Generative AI is everywhere right now. It dominates conference agendas, fills LinkedIn feeds, and is reshaping how many businesses think about organic search.
Brands are racing to optimize for AI Overviews, build vector embeddings, map semantic clusters, and rework content models around LLMs.
What gets far less attention is a basic reality: for most websites, AI platforms still drive a small share of overall traffic.
AI search is growing, no question.
But in most cases, total referral sessions from all LLM platforms combined amount to only about 2% to 3% of the organic traffic Google alone delivers.
Despite that gap, many teams are spending more time chasing AI strategies than fixing simple, high-impact SEO fundamentals that continue to drive measurable results.
Instead of improving what matters most today, they are overinvesting in the future while underperforming in the present.
This article examines how a narrow focus on AI can obscure proven SEO tactics and highlights practical examples and real-world data showing how those fundamentals still move the needle today.
1. Quick SEO wins are still delivering outsized gains
In an era where everyone is obsessed with things like vector embeddings and semantic relationships, it’s easy to forget that small updates can have a big impact.
For example, title tags are still one of the simplest and most effective SEO levers to pull.
And they are often one of the on-page elements that most websites get wrong, either by targeting the wrong keywords, not including variations, or targeting nothing at all.
Just a few weeks ago, a client saw a win by simply adding “& [keyword]” to the existing title tag on their homepage. Nothing else was changed.
Keyword rankings shot up, as did clicks and impressions for queries containing that keyword.
This was all achieved simply by changing the title tag on one page.
Couple that with other tactics, such as on-page copy edits, internal linking, and backlinking across multiple pages, and growth will continue.
It may seem basic, but it still works.
And if you only focus on advanced GEO strategies, you may overlook simple tactics that provide immediate, observable impact.
2. Content freshness and authority still matter for competitive keywords
Another tactic that has faded from view with the rise of AI is what’s often called the skyscraper technique.
It involves identifying a set of keywords and the pages that already rank for them, then publishing a materially stronger version designed to outperform the existing results.
It’s true that the web is saturated with content on similar topics, especially for keywords visible in most research tools.
But when a site has sufficient authority, a clear right to win, and content freshness, this approach can still be highly effective.
I’ve seen this work repeatedly.
Here’s Google Search Console data from a recent article we published for a client on a popular, long-standing topic with many competing pages already ranking.
The post climbed to No. 2 almost immediately and began generating net-new clicks and impressions.
Why did it work?
The site has strong authority, and much of the content ranking ahead of it was outdated and stale.
If you’re hesitant to publish the thousandth article on an established topic, that hesitation is understandable.
This approach won’t work for every site. But ignoring it entirely can mean passing up clear, high-confidence wins like these.
3. User experience remains a critical conversion lever
Hype around AI-driven shopping experiences has led some teams to believe traditional website optimization is becoming obsolete.
There is a growing assumption that AI assistants will soon handle most interactions or that users will convert directly within AI platforms without ever reaching a website.
Some of that future is beginning to take shape, particularly for ecommerce brands experimenting with features like Instant Checkout in ChatGPT.
But many websites are not selling products.
And even for those that are, most brands still receive a significant volume of traffic from traditional search and continue to rely on calls to action and on-page signals to drive conversions.
It also makes little difference how a user arrives – via organic search, paid search, AI referrals, or direct visits.
A fast site, a strong user experience, and a clear conversion funnel remain essential.
There are also clear performance gains tied to optimizing these elements.
Here are the results we recently achieved for a client following a simple CTR test:
Brands that continue to invest in user experience and conversion rate optimization will outperform those that do not.
That gap is likely to widen the longer teams wait for AI to fully replace the conversion funnel.
AI is reshaping search, but what works still matters
There is no dispute that AI is reshaping the search landscape.
It’s changing user behavior, influencing SERPs, and complicating attribution models.
The bigger risk for many businesses, however, is not underestimating AI but overcorrecting for it.
Traditional organic search remains the primary traffic source for most websites, and SEO fundamentals still deliver when executed well.
Quick wins are real.
Higher-quality content continues to be rewarded.
User experience optimization shows no signs of becoming irrelevant.
These are just a few examples of tactics that remain effective today.
Importantly, these efforts do not operate in isolation.
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Increased enforcement against incentivized reviews.
Local regulatory pressure.
Together, these forces have significant implications for businesses and local search visibility.
Review deletions are on the up globally
Data collected from tens of thousands of Google Business Profile listings across multiple countries by GMBapi.com show a sharp increase in deleted reviews between January and July 2025.
The surge began accelerating toward the end of Q1 and gained momentum mid-year, with a growing share of monitored locations experiencing at least one review removal in a given week.
This is not limited to negative feedback.
While one-star reviews continue to be taken down, five-star reviews now account for a sizable share of deletions.
That pattern suggests Google is applying stricter enforcement, including on positive reviews, as it works to maintain authenticity and trust.
More recently, Google has begun asking members of its Local Guide community whether businesses are incentivizing reviews, likely in response to AI-driven flags for suspicious activity.
Review deletion patterns vary significantly by business category.
Restaurants account for the highest volume of deleted reviews, followed by home services, brick-and-mortar retail, and construction.
These categories generate large volumes of reviews, and removals occur across both recent and older submissions.
That distribution points to ongoing enforcement, not isolated cleanup efforts.
By contrast, medical services, beauty, and professional services see fewer deletions overall.
However, closer analysis reveals distinct and consistent patterns within those categories.
What review ratings reveal about industry bias
Looking at deleted reviews as a share of total removals within each category reveals distinct moderation patterns.
In restaurants and general retail, deleted reviews are relatively evenly distributed across one- to five-star ratings.
By contrast, medical services and home services show a strong skew toward five-star review deletions, with far fewer removals in the middle of the rating spectrum.
That imbalance suggests positive reviews in higher-risk or regulated categories face closer scrutiny, likely tied to concerns around trust, safety, and compliance.
These differences do not appear to stem from manual, category-specific policy decisions.
Instead, they reflect how Google’s automated systems adjust enforcement based on perceived industry risk.
The age of a review plays a significant role in when it is removed.
In medical and home services, a large share of deleted reviews disappear within the first six months after posting.
That timing points to early intervention by automated systems evaluating language, reviewer behavior, and other risk signals.
Restaurants and brick-and-mortar retail show a different pattern.
Many deleted reviews in these categories are more than two years old, suggesting retroactive enforcement as detection systems improve or new suspicious patterns emerge.
It may also reflect efforts to refresh older review profiles.
For businesses, this means reviews can disappear long after they are posted, often without warning.
Geography adds further complexity
Industry alone does not tell the full story. Location matters.
In English-speaking markets such as the U.S., UK, Canada, and Australia, deleted reviews skew heavily toward five-star ratings.
That trend aligns with increased AI-driven moderation aimed at reducing review spam and incentivized positive feedback.
Germany stands apart.
Analysis of thousands of German business listings shows a higher share of deleted reviews are low-rated, and most are removed within weeks of posting.
This pattern aligns with Germany’s strict defamation laws, which permit businesses to legally challenge negative reviews and require platforms to take prompt action upon notification.
In short:
AI-driven enforcement dominates in many English-speaking markets.
Legal takedowns play a much larger role in Germany.
What this means for local SEO and small business owners
The rise in review deletions creates two primary challenges.
Trust erosion: When legitimate reviews, whether positive or negative, disappear without explanation, confidence in review platforms begins to weaken.
Data distortion: Deleted reviews affect star ratings, performance benchmarks, and conversion signals that businesses rely on for local SEO and reputation management.
For SEO practitioners, small businesses, and multi-location brands, review monitoring is no longer optional.
Understanding when, where, and which reviews are removed is now as important as generating them.
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For the past decade, image SEO was largely a matter of technical hygiene:
Compressing JPEGs to appease impatient visitors.
Writing alt text for accessibility.
Implementing lazy loading to keep LCP scores in the green.
While these practices remain foundational to a healthy site, the rise of large, multimodal models such as ChatGPT and Gemini has introduced new possibilities and challenges.
Generative search makes most content machine-readable by segmenting media into chunks and extracting text from visuals through optical character recognition (OCR).
Images must be legible to the machine eye.
If an AI cannot parse the text on product packaging due to low contrast or hallucinates details because of poor resolution, that is a serious problem.
This article deconstructs the machine gaze, shifting the focus from loading speed to machine readability.
Technical hygiene still matters
Before optimizing for machine comprehension, we must respect the gatekeeper: performance.
Images are a double-edged sword.
They drive engagement but are often the primary cause of layout instability and slow speeds.
The standard for “good enough” has moved beyond WebP.
Designing for the machine eye: Pixel-level readability
To large language models (LLMs), images, audio, and video are sources of structured data.
They use a process called visual tokenization to break an image into a grid of patches, or visual tokens, converting raw pixels into a sequence of vectors.
This unified modeling allows AI to process “a picture of a [image token] on a table” as a single coherent sentence.
These systems rely on OCR to extract text directly from visuals.
This is where quality becomes a ranking factor.
If an image is heavily compressed with lossy artifacts, the resulting visual tokens become noisy.
Poor resolution can cause the model to misinterpret those tokens, leading to hallucinations in which the AI confidently describes objects or text that do not actually exist because the “visual words” were unclear.
Reframing alt text as grounding
For large language models, alt text serves a new function: grounding.
It acts as a semantic signpost that forces the model to resolve ambiguous visual tokens, helping confirm its interpretation of an image.
“By inserting text tokens near relevant visual patches, we create semantic signposts that reveal true content-based cross-modal attention scores, guiding the model.”
Tip: By describing the physical aspects of the image – the lighting, the layout, and the text on the object – you provide the high-quality training data that helps the machine eye correlate visual tokens with text tokens.
The OCR failure points audit
Search agents like Google Lens and Gemini use OCR to read ingredients, instructions, and features directly from images.
