Yelp is rolling out its most significant AI update yet, centered on a new conversational “Yelp Assistant” designed to move users from searching to actually booking, ordering, and scheduling — all in one flow.
What’s new. Yelp Assistant sits at the center of the update, acting as a chatbot that can answer complex queries, recommend businesses, and complete actions like reservations or appointments without leaving the app.
Zoom in. The assistant pulls from Yelp’s massive base of user reviews and photos to generate tailored recommendations, explain why a business fits, and let users refine results conversationally. It can then take the next step — booking a table, ordering food, or requesting a quote — directly within the same interaction.
What else is new. Yelp is expanding integrations with platforms like Vagaro, Zocdoc, and Calendly to streamline bookings across categories like beauty, healthcare, and home services, while deepening delivery ties with DoorDash.
Also notable. An upgraded “Menu Vision” feature uses AI and visual overlays to show dishes, reviews, and photos in real time when scanning a menu, helping users decide what to order faster.
Why we care. Yelp is shifting from a discovery platform to a transaction-driven experience powered by AI. With Yelp Assistant handling recommendations and bookings in one flow, visibility alone may not be enough — businesses will need to be optimized for conversion within the platform. The update also signals more competition for high-intent users as Yelp tightens control over the path from search to purchase.
Between the lines. Yelp is leaning into AI not just for discovery, but for conversion — turning intent into transactions without sending users elsewhere.
What’s next. The assistant is live on iOS and Android with broader expansion across categories and desktop coming later this year.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-04-21 11:00:002026-04-21 11:00:00Yelp launches AI-powered Assistant to streamline local search and bookings
For 20 years, Google Ads management has followed the same basic model: you log in, review performance, make changes, and hope they work before the next check-in.
Agencies, freelancers, and in-house teams all work this way, even as the tools have changed. Spreadsheets gave way to scripts, and scripts gave way to automated bidding, but the core loop never changed — someone still had to sit in the account.
groas aims to change that model by introducing a system designed to automate campaign execution end-to-end.
Our company announced today it has developed a fully end-to-end autonomous system that’s designed to match or exceed PPC performance benchmarks observed in internal testing. It’s designed to operate without routine manual approvals or constant dashboard monitoring.
From campaign creation through bid management, ad copy generation, keyword expansion, negative keyword pruning, budget allocation, and dynamic landing page deployment — along with everything else you can do in the Google Ads console and beyond — the entire workflow now runs autonomously, 24/7.
The system runs on a distributed network of specialized AI agents that handle different parts of campaign management and communicate in real time.
We didn’t start here.
A year ago, groas launched as a lightweight product that surfaced optimization recommendations for you to review and implement. The same model most PPC products still follow.
By the founder’s own admission, it was a fairly unremarkable v1. But what it lacked in sophistication, it made up for in something more valuable: real data from large volumes of real campaigns at scale.
Hundreds of early customers across the world signed up and connected their Google Ads accounts, representing a wide range of ad spend levels, campaign structures, and conversion goals.
These weren’t a narrow slice of one vertical. They spanned dozens of industries and niches — from local service businesses spending a few thousand a month to large agencies managing seven-figure monthly budgets across full client portfolios.
That diversity became the most important asset groas built.
The custom-trained, fine-tuned models that now power the system were shaped by this breadth — not a static dataset or simulation, but live campaigns with real money on the line across every industry and budget tier.
Without that base of early adopters, what groas is today couldn’t exist. The training data that enables autonomous management came from actively managing real dollars across real campaigns, learning what worked and what didn’t in conditions no synthetic environment could replicate.
David Pourquery, founder and CEO of groas, said:
“We kept seeing the same pattern. We’d surface a recommendation that would clearly improve performance, and it would sit there for days or weeks because the account manager was busy, or the client needed to approve it, or someone was on vacation. The insight had a shelf life, and by the time it got implemented, the data had moved on. So we stopped recommending and started doing.”
That realization drove a complete six-month rebuild. The result is a system of interconnected AI agents, each specialized in a different part of campaign management, collectively processing over 100,000 data points per hour per campaign.
The network handles a wide range of tasks typically performed inside the Google Ads console without the limits of working hours, cognitive load, or the tradeoffs that come with managing multiple accounts. The system automates most day-to-day campaign management tasks that would typically require manual input. If you wouldn’t have time to do it, the agents would.
From day one, groas built dynamic landing pages into the system, deployed and continuously A/B tested to find winning combinations of messaging, layout, and calls to action for every campaign. groas deploys them with a single line of JavaScript on your existing site — no developer resources, no new hosting, no CMS changes. The system tests and iterates 24/7, designed to improve conversion rates through continuous testing.
There’s a full undo capability for each agent action, but the point is you don’t need to regularly check into groas or Google Ads. Weekly reports are emailed, summarizing what was done, while a dedicated human PPC account manager oversees everything groas does around the clock.
Onboarding is fully hands-off. After sign-up, your groas account manager learns your business, audits your existing Google Ads accounts, and delivers a detailed action plan within 24 hours. From there, they implement everything across groas and Google Ads with zero work on your side.
In less than a year since shifting to full autonomy, groas now manages eight figures in monthly ad spend across its client base. Every account came through organic discovery or direct referrals — the company hasn’t spent anything on paid acquisition to date.
The client base has consolidated around two profiles:
Businesses moving away from agency relationships where results haven’t kept pace with cost. These are companies paying $5,000 to $15,000 per month and looking for more consistent performance and transparency. groas provides an alternative by automating day-to-day execution while reducing management overhead.
Agencies. This is now the larger segment. Agencies plug groas into their clients’ accounts behind the scenes, bundle the cost into your existing fees, and let the agent network handle day-to-day execution while their teams focus on strategy, creative direction, and client relationships. The implementation runs behind the scenes within agency workflows. groas turns a labor-intensive, low-margin service into something that scales without added headcount. groas offers a 30% lifetime recurring commission for referrals, but most of you choose to pay for it yourselves and keep the margin.
Google’s automation — from Performance Max to AI Max to broad match expansion — has pushed the industry toward more black-box control for years. Many advertisers feel they are losing visibility into what’s actually happening inside their campaigns. Meanwhile, agencies and recommendation-based products still run the old loop: review, recommend, wait for approval, implement, repeat.
groas occupies a category that didn’t exist. Instead of helping you manage campaigns better or relying on Google’s automation, it removes you from the execution loop while keeping you in the strategic loop through a dedicated account manager.
The PPC industry has spent two decades debating how much to automate. groas is the first to answer “everything” and back it up with eight figures in managed spend.
The growth points to something the industry has been circling for years without arriving at. The bottleneck in Google Ads performance has often been the limits of manual execution — constrained by time, attention, and the volume of data modern campaigns generate.
groas didn’t build a better recommendation engine — it reduced the need for traditional recommendation-based workflows.
groas starts at $999 per month for up to $15,000 in managed ad spend, scaling to $6,999 per month for up to $150,000. No contracts, lock-ins, or setup fees. The only requirement is at least $2,000 per month in Google Ads spend — below that, there isn’t enough data for the agents to optimize effectively.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-04-21 11:00:002026-04-21 11:00:00groas introduces a fully autonomous approach to Google Ads management by groas
SEO in 2026 is expanding, not changing. Traditional search still matters, but now SEO also includes AI-driven discovery, social platforms, and chatbots. The principles are the same, like clarity, structure, authority, and relevance, but the platforms are multiplying. We surveyed 59 SEOs to see how they’re handling these changes.
Some have less than a year of experience. Others have been in the field for over a decade. Their answers show an industry figuring things out. A few are ahead of the curve, but most are still catching up.
The best SEOs aren’t just reacting to AI. They’re using it to strengthen what already works: technical foundations, high-quality content, and real authority. Others are stuck debating whether SEO should even keep its name.
Here’s what stood out, and where Yoast fits into the conversation of what SEO means in 2026.
You can find the full results, with more questions and deeper insights from Yoast’s principal SEOs, Carolyn Shelby and Alex Moss, in a downloadable PDF. Sign up below!
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51% of respondents consider SEO to be “evolving”. 33% say it’s “thriving”. Only 10% think it’s “declining”.
This is an interesting divide, but it’s not random. In the results, those with 10+ years of experience say SEO is thriving, while newcomers say it is not. It might be that experts know the landscape better and see change as a constant.
Alex Moss’s take:“SEO has always adapted to changes in the SERP, and now it’s adapting again. The traditional SERP is gone, but SEO isn’t.”
Carolyn Shelby’s take:“SEO is evolving, but not because its fundamentals are breaking. The interfaces between users and information are changing. Search is no longer confined to ten blue links, but the need for structured, relevant, trustworthy content hasn’t diminished.”
The Yoast Perspective: We think SEO isn’t going anywhere, but there are changes happening. Traditional search from Google and Bing still drives traffic, but AI-driven discovery from LLM-powered assistants shapes perception and discovery. Therefore, the best SEOs don’t choose sides in this fight; they are mastering both directions.
2. Keep the name Search Engine Optimization
39% say SEO should be relabeled “Search Everywhere Optimization”. Only 32% want to keep “Search Engine Optimization”.
Big support for relabeling SEO, and even among veterans, 41% prefer Search Everywhere Optimization. Of course, this doesn’t mean that we should do this.
Alex Moss’s take:“The term ‘SEO’ will stay. The role will widen to include AI and other disciplines, but the name doesn’t need to change.”
Carolyn Shelby’s take:“The term ‘SEO’ still holds shared meaning, credibility, and market recognition. There’s no strong evidence that rebranding the discipline itself is necessary or beneficial. Responses favoring ‘Search Everywhere Optimization’ reflect where SEO outcomes now surface, not a fundamentally different practice.”
The Yoast Perspective: We at Yoast don’t think the term SEO is broken. Yes, there is a lot of change happening, especially in search, with AI overviews, chatbots, and social media platforms, but what about the core SEO work? You still have to focus on technical foundations, content quality, brand building, and authority.
