: Hallucinated numbers are a common issue. If the stat sounds surprising or overly convenient, verify it with a trusted source.
  • Contradictions inside the same answer: We experienced cases where an AI said one thing in the first paragraph and contradicted itself by the end. That’s a major warning sign.
  • “Real” examples that don’t exist: Some hallucinations involve fake product names, companies, case studies, or legal precedents. These details feel legit, but a quick search reveals no facts to verify these claims.

The more complex your prompt, the more important it is to sanity-check the output. If something feels even slightly off, assume it’s worth a second look. After all, subtle hallucinations are the ones most likely to slip through the cracks.

Best Practices for Avoiding AI Hallucinations and Errors

You can’t eliminate AI hallucinations completely, but you can make it a lot less likely they slip through. Here’s how to stay ahead of the risk:

  • Always request and verify sources: Some models will confidently provide links that look legit but don’t exist. Others reference real studies or stats, but take them out of context. Before you copy/paste, click through. This matters even more for AI SEO work, where accuracy and citation quality directly affect rankings and trust.
  • Fine-tune your prompts: Vague prompts are hallucination magnets, so be clear about what you want the model to reference or avoid. That might mean building prompt template libraries or using follow-up prompts to guide models more effectively. That’s exactly what LLM optimization (LLMO) focuses on.
  • Assign a dedicated fact-checker: Our survey results showed this to be one of the most effective internal safeguards. Human review might take more time, but it’s how you keep hallucinated claims from damaging trust or a brand’s credibility.
  • Set clear internal guidelines: Many teams now treat AI like a junior content assistant: It can draft, synthesize, and suggest, but humans own the final version. That means reviewing and fact-checking outputs and correcting anything that doesn’t hold up. This approach lines up with the data. Nearly half (48.3 percent) of marketers support industry-wide standards for responsible AI use.
  • Add a final review layer every time: Even fast-moving brands are building in one more layer of review for AI-assisted work. In fact, the most common adjustment marketers reported making was adding a new round of content review to catch AI errors. That said, 23 percent of respondents reported skipping human review if they trust the tool enough. That’s a risky move.
  • Don’t blindly trust brand-safe output: AI can sound polished even when it’s wrong. In our LLM testing, some of the most confidently written outputs were factually incorrect or missing key context. If it feels too clean, double-check it.

FAQs

What are AI hallucinations?

AI hallucinations occur when an AI tool gives you an answer that sounds accurate, but it’s not. These mistakes can include made-up facts, fake citations, or outdated info packaged in confident language.

Why Does AI hallucinate?

AI models don’t “know” facts. They generate responses based on patterns in the data they were trained on. When there’s a gap or ambiguity, the model fills it in with what sounds most likely (even if it’s completely wrong).

What causes AI hallucinations?

Hallucinations usually happen when prompts are vague, complex, or involve topics the model hasn’t seen enough data on. They’re also more common in fast-changing fields like SEO and crypto.

Can you stop AI from hallucinating?

Not entirely. Even the best models make things up sometimes. That’s because LLMs are built to generate language, not verify facts. Occasional hallucinations are baked into how they work.

How can you reduce AI hallucinations?

Use more specific prompts, request citation sources, and always double-check the output for accuracy. Add a human review step before anything goes live. The more structure and context you give the AI, the fewer hallucinations you’ll run into.

Conclusion

AI is powerful, but it’s not perfect. 

Our research shows that hallucinations happen regularly, even with the best tools. From made-up stats to misinterpreted prompts, the risks are real. That’s especially the case for fast-moving marketers.

If you’re using AI to create content or guide strategy, knowing where these tools fall short is like a cheat code. 

The best defense? Smarter prompts, tighter reviews, and clear internal guidelines that treat AI as a co-pilot (not the driver).

Want help building a more reliable AI workflow? Talk to our team at NP Digital if you’re ready to scale content without compromising accuracy. Also, you can check out the full report here on the NP Digital website.

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