They can then answer complex user queries.
As a result, image SEO now extends to physical packaging.
Current labeling regulations – FDA 21 CFR 101.2 and EU 1169/2011 – allow type sizes as small as 4.5 pt to 6 pt, or 0.9 mm, on compact packaging.
“In case of packaging or containers the largest surface of which has an area of less than 80 cm², the x-height of the font size referred to in paragraph 2 shall be equal to or greater than 0.9 mm.”
While this satisfies the human eye, it fails the machine gaze.
Glossy packaging reflects light, producing glare that obscures text.
Packaging should be treated as a machine-readability feature.
If an AI cannot parse a packaging photo because of glare or a script font, it may hallucinate information or, worse, omit the product entirely.
Originality as a proxy for experience and effort
Originality can feel like a subjective creative trait, but it can be quantified as a measurable data point.
Original images act as a canonical signal.
The Google Cloud Vision API includes a feature called WebDetection, which returns lists of fullMatchingImages – exact duplicates found across the web – and pagesWithMatchingImages.
If your URL has the earliest index date for a unique set of visual tokens (i.e., a specific product angle), Google credits your page as the origin of that visual information, boosting its “experience” score.
Good to know: mid contains a machine-generated identifier (MID) corresponding to a label’s Google Knowledge Graph entry.
The API does not know whether this context is good or bad.
You do, so check whether the visual neighbors are telling the same story as your price tag.
By photographing a blue leather watch next to a vintage brass compass and a warm wood-grain surface, Lord Leathercraft engineers a specific semantic signal: heritage exploration.
The co-occurrence of analog mechanics, aged metal, and tactile suede infers a persona of timeless adventure and old-world sophistication.
Photograph that same watch next to a neon energy drink and a plastic digital stopwatch, and the narrative shifts through dissonance.
The visual context now signals mass-market utility, diluting the entity’s perceived value.
Beyond objects, these models are increasingly adept at reading sentiment.
APIs, such as Google Cloud Vision, can quantify emotional attributes by assigning confidence scores to emotions like “joy,” “sorrow,” and “surprise” detected in human faces.
This creates a new optimization vector: emotional alignment.
If you are selling fun summer outfits, but the models appear moody or neutral – a common trope in high-fashion photography – the AI may de-prioritize the image for that query because the visual sentiment conflicts with search intent.
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Discovery now happens before search demand is visible in Google.
In 2026, interest forms across social feeds, communities, and AI-generated answers – long before it shows up as keyword search volume.
By the time demand appears in SEO tools, the opportunity to shape how a concept is understood has already passed.
This creates a problem for how search marketing research is typically done.
Keyword tools, search volume, and Google Trends are lagging indicators.
They reveal what people cared about yesterday, not what they are starting to explore now.
In a landscape shaped by AI Overviews, social SERPs, and shrinking organic real estate, arriving late means competing inside narratives already defined by someone else.
It helps surface emerging themes, behaviors, and conversations while they are still forming – before they harden into keywords, content clusters, and product categories.
Used properly, it is not just a trend tool. It is a way to plan SEO, content, digital PR, and social-led search proactively.
This article breaks down how to use Exploding Topics to identify future entities, validate them through social search, and build search visibility before demand peaks.
Use Exploding Topics Trend Analytics to identify future entities – not just topics
Most marketers who use Exploding Topics already understand its value for content ideation, and we will cover that.
But its bigger opportunity is identifying future entities – concepts that search engines and AI systems will soon recognize as distinct “things,” not just keyword variations.
This matters because modern search no longer operates purely on keywords.
Google’s AI Overviews, ChatGPT, and other LLM-powered systems organize information around entities and relationships.
Once an entity is established, the narrative around it hardens.
Arrive late, and you are competing inside a story that has already been defined.
Exploding Topics gives you visibility early enough to act before that happens.
Example: Weighted sleep masks
In Exploding Topics, you might notice “weighted sleep mask” rising steadily.
Search volume remains low, and most keyword tools understate its importance.
At a glance, it looks like a niche product trend that is easy to ignore.
Look closer, and the signals are stronger:
The phrase is consistent and repeatable.
Adjacent topics are rising alongside it, including deep pressure sleep, anxiety sleep tools, and vagus nerve stimulation.
Questions that signal intent are increasing.
Early discussion focuses on understanding the concept, not just buying a product.
This is the point where something shifts from being a product with an adjective to a named solution. In other words, it is becoming an entity.
The traditional play
Most brands wait until:
Search demand becomes obvious, acting in December 2025 rather than July 2025.
Competitors launch dedicated product pages.
Affiliates and publishers surface “best” and “vs.” content.
Only then do they create:
A category page.
A “What is a weighted sleep mask?” article or social-search activation.
SEO content designed to chase presence, such as FAQs, SERP features, and rankings.
By this point, the entity already exists, and the story around it has largely been written by someone else.
In this case, NodPod is clearly dominating the entity.
Acting earlier, while the entity is forming
Using Exploding Topics well means acting earlier, while the entity is still being defined. Instead of starting with a product page, you:
Publish a clear, authoritative explanation of what a weighted sleep mask is.
Explain why deep pressure can help with sleep and anxiety.
Address who it is for – and who it is not.
Create supporting content that adds context, such as comparisons with weighted blankets or safety considerations.
This work can be done quickly and at scale through reactive PR and social search activations.
You are not optimizing for keywords yet.
You are teaching social algorithms, search engines, and AI systems what the concept means and associating your brand with that explanation from the start.
This is how brands can win at search in 2026 and beyond.
This early, proactive approach:
Helps search systems understand new concepts faster.
Increases the chance your framing is reused in AI-generated answers.
Positions your brand as the authority on the entity – not just a seller within the conversation.
Identifying an emerging entity is only the first step.
The real risk is not being early to a conversation. It is being early to something that never takes off.
This is where many SEO teams stall.
They wait for search volume and arrive too late, publish on instinct and hope demand follows, or freeze under uncertainty and do nothing.
There is a better middle ground: validate emerging entities through social search research and activation tests before scaling them into owned SEO and on-site experiences.
Exploding Topics is straightforward. It shows what might matter. Social platforms tell you whether your audience actually cares.
How social search becomes your validation layer
Once Exploding Topics surfaces a potential emerging entity, the next step is not Keyword Planner.
It is native search across platforms such as TikTok, Reddit, and YouTube, using either built-in trend tools or basic platform search.
You are looking for signals like:
Multiple creators independently explaining the same concept.
Comment sections filled with questions such as “Does this actually work?” or “Is this safe?”.
Repeated framing, metaphors, or demonstrations.
Early how-to or comparison content, even if production quality is low.
These signals point to intent.
Curiosity is turning into understanding.
Historically, this phase has always preceded measurable search demand.
Revisiting the weighted sleep mask example
After spotting “weighted sleep mask” in Exploding Topics, you might search for it on TikTok.
What you want to see is a lack of heavy brand advertising.
Mature ecommerce pushes or TikTok Shop funnels suggest the market is already established.
Instead, look for creators – not brand channels – testing products, discussing solutions, and exploring the underlying problem.
Focus on videos that explain pains, needs, and motivations, such as why pressure may help with anxiety.
Check the comments for comparisons to other solutions.
Look for questions raised in videos and comment threads.
Tools like Buzzabout.AI can help do this at scale through topic analysis and AI-assisted research.
These signals answer two critical questions:
Are people actively trying to understand this concept?
What language, framing, and objections are forming before SEO data exists?
That is validation.
Rethinking how SEO strategy gets built
This is where search strategy shifts.
Instead of asking, “Is there enough volume to justify content creation?” the better question is, “Is there enough curiosity to justify building authority early?”
If social signals are weak:
Pause.
De-risk by testing with creators outside your owned channels.
Avoid heavy investment in content that takes months to rank.
If signals are strong:
Scale with confidence.
Work with creators and activate brand channels.
Invest in entity pages, hubs, FAQs, comparisons, and PLP optimization.
In this model, fast-moving social platforms become the testing layer.
SEO is not the experiment, it’is the compounding layer.
Editorial digital PR that earns links and LLM citations
Most digital PR still works backward.
A trend reaches mainstream awareness.
Journalists write about it.
Brands scramble to comment.
PR teams try to extract links from a story that already exists.
The result is short-term coverage, diluted impact, and little lasting search advantage.
Exploding Topics makes it possible to reverse that dynamic by surfacing editorial narratives before they are obvious and positioning your brand as one of the sources that helps define them.
In 2026, this matters more than ever.
Links still matter, but they are no longer the only outcome that counts.
Brand mentions, explanations, and citations increasingly feed the systems behind AI Overviews, ChatGPT, Perplexity, and other LLM-driven discovery experiences.
Why early narratives outperform reactive PR
When a topic is everywhere, journalists are aggregating. When a topic is emerging, they are still asking questions.
Exploding Topics surfaces concepts at the stage where:
There is no consensus narrative.
Definitions are inconsistent.
Journalists are looking for clarity, not quotes.
“What is this?” stories have not yet been written.
This is the point where brands can move from commenting on a conversation to shaping it.
From trend-jacker to narrative owner
Instead of pitching “our brand’s take on X,” you lead with early signals you are seeing, why a concept is emerging now, and what it suggests about consumer behavior or the market.
The difference is subtle but important.
You are no longer reacting to coverage that already exists.
You are creating the framing that journalists, publishers, and, eventually, AI systems reuse.
LLMs do not learn from rankings alone.
They learn from editorial context, repeated explanations, and how trusted publications describe and define emerging concepts over time.
Done consistently, this approach compounds.
As your brand becomes associated with spotting and explaining emerging narratives early, you move from reactive commentary to trusted source.
Journalists begin to recognize where useful insight comes from, and that trust carries into more established coverage later on. You are no longer pitching for inclusion.
Your perspective is actively sought out.
The result is early narrative ownership and stronger access when mainstream coverage follows.