‘Search Everywhere Optimization’ might describe where SEO happens, but it doesn’t change what SEO is. The name ‘SEO’ still works, but we just need to explain how it applies to AI and social platforms.
3. Good SEO is LLM optimization
64% agree LLM optimization is essentially the same as traditional SEO. 59% aren’t even actively optimizing for LLMs.
You might call this laziness, but you could also call it efficiency. It oftentimes comes down to the same thing.
There’s also the 9% who strongly disagree with this statement. These respondents say LLMs prioritize synthesis over rankings, so focusing on structured data and brand mentions makes more sense for them. Of course, they are not wrong, but they don’t contradict what others have said. LLMs don’t require new tactics; they just reward the same SEO principles more strictly.
Alex Moss’s take:“If you’re undertaking good SEO, you’re already optimizing well for LLMs. The tactics don’t change—just the audience.”
Carolyn Shelby’s take:“The same practices that make content discoverable and trustworthy for search engines also make it usable for LLMs. The confusion arises when people treat LLMs as a completely separate system. In reality, LLM visibility rewards clarity, relevance, and authority—all long-standing SEO principles.”
LLM optimization isn’t a separate discipline because it’s SEO for AI. The same principles apply: clarity, structure, and authority. The difference? AI systems are less forgiving of mediocre content, so the bar for quality is higher.
4. Rankings still matter, but not like they used to
52% say rankings are “equally important” as before. 30% say they’re “less important”.
This is a sensible shift. Google’s AI overviews and other zero-click results mean visibility does not equal traffic. For AI systems, rankings are still an authority signal.
Alex Moss’s take:“Traditional rankings are still important because agents still search the web to ingest information. If you aren’t visible there, it’s less likely an agent will identify and select you into their responses.”
Carolyn Shelby’s take:“Rankings still matter, but they are no longer the end goal. They are a proxy for visibility, not a guarantee of impact.”
The Yoast Perspective: We need to stop obsessing over ranking number one, so start tracking visibility and presence. Check whether you are cited in AI-driven answers, and try to be mentioned in industry discussions. AI visibility and citations are the new rankings.
5. Organic traffic is still king, but for how long?
55% say “organic traffic” is their top metric. Yet 49% cite “reducing organic clicks” as their biggest challenge.
We see this as the great paradox of 2026. Traffic is down, but the value of that traffic could be up. You might get less traffic, but the clicks that do happen have a better intent.
Carolyn Shelby’s take: “As AI reduces the need for some visits, success looks like being represented correctly rather than merely visited. Visibility in AI overviews doesn’t always drive clicks, but it builds legitimacy. Being included signals that you’re a credible source, even when users don’t click.”
Our advice:
Work on AI visibility, as this is the new SEO metric. Just as rankings show your visibility in traditional search, citations in AI overviews show your authority in AI-driven discovery. Track it alongside rankings and traffic
Keep an eye on branded search volume to learn whether people are looking for you by name
Monitor citations to see if others are referencing your content online
6. Content saturation is a big threat
39% say “competing with AI-generated content” is their top challenge. Only 4% cite a “talent gap.”
We know AI can write bad content. But it’s a bigger challenge when AI writes good enough content at scale. This will flood the web with noise, making it hard to penetrate.
Alex Moss’s take:“AI-generated content is artificial. Humans connect with stories, not regurgitated lists.”
Carolyn Shelby’s take:“AI doesn’t change what good content is, but just raises the bar. Mediocrity doesn’t just rank lower; it disappears.”
Our advice:
Focus on building your EEAT, because AI can’t fake real-world expertise and authority
Prioritize quality over quantity, as a single great piece of content can beat ten average ones
Use AI, but be careful and always use it as a tool, not as a replacement
7. Most SEOs are ignoring a fast-growing search channel
Traditional search (Google/Bing) is still #1. But TikTok search ranks #5, lower than Amazon.
This might be something of a blind spot for many. Younger generations use TikTok and other video platforms for entertainment, recommendations, tutorials, and even B2B advice.
Alex Moss’s take:“Social platforms influence how LLMs perceive freshness and authority. Ignoring them means missing out on signals that AI systems value.”
Carolyn Shelby’s take:“You don’t need to rank on TikTok, but you do need to be discoverable there. LLMs scrape social platforms for real-world signals.”
The Yoast Perspective: SEO now includes social platforms like TikTok. You don’t need to rank there, but you do need to be discoverable, because LLMs scrape these platforms for fresh, authoritative content. A great video channel can boost your authority in AI responses.
Our advice:
Repurpose content for video platforms like TikTok and YouTube
Check brand mentions in these platforms
Improve your video SEO in general
What Yoast’s experts really think
The data shows trends, but the real wisdom comes from Yoast’s SEO leaders, Carolyn Shelby and Alex Moss. Here is a small peek at the insights they share about the various debates:
On “Search Everywhere Optimization”:
Alex: “The term ‘SEO’ will stay. The role will widen, but the name doesn’t need to change.”
Carolyn: “Rebranding risks fragmenting understanding. ‘SEO’ is already well-established outside the industry.”
On the future of SEO metrics:
Alex: “As we move from being seen to being selected, visits don’t hold the same value they used to. The business goal should be the most important metric.”
Carolyn: “Visibility in AI overviews doesn’t always drive clicks, but it builds legitimacy. Being included signals that you’re a credible source.”
On rankings vs. influence:
Alex: “Rankings still matter because agents search the web to ingest information.”
Carolyn: “Rankings are a proxy for visibility, not a guarantee of impact. Focus on presence.”
On the role of SEOs in 2026:
Alex: “100% all three: marketers, brand builders, and SEO specialists. Brand and marketing have become intertwined with SEO as our role expands.”
Carolyn: “A blended mindset is essential. SEO can’t operate in isolation from brand, product, or communications.”
Do you want to read the full story?
These insights are just a small taster for you. In the full Yoast SEO report, you’ll find much more:
Includes the full answers to all 25 questions
In-depth commentary from Yoast’s SEO experts, Carolyn Shelby and Alex Moss
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-04-21 10:29:142026-04-21 10:29:14The Yoast Perspective 2026: 7 things we learned from the SEO industry
New privacy laws will require marketers to think carefully about how they collect and manage data.
While many marketers aren’t overly concerned about the depreciation of third-party cookies, it’s recommended to explore cookieless solutions like Enhanced Conversions.
Several emerging platforms are on the rise, including gaming networks, digital out-of-home (DOOH) ads, connected TV (CTV) ads, and Brave Ads.
Paid social is as popular as ever thanks to the evolution of social media platforms as search engines and e-commerce hubs.
Want to mix up your paid ads strategy in 2026? Try user-generated content (UGC) or conversational advertising.
Short-form videos on TikTok or YouTube Shorts grab attention fast—just make sure your first five seconds are strong to hook viewers.
Premium placements like TikTok Pulse and YouTube Select allow advertisers to position their brands alongside trending, top-performing content for added credibility.
AI tools like Performance Max or Jasper can optimize bidding, generate creative, and streamline campaigns, helping marketers save time and improve ROI.
Want to upgrade your paid media strategy for the rest of 2026 and beyond? Whether you want to take advantage of AI, discover new platforms, or prepare to advertise in a world without third-party cookies, this guide is here to help.
Just as our Search Engine Rewind did for SEO trends, this guide combines expert insights from the paid media team at NP Digital and other paid marketers in the field.
You’ll learn which new paid media platforms you should be taking advantage of, how the paid social landscape is shifting, and the best practices you should focus on.
So, if you’re ready to discover the biggest paid media trends of 2026, read on.
Methodology
The insights in this guide come from two super knowledgeable resources.
The first is the team at NP Digital, which includes industry experts with decades of experience in paid search, social, and programmatic advertising.
The second is a group of 309 paid marketers who work at companies with over 250 employees. We surveyed marketers from across the U.S. to understand how they see the paid search landscape changing.
We asked this group about a range of paid search topics, such as emerging platforms, new targeting strategies, and the impact of artificial intelligence.
Privacy Concerns and Expanded Privacy Regulations
New privacy laws are changing how platforms and advertisers collect and use consumer data. A slew of U.S. states have recently enacted data privacy laws, including California (which amended and expanded the California Consumer Privacy Act to the California Privacy Rights Act), Colorado, Florida, Montana, Oregon, and Texas.
Google has reacted by adjusting its data collection practices. It has enabled Restricted Data Processing (RDP), which means the search platform shows only non-personalized ads to users in these states.
In Europe, the Digital Markets Act (DMA), which became applicable halfway through 2023, means major platforms like Google and Meta need explicit consent to collect and use personal data from consumers in the European Economic Area (EEA). That’s unlike most state-based privacy laws in the U.S., which let users opt out of personalized ads, but still displays them by default.
Data protection laws are also being introduced across Africa, the Middle East, and Asia. In most cases, businesses will need to introduce a cookie banner that lets users choose their preferences. You can also implement Google’s consent mode v2, which enables you to collect conversion data through a tag-based system and comply with regulations.
While platforms like Google generally help companies comply with privacy laws, you may find it easier to use a compliance management platform (CMP) that streamlines your paid media compliance efforts.
One privacy-related issue marketers don’t have to worry about just yet is the deprecation of third-party cookies in Chrome. Google has reversed its decision to remove cookies from its browser in response to concerns from the U.K.’s Competition and Markets Authority, along with demand from advertisers. Instead, it will continue to work on its Privacy Sandbox.
Even so, the majority (57.9 percent) of our survey respondents felt that the deprecation of cookies wouldn’t impact their paid marketing plans much.
That might be because they’ve already switched to less cookie-focused tracking methods like Google Ads’ enhanced conversion tracking. This method works in addition to your existing conversion tags, sending hashed first-party data from your website to Google in a privacy-focused manner.
The most popular paid marketing channels can be expensive. Four in 10 of our respondents said their pay-per-click (PPC) costs had increased over the past year, yet almost half (49.6 percent) of that group planned to double down on spending.