An editorial window before mainstream coverage
Before “weighted sleep mask” became a crowded ecommerce term in early 2025, there was a clear editorial window.
Journalists had not yet published stories asking:
“What is a weighted sleep mask?”
“Are weighted sleep masks safe?”
“Do they actually work for anxiety?”
That was the opportunity.
A PR-led approach at this stage includes:
Supplying journalists with expert explanations of deep pressure and sleep.
Sharing early insight into why the product category is emerging.
Contextualizing it alongside weighted blankets and other anxiety tools.
The result is not just coverage. It connects PR to search, curiosity, and discovery by helping define the concept itself.
That earns links, builds brand mentions, and signals authority around emerging entities that LLMs are more likely to cite and summarize over time.
Content roadmaps and briefs that don’t rely on search volume
Search volume is a poor starting point for content briefing.
It reflects interest only after a topic is established, language has stabilized, and the SERP is already crowded.
Used as a primary input, it pushes teams to chase demand instead of building authority.
That is why so many brands end up rewriting the same “What is X?” post year after year.
Better briefs start upstream.
They use Exploding Topics to spot what is forming and social search to understand how people are trying to make sense of it.
Reframing the briefing process
The core shift is moving away from briefs built around keywords and volumes and toward briefs built around audience intent.
That means focusing on three things:
Problems people are beginning to articulate.
Concepts that are not yet clearly defined or are actively debated.
Language that is inconsistent, emotional, or exploratory.
When content is approached this way, the objective changes.
It is no longer “create X to rank for Y.”
It becomes “explain X so the audience does not experience Y.”
That shift matters.
Designing content that compounds instead of expiring
The goal for SEO content teams in 2026 and beyond should be to brief content that defines a concept clearly. That includes:
Connecting it to adjacent ideas.
Comparing it to established solutions.
Answering questions within conversations that are still forming.
This does not always require written content.
The same work can happen through social search activations or digital PR.
Approached this way, content grows into demand rather than chasing it.
Instead of being rewritten every time search volume changes, it evolves through updates, expansion, and, where possible, stronger internal linking.
As interest grows, the content does not need replacing. It needs refining.
This is the type of material AI and LLMs tend to reference – timely, clear, explanatory, and grounded in real questions.
Publication isn’t the end
Publishing and waiting for content to rank is no longer the end of the brief.
Teams need a clear plan for distribution and reuse.
For emerging topics, that means contributing insight in relevant Reddit threads, Discord communities, niche forums, and creator comment sections.
Not to drop links, but to answer questions, share explanations, and test framing in public.
Those conversations feed back into the content itself, improving clarity and increasing the likelihood that your explanation is the one others repeat.
With a social search activation approach, brands can scale messaging quickly by working with partners who interpret and distribute the brief in their own voice.
When this works, SEO content stops being static and starts acting like a living reference point – one that contributes to culture and builds lasting brand recognition.
It develops across social platforms, communities, and AI-driven discovery long before it registers as keyword volume.
Exploding Topics helps surface what is emerging.
Social search shows whether people are trying to understand it.
Digital PR shapes how those ideas are defined and cited.
SEO compounds that work by reinforcing narratives that are already taking shape, rather than trying to test or invent them after the fact.
In this model, SEO is the layer that turns early insight and clear explanation into durable visibility across Google, social platforms, and AI-generated answers.
Search no longer starts on Google. The teams that act on that reality will influence what people search for next.
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When targeting businesses vs. customers with your SEO tactics, there are different formulas that come into play.
But the answer is always the same: “Content matters.”
This is especially true in the world of B2B, where conversions tend to take longer to occur, and customers typically have a deeper understanding of their specific niche.
The right keywords mean people can find you when searching for products and services like yours. And, in the modern marketplace, it’s all about personalization.
Choosing keywords worth targeting, meaning ones that will actually lead to conversions, means matching your research to your target audience. Gone are the days where you can simply focus on target keywords for a given industry. You need to get clear on who your ideal customer is (a customer persona is the best way), work backwards from there, and conduct your keyword research accordingly.
Let’s see how you can use it to supercharge the conversions in your business.
Key Takeaways
Intent beats volume in B2B. Long-tail, comparison, integration, and pain-point keywords bring the highest-quality traffic because they mirror how real buyers evaluate solutions.
Your best keywords come from conversations, not tools. Sales teams and customers surface language and questions that keyword tools can’t predict.
B2B funnels require keyword mapping. TOFU, MOFU, and BOFU terms attract different stakeholders at different readiness levels. If you skip a stage, you break your pipeline.
Clusters win in B2B SEO. Organizing keywords into pillars and supporting clusters builds authority and guides buyers naturally through research and evaluation.
Keyword lists are only valuable when activated. Use them for on-page optimization, schema, content hubs, repurposed formats, and now LLMO to appear in AI-generated answers.
B2B vs B2C Keyword Research
With both B2B and B2C keyword research, your ideal user or customer should be at the center of what you do.
With B2B marketing, you focus on various decision-makers, like a team lead, manager, or even the CEO. These keywords are typically lower volume, but are higher value when you rank well for them.
With B2C marketing, the only decision-maker you’re worried about is the customer. Your marketing should be geared directly towards them, which makes understanding your target audience even more important.
One of the challenges with B2B marketing is the sales cycle. Business-to-business conversions generally take longer than B2C. There’s a big difference between someone buying a pair of socks versus investing in a software suite for a whole company.
There are some parallels, but by and large, B2B buyers have different behavior. This is where accurate intent mapping comes into play. Understanding which keywords are ranking is only half the battle. Matching the intent behind the search for each query gives a much clearer picture of what will move your target customers further along their buyer journey, ultimately leading to a conversion.
The good news is that in some ways, your best practices stay the same.
Know your product, then move to understand your market and competition to build the best B2B keyword list.
B2B keyword research helps you win over the decision-makers at hand, but this can be tricky.
There’s a different drive to the transaction. You need to take a different approach to earn their buyer intent.
To address their unique needs, you need to demonstrate your expertise not only in the niche but also in the specific pain points within that niche. That means picking the right keywords for your content and pages. To inspire your B2B keyword research, ask yourself:
What kind of businesses am I targeting? How big are their teams? Are they in industries I can flourish in?
Am I trying to reach businesses at the executive, manager, or employee level?
Of the decision-makers I’m targeting, what challenges are they up against? How is their current system failing them?
If you don’t keep these questions in mind during your keyword research, you’ll have a tough time reaching your B2B SEO goals.
Taking the time to get it right is critical to long-term growth.
What Makes the B2B Buyer Journey Unique (and how it impacts keywords)
B2B buyers don’t search like consumers. They ask more questions and involve more decision-makers. That means your keyword strategy needs to map to every stage of the funnel, because each stage comes with its own unique intent.
At the top of the funnel (TOFU), people are looking to understand the problem. Think keywords like “what is lead nurturing” or “how to qualify B2B leads.”
In the middle (MOFU), they’re evaluating options. That’s where terms like “best B2B CRM platforms” or “HubSpot vs. Salesforce” show up.
At the bottom (BOFU), they’re ready to buy. They’ll search for things like “HubSpot onboarding consultant” or “best CRM for B2B SaaS.”
If you skip a stage, you risk confusing or losing your audience. Match your keywords to where buyers actually are, not where you hope they are.
How To Find High-Intent B2B Keywords That Actually Convert
To drive real leads, you need more than traffic. Here’s how to find keywords that match intent and move B2B buyers toward a decision.
Step 1. Interview Your Sales Team and Customers
If you want high-intent keywords, talk to the people on the front lines.
Your sales team knows exactly what questions prospects ask before they buy. They hear the same objections, pain points, and decision criteria repeatedly. That language? It’s keyword fuel. Ask them: What are the top questions you hear? What phrases come up in discovery calls? What signals buying intent?
Then talk to a few current customers. Ask what they Googled before they found you. What words did they use to describe their problem? Why did they choose you over a competitor?
These conversations don’t have to be formal. A quick 15-minute chat can uncover terms your audience actually uses that your keyword tool might miss.
Log every phrase, question, and pain point. You’ll use them later to validate topics and shape content that speaks directly to your buyer’s intent.
Step 2. Use Tools To Expand Your Keyword Set
Once you’ve got seed terms from sales and customers, plug them into keyword tools to scale.
Start with Ubersuggest or Semrush to find related phrases, autocomplete suggestions, and questions your audience is already searching for.
AnswerThePublic is great for uncovering long-tail keywords phrased as real questions—perfect for B2B blog content and landing pages.
Focus on commercial-intent keywords, terms that suggest the searcher is in buying mode. Look for modifiers like “best,” “vs,” “top,” or “software for [industry].”
Don’t just chase volume. Check keyword difficulty to make sure you can rank, and look at CPC (cost per click) to gauge how valuable a keyword is to advertisers. High CPC usually means it’s converting for someone.
This is where you turn insights into opportunity. The right tools help you see the full landscape and find the gaps your competitors missed.
Step 3. Spy on Competitors (Especially in Niche B2B)
If your competitors are already ranking, reverse-engineer what’s working for them.
Tools like Semrush and Ahrefs let you plug in a competitor’s domain and see the exact keywords they rank for, along with positions, search volume, and traffic estimates. This gives you a fast snapshot of what’s driving their visibility.
Look for content gaps. Are there high-value keywords they missed? Are there topics they cover that you could go deeper on, with more data, better examples, or stronger CTAs?
In niche B2B markets, you won’t find millions of searches—but that’s the point. The right long-tail keyword with even 100 searches a month could drive qualified leads if the intent is strong and the competition is low.
Don’t copy what they’ve done. Use it as a launchpad. Then build something more useful, more specific, and more aligned with your buyer’s needs.
Step 4. Analyze Intent, Not Just Volume
In B2B, high search volume doesn’t always mean high value.
A keyword like “lead generation” might pull in thousands of searches, but it’s broad and packed with top-of-funnel traffic. Instead, go after long-tail keywords that signal real buying intent.
Look for terms like:
“SOC 2 vs ISO 27001” – These comparison searches show the buyer is actively evaluating solutions.
“Lead scoring software for SaaS” – This one’s specific, solution-aware, and vertical-focused. A perfect match for bottom-of-funnel content.
Intent > volume. That’s the rule.
Use keyword tools to filter by modifiers like “vs,” “best,” “alternatives,” or “[industry] software.” These often have lower volume, but they attract leads who are closer to buying and more likely to convert.
Build your keyword strategy around relevance and readiness, not raw traffic. That’s how you attract the right people at the right time.
Step 5. Group Keywords Into Pillars and Clusters
Don’t just build a list, build a structure.
Once you’ve nailed down your keyword set, organize it into pillars and clusters. A pillar page targets a broad, high-value topic like “email marketing software.” Around it, you build supporting content, think clusters like “email automation for B2B,”“lead nurturing workflows,”and“best B2B email sequences.”
This approach does two things:
It strengthens your SEO by signaling topical authority.
It aligns with the B2B buyer journey, letting prospects go deeper as they move from problem-aware to solution-ready.
Each cluster targets a long-tail, intent-driven keyword and links back to the pillar. The result? Better rankings and clearer paths to conversion.
Use tools like Ubersuggest or SEMrush’s keyword grouping to speed this up. Just make sure every piece has a purpose in your funnel.
B2B Keyword Types You Should Actually Focus On
Not all keywords are created equal, especially in B2B. Some attract the right audience, move them through the funnel, and convert. Others just bring “fluff traffic” that never turns into leads.
Here are the four keyword types that consistently deliver in B2B:
Comparisons
These are high-intent gold. When someone searches “HubSpot vs Salesforce” or “SOC 2 vs ISO 27001,” they’re in evaluation mode. They’re comparing options and looking for a clear winner.
Create content that breaks down the pros and cons honestly. Side-by-side features, pricing, integrations, and who it’s best for. This is where trust gets built and decisions get made.
Integrations
In B2B, tools rarely stand alone. That’s why keywords like “Slack integration with project management software” or “CRM that integrates with QuickBooks” pull in traffic that’s ready to act.
These searches signal product fit and technical alignment—key for conversion. If your product integrates with other tools, optimize for those terms.
Use-Case Specific
Broad keywords miss the mark. “Lead scoring software” is nice, but “lead scoring software for SaaS” is better. Even better? “lead scoring software for early-stage B2B SaaS.”
The more specific the use case, the higher the intent. Create content that addresses your audience’s specific needs and concerns.
Pain Point Phrases
These are often phrased as questions: “How to reduce churn in B2B SaaS” or “Why aren’t my sales qualified leads converting?” These aren’t just TOFU, they’re strong entry points for solution-aware buyers.
Targeting these keywords helps you show up early in the journey and guide buyers toward your solution.
What to Do After You Have Your Keywords?
Now what?
You know your keyword opportunities. It’s time to put them to work.
In addition, by implementing keywords appropriately in areas such as schema markup like FAQs or price listings for e-commerce, you can both have a more optimized and useful listing. Use Ubersuggest or AnswerThePublic to pinpoint the questions your target decision-makers may have. (Hint: They’re already searching for them, and these tools will show you what they are.)
As far as working more B2B SEO keywords into your content, make sure the content is directly related to your existing target B2B keywords.
Another quick way to optimize for your target keywords is to structure your internal links in a way that creates content hubs on your site for pieces relevant to your B2B content strategy.
Below you can see Zapier’s Remote Work Guide as a content hub touchpoint example. This page acts as a content hub, with many “spokes” out to different resources around tools and tactics for the main subject: remote work.
Today, your B2B keyword strategy is more about being the source across search, AI, and voice.
Fortunately, you can also use your keyword list to guide large language model optimization (LLMO). Tools like ChatGPT, Gemini, and Claude often cite content when answering B2B queries. If your page is optimized for specific long-tail or question-based keywords, you increase the odds of being surfaced in AI-generated answers.
Using your keywords to shape new content formats is another smart move. Turn question-based terms into short-form video or slideshows. Repurposing like this builds topical authority across channels and sends strong signals back to your core site.
Finally, don’t let your keyword list sit in a spreadsheet. Plug it into your editorial calendar. Map keywords to specific goals, funnel stages, and audience segments. That’s how you turn SEO research into actual business growth.
FAQs
Does SEO work for B2B?
Yes, SEO is a valuable tactic to use to win over buyers. Good organic visibility throughout the sales funnel is a proven technique to drive growth and, in turn, increase interest.
Why is SEO important for B2B?
SEO generates valuable leads and makes it easier for potential buyers to find you. When they’re searching for products or services in relation to yours, you’re more likely to show up in their search results thanks to SEO tactics like using B2B SEO keywords.
How do I create a B2B SEO strategy?
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If you want a solid B2B SEO strategy, follow these quick tips: 1. Conduct B2B keyword research. (Hint: Use Ubersuggest to help you get valuable results.) 2. Understand what matters to your target decision-makers and nurture them through your sales funnel. 3. Optimize your site to target your ideal audience by updating aspects like meta descriptions and internal linking. 4. From the B2B keywords, formulate content to position yourself as the answer to your audience’s needs. 5. Promote your content and grow your audience and domain authority through backlinks.
Conclusion
Now that you know how to conduct B2B keyword research using Ubersuggest, you can unlock hidden opportunities for your brand.
Getting the lay of the land in your niche will help. From your competitor analysis on your target B2B keywords, ask yourself: Where do you stand? How can you satisfy buyers in a way that your competitors aren’t?
The goal with B2B content tactics is to position yourself as the answer decision-makers need.
Your keyword research will reveal the topics that reel in buyers, and the content you create will help secure conversions.
Folding B2B SEO keywords into your strategy is a core step in gaining the attention and influence of the brands you’re targeting.
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Most site owners don’t realize how much of their content large language models (LLMs) already gather. ChatGPT, Claude, and Gemini pull from publicly available pages unless you tell them otherwise. That’s where LLMs.txt for SEO comes into the picture.
LLMs.txt gives you a straightforward way to tell AI crawlers how your content can be used. It doesn’t change rankings, but it adds a layer of control over model training, something that wasn’t available before.
This matters as AI-generated answers take up more real estate in search results nowadays. Your content may feed those answers unless you explicitly opt out. LLMs.txt provides clear rules for what’s allowed and what isn’t, giving you leverage in a space that has grown quickly without much input from site owners.
Whether you allow or restrict access, having LLMs.txt in place sets a baseline for managing how your content appears in AI-driven experiences.
Key Takeaways
LLMs.txt lets you control how AI crawlers such as GPTBot, ClaudeBot, and Google-Extended use your content for model training.
It functions similarly to robots.txt but focuses on AI data usage rather than traditional crawling and indexing.
Major LLM providers are rapidly adopting LLMs.txt, creating a clearer standard for consent.
Allowing access may strengthen your presence in AI-generated answers; blocking access protects proprietary material.
LLMs.txt doesn’t impact rankings now, but it helps define your position in emerging AI search ecosystems.
What is LLMs.txt?
LLMs.txt is a simple text file you place at the root of your domain to signal how AI crawlers can interact with your content. If robots.txt guides search engine crawlers, LLMs.txt guides LLM crawlers. Its goal is to define whether your public content becomes part of training datasets used by models such as GPT-4, Claude, or Gemini.
Here’s what the file controls:
Access permissions for each AI crawler
Whether specific content can be used for training
How your site participates in AI-generated answers
Transparent documentation of your data-sharing rules
This protocol exists because AI companies gather training data at scale. Your content may already appear in datasets unless you explicitly opt out. LLMs.txt adds a consent layer that didn’t previously exist, giving you a direct way to express boundaries.
OpenAI, Anthropic, and Google introduced support for LLMs.txt in response to rising concerns around ownership and unauthorized data use. Adoption isn’t universal yet, but momentum is growing quickly as more organizations ask for clarity around AI access.
LLMs.txt isn’t replacing robots.txt because the two files handle different responsibilities. Robots.txt manages crawling for search engines, while LLMs.txt manages training permissions for AI models. Together, they help you protect your content, define visibility rules, and prepare for a future where AI-driven search continues to expand.
Why is LLMs.txt a Priority Now?
Model developers gather massive datasets, and most of that comes from publicly accessible content. When OpenAI introduced GPTBot in 2023, it also introduced a pathway for websites to opt out. Google followed with Google-Extended, allowing publishers to restrict their content from AI training. Anthropic and others soon implemented similar mechanisms.
This shift matters for one reason: your content may already be part of the AI ecosystem unless you explicitly say otherwise.
LLMs.txt is becoming a standard because site owners want clarity. Until recently, there was no formal way to express whether your content could be repurposed inside model training pipelines. Now you can define that choice with a single file.
There’s another angle to this. Generative search tools increasingly rely on trained data to produce answers. If you block AI crawlers, your content may not appear in those outputs. If you allow access, your content becomes eligible for reference in conversational responses, something closely tied to how brands approach LLM SEO strategies.
Neither approach is right for everyone. Some companies want tighter content control. Others want stronger visibility in AI-driven areas. LLMs.txt helps you set a position instead of defaulting into one.
As AI-generated search becomes more prominent, the importance of LLMS.txt grows. You can adjust your directives over time, but having the file in place keeps you in control of how your content is used today.
How LLMs.txt Works
LLMs.txt is a plain text file located at the root of your domain. AI crawlers that support the protocol read it to understand which parts of your content they can use. You set the rules, upload the file once, and update it anytime your strategy evolves.