Cost per click (CPC) may become even more expensive due to the upcoming U.S. presidential election. The majority (70 percent) of marketers in our survey think the presidential election will impact paid advertising. This may stem from the fact that the results of presidential elections tend to have ripple effects in the business world as companies react to the potential economic policies of the incoming administration.
Those who say they are seeing higher PPC costs are more likely to seek out new forms of paid marketing—and there are plenty of up-and-coming paid media platforms and channels to choose from.
We’ve highlighted five of these new frontiers below.
Gaming
Gaming is a $350 billion industry with a highly engaged audience, so it’s no surprise that gaming ads are becoming more popular.
Our survey found that 57.3% percent of marketers were advertising there, with Twitch, Discord, and in-game ads as the most popular channels.
If you want to get started on Twitch, there are eight formats to choose from:
Headliner
Homepage Carousel
Medium Rectangle
Stream Display Ads
Streamable Ads
Super Leaderboard Ads
Twitch Premium Video Ads
First Impression Takeover
Of these formats, brands will be most interested in:
Headliner ads, which appear behind the paid-for carousel ads used by streamers
Stream display ads, which appear during live streams
Super leaderboard ads, which appear at the top of the page
Twitch premium video ads, short pre-roll, and mid-roll unskippable video ads
Note that you have the option to run self-service ads on Twitch through Amazon’s DSP, similar to the self-serve platforms of Meta and Google.
If you’d rather target gamers mid-game, a solution like Activision Blizzard’s Rewarded Video may do the trick.
These user-initiated in-game ads (like the Ben & Jerry’s ad above) let you connect with highly engaged gamers at key moments in combination with desirable player rewards they can use to advance through their game of choice. Brands can also use interactive end cards to increase audience engagement and drive higher click-through rates.
In-App Ads
In-app advertising is a win-win for brands and developers. It gives developers a source of additional revenue while letting brands reach targeted and engaged audiences.
Spending on this form of paid media is forecast to reach $352.7 billion in 2024 and $534.1 billion by 2029.
In-app advertising comes in various forms, including:
Interstitial ads
Video ads
Banner ads
Native ads
Here’s an example of an interstitial ad from Google’s AdMob network:
Advertisers can choose from a range of pricing models, too:
Cost per mille (CPM): Pay for every thousand views.
Cost per click (CPC): Only pay when your ad is clicked.
Cost per view (CPV): Only pay when users watch your video ad.
Cost per install (CPI): An app-specific pricing model through which you only pay when users install your app.
Cost per action (CPA): Only pay when users complete a predefined action.
In-app ads are an ideal and effective way for brands to target smartphone users. Research shows 42.6 percent of consumers have bought something after clicking on an in-app ad, and 56.5 percent of consumers have downloaded an app after an in-app ad recommended it.
The other great thing about in-app ads is that you aren’t just limited to using a single advertising platform. There are several to choose from, including Google’s AdMob, AppLovin, Publift, PropellerAds, and RevX.
Digital Out-Of-Home (DOOH)
Digital out-of-home media (DOOH) is one of the fastest-growing paid media sectors. Used by over 43 percent of respondents, it was the second most popular trending paid media option in our survey.
DOOH is a form of dynamic and data-driven outdoor advertising. Brands can tailor ads based on things like the time of day, the location of the signage, and even the weather. DOOH includes digital billboards and outdoor signage—as large as the billboards in Times Square or as small as the sign at your local bus stop.
DOOH offers several benefits over both online ads and traditional outdoor media:
There are no online ad blockers, meaning consumers will always be exposed to your ad.
You can engage consumers close to the point of sales with geofenced advertising.
Signage is dynamic, allowing brands to only pay for their ads to be shown at specific times of the day.
DOOH advertising isn’t perfect. It’s much harder to measure than online advertising since there’s no way of knowing for sure how many people walked past your ad or saw it. Inventory is also spread between multiple platforms, which can make campaigns hard to manage.
Connected TV (CTV) and Over-the-Top (OTT) Advertising
Want to reach consumers while they are watching TV? Connected TV ads and over-the-top advertising are two emerging channels to try.
Some use these two terms interchangeably, but they are slightly different.
Connected TV (CTV) advertising delivers targeted ads through internet-connected smart TVs. Over-the-top (OTT) advertising delivers ads through video streaming services like Hulu and Paramount+.
Regardless of which paid media format you use, both types of advertising have advantages over traditional TV ads.
Unlike traditional TV ads, which have to appeal to a broad audience, CTV ads can be hyperpersonalized and targeted. Brands can target audiences based on their viewing interests, for example, as well as their location and the time of day.
These ads are also much more measurable than traditional TV ads. Brands can track viewing, engagement, and conversion metrics like return on ad spend (ROAS), cost per completed view (CPCV), and gross rating points (GRPs) to optimize campaigns in real time.
CTV also has integrated ad buying options at your fingertips through Microsoft Advertising. Advertisers can manage CTV campaigns alongside search and display ads, simplifying ad management and removing the need for separate DSPs. Other benefits include:
Easy setup
No onboarding fees
Budget flexibility
Impression-based remarketing
Cohesive messaging across CTV and other formats
Leverages Microsoft’s vast first-party data to enhance personalization and performance.
CTV ads and over-the-top advertising were used by 40.8 percent and 43.3 percent of respondents in our survey, respectively.
But these ads are only going to become more popular. Connected TV ad spending is expected to reach $21.45 billion by the end of the year and grow by 13.9 percent year on year. OTT, on the other hand, projects to reach $11.14 billion and account for 3.7% of all digital ad spend.
Brave Ads
One emerging ad format used by only 29.1 percent of respondents in our survey (although 17.27 percent said they soon would) was Brave Ads.
If you don’t know much about Brave, it’s a free, open-source, Chromium-based browser that puts a premium on speed and privacy. It boasts over 78 million monthly active users and is popular with those who like Chrome but want a more private browsing experience. The browser has built-in ad blockers, its own crypto wallet, and even a search engine.
Brave Ads are ideal for reaching a unique audience of tech-savvy, privacy-first individuals who are harder to reach on other platforms. There are several ad formats you can choose from that cover both the platform’s browser and search engine:
Search ads:
A privacy-preserving text-based ad that appears at the top of the Brave search results page.
Tab takeover ads:
High-quality full-page images that feature in Brave’s new tab rotation.
Newsfeed ads:
Display ads that show in the private and customizable news feed that appears every time someone opens a new tab
Notifications ads:
Text-based ads with a call-to-action that appear during user browsing sessions.
One of the biggest benefits of Brave Ads is the ability to reach a unique target audience. Brave’s userbase spreads across over 200 countries. These users tend to be younger and privacy-conscious individuals—the kind of people who would have ad blockers on another browser like Chrome or Safari. Because these users have more trust in the Brave platform, ads on the platform don’t have the same perception of “spammy” or “intrusive” conventional paid ads might.
Perplexity Ads
Another emerging platform for advertisers is the AI generative search engine Perplexity, which boasts around 100 million search queries per week. In November, the platform announced that it would start launching ads in the form of sponsored follow-up questions. Note that these follow-ups would be generated by Perplexity, not your brand.
Some of the initial brands taking part include Indeed, Whole Foods Market, Universal McCann, PMG, and others. But what does an ad on a generative search engine look like?
Here’s an example: First, here’s a typical Perplexity question and response:
Now, here’s an example of a sponsored followup question.
Perplexity has the potential to open up a new potential audience for advertisers, even though the concept is in its relative infancy. The platform has shared that:
80% of its users have undergraduate degrees.
30% have what they call a “senior leadership position.”
65% are considered to work in “high-income white-collar professions.” Examples include law, medicine, and software engineering.
If you’re targeting that high-earning, well-educated audience, or want the lower competition of an emerging platform, Perplexity could be a good way to get started on the ground floor.
Social Media Landscape Shifts
Over 5 billion people around the world use social media, and 259 million new users have come online in the past year.
These networks are becoming much more than a way to connect with friends. Modern social platforms are news outlets, search engines, and storefronts.
The popularity of social media advertising isn’t in doubt—it was the most popular paid media campaign style among our respondents. But how marketers think about the network is changing.
Below, we look at four of the biggest paid social trends this year so far.
Expansion of Social Commerce
Social commerce is the fusion of social media and e-commerce. It’s a powerful combination that enables users to make purchases directly from their favorite social platform. U.S. social commerce was valued at $89.11 billion in 2022 and is expected to grow at a compound annual growth rate of 29.2 percent.
Facebook is the most popular social commerce platform overall, although platforms like Instagram, YouTube, and TikTok are leading the charge in social commerce, especially among Gen Z, who favor seamless in-app shopping experiences.
The great thing about social commerce from an advertiser’s point of view is they no longer have to convince users to leave the platform to make a purchase. Shoppable ads on platforms like Instagram, TikTok, and Facebook mean users can click a link in the post’s description, see the price, and make a purchase.
So, how can you get started?
Having your product feed in order is essential. These are the cornerstones of shoppable ads on most social media platforms, such as Facebook and TikTok.
These feeds let you take advantage of each platform’s unique shopping ad formats. On Facebook, that includes carousel collections, photo ads, and video ads.
Ad-Free Experiences
Social media platforms have always relied on ads to generate revenue. But that may not be the case for much longer since several social media platforms are trialing ad-free subscription models.
This includes X, with a Premium+ tier that costs $16 per month, Meta (which announced it will offer ad-free subscriptions to European users for €9.99—just under $11 in U.S. currency), and TikTok, which is piloting an ad-free subscription priced at $4.99.
While it remains to be seen just how many users will pay a subscription to use social media, advertisers should consider how to navigate ad-free social media networks in the future.
One obvious option is to invest more in influencer marketing. Paid users will still follow their favorite influencers, making this an easy way for brands to reach these consumers. Investing in nano- and micro-influencers, specifically, could help brands maintain reach in ad-free environments while keeping campaigns cost-effective.