Where it Lives
LLMs.txt must be placed at:
yoursite.com/llms.txt
This mirrors the structure of robots.txt and keeps things predictable for crawlers. Every supported AI bot checks this exact location to find your rules. It must be in the root directory to work correctly, subfolders won’t register.
The file is intentionally public. Anyone can view it by navigating directly to the URL. This transparency allows AI companies, researchers, and compliance teams to see your stated preferences.
What You Can Control
Inside LLMs.txt, you specify allow or disallow directives for individual AI crawlers. Example:
User-agent: GPTBot Disallow: /
User-agent: Google-Extended Allow: /
You can grant universal permissions or block everything. The file gives you fine-grained control over how your public content flows into AI training datasets.
Current LLMs That Respect It
Several major AI crawlers already check LLMs.txt automatically:
GPTBot (OpenAI) — supports opt-in and opt-out training rules
Google-Extended — used for Google’s generative AI systems
CCBot (Common Crawl) — contributes to datasets used by many models
PerplexityBot — early adopter in 2024
Support varies across the industry, but the direction is clear: more crawlers are aligning around LLMs.txt as a standardized method for training consent.
LLMs.txt vs Robots.txt: What’s the Difference?
Robots.txt and LLMs.txt serve complementary but distinct purposes.
Robots.txt controls how traditional search engine crawlers access and index your content. Its focus is SEO: discoverability, crawl budgets, and how pages appear in search results.
LLMs.txt, in contrast, governs how AI models may use your content for training. These directives tell model crawlers whether they can read, store, and learn from your pages.
Here’s how they differ:
Different crawlers: Googlebot and Bingbot follow robots.txt; GPTBot, ClaudeBot, and Google-Extended read LLMs.txt.
Different outcomes: Robots.txt influences rankings and indexing. LLMs.txt influences how your content appears in generative AI systems.
Different risks and rewards: Robots.txt affects search visibility. LLMs.txt affects brand exposure inside AI-generated answers — and your control over proprietary content.
Both files are becoming foundational as search shifts toward blended AI and traditional results. You’ll likely need each one working together as AI-driven discovery expands.
Should You Use LLMs.txt for SEO?
LLMs.txt doesn’t provide a direct ranking benefit today. Search engines don’t interpret it for SEO purposes. Still, it influences how your content participates in generative results, and that matters.
Allowing AI crawlers gives models more context to work with, improving the odds that your content appears in synthesized answers. Blocking crawlers protects proprietary or sensitive content but removes you from those AI-based touchpoints.
Your approach depends on your goals. Brands focused on reach often allow access. Brands focused on exclusivity or IP protection typically restrict it.
LLMs.txt also pairs well with thoughtful LLM optimization work. Content structured for clarity, strong signals, and contextual relevance helps models interpret your material more accurately. LLMs.txt simply defines whether they’re allowed to learn from it.
“LLMs.txt doesn’t shift rankings today, but it sets early rules for how your content interacts with AI systems. Think of it like robots.txt in its early years: small now, foundational later.” explains Anna Holmquist, Senior SEO Manager at NP Digital.
Who Actually Needs LLMs.txt?
Some websites benefit more than others from adopting LLMs.txt early.
Content-heavy sites Publishers, educators, and documentation libraries often prefer structure around how their content is reused by AI systems.
Brands with proprietary material If your revenue depends on premium reports, gated content, or specialized datasets, LLMs.txt offers a necessary layer of protection.
SEOs planning for AI search As generative results become more common, brands want control over how content feeds into those answer engines. LLMs.txt helps set boundaries while still supporting visibility.
Industries with compliance requirements Healthcare, finance, and legal organizations often need strict data-handling rules. Blocking AI crawlers becomes part of their governance approach.
LLMs.txt doesn’t lock you into a long-term decision. You can update it as AI search evolves.
How To Set Up an LLMs.txt File
Setting up an LLMs.txt file is simple. Here’s the process. If you want assistance doing this, there are tools and generators that can assist.
It’s a file that tells AI crawlers whether they can train on your content. It’s similar to robots.txt but designed specifically for LLMs.
Does ChatGPT use LLMs.txt?
Yes. OpenAI’s GPTBot checks LLMs.txt and follows the rules you specify.
How do I create an LLMs.txt file?
Create a plain text file, add crawler rules, and upload it to your site’s root directory. Use the examples above to set your directives.
Conclusion
LLMs.txt gives publishers a way to define how their content interacts with AI training systems. As AI-generated search expands, having explicit rules helps protect your work while giving you control over how your brand appears inside model-generated answers.
This file pairs naturally with stronger LLM SEO strategies as you shape how your content is discovered in AI-driven environments. And if you’re already improving your content structure for model comprehension, LLMs.txt fits neatly beside ongoing LLM optimization efforts.
If you need help setting up LLMs.txt or planning for AI search visibility, my team at NP Digital can guide you.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-12-20 20:22:272025-12-20 20:22:27What Is LLMs.txt? & Do You Need One?
AI is reshaping how people shop online. Search isn’t just about keywords anymore. Tools like Google’s AI Overviews, ChatGPT shopping features, and Perplexity product recommendations analyze huge amounts of product data to decide what to show users. That shift means e-commerce brands need to rethink the way their product information is structured.
If you want visibility in these AI-powered shopping journeys, your product data has to be clean, complete, and enriched. AI models lean heavily on structured feeds, trusted marketplaces, and high-quality product attributes to understand exactly what you sell.
That’s why AIsearch for e-commerce matters right now. Brands that optimize their feeds will show up in conversational queries, comparison results, and visual search responses. Brands that don’t will struggle to appear even if they’ve done traditional SEO well.
This foundation will help you give AI systems the clarity they need to recommend your products with confidence.
Key Takeaways
AI search engines rely heavily on structured product feed data instead of just site content to understand and surface products.
Clean, complete feeds lead to higher visibility across Google Shopping, ChatGPT shopping research, Perplexity results, and other LLMs.
Strong titles, enriched attributes, and quality images make it easier for AI systems to match your products to real user needs.
Brands with clear, structured product data will outperform competitors in AI-driven shopping experiences.
How AI Search Is Reshaping Product Discovery
AI is changing the way customers find products long before they reach your website. Instead of typing traditional keywords, shoppers now describe what they want in plain language: “lightweight waterproof hiking boots,” “a gift for a 12-year-old who loves science,” “a mid-century floor lamp under $150.”
AI systems interpret these natural-language queries using semantic understanding instead of exact keyword matches. That shift affects everything from Google Shopping listings to ChatGPT’s built-in shopping tools. It also impacts how AI-driven platforms rank your products when answering conversational or comparison-based queries.
If you’ve been following the evolution of AI in e-commerce, you already know AI is moving deeper into product search, recommendation, and personalization. But behind the scenes, the link between your product data and AI visibility is tightening.
AI models rely on structured, trustworthy data sources, including product feeds, schema markup, and marketplace listings. If your feed lacks attributes or clarity, AI can’t confidently connect your product to a user’s need, even if your website is strong.
Optimizing your feed is no longer a backend task. It’s a visibility strategy.
What Is a Product Feed (and Why AI Cares About It)
A product feed is a structured data file that contains detailed information about every item you sell. It includes attributes like product title, description, brand, size, color, price, availability, GTIN, and more. Platforms such as Google Shopping, Meta, Amazon, and TikTok Shops rely on these feeds to understand your inventory and decide when to show your products.
AI systems depend on the same structure. Instead of scanning pages manually, they pull product details from feeds because the information is cleaner, more complete, and easier to interpret at scale.
If your feed includes rich attributes, AI can match your items to complex user queries. When attributes are missing or titles are vague, your products become invisible in AI-driven discovery, regardless of how strong your website content might be.
This is why optimizing product feeds is a priority for e-commerce brands right now. Clean, enriched feeds increase your visibility across AI-powered shopping experiences and visual search tools like Google Lens.
Now, your product feed isn’t just for ads, but is a core input for AI search.
What AI Needs From Your Product Feed (Titles, Attributes, Images)
AI systems don’t guess what your products are, instead analyzing the data you provide. These are the elements that matter most.
Titles and Descriptions
AI models prefer natural, descriptive, human-sounding titles. Short, vague titles like “Running Shoes” don’t give AI enough context. But a title such as:
instantly signals the audience, category, and key benefits.
Descriptions should reinforce the title and add details that help AI understand use cases, materials, fit, and core value.
Avoid keyword stuffing. AI systems would likely reference sites with ambiguity less because they would have less info to understand it.
Product Attributes
AI engines rely heavily on structured attributes such as:
Size
Color
Material
Fit
Style
GTIN/MPN
Age range
Intended use
Missing attributes = missing visibility.
Attributes help AI refine products when users ask things like: “Show me a size 8,” “Only vegan options,” “Something in walnut or dark wood.”
The more complete your attributes, the better your likelihood of appearing in those filtered results.
Product Images and Alt Text
AI increasingly “reads” images using vision models. Google Lens, Pinterest Lens, and multimodal AI systems analyze colors, textures, shapes, and packaging.
Clear, high-resolution images paired with alt text provide two inputs: visual interpretation and descriptive language.
Example alt text: “Women’s waterproof trail running shoe with rubber sole, breathable mesh upper, and reinforced toe cap in blue.”
Visual clarity improves both AI understanding and user experience.
Steps To Optimize Product Feeds for AI Visibility
Here’s the practical workflow to upgrade your product feed for AI search visibility.
1. Audit Your Current Product Feed
Start with a complete audit using tools like Google Merchant Center, Feedonomics, or GoDataFeed. Look for:
Missing GTINs or invalid identifiers
Weak or vague product titles
Incomplete attributes
Duplicate listings
Mismatched availability or pricing
Blank fields or generic descriptions
AI search systems penalize incomplete or ambiguous data.
Include product schema markup on all product pages, especially:
Product
Review
Price
Availability
AI search engines treat structured schema as a trust signal.
Also include descriptive alt text on all product images to support accessibility and AI interpretation.