The other solution is to grow organic followings using a paid ad strategy while you can. An ad-free social media experience doesn’t mean people won’t follow their favorite brands. So, community building may be the order of the day before consumers choose to forgo ads for good.
Social Media Is Becoming Its Own Search Platform
Social media platforms have become search engines for millions across the globe. Research by Adobe finds that two in five Americans use TikTok as a search engine, and 10 percent of Gen Z users are more likely to turn to TikTok than Google.
Platforms like YouTube and Instagram are also popular among people of all ages looking for information fast.
Speed is one reason consumers turn to social media instead of search engines. But so are trust and convenience. When people are on social media a lot more than Google, it’s much easier to turn to trusted sources like your favorite influencer or the app’s discovery section for advice.
Some social platforms are embracing this phenomenon, and advertisers would do well to follow. TikTok, for example, is launching Search Ads Toggle in Beta—a feature that allows brands to advertise in search results.
TikTok automatically creates ads using the advertiser’s existing content and serves them alongside organic results, as you can see above.
Nano- and Micro-Influencers Take Center Stage
Move aside, celebrities and social media mega influencers. The time of the micro- and nano-influencers has arrived.
Micro-influencers (people with 10,000 to 50,000 followers) and nano-influencers (people with 1,000 to 10,000 followers) may not have the follower counts of people like Jake Paul. But they have a lot of other characteristics that appeal to brand advertisers.
This includes:
Highly engaged audiences
Authenticity and relatability
Cost-effectiveness
While macro-influencers were the most popular with marketers in our survey, that’s primarily because they fit their business audience. Of the marketers who preferred nano-influencers, one-third said it was because they are more cost-effective.
Even the biggest brands work with micro-influencers.
Take Melizza Black, for example, a fangirl fashionista who partners with the likes of Disney, Pixar, and Universal Studios to promote new films, product ranges, and clothing merchandise.
In fact, survey respondents with budgets topping $200,000 were more likely to work with micro-influencers because of better campaign performance.
Linkedin Influencers For B2B Campaigns
Targeted B2B audiences with paid media campaigns has always had its unique challenges due to the longer buying cycles and more discerning preferences compared to B2B. Bringing Linkedin influencers into your paid campaigns can be a difference-maker in terms of providing that credibility and reach you need.
LinkedIn influencers bring a unique advantage to paid media campaigns due to their highly professional and engaged audiences. Since they are often focused on professional growth and industry-specific insights, they make a perfect fit for B2B campaigns.
Collaborating with Linkedin influencers allows brands to target a niche audience with tailored messaging, increasing conversion rates and ROI.
With that in mind, how do you fully harness the power of this growing option for paid media? Start by identifying individuals whose audience aligns with your target demographics.
For example, NP Digital co-founder Neil Patel is a successful Linkedin influencer, buiilding an audience of over 680k followers on the platform.
This would make him an ideal fit for any paid campaign related to digital marketing services or tools due to his established success in the space.
Use tools like LinkedIn’s Creator Mode analytics or platforms like BuzzSumo to evaluate influencer performance metrics such as engagement rates and follower authenticity.
Once you start your Linkedin partnership, prioritize authenticity in your paid campaigns. LinkedIn audiences are particularly sensitive to overtly promotional content, so it’s essential to frame your message as an industry-relevant insight or case study. Personal anecdotes or professional experiences tied to your product can boost credibility and audience trust as well.
Developing Practices for Paid Media
The strategies advertisers use to craft the best paid ads are constantly changing. Whether it’s the format, platform, or bidding strategy, it’s important to stay ahead of the curve and use the latest techniques to create ads that resonate with audiences and drive return on investment (ROI).
Short-Form Video
Want to seize a user’s attention? Short-form videos are the way to go.
A short-form video is between three and 90 seconds long. Most commonly found on TikTok, this video format has quickly spread to YouTube, Instagram, and Facebook.
Short-form videos are popular for both organic and paid marketing efforts thanks to high engagement rates, lower costs, and simple messaging.
You can create short-form video ads on any of these platforms, but TikTok and YouTube Shorts are the most popular and effective. YouTube Shorts boasts more than 2.3 billion monthly users, and 70 billion daily views, for example, while TikTok has over 1 billion global monthly active users.
Google’s making several new initiatives for YouTube Shorts advertising to take advantage of this newfound popularity, including:
The introduction of the YouTube Select Shorts lineup, where advertisers can place ads next to curated popular Shorts content.
Tailoring its ABCD framework for short-form content, which emphasizes strategies like capturing attention quickly and integrating branding early. Additionally,
Three things are critical to succeed with short-form video ads. The first is to make the first five seconds of your video as captivating as possible. The better your hook, the fewer users will click skip.
Second, ensure you have a single, clear message. Don’t try to highlight multiple unique selling propositions (USPs) or target different audiences in a 30-second short. Short, sweet, and to the point is the order of the day.
Lastly, Google reports that creator-led ads on YouTube Shorts can achieve up to 20% higher conversions than traditional branded ads. You can apply this lesson to any short-video platform, and work with relevant influencers on the platform to increase your reach and credibility.
Immersive and Interactive Experiences (AR and VR)
You probably can’t remember the last ad you saw. But it’s more likely you can remember the last time you experienced augmented reality (AR) or virtual reality (VR).
That’s why AR and VR ad experiences are becoming increasingly popular with major brands. The AR market alone is expected to hit $5.2 billion by the end of the year.
While traditional ads are passive affairs that struggle to capture our attention, augmented and virtual reality ads create an interactive and memorable experience.
Take Coca-Cola Zero Sugar’s #TakeATasteNow out-of-home augmented reality campaign that launched at the end of 2023. Rolled out across 13 U.K. locations, the AR ad experience enabled customers to interact with digital billboards in real time by scanning a QR code to redeem a Coca-Cola in a nearby store.
AR and VR ads don’t have to be high-budget guerilla advertising campaigns, however. IKEA has seen success with IKEA Place, an app that launched in 2017 and lets customers use AR to place IKEA products around their homes.
User-Generated Content (UGC)
User-generated content (UGC) is a popular organic social media strategy, but you can also use it in paid campaigns.
In fact, it can go a long way toward improving your ad conversion rates, given that visitors who interact with UGC convert 102.4 percent higher than average.
UGC also improves the authenticity of your ads. It’s hard to make a paid ad truly authentic, but reviews and recommendations from real customers help humanize your campaigns.
Integrating UGC into your paid ads campaign can be as simple as dropping a testimonial into your next creative, as Peet’s Coffee does here:
Or you can use video reviews as your ad’s main creative, as the beauty brand Prose does below:
Conversational advertising uses personalized and automated conversations to encourage users to take action.
Rather than a static image or scripted video, conversational ads use a chat interface to mimic a real-life conversation. Users can select predefined messages that can change the nature of the conversation and lead to different outcomes.
Facebook and LinkedIn are the two social platforms that work best for this paid media strategy. LinkedIn Message Ads lets you send sponsored direct messages to a specific set of users on the platform, for example. Facebook Messenger Ads are conversational ads that appear in the Messenger app.
But social media isn’t the only place you’ll find these ads. You can also re-create a chatbot interface using banner ads. Here’s an example from Emirates Vacations:
Contextual Targeting
Contextual advertising is a type of targeted digital advertising where ads change depending on the content of the webpage.
Contextual targeting can either be keyword-based or semantic:
Keywords: Platforms use on-page keywords to serve targeted ads.
Semantic: Platforms use AI to understand the meaning of the page rather than just identifying keywords.
Contextual targeting is an excellent way to serve relevant ads to an engaged audience without relying on third-party cookies. If someone is reading a page about tourist destinations in Bali, you can be pretty sure they’ll be interested in a hotel or flight to the region.
Contextual targeting can also be quite cost-effective, especially compared with behavioral marketing campaigns that require massive amounts of user data. This makes it easier to implement, too.
There are plenty of platforms you can use to get started with contextual ads, including Google AdSense, DV360, the Yahoo! Bing Network, and Media.net.
Premium Inventory Opportunities
Want to give your ads the best chance of success in 2026 and beyond? Consider leveraging the premium inventory opportunities available on several social media platforms.
Premium ad inventories are the most expensive and exclusive ad spaces on these platforms. They typically appear under or alongside the most on-trend and relevant content.
TikTok Pulse suite, for example, places your ads immediately after the best in-feed content. There are several options, including:
Max Pulse: Ads placed next to the top 4 percent of content on the platform, according to the Pulse Score.
Category Lineups: Ads inside TikTok’s Pulse Lineups, a collection of top-performing content across a dozen categories.
Seasonal Lineups: The seasonal equivalent of category lineups, where ads are placed alongside top-performing content from holiday events like Thanksgiving and the Fourth of July.
Pulse Premiere: Ads placed after top-performing content in the lifestyle & education, sports, and entertainment categories.
YouTube Select is a similar program that shows ads alongside the top 5 percent of the platform’s most viewed content.
Premium inventory opportunities are a fantastic way to associate your business with the biggest brands and content creators. This network effect can be a great way to increase brand awareness. Thirty-four percent of survey respondents said they are already using opportunities like this, with another 20 percent planning to start using them in the next year.
Gen Z And Social Responsibility
Want to attract younger consumers with paid media? Then you better be a socially responsible corporation.
Gen Z has strong values and expects brands to share them. McKinsey research finds that almost three-quarters (73 percent) of Gen Z try to purchase from ethical companies. Nine in 10 believe companies have a responsibility to address social and environmental issues.
As “digital natives”—the first generation to grow up surrounded by technology—your best bet for reaching Gen Z is through digital channels. But not just any ad will do. Sprout Social found that 73 percent of Gen Z consumers think brands should raise awareness and take a stand on sensitive issues.
By taking a stand against fast fashion and partnering with well-known social activists, including Xiye Bastida, Emma Chamberlain, and Marcus Rashford, the brand raised awareness of our shared environmental responsibility while promoting the quality of its products and attracting a new, younger generation of customers.