5. Set Up Feed Rules and Automations
Automate cleanup tasks such as:
Adding missing colors to titles
Appending product type or material
Standardizing capitalization
Populating missing attributes with known defaults
Flagging products with incomplete data
Automation keeps your feed consistent as your catalog changes.
How AI Assistants Use Product Data
AI shopping assistants are rapidly changing how customers discover and compare products.
To generate these answers, AI systems pull from:
Merchant Center feeds
Structured schema markup
Marketplace listings
Verified product databases
High-quality product images
Trusted review sources
This creates a composite understanding of your product beyond just what your site says about it.
If you’ve explored the role of AI shopping assistants, you’ve likely seen how quickly they recommend products based on attributes like size, color, performance, ratings, and price. Those signals come directly from your feed and structured product data.
Brands with richer data sets see higher inclusion rates in:
AI systems don’t guess. They promote products they can understand clearly and ignore the rest.
Common Mistakes That Hurt AI Visibility
Most feed problems fall into a few categories, and each one reduces visibility in AI search engines.
1. Vague or Duplicated Titles
Titles like “Running Shoes” or “LED Lamp” provide no usable context. AI deprioritizes these compared to richer alternatives.
2. Missing Key Attributes
Many merchants skip fields like size, color, material, GTIN, or gender. AI relies heavily on these attributes when matching products to specific user requests.
3. Keyword-Stuffed or Fluffy Descriptions
Descriptions should be informative, not bloated. AI models prefer specific phrasing over repetitive keywords.
4. Inconsistent Pricing or Availability
If your feed shows “in stock” but your page says “out of stock,” AI systems flag inconsistencies and may reduce your visibility.
5. Low-Quality Images or Missing Alt Text
Visual AI models need clarity. Poor images or missing alt text make your product harder to classify.
Fixing these issues has a measurable impact on how often your products appear in AI-driven recommendations.
FAQs
What is AI e-commerce?
AI e-commerce refers to using artificial intelligence to improve product discovery, recommendations, personalization, and automation throughout the online shopping experience.
How is AI changing e-commerce?
AI is shifting product discovery toward natural-language search, visual identification, and conversational shopping assistants. Brands now need structured, enriched product data to stay visible.
How do you optimize a product feed for AI search?
Create clear titles, use complete attributes, include schema markup, strengthen product images, and use automation to maintain consistency. A detailed feed helps AI understand your products accurately.
Conclusion
Brands that invest in structured data, enriched attributes, and clear product information will outperform competitors as AI-driven shopping grows.
Feed optimization also strengthens your broader search strategy. The same structured data powering AI engines aligns with strong AI in e-commerce practices, and the same clarity helps conversational systems recommend your products more confidently.
Visibility in AI search isn’t random. It comes from data quality. And improving that data is one of the highest-impact steps an e-commerce brand can take today.
Missed the final SEO Update by Yoast of 2025? Our in-house principal SEOs, Carolyn Shelby and Alex Moss, broke down December’s biggest search shifts, from Gemini’s integration to Google’s publisher deals, and answered your burning questions. Don’t forget to watch the replay and sign up for the next edition!
Watch the full replay below (or read on for the highlights).
2025 in a nutshell: The three biggest SEO shifts
2025 was the year AI officially took over search. Here’s what mattered most:
From rankings to retrieval: AI overviews and chat interfaces made being cited more important than ranking #1.
EEAT became non-negotiable: Google (and users) demanded real expertise, not just keyword-stuffed content.
Publishers vs. AI: Lawsuits and deals reshaped how content is licensed and monetized.
Want the full breakdown? Our in-depth 2025 SEO recap post will be released next week. Also, hear Carolyn and Alex share their insights in the December SEO Update by Yoast on YouTube.
Google Ads in AI mode? Some users have reported seeing sponsored results in AI overviews, which occupy half the screen. Expect more tests in 2026.
OpenAI’s “Code Red”: After user feedback favored Gemini, OpenAI rushed out GPT-5.2. The takeaway? AI platforms are in a feature war, and your content needs to work across all of them.
Action: Audit your content for retrieval (not just rankings). Use tools like Yoast’s Brand Insights AI visibility tracker to see where you’re cited in AI responses in LLMs like ChatGPT and Perplexity.
Google’s publisher deals: A band-aid or the future?
Our take: It’s low-effort, so why not? Yoast SEO’s free tool generates a valid llms.txt file in seconds.
Action: Add llms.txt if you’re curious, but don’t expect miracles.
Q&A highlights
Carolyn and Alex answered live questions during the webinar. Here are the top three:
1. Should we stop using background images to improve load speed?
Carolyn: “Optimize them, but don’t stress. Focus on non-blocking load times. If the image is lazy-loaded and doesn’t delay interactivity, it’s fine.”
Alex: “Test it. If your audience cares about visuals (e.g., fashion, design), keep them. If not, simplify.”
2. Can we make big changes during a Google core update?
Carolyn: “Act like there’s no update. If you need to make changes, make them. Google’s updates are continuous, so they’re not a deadline.”
Alex: “Worst case? You’ll see fluctuations. But if your site’s broken, fix it now.”
3. FAQ pages or FAQs on every page?
Alex: “Both. Put unique FAQs on product/service pages. Use a central FAQ for shared questions (e.g., shipping, returns).”
Carolyn: “Avoid hiding answers in toggles, because AI won’t read them.”
Stay ahead in 2026
The news in this December edition of the SEO Update by Yoast proves one thing: SEO is changing faster than ever. Whether it’s AI-driven search, publisher deals, or smarter tools, the rules are being rewritten at a rapid pace.
Here’s how to keep up:
Join us for the next SEO Update by Yoast on January 27, 2026. We’ll dive into the latest trends and explore their implications for your strategy. Sign up now.
Missed the 2025 recap? Our in-depth post will be released next week; don’t forget to read it!
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-12-18 14:03:402025-12-18 14:03:40The December 2025 edition of the SEO Update by Yoast: AI search, publisher deals & more
Search engines have become significantly more intelligent than they were in the past. You no longer need to repeat the same keyword a dozen times to be noticed. Google’s AI models, as well as large language models like ChatGPT and Gemini, now understand meaning, intent, and context. So, does that mean you don’t need to use synonyms and related keywords? Of course not, and to avoid any confusion, you should definitely. Using synonyms and related keywords isn’t just about improving your writing style. It also helps both people and search engines interpret what your page is about.
Search engines now understand context, making it essential to use synonyms and related keywords for improved clarity.
Using synonyms enhances readability and helps both people and search engines understand the content’s meaning more effectively.
Tools like Yoast SEO Premium can suggest related keywords, making the writing process easier.
Focus on natural language rather than keyword density to enhance your SEO strategy.
Writing for readers and AI involves selecting word choices that create engaging and informative content.
What are synonyms and related keyphrases?
A synonym is a word that shares the same or a very similar meaning as another. For example, “fast” and “quick” are synonyms.
A related keyphrase, on the other hand, isn’t necessarily a direct synonym; it’s a word or phrase connected to the same topic. If your main keyphrase is chocolate candy, then sweets, dessert, or sugary treats could all be related keyphrases.
When you use synonyms and related keywords, you make your writing more natural to read and more informative. You also help search engines understand your topic in greater depth, which improves your chances of appearing in relevant searches.
Why variation matters for SEO and readability
Modern SEO copywriting and readability are all about helping people and search engines understand the context of your writing. When you vary your word choice and use synonyms and related keywords, you make your text more engaging for readers and clearer for algorithms.
If you’ve ever read a page that repeats the same keyword endlessly, you know how mechanical it feels. Years ago, that might have worked. Today, it can frustrate readers and even harm your SEO.
Using synonyms and related keywords also improves your readability score, a ranking factor that reflects how easy your text is to follow. When your content is varied, visitors stay longer, bounce less, and gain a better understanding of you.
How search engines and AI understand language today
Search engines rely on natural language processing (NLP) and machine learning to interpret meaning. Instead of simply counting keywords, they analyze how words relate to each other in context.
That’s why a post about AI copywriting tools can appear in searches for AI content writing software. Google understands those terms belong to the same topic. This is part of semantic SEO, which involves optimizing content so that search engines can grasp its overall context rather than just individual words.
By naturally incorporating synonyms and related keywords, you help Google recognize that your content addresses a broader range of questions related to your topic.
Understanding search intent is crucial here. Once you know what users expect to find, you can use language that naturally fits their intent while still covering your main keyphrases.
Keyword density versus natural language
In early SEO, keyword density, or the percentage of times a keyword appeared in your text, was seen as a signal of relevance. But search engines have outgrown that. Today, keyword density has little to no impact on search engine rankings.
Still, using your synonyms and related keywords naturally throughout your text can help clarify context. The key is balance: write as if you’re explaining the topic to a colleague or client.
Yoast SEO’s readability and SEO guidelines highlight why tone, pacing, and sentence length are now essential parts of optimization. The goal isn’t to count words, it’s to communicate clearly.
If you remember the candy shop analogy from before, let’s look at a real-world example. If you type in ‘best candy store New York’ on Google, the results will show pages about ‘candy stores’ and ‘candy shops’. Google understands that ‘store’ and ‘shop’ are synonyms and treats them as such.
Snippets from the search result page for the search ‘best candy store New York’
This doesn’t detract from the fact that you should still incorporate your focus keyword a few times throughout your post. After all, the focus keyword is still the word or phrase your audience was searching for. These are the words your audience uses and will expect to find in your text. That exact match remains important. However, to avoid using your keyword too many times, also known as keyword stuffing, you can use synonyms and related keywords to achieve a more natural flow of language. That way, you can rank on these keywords while keeping your text attractive and readable.
Find related keyphrases using our Semrush integration
Yoast SEO can help you find related keyphrases based on your focus keyword, saving you time and hassle. All you need to do is click the button to ‘Get related keyphrases’; you’ll find it right underneath your focus keyword in the Yoast SEO sidebar. You’ll see a list of related keywords and search trend data when you click that button.