AI’s Impact on Advertising
It wouldn’t be a 2026 trends article if we didn’t end with a section about AI, would it? The truth is there’s a lot to cover. AI has become so pervasive that it’s impacting almost every part of the paid media world—and the majority of our survey respondents are already using it in their campaigns.
Ad creation is one common use case. Google’s Dynamic Search Ads feature uses AI algorithms and content on your existing website to automatically create relevant ads and show them to customers searching for the exact product or service you offer.
Meta also has a Dynamic Ads offering, which can automatically promote all your products across Facebook and Instagram.
Then there are generative AI tools like ChatGPT or Jasper, which you can use to create ad copy in seconds.
Soon enough, though, you won’t even need to leave your native ad platform of choice for generative AI support. Many of the major players, like Google, Meta, and TikTok are all building native creative solutions that can auto-generate copy and creative.
Google, in particular, is taking this capability to the next level, using AI to assist advertisers with video and voice-over capabilities, enhancing the efficiency and quality of ad creation. The platform includes features such as AI-powered video editing and text-to-speech functionality for YouTube ads.
Imagine being able to input a script, choose a variety of voices, and create natural-sounding audio overlays, This allows for flexible and quick adjustments to match your brand voice, making video content production more efficient and accessible than ever.
AI can run your campaign for you, too. Over one-third of our respondents used AI-powered paid bidding strategies, for example, which was the top use case in our survey. In addition, when it comes to taking advantage of Google’s capabilities to generate creative using AI, our survey respondents were very intrigued.
Speaking of Google’s AI improvements, Performance Max (PMax) campaigns are a great example of this technology. Performance Max campaigns deliver broad, conversion-focused coverage with the use of AI. The results are impressive, with Google claiming marketers are seeing 27 percent more conversions.
Not that Google is resting on its laurels. The search giant is making a series of improvements to PMax by:
Optimizing broad matches by 10% for advertisers using smart bidding
Adding the ability to see ad performance by creative and placement details
Implementing YouTube exclusions
Introducing a profit optimization goal in Smart Bidding, which optimizes for profit using data from cart-level conversions and your Merchant Center account
If you already have paid media campaigns set up, there are several third-party platforms that let you leverage AI to improve campaign performance. These include Trapica and Adsmurai.
What do all the AI leaps mean for paid media marketers? Well, the role is evolving. The advent of AI simplifying tasks like content generation, bidding, and more means that there is less time spent on tedium and more time on higher-level tasks, with supervision for that AI output. Paid media professionals are becoming more a hybrid campaign/creative/strategy manager role.
FAQs
How have paid media trends shifted for healthcare recently?
Healthcare paid media has shifted from aggressive targeting and quick conversions to privacy-first, trust-driven marketing.
You can’t rely on hyper-targeting like before. Privacy rules and platform changes have limited how precisely you can track and target users. So the focus has moved to intent and context—think search queries, location, and content people are actively engaging with.
At the same time, the strategy has expanded beyond just paid search. Search still captures demand, but channels like YouTube, social, and even connected TV are now critical for building trust before someone ever clicks.
Content plays a bigger role too. Educational ads, doctor-led videos, and patient stories outperform hard “book now” pushes because healthcare decisions take time.
Bottom line: healthcare paid media isn’t just about driving clicks anymore. It’s about showing up early, building credibility, and staying visible until the patient is ready to act.
Conclusion
Paid media is one of the most dynamic and fast-paced marketing environments. Things can change significantly from month to month, and there are always new trends and platforms you can use to drive more revenue and generate higher returns on your investment.
New trends may not last, but that doesn’t mean you shouldn’t be experimenting. You never know: Contextual advertising, short-form video, or in-app ads may be your new highest-converting channel.
Then it’s all about executing. And remember, the faster you execute, the better positioned you’ll be to take advantage of new paid marketing trends and digital marketing predictions in 2026 and beyond.
If you want even more insights on these upcoming trends, check out our full report on the NP Digital website.
The Ubersuggest Chrome Extension surfaces keyword data like volume, CPC, and competition scores directly on Google, YouTube, and Amazon search results pages without leaving your browser.
The extension works on both Windows and Mac, and activates automatically once installed.
YouTube and Amazon support expands your keyword research beyond Google, covering video and ecommerce search behavior in one tool.
The extension connects to the full Ubersuggest platform, where AI visibility features help you track how your brand and content appear in AI-generated search results.
Traditional organic rankings are no longer the whole picture. Ubersuggest helps you monitor visibility across both classic search and the AI-powered results shaping where traffic goes next.
The Ubersuggest Chrome Extension has seen some significant updates over the years, and the latest version is the most useful one yet.
The improvements go well beyond a simple refresh. Keyword research is faster, more intuitive, and built around how SEO professionals actually work. This post breaks down what the extension does, what’s changed, and how to get the most out of it.
Here’s what Ubersuggest Chrome can do for you…
The Ubersuggest Chrome Extension brings SEO data directly into your browser, so you’re not jumping between tabs or tools to get the numbers you need.
Once installed, the extension activates automatically whenever you run a Google search. Right there on the results page, you’ll see keyword data layered in: monthly search volume, cost-per-click (CPC), and competition scores for your search term. No extra steps required.
Here’s how to put it to work across a few common use cases:
Researching keywords: Search any term on Google and the extension surfaces volume and difficulty data instantly. Use that to qualify whether a keyword is worth targeting before you invest time in content.
Scoping out the competition: Click into any organic result directly from the SERP and the extension pulls up domain-level authority data. It’s a fast way to size up who you’re up against. For a deeper look, pair it with a full competitor analysis to understand where gaps exist.
Finding related keywords: The extension also surfaces keyword suggestions tied to your search. These are useful for building out topic clusters or identifying secondary terms to work into existing content.
It works the same way whether you’re on Windows or Mac, as long as you’re running Chrome. The experience is consistent across both operating systems, so the workflow above applies regardless of your setup.
Keyword research beyond Google
Google isn’t the only place people search. YouTube is the second-largest search engine in the world, and Amazon drives more product searches than Google does in the ecommerce space. The Ubersuggest Chrome Extension works across both platforms, giving you keyword data wherever your audience is actually searching.
On YouTube, the extension surfaces keyword data directly as you type. Search any term and you’ll see volume and competition data on suggested keywords in real time.
Click “view all” next to the search bar for a deeper breakdown. You’ll see monthly search volume trends over the past 12 months, seasonal patterns, competition scores, CPC data, click rates, and audience age ranges. The extension also shows whether a keyword skews toward mobile or desktop searches, which matters for conversion strategy. Desktop searches tend to convert at higher rates, so prioritizing those terms can sharpen your targeting.
The right-hand side of any YouTube results page also surfaces related keyword suggestions, giving you additional terms to evaluate without leaving the page.
Amazon works a little differently. Rather than overlaying data on the results page, the extension surfaces keyword information directly on search suggestions, keeping the shopping experience intact while still giving you the data you need for ecommerce keyword research.
Think of the extension as your on-ramp to smarter research across the open web. The data it surfaces on Google, YouTube, and Amazon is just the starting point. The full Ubersuggest platform goes deeper, with features like AI visibility tracking and AI-powered keyword overviews that help you understand not just where you rank, but how AI search surfaces your brand. Those capabilities live in the platform, and the extension is the fastest way to get there.
What does that mean for you?
Search has changed. Google’s AI Overviews now answer questions directly on the results page, and platforms like TikTok, YouTube, and Amazon have become legitimate search destinations in their own right. Ranking on Google alone no longer guarantees visibility.
That’s where Ubersuggest’s newer AI-focused features become relevant. The platform can show you how your brand and content are surfacing across AI-generated results, not just traditional organic rankings. That kind of insight is hard to get anywhere else, and it’s increasingly necessary for staying competitive.
The Chrome Extension is your entry point into all of it. It gives you real-time keyword data as you browse, and connects directly to the Ubersuggest platform where the deeper AI visibility analysis lives. Together, they give you a clearer picture of where you stand across both traditional and AI-powered search.
In short: the extension makes everyday research faster, and the platform helps you think bigger about where search is headed.
But wait, There’s more…
There is also one other important change made to the extension that you may have already noticed if you use Google on a regular basis.
When you search on Google, there is now traffic estimations under each URL.
This traffic estimation is for organic search traffic and it is done on a domain level. Eventually, we will tweak it to be page-based and even show you the other keywords each page ranks for… but for now, we are providing you with organic traffic estimates for each domain.
And if you are more of a visual person, in the right-hand sidebar you can also see the traffic estimation for any result in the top 10.
FAQs
How do I use the Ubersuggest Chrome extension?
Once you download and install the Ubersuggest Chrome Extension, it runs automatically in the background. Go to Google and run any search, you’ll see keyword data including monthly search volume, CPC, and competition scores layered directly onto the results page. The same applies on YouTube and Amazon. Search a term and the extension surfaces keyword suggestions with supporting data as you type. No setup or configuration is needed beyond the initial install.
Conclusion
Already using the Ubersuggest Chrome extension? It’s still a great way to get quick insights as you browse.
But here’s the real play: use it as your entry point into the full platform.
When you click through, you’ll unlock deeper keyword data, competitor insights, and our newer AI features that help you generate content ideas, analyze pages, and move faster without guessing.
Don’t have the extension yet? Install it here and start turning everyday browsing into real SEO opportunities.
At one end: a human asks an AI a question and gets a fast, generated response.
At the other: an AI receives a goal and browses the web on a human’s behalf. It evaluates your brand, makes a decision, and leaves no trace in your analytics.
That’s agentic search.
And it’s already emerging.
ChatGPT’s deep research, Gemini’s agentic mode, and Perplexity’s research features are early expressions of it. Shopping within ChatGPT and booking tables without ever visiting a website are where it’s heading.
AI systems are already running multi-step evaluations with less human direction at each step.
The brands that show up in those evaluations aren’t waiting to see how this develops.
They’re optimizing for it now.
By the end of this guide, you’ll know what agentic search is, how it differs from typical AI search, and how you can prepare your brand for it.
What Agentic Search Actually Is
Agentic search is AI that searches and acts on your behalf — not just composing an answer from its training data, but going out to find information, use tools, and complete tasks.
At the simpler end of the agentic search spectrum, the AI retrieves sources and synthesizes a response.
At the more complex end, the AI agent receives a search goal, breaks it into sub-tasks, searches across multiple sources, cross-references what it finds, and takes action, without waiting for your input at each stage.
Examples of Agentic Search in Action
At the simpler end of the agentic search spectrum, you give an AI tool a prompt like “Which project management software is best for a remote team of ten?”
It won’t just produce an answer from its training data. It’ll go online, search for comparison articles, pull pricing and feature information from review platforms, and synthesize a recommendation.
Move further along the AI search spectrum and the behavior gets more complex.
For instance, imagine you ask the AI to research the competitive landscape in your market. It formulates a plan, then runs multiple searches across different source types — news coverage, review platforms, company pages, industry analysis.
It cross-references what it finds, and you get a structured report.
You’re still the one taking action based on this report, but this is a step up from the fairly simple, synthesized response we’re now used to.
Further still: some agents don’t need a prompt at all. Configured with a recurring search task, like monitoring competitor pricing, flagging new entrants, or summarizing industry news weekly, they run on a schedule.
And at the furthest end of the agentic search spectrum, the AI finds the right option, evaluates it against alternatives, and completes a transaction on your behalf. You asked for a recommendation. It booked the table.
Both OpenAI and Google have published open protocols specifically designed to make this possible (more on them soon).
Why This Is Different from What SEOs Already Know
Agentic search challenges some of the core assumptions SEO has operated on for years.
Here are the three that matter most.
Rankings Matter Less Than Before for Overall Visibility
AI tools are built to pull from a deliberately diverse range of sources, not just the highest-ranking pages.
A single search query triggers retrieval across multiple source types: editorial content, review platforms, community forums, company pages. No single ranking position dominates that process.
AI tools also heavily weigh up content and brand relevance when forming responses, versus factors like website authority, which is more important for SEO.
That doesn’t mean backlinks don’t matter — they do. But topical depth and relevance to the searcher’s intent are the focus in these tools.
Finally, when an AI tool processes a search, it generates multiple related sub-queries, pulling from the results of each. This is called query fan-out.
Your ranking for the original keyword is just one input into a much wider retrieval process. This makes broader topical coverage a key component of AI search in general. This is how you show AI agents that you’re worth citing, recommending, and taking action on.
Your Content Depth Is Now a Competitive Advantage
As Crystal Carter, Head of AI Search & SEO at Wix, puts it: “LLMs don’t get tired of reading 45 pages about your business.”
The average user won’t read countless pages of product documentation. But an agent will — and it’ll use what it finds to make a recommendation.
FAQs, knowledge base articles, documentation, case studies — content that might rarely surface in a standard browsing session becomes evidence in an agentic evaluation.
Crystal gives Levi’s sustainability documentation as an example.
A human visitor might not find it. If you were wondering if Levi’s were sustainable, you’d probably look them up on a single trusted site.
Compare that with what Perplexity AI does to answer the question “Are Levi’s sustainable?”
It conducts a deep dive into Levi’s site.
It evaluates evidence from 15 different sources.
It reads multiple pages from Levi’s own site, including their sustainability report, details on the sustainability of their fibers, their stance on human rights, and a page on slavery from a domain in a separate geography (Levi’s UK).
To succeed in agentic search, you need to make sure agents can answer any questions about your brand your users may have.
AI systems don’t simply retrieve results. They actively research, compare, and filter brands before a human ever sees a recommendation.
Your brand isn’t being ranked once. It’s being audited across sources.
If we take the Levi’s example again, ChatGPT doesn’t just look at Levi’s own content to answer the sustainability question.
It also looks at official rating bodies, third-party research, and media publications. It acts more like a professional researcher than a human conducting a low-stakes product search question.
An agentic system evaluates brands through layered filters like:
Can it find you clearly?
Does it understand you correctly?
Are you validated elsewhere?
Does it trust you enough to recommend you?
If you fail any of those layers, you can disappear entirely from the final answer.
Your Site Needs to Be Usable By Agents, Not Just People
Increasingly, AI agents interact with businesses through structured agentic protocols designed for machine-to-machine communication.
Instead of just relying on what’s in a page’s HTML, AI agents are moving toward standardized protocols, like the Agentic Commerce Protocol (ACP) and Natural Language Web (NLWeb).
This changes what “being accessible” actually means.
Content that only exists inside a visual interface — FAQs that expand on click, pricing tables rendered dynamically, product comparisons loaded via JavaScript — may never exist in the structured layer agents rely on to retrieve and execute actions.
And if they can’t access it, they can’t use it.
That matters because AI agents are increasingly the ones deciding what to include in their recommendations and what to ignore. The human only sees your site if you’re in those recommendations.
So the question is no longer just: “Can people find my website?”
It’s: “Can AI systems clearly understand and use my business information without friction?”
Because in this new system, if your business isn’t easy for AI to access and act on, you may not show up at all.
An agent evaluating your brand might find everything it needs on a single page of your website.
But when it does go looking further, it’s not just gathering information. It’s also checking whether the rest of its sources agree.
An agent corroborates, actively checking whether the picture is consistent across everything it finds.
Here are some of the key places agents look:
Your Website
Agents are likely to prioritize sites that are easy to parse and extract from. They look for:
Clear, up-to-date pricing in plain HTML (not hidden behind interactions).
Feature descriptions that explain capabilities — not just marketing claims.
Positioning that makes it obvious who the product is for (and who it isn’t).
Review Platforms (G2, Capterra, Trustpilot)
Agents read review content for specificity, covering things like use case, company size, outcomes, and integrations.
Community Signals (Reddit & Other Forums)
Agents look at user sentiment on community platforms to cross-check vendor claims.
A brand that talks about itself one way and gets discussed differently in communities creates a consistency gap that leaves agents hesitant to recommend your brand (at least without caveats).
Third-Party Editorial
Agents also look at comparison articles, analyst coverage, and industry endorsements.
Appearing consistently in credible “best X for Y” content is a positive signal.
6 Things to Do Before Agentic Search Goes Mainstream
Agentic search isn’t fully mainstream yet, but the infrastructure is being built now.
The brands that will be well-positioned are the ones that start taking action before their rivals are even aware of what agentic search is.
Here’s how to make sure you’re one of those brands.
1. Run a Cross-Source Consistency Audit
Check your pricing, features, and positioning across your own site, your G2 and Capterra profiles (or any other platforms your target audience users), and comparison articles where your brand appears.
Flag and correct every contradiction.
Make this a recurring part of your workflow. Old positioning language lingers in third-party content long after you’ve updated your own pages.
2. Build Hub Pages for Your Highest-Value Queries
If you don’t have them already, create new standalone pages that fully answer the key questions: what you do, who it’s for, how it compares to other solutions, what it costs, and what customers say.
3. Pressure-Test Your Declared Audience
Pull up your homepage, pricing page, and top comparison content.
Ask: can an agent clearly extract who this is for, what problem it solves, and what makes it right for a specific profile?
To make this concrete, paste the content into an AI tool and use this prompt:
“You are an AI agent evaluating this company. Based only on the content provided, extract: (1) who this product is for, (2) what problem it solves, (3) key use cases, and (4) what differentiates it from alternatives. Then highlight any ambiguity or contradictions.”
If the output is vague or generic, your positioning is too.
4. Ask Customers for More Detailed Reviews
Most reviews are vague: “Great product, really helpful team.”
That doesn’t help AI systems understand when your product is actually a good fit.
Instead, ask customers to be more specific about how they use it and what changed.
For example, in your review requests, you can say:
“If you’re happy to leave a review, it would be really helpful if you could include:
What you use the product for
Your company size or team type
The problem you were trying to solve
The outcome or result you saw
Any tools you integrate with”
5. Check Your Accessibility
Make sure your pricing, FAQs, and feature comparisons are in plain HTML.
Also check your forms and CTAs. If an agent needs to book, enquire, or transact on a user’s behalf, it needs to be able to find and use the form. So don’t hide them behind JavaScript.
6. Implement Agentic Search Protocols
While agentic search protocols are still new and being actively developed, understanding how they work and implementing them on your site can help you prepare for wider rollouts.
For more information on which protocols matter and what they do, read our guide to agentic search protocols.
7. Monitor Your AI Footprint
Right now, here are two things you can actually track to monitor your AI footprint:
Run Regular Brand Queries
Open ChatGPT, Perplexity, and Google AI Mode, and search for your brand by name.
Then search for the category queries a buyer would use — “best [product type] for [your target audience].”
In both cases, document what comes back. Is your brand mentioned? Is what’s being said accurate? Is it consistent with your current positioning?
Do this monthly and track how things change over time.
If your positioning is wrong or outdated, update your homepage, pricing, and comparison pages first (these are usually the sources AI systems rely on most).
If competitors are being favoured, strengthen your comparison content and aim to get more third-party reviews.
If you’re missing entirely, check whether your key pages are crawlable, indexable, and clearly describe your use case.
Agentic search is already here. And as time goes on, complex agentic tasks — like signing up for a tool or buying on behalf of the user — will only become more common. That’s why it’s worth preparing for full agentic search right now.
Start by figuring out where you stand currently.
Tools like Semrush’s AI Visibility Toolkit show you how AI systems currently perceive your brand across platforms. That’s your baseline before you tackle anything else. Learn how to use it in our Semrush AI visibility guide.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-04-20 13:00:142026-04-20 13:00:14What Is Agentic Search? (And Why SEOs Need to Pay Attention)
AI is changing search and rewriting the rules. If your brand isn’t visible in AI-generated answers, you have a bigger problem than just traffic. You’re missing out on trust, credibility, and customers who now expect AI to recommend the best options everywhere.
We see that traditional SEO isn’t enough anymore. Today, it’s possible to rank #1 on Google and still be invisible in the AI responses people now often turn to for recommendations.