This is how the related keyphrases feature looks in Yoast SEO
As a Yoast SEO Premium or Yoast SEO for Shopify user, you can add up to five related keyphrases to your SEO analysis. This lets you optimize your text for these additional terms similarly to your focus keyphrase. As always, you’ll see our familiar feedback bullets to guide you. If you’re a Yoast SEO Free user, you can explore related keyphrases using the tool, but you won’t be able to add these to your SEO analysis.
Yoast SEO can help you balance the use of your keywords, synonyms, and related keywords by recognizing word forms in different languages. If you want to know more, you can read about the related keywords feature in Yoast SEO for WordPress and the related keywords featured in Yoast SEO for Shopify.
How often should you use synonyms and related keywords?
The use of synonyms versus the use of focus keywords is not an exact science. The most important criterion is the way readers will experience your text. So, read and re-read it. Is it engaging and easy to read? Or are you getting annoyed by the constant use of a certain term? Be critical of your writing and ask others for feedback on your text.
As mentioned earlier, you can add your related keywords to the analysis in Yoast SEO Premium and Yoast SEO for Shopify. By adding these, the plugin can check whether you’re using them in your text. Your focus keyword remains the most important keyword, though, and that’s why the plugin is less strict in its analysis of your related keyphrases.
You can add keyphrases that are related to your focus keyphrase in Yoast SEO Premium and Yoast SEO for Shopify
You’ll also be able to add synonyms of your focus and related keywords when you use our Premium SEO analysis or Yoast SEO for Shopify. These analyses include checks to ensure you’ve used these synonyms in your text and your meta description, introduction, subheadings, or image alt text. Moreover, our keyphrase distribution check will reward you for alternately using your keyphrase and its synonyms throughout your text.
You can add multiple synonyms for your focus keyphrase in Yoast SEO Premium and Yoast SEO for Shopify
Make those related keyphrases and synonyms work for you
As we mentioned earlier, Google has come a long way since its early days in the field of SEO. It can understand texts, consider related concepts and synonyms, and recognize related entities. This enables it to serve its users with the best results. And part of being the best result is ensuring your texts are easy to read. Google wants to serve readable texts.
So make sure you deliver! Consider synonyms for your keyword or keyphrase and utilize them to your advantage. Take a moment to come up with a few alternatives for your keyword. Additionally, consider topics closely related to your keyword. You’ll notice that writing a naturally flowing text becomes much easier when you don’t have to use your focus keyword in every other sentence. Using synonyms and related key phrases helps Google understand the context of your text, which increases your chances of ranking.
In conclusion
Using synonyms and related keywords isn’t about tricking algorithms. It’s about writing naturally for humans while helping machines interpret meaning. When you vary your word choice, your writing becomes clearer, richer, and more engaging. That’s what today’s search engines reward: real content that genuinely helps users and shows topical depth.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-12-18 10:00:192025-12-18 10:00:19Why you should use synonyms and related keywords
People trust the creators they follow every day. That’s why influencer marketing has become such a big part of how brands grow.
The numbers back it up.
A staggering 92 percent of consumers trust recommendations from influencers more than traditional ads or celebrity endorsements.
That credibility is a big reason why influencer marketing keeps growing each year.
Brands also turn to influencers because people want authenticity. They want real voices and real experiences. They want content that feels personal.
What happens when you combine that with the massive reach creators have on platforms like Instagram, TikTok, and YouTube? You get a marketing channel that can drive awareness, traffic, and sales faster than most alternatives. That includes standard social media marketing.
In this guide, you’ll learn what influencers do, how influencer marketing works, and how to find the right partners for your brand.
Key Takeaways
Influencer marketing is about tapping into creators who already have trust and attention with your ideal customers. You’re not just renting their reach for a single post.
Every tier has a role: Mega- and macro-influencers drive reach, while mid-tier, micro-, and nano-creators tend to deliver stronger engagement and conversions.
Almost any business can use influencer marketing, including B2B. The key is audience fit, not brand size or industry.
Costs range widely by platform and tier, so treat rate charts as benchmarks. Pay for impact you can measure, not just vanity metrics.
Strong programs follow a process: clear goals, the right creators, tight contracts, transparent FTC-compliant disclosure, and performance tracking with links, codes, and real business metrics.
What Is Influencer Marketing
An influencer is someone who can shape opinions or buying decisions because people trust their voice.
Social platforms made that influence accessible to almost anyone. Creators like Charli D’Amelio and Khaby Lame (pictured below) built massive audiences from scratch, while celebrities like Kylie Jenner amplify the reach they already have.
Influencer marketing uses that trust to promote products or services.
Brands work with influencers to create content that feels native to the platform:
Short-form videos
Product reviews
Tutorials
Unboxings
Livestreams
Some brands even build full campaign partnerships with influencers, bringing them into everything from creative planning to multi-post launches that run across several platforms.
These formats work because they blend into the creator’s everyday content instead of feeling like traditional ads.
The influencer industry keeps expanding. The global influencer marketing market topped $30 billion in 2025 and is on pace to surpass $120 billion by 2030. And brands are investing heavily, with 80 percent of them maintaining or increasing their influencer marketing budgets in 2025.
Who Can Use Influencer Marketing?
You’ll see these collaborations across every sector. Fashion, beauty, e-commerce, entertainment, you name it. Heck, even the World Health Organization used a virtual influencer to lead a COVID-19 prevention campaign.
Close to 90 percent of companies with more than 100 employees planned to use influencers in 2025, showing just how mainstream influencer marketing has become.
On the B2C side, creators drive real traction for e-commerce, food and beverage, fitness, home décor, travel, and entertainment. These industries benefit from how visual and fast-moving platforms like TikTok, Instagram, and YouTube are.
Experts, analysts, and technical creators now build large followings on LinkedIn, X, and YouTube by teaching complex topics in simple ways. Brands tap these voices to explain products, review tools, share workflows, or demonstrate use cases. And it works because the creator already has trust and credibility with the exact audience you want.
If your customers spend time online (and they do), there’s an influencer with reach in your category.
Why Is Influencer Marketing Important?
Influencer marketing matters because people trust other people more than they trust brands. Your brand becomes more enticing and trustworthy just by association.
It makes sense if you think about it, particularly in a world where AI-generated content floods every corner of the internet.
When consumers aren’t sure what’s real, they lean on creators they already follow—people who show their faces and share their processes.
Think of it this way: You probably wouldn’t trust a person at a cocktail party who brags about themselves, trying to convince you to become their friend. But chances are you’ll believe the mutual friend who vouches for that person.
An influencer is that mutual friend.
Their credibility transfers to you. If a creator your audience respects talks about your product, it lands differently than a traditional ad or a brand-written post. It feels like a recommendation from someone they know.
A quick walkthrough, a tutorial, or a “day in the life” clip can show how your product works far better than a polished studio ad. It helps buyers understand what you do before they ever hit your site.
Influencers also give you reach you can’t get on your own. Their communities already exist. You tap into that attention instantly, instead of building it from scratch.
Why Is Influencer Marketing Effective?
There must be a reason so many brands are investing hundreds of thousands of dollars into influencer marketing, right? Consider this staggering statistic: 94 percent of marketers say influencer marketing drives more return on investment (ROI) than traditional digital advertising.
But what makes it so effective?
The unstoppable rise of social media obviously plays a part. There are now well over 5 billion social media users around the world, equating to more than two-thirds of people on Earth. On average, users spend more than two hours per day on average on social media.
Then there’s the person-to-person connection.
It seems that a lot of consumers trust the opinions of influencers. In fact, research shows that a whopping 77 percent of consumers trust content from influencers over traditional ads.
There’s also a generational element to it. Younger buyers—Gen Z and millennials—tend to rely heavily on influencer guidance when making purchase decisions. In fact, Gen Z is twice as likely to trust influencers as baby boomers.
Influencers also explain products in ways people understand. A quick tutorial, demo, or “I use this every day” clip can communicate value faster than any static ad. It reduces uncertainty and helps potential customers visualize your product in their world.
What Are the Different Types of Influencers?
Influencers are typically grouped by audience size, not just by what they’re known for. Most brands work within five tiers:
Mega-influencers
Macro-influencers
Mid-tier influencers
Micro-influencers
Nano-influencers
Each group brings different reach and engagement levels (price points, too, but more on that later). And each plays a unique role in a campaign.
Mega-Influencers
Follower count: Typically more than 1 million
Mega-influencers are some of the biggest and most popular influencers.
Many are celebrities who became famous away from social media. Think movie stars, pop stars, sports stars, and TV personalities, like Ryan Reynolds or Kim Kardashian.
Others are creators who built massive audiences entirely online. Think people like Khaby Lame, Addison Rae, or MrBeast, whose platforms turned them into global names.
This tier works best when the goal is broad awareness. You’re paying for cultural reach, not deep engagement. That’s why they’re often used for major launches, national campaigns, product drops, or high-impact moments.
Brands need bigger budgets to work with mega-influencers, but the payoff is unmatched scale.
Macro-Influencers
Follower count: Typically between 100,000 and 1 million
Macro-influencers tend to be well-known creators, podcasters, YouTubers, or niche personalities. They’ve often built their fame online and not through traditional celebrity channels.
They hit a sweet spot for many brands: They offer high reach, but with more consistent engagement than mega-influencers. Their audiences tend to be more aligned with their niches, like travel, fitness, tech, gaming, beauty, and more. This gives brands stronger relevance and better targeting.
Think of creators like Taryn Truly, a body-positive fashion creator whose Instagram profile is pictured below, or Mina Le, a fashion and culture commentator.
Macro-influencers may not be household names to everyone, but they wield major influence in specific categories. Their endorsement carries real weight with fans.