Yoast AI Brand Insights is a great tool that shows you exactly how your brand appears in AI-generated answers from ChatGPT, Perplexity, or Google Gemini. It tracks sentiments and benchmarks against competitors. What’s more, it doesn’t just help build your AI visibility, but also helps control your brand’s narrative.
Key takeaways
AI visibility matters; brands absent in AI responses lose trust and customers.
Yoast AI Brand Insights helps track brand mentions, sentiment, and credibility across AI platforms.
Modern SEO now focuses on AI visibility, moving beyond traditional search engines.
To improve AI brand visibility, brands should publish authoritative content and optimize for AI citations.
Active participation in online communities enhances brand visibility on AI platforms.
Why modern SEO is about AI visibility
People are no longer just searching on Google. Every day, more people are asking AI tools and Large Language Models (LLMs) like ChatGPT, Gemini, and Perplexity for recommendations. Unlike classic search engines, these tools don’t just list links; they curate answers by combining trained knowledge with information they’ve learned from the web.
AI platforms combine information from multiple sources to provide a single, context-aware, and custom answer. People even start treating these AI answers as personal advice, not just generic search results. This will happen more and more as search engines like Google increasingly integrate AI into their search results. As a result, the boundaries between traditional search and AI-generated answers are blurring.
AI search is a blind spot for most
Classic SEO tools track rankings, but they don’t track how your brand appears in AI answers. This leads to blind spots where your competitors might be all over the AI recommendations in your market without you realizing it.
What’s more, you might rank well on Google, but you could be invisible to a growing audience if AI systems ignore your brand. Your competitors can appear more often or more positively in AI recommendations. Or there’s negative sentiment in AI responses that can harm your reputation without you even knowing.
Controlling the narrative of your brand
AI platforms like ChatGPT, Perplexity, and Gemini piece together your brand’s story from scattered sources, like reviews, news articles, social media, and your own content. If these send mixed signals, the answers an AI gives will too. That’s why you need to send a unified, consistent message. This is one of the most effective ways to reinforce your narrative across every platform.
Repeat your main message, whether that’s “affordable luxury” or “sustainable innovation,” everywhere, from your site content to press releases and from social media to external interviews.
Quickly address misinformation and respond to inaccurate reviews by publishing clarifications online. By doing this, you prevent the AI from amplifying outdated or incorrect details.
Support your brand’s most important attributes with structured data. Add the awards your brand won, or its unique selling points, so you can give the AI platform an all-encompassing framework to reference.
Remember, consistency is about repeating your most important brand aspects everywhere. Shape the narrative in such a way that the AI has no choice but to reflect the brand the way you want it to project.
Yoast AI Brand Insights is here to help
Yoast AI Brand Insights is a helpful tool that tracks how your brand appears in AI answers. It provides a clear, actionable view of your brand’s visibility, sentiment, and credibility across major AI platforms.
Yoast AI Brand Insights helps you:
Understand if and how your brand is mentioned in AI responses
Track sentiment and see if AI platforms describe your brand positively or negatively
Identify the sources to see what AI references when mentioning your brand
Benchmark against competitors to see how you stack up
We didn’t build this to get you some data, but to turn that AI black box into actionable insights.
The main page of the Yoast AI Brand Insights shows your main metrics, and you can delve deeper into your analysis by going to Analysis details
Understanding the AI visibility metrics
Using the Yoast AI Brand Insights metrics helps you measure and improve your brand’s visibility in AI platforms. To make the most of it, you have to understand what metrics mean and why they matter.
AI Visibility Index (AIVI)
The AI Visibility Index (AIVI) scores (on a scale of 100) how visible your brand is on AI platforms such as ChatGPT, Perplexity, and Gemini. It consists of the following metrics:
Mentions, or how often your brand is cited in AI answers
Citations, or the number of authoritative sources referencing your brand
Sentiment, or the rate of positive vs. negative keywords associated with your brand
Rankings, or the relative position of your brand mentions compared to your competitors
The higher the AIVI score (on a scale of 0-100), the more visible your brand is in AI search results for the tracked terms. If you find that your score is low, you should focus on getting more mentions and citations. You should also work on positive sentiment around your business.
You build your relevance by publishing authoritative content. Try to get featured on relevant sites and monitor and improve negative sentiment around your brand. Learn more about how AI shapes brand perception.
The higher the AIVI score (on a scale of 0-100), the more visible your brand is in AI search results for the tracked terms
Mentions
The Mentions section tracks the specific queries for which your brand appears in AI responses. So, if someone asks, “What is the best low-cost CRM system for small businesses?” and your brand is in the results, that is a mention.
It’s not hard to understand why this is important. More mentions generally lead to greater visibility. If you don’t show up for the terms and queries relevant to your brand, you need to start improving your content.
Use the built-in AI-generated brand queries to find high-intent questions and write content that answers those questions thoroughly. These could be blog posts or FAQ pages, or whatever makes sense. Also optimize for conversational queries, such as “Is brand X good for startups?”
The mentions section tracks the specific queries for which your brand appears in AI responses
Sentiment
Sentiment measures the percentage of negative vs. positive words in the query results associated with your brand. So, if the AI describes your brand as “innovative” or “reliable”, that counts as positive sentiment. However, if they use terms like “overpriced” or “unreliable”, that’s negative sentiment.
Positive sentiment helps build trust, while negative sentiment can drive potential customers away. That’s why you should always actively address negative sentiments online. Don’t leave those bad online reviews unresponded to. You can also publish testimonials on your site to amplify positive voices, and you can do the same in your marketing messaging by talking about “a brand loved by thousands” or “award-winning” products.
Keep an eye on trends in your online sentiment and catch and fix issues early.
Sentiment measures the percentage of negative vs. positive words in the query results associated with your brand
Citations
Citations refer to the sources that AI platforms explicitly reference when generating an answer, not the brands mentioned within those sources. For example, if Gemini answers a query about “the best credit cards” and cites a New York Times article about best credit cards, that New York Times page is the citation. Even if the article includes brands like American Express or Chase, the citation is attributed to the publisher, not to the individual brands.
That said, appearing in those cited sources still matters a great deal. If your brand is consistently featured in relevant, high-authority publications like The New York Times, it increases the likelihood that AI systems will surface your brand in their responses over time. In other words, you may not receive a direct citation, but you benefit from being part of the content that AI platforms trust and rely on.
Over time, your brand (say, American Express or Chase) becomes more likely to be included in AI responses to queries like “best credit cards,” especially if it consistently appears in trusted sources.
AI platforms use citations to validate their answers. Citations from top sources, such as industry publications, enhance credibility. Find where there’s a natural match between your customers and their audience, and publish the type of content people will want to link to.
Citations refer to the sources that AI platforms explicitly reference when generating an answer
5 Ways to improve your AI brand visibility
Now that you understand the metrics, here’s how to use insights from Yoast AI Brand Insights to improve your AI visibility.
Optimize for AI citations
AI platforms like Gemini, Perplexity, and ChatGPT use citations to validate their responses. So, citations increase the likelihood of your brand being included and trusted in AI-generated answers
Try to get featured on relevant, authoritative sites and publish guest posts on industry sites, news sites, or educational domains. Get mentioned in roundup articles, like “Top 10 tools for doing X”. Ask customers to write reviews on platforms like Capterra, G2, and Trustpilot. All of these tactics can act as proof that your brand is a well-trusted source. Remember, it must be relevant citations.
Make sure your content is structured so the AI can read it easily. Use clear, hierarchical headings and bullet points to make the content easy to scan. Add FAQs and publish direct answers to common questions. It is also a good idea to add schema markup to help the AI crawlers understand your content.
Don’t forget to update old content regularly. The AI platforms prioritize fresh, up-to-date information when retrieving sources, so refresh your content regularly to stay relevant.
Monitor and improve brand sentiment
By mentioning your brand, the AI platforms also shape how people see it. If those sentiments in the AI’s answers are negative, it can hurt your trustworthiness and cost conversions. This could signal the need for a broader reconsideration of business strategy priorities.
Once you find AI platforms associate your brand with negative terms (like “slow customer service”), respond to this issue publicly. For instance, you could contact customers on review sites to resolve complaints. You can also publish case studies and testimonials to steer the AI towards positive perceptions.
In your monitoring, you’ll also find the positive terms AI platforms associate with your brand, such as “trusted” or “innovative”. Use these terms in your marketing, in your site content, and on social media.
The weekly scans in Yoast AI Brand Insights track sentiment shifts for your queries over time. If sentiment drops, investigate the cause, like a recent PR issue or a product recall.
Benchmark against competitors
AI visibility is also about how you compare to the competition. If they are mentioned more often or in a better light than you, they will appear more often in recommendations made by AI platforms.
See how your brand stacks up against competitors. Use Yoast’s Competitor ranking tab to see which brands show up a lot in AI answers. Analyze their content strategy. Do they publish more case studies? Are they active on review sites?
This tool shows how AI describes your brand compared to others in your market. For example, if you’re a coffee company like Taylor’s of Harrogate, you might find that Lavazza is consistently labeled as “the Italian espresso expert.” Now you know exactly what to highlight, whether it’s your heritage, roasting process, or sustainability, to stand out. Use these insights to sharpen your messaging and compete more effectively.
Don’t forget to check your weekly competitor analyses to see if your AI visibility is improving. Double down on the strategy that works for you. The tool also includes an historical view. This lets you look back at earlier analyses by selecting a past date, helping you compare visibility and sentiment across different points in time.
For each tracked query, Yoast AI Brand Insights gives specific insights into how your brand performs versus the competition
Answer brand-specific questions
AI platforms are very good at answering specific questions, such as “Is brand X reliable?” or “What’s the best tool to do Y?” You’re missing out on a lot of potential customers when your brand isn’t in these answers.