This influencer tier works well for brands that want meaningful visibility without the mega-influencer price tag. They’re extra valuable for things like product launches, category education, or content series that need a consistent creator presence.
Mid-Tier Influencers
Follower count: Typically between 50,000 and 100,000
Mid-tier influencers are established creators who have proven they can grow and sustain an audience, but they aren’t yet operating at celebrity scale.
What makes mid-tier influencers valuable is the balance of reach and strong engagement. Their communities are still highly invested in their content, and their rates are more accessible than macro-level talent. That combination makes them ideal for performance-driven campaigns where you want conversions, affiliate sales, tutorials, or product demos that feel personal and trusted.
Maya Abdallah (wellness) is a great example of a mid-tier voice who consistently moves her audiences to action.
This tier is often the “workhorse” of influencer marketing, as they’re scalable and cost-efficient.
Micro-Influencers
Follower count: Typically between 10,000 and 50,000
Micro-influencers are known for having some of the most engaged communities online. Their audiences follow them for specific expertise, like fitness, wellness, tech tips, budgeting, parenting, home decor, you name it.
Because their followers trust them deeply, micro-influencers often outperform larger influencers on engagement rate and conversion rate. They’re perfect for brands that need authenticity or niche targeting.
A strong example is creators like Jen Lauren, who built a tight-knit community around self-care and women’s fitness. Partners in wellness, boutique fitness, and online coaching spaces often see better ROI with creators like this than with larger names.
Micro-influencers are especially helpful for small and mid-sized brands. They’re also a great place to start if you’re testing influencer marketing.
Nano-Influencers
Follower count: Typically between 1,000 and 10,000
Nano-influencers have extremely tight, loyal communities. Their audiences know them personally or feel like they do, which can drive high engagement rates.
This group is powerful for brands that want authentic word-of-mouth or hyper-local impact. They’re great for early-stage launches or local business marketing. With nano-influencers, it’s all about campaigns where credibility matters more than massive reach.
You’ll see nano-influencers thriving in categories like beauty, food, wellness, fashion basics, small business recommendations, and travel. An example is Marc Wanderlust, a nano travel creator whose tight-knit audience trusts his quick, practical destination tips.
Nano-influencers help brands show up in real conversations with audiences that actually care, making them one of the more cost-effective influencer tiers.
How Much Do Influencers Cost?
The short answer: It varies. Rates depend on audience size, engagement, niche, platform, and the type of content you need.
Mega: $10,000 or more per post; could be $1 million or more for some celebrities
TikTok
Nano: $5–$25 per post
Micro: $25–$125 per post
Mid-tier: $125–$1,250 per post
Macro: $1,250–$2,500 per post (or more)
Mega: $2,500–$20,000 per post (or more)
Most creators work on flat-fee pricing, but affiliate commissions, usage rights, content licensing, and whitelisting can add to the cost. Product-only compensation is usually limited to nano creators and early-stage campaigns.
Influencer campaigns can reach five or six figures depending on talent and scope, so the key is paying at a level where you can realistically drive ROI.
How to Get Started With Influencer Marketing
Ready to run your first influencer campaign? Here’s a clear, practical process you can follow from start to finish.
1. Set clear goals.
Decide what you want to achieve before you reach out to anyone. Maybe that’s brand awareness, traffic, lead generation, content creation, or sales. Whatever the case, your goal determines the type of creator you work with and how you measure success.
2. Understand your audience.
Look at what your customers actually watch and follow online. Pay attention to the platforms they prefer and the types of creators they already trust. If I wanted to promote Ubersuggest, for example, I might look for SEO educators and marketing YouTubers.
3. Build a shortlist of relevant influencers.
We’ll cover this in depth below, but use influencer-discovery tools, social search, hashtags, competitor research, or even your own follower lists to find creators who already reach your target audience. Relevance beats reach every time.
4. Make your pitch.
Keep outreach simple and personal. Explain why you chose them, what you’re proposing, and what they’d get in return. Bigger creators may prefer email or agency contact; smaller creators often respond quickly to DMs.
5. Negotiate the scope and contract.
Outline deliverables, deadlines, usage rights, exclusivity, compensation, and Federal Trade Commission (FTC) disclosure requirements (more on this later). A straightforward contract protects both sides and keeps the project on track.
6. Launch and measure performance.
Use trackable links, codes, or UTM parameters to see what each creator drives. Review engagement, traffic, reach, saves, comments, and sales—whatever aligns with your original goals.
Influencer marketing works best when you treat it like a repeatable process and not a one-off post. Each campaign gives you data you can use to refine the next.
How to Find Your Ideal Influencers
Once you know your goals and your audience, you can start identifying influencers who actually make sense for your brand. I like to use three simple criteria to find influencers:
Context: Does the creator naturally talk about topics related to your product or category?
Reach: Do they have enough visibility for the results you want? Bigger isn’t always better, but your goals should match their audience size.
Actionability: Can they inspire their followers to take action? Creators with the right niche and trust tend to perform best.
Use multiple discovery methods (not just one), and build a shortlist of creators who consistently show up in conversations your audience already cares about.
Social Media Monitoring
Brand advocates are the loudest influencers your brand can have. They’re already talking about you, and they’re reaching people who trust their recommendations.
You can find them by tuning in to your social media mentions and blog posts about your brand. Track who tags you, reviews your product, or mentions your name in posts or videos. Tools like Brandwatch, Sprout Social, Hootsuite Streams, and Mention make this easier by pulling all relevant mentions into one dashboard.
Social listening with tools like AnswerThePublic can also help you spot creators who consistently talk about your niche, even if they haven’t discovered your brand yet. For example, a skincare brand might find rising creators who frequently review moisturizers or post “routine” content that aligns with their audience.
Start by adding promising creators to a shortlist and tracking their engagement and niche fit.
Research Hashtags
Identify the hashtags that your target market is using. Tuning in to the conversations surrounding these hashtags won’t just show potential influencers, you can also use it to identify blog topics, too.
Hashtags are one of the fastest ways to find creators your audience already follows. Search for hashtags related to your niche or customer interests and not just your brand name. This is especially effective on Instagram and TikTok, where creators tag content by topic, format, or trend.
For example, if you sell running shoes, hashtags like #runnersofinstagram or #runtok will surface creators who post content your customers care about. You’ll quickly spot who gets real engagement versus who’s posting generic or low-quality content.
Scroll the TikTok grid for #runtok, and you’ll see it’s packed with running creators your audience already follows.
Once you identify potential influencers, follow them for a while. Note their engagement rate, tone and the types of products they naturally feature.
Save strong candidates to a spreadsheet so you can compare them later.
Dedicated Influencer Platforms
Influencer platforms make discovery much easier by giving you searchable databases of creators, complete with audience insights, engagement metrics, and pricing estimates. Many also include campaign management tools.
Some of the most widely used platforms include:
Aspire
Upfluence
GRIN
Tagger
CreatorIQ
Impact.com
These tools let you filter by niche, location, platform, follower count, demographics, and brand affinity. That way, you can build a targeted shortlist in minutes instead of hours.
They’re especially useful if you want to scale your influencer program or track ROI more accurately.
Even if you’re running a small pilot, using a platform can help you confirm whether an influencer’s audience is real and aligned with your ideal customer.
Influencers and Disclosure
Influencer marketing only works when it’s transparent. The FTC has made that crystal clear. If a creator is paid, receives free products, earns affiliate commissions, or has any kind of material connection to your brand, they must disclose it clearly and up front.
That means no buried hashtags, no vague captions, and no “implied” relationships. The FTC expects disclosures like “#ad,” “#paidpartner,” or “Sponsored by…” to appear where viewers will actually see them. They can’t be hidden at the end of a long caption or inside a collapsed list of hashtags.
On video platforms, disclosures need to be spoken and written on screen, not just added in the description.
As a brand, you’re on the hook, too. Put disclosure requirements in your contracts, then actually enforce them by reviewing posts before they go live and saving copies of what was approved. That paper trail and clear labeling aren’t just to keep regulators off your back. They also signal to your audience that you’re being straight with them.
If your creator partnerships are transparent, everybody wins. That includes your customers.
FAQs
What is influencer marketing?
Influencer marketing is a strategy where brands partner with creators who have built trust and attention with a specific audience, then collaborate on content that features the brand or product. Instead of running a traditional ad, you tap into the influencer’s relationship with their followers through things like reviews, tutorials, “day in the life” videos, or sponsored posts.
Do influencers get paid?
Yes! In fact, many influencers make a full-time living out of their social media presence. Brands pay them to promote their products or services to their followers.
Does influencer marketing work?
Influencers have a loyal following of people who trust their opinions and recommendations. By partnering with an influencer, brands can tap into this trust and reach a wider audience than ever before. Plus, influencers often create visually appealing and engaging content that can help capture viewers’ attention.
How do you create an influencer marketing strategy?
Start with a clear goal (awareness, leads, or sales), then pick the platforms your audience actually uses. Set a budget, choose the right influencer tier, and shortlist creators who fit your niche. Agree on deliverables, timelines, and success metrics before you sign anything.
How do you track influencer marketing?
Give each creator unique links or codes, then watch what they drive—traffic, sign-ups, and sales, not just likes. Use your analytics tools to compare creators and content types. Double down on the partnerships that move real numbers and drop the ones that don’t.
Conclusion
The internet has changed, but the idea behind influencer marketing hasn’t.
Brands still want their products in the hands of people who can shape opinions. Today, influencer marketing is simply a faster, more targeted way to do it.
You might partner with a celebrity who can put your brand in front of millions, or build a roster of micro-influencers with smaller but highly engaged communities.
Both approaches can work when they match your goals and budget. If you’re serious about doing this at scale, check out our guide on using ChatGPT to automate parts of the influencer marketing process.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-12-17 20:00:002025-12-17 20:00:00What Is an Influencer? A Guide to Influencer Marketing