Yoast AI Brand Insights suggests queries you should monitor based on your input, such as “Is [Your Brand] good for small businesses?” In addition, do deep research into the common questions asked in your industry using tools like AnswerThePublic, AlsoAsked, or simply by checking Google’s People Also Ask section.
With the insights gathered, publish blog posts, FAQs, or landing pages and directly answer those brand-related queries. Support the content with properly structured data, such as FAQ and how-to schema, to give AI platforms more tools to understand your content.
In Yoast AI Brand Insights, track which questions get the most mentions from AI platforms. Don’t forget to keep your content up to date to keep it accurate and relevant.
During the setup, Yoast AI Brand Insights generates five highly relevant queries based on your input. You can change them if you like
Track progress with the AI Visibility Index
Improving the AI visibility of your brand isn’t a one-time task, but a recurring effort. Luckily, Yoast’s AI Visibility Index gives you an easy-to-understand metric that you can use to track your progress over time.
Run your first scan to establish the starting point for your AI Visibility Index. Note which areas, like citations or sentiment, are strongest and weakest.
Yoast AI Brand Insights runs weekly scans. Please review them to track progress. Check the historical view, but remember these cannot be viewed together. Select the week before and then reselect this week to spot changes. Look for trends, such as improvements in sentiment or a sudden increase in citations.
If your score doesn’t improve, revisit the strategies above, such as optimizing for citations and improving sentiment. Be sure to experiment with new tactics and publish original research to secure more earned media.
How to influence LLMs to mention your brand
Imagine this: A potential customer asks ChatGPT, “What’s the best CRM for small businesses?” If your brand isn’t mentioned in the answer, you’ve lost a customer before they even knew you existed.
LLMs like ChatGPT, Gemini, and Perplexity don’t just pull answers out of thin air. They rely on data, citations, and patterns to generate responses. If your brand isn’t part of those patterns, it’s far less likely to be mentioned, no matter how well you rank on Google.
Publish authoritative content
LLMs are looking for well-structured, factually accurate content. These AI platforms love sources that provide unique insights or expert opinions, so be sure to focus on that.
Start with original research. Publish surveys, case studies, or industry reports with unique data. For example, “2026 State of [Your Industry] Report: Key Trends and Insights” positions your brand as an authority and gives AI platforms a reason to cite you.
Use the proven inverted pyramid structure in your content. Start with the most important information, like key findings and conclusions, follow with supporting details, and end with background information. This makes it easier for AI to extract, digest, and use your content.
Don’t forget to optimize for facts. Include statistics, quotes from experts, and actionable insights. For example, instead of “Our tool is great for marketers,” say “Our tool increased conversion rates by 30% for 500+ marketers in 2025, according to our latest case study.”
For example, HubSpot built its authority by publishing ultimate guides, like “The Ultimate Guide to Inbound Marketing.” These guides became go-to resources for marketers, earning backlinks from industry blogs, news sites, and even competitors. As a result, HubSpot is now frequently cited in AI responses about marketing tools.
Get mentioned on relevant, high-authority sites
LLMs trust reputable sources like industry publications, news sites, and review platforms. The more your brand is mentioned on these sites, the more likely it is to appear in AI responses. Please keep in mind that relevance is key here. For instance, if Yoast gets mentioned in Gardeners’ World or Home and Garden, it will do little to nothing for our brand. Find the most important and relevant sources and focus on those.
Pitch stories to journalists or industry blogs. For example, try to get featured in “Top 10 [Your Industry] Tools in 2026” lists.
Encourage customers to leave reviews on G2, Capterra, Trustpilot, or Google Reviews. Don’t forget to respond to (negative) reviews to show engagement and transparency.
If possible, try to reach out to sites like HubSpot, Search Engine Journal, or industry-specific blogs and ask to write for them. Be sure to include a bio with your brand name to reinforce recognition.
Optimize for conversational queries
LLMs are designed to answer natural language questions. This means you have to optimize your content for conversational queries. Conversational queries are things like “What’s the best CRM for startups?” rather than “best CRM”.
In your content, you should use question-focused headings. For example, answer the question “Is [Your Brand] good for small businesses?” directly in the first paragraph to make it clear and easy to understand.
LLMs often answer long-tail questions, so you should target long-tail keywords. For example, instead of “project management tool,” target “best project management tool for remote teams in 2026.”
In support of all of this, create FAQ pages with schema markup to help AI better understand your content.
Build citations
Build up a network of high-quality mentions that reinforce your brand’s authority. The more high-quality, relevant citations you have, the more likely LLMs are to mention your brand.
Publish assets like ultimate guides, templates, or tools that others want to reference and link to. For example, “The Ultimate Guide to [Your Industry] in 2026.”
Reach out to bloggers, journalists, and influencers to reference your content. For example, “We noticed you mentioned [Competitor] in your article. Here’s why [Your Brand] might be a better fit.”
Get featured in press releases, podcasts, or webinars. For example, “[Your Brand] Announces Groundbreaking Feature for [Industry].”
Make sure AI crawlers can reach your site
It’s important to ensure that AI crawlers can discover and index your content. If your site is invisible to them for whatever reason, your brand won’t appear in AI responses.
Your site should be technically sound, but you can also help LLMs in other ways. Make sure your site loads fast and is mobile-friendly. Use clean URLs, good meta tags and descriptions, and alt text for images. Also, use schema on your site to help AI crawlers understand what your site is about and how it all ties together.
In Yoast SEO, you can activate an llms.txt file. This proposed standard helps point AI crawlers to your most important content. Also, check whether your robots.txt file inadvertently blocks AI crawlers from accessing your content.
The llms.txt file in Yoast SEO helps point AI crawlers to your most important content
Be active in online communities
LLMs are trained on and can retrieve information from forums, social media, and community platforms such as Reddit, Quora, and LinkedIn. It can improve your brand’s visibility on AI platforms if you participate there.
Answer questions on Quora and Reddit. Provide valuable, non-promotional answers that naturally mention your brand. For example, “As a [Your Industry] expert, I recommend [Your Brand] because…”
Join discussions on Slack, Discord, or niche forums. Share insights and link to your content when relevant. Post thought leadership content on LinkedIn, Twitter, or Facebook. For example, “Here’s why [Your Industry] is changing in 2026, and how [Your Brand] is leading the way.”
The future of brand visibility is AI-driven
We’ve seen that AI is changing how people discover brands. There’s a simple rule: if your brand isn’t visible in AI responses, you are missing out on an ever-growing audience.
Luckily, Yoast AI Brand Insights gives you the tools to:
Track mentions, sentiment, and citations across AI platforms
Benchmark against competitors to identify gaps
Optimize for high-intent queries to capture more attention
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-04-16 14:45:232026-04-16 14:45:235 ways to improve your AI brand visibility (Using Yoast AI Brand Insights)
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-04-15 18:32:082026-04-15 18:32:08Top 10 Best SaaS SEO Agencies To Help You Grow in 2026
Knowing how to measure marketing ROI requires moving beyond credit assignment toward causal proof. Attribution shows what happened; incrementality shows what marketing actually caused.
Marketing leaders face structural visibility gaps from walled gardens, cross-device behavior, offline conversions, and AI-mediated discovery that no single tool can fully account for.
Marketing investment ROI looks different at different funnel stages. Lower-funnel channels support high statistical confidence. Upper-funnel activity requires directional signals and longer evaluation windows.
The payback curve problem means short reporting cycles systematically under-value brand and upper-funnel investment, even when those channels drive the most long-term growth.
Learning velocity matters as much as measurement precision. A confident direction pursued quickly outperforms a perfect answer that arrives too late.
The Real Measurement Challenge CMOs Face
The core challenge in figuring out how to measure marketing ROI is not a lack of data. Most marketing teams have more data than they can act on. The challenge is that the data they have mostly reflects activity, not impact. And the visibility gaps that matter most are structural, not fixable with a better dashboard or a new marketing measurement plan.
Consider what falls outside standard analytics. Walled garden platforms like Google, Meta, and Amazon run their own measurement systems optimized to report performance favorably within their ecosystems. Cross-device behavior means a buyer who saw an ad on mobile and converted on desktop may never be connected in a single attribution path. Offline conversions, from phone calls to in-store visits to deals closed in a CRM, are underrepresented or missing entirely. Private sharing channels, where recommendations travel through direct messages and group chats, show up as direct traffic if they register at all. And AI-mediated discovery, where a buyer forms a view of a brand through an AI-generated answer before ever visiting a website, leaves no footprint in standard reporting.
NP Digital research found that the average customer journey grew from 8.5 touchpoints in 2021 to 11.1 touchpoints in 2025. The interactions most likely to have shaped a purchase decision are the ones least likely to appear in a marketing report.
Marketing leaders who understand this stop expecting their measurement stack to show a complete picture. Instead they ask which signals are reliable enough to act on, which decisions require stronger proof, and where directional confidence is sufficient to move forward.
Attribution modeling remains one of the most widely used marketing measurement tools available, and it has a genuine role to play in day-to-day campaign management. The problem is that when it gets used to answer questions it was not built to answer.
Attribution shows which touchpoints preceded a conversion. It does not show whether those touchpoints caused the conversion. That distinction sounds subtle, but it has significant implications for budget decisions. When Airbnb paused its performance marketing budget, bookings did not drop. When Uber cut spend in certain channels, rider acquisition was largely unaffected. In both cases, the attribution system had been crediting spend for outcomes that would have occurred regardless. The marketing was capturing demand, not creating it.
The questions leadership most often asks are precisely the ones attribution cannot answer reliably. Did this campaign generate new demand, or intercept demand that already existed? Would revenue have changed if this activity had not run? Which channels are actually changing the economics of the business? These are questions about causality. Attribution is built around correlation.
According to NP Digital research, nearly 47 percent of marketers lack confidence in their current attribution model. Yet most organizations still use attribution reports as the primary input for strategic budget decisions. Understanding where attribution blind spots appear is the first step toward building a marketing measurement plan that can support those decisions more reliably.
The Four Questions Modern Marketing Measurement Must Answer
Rather than starting with a dashboard, high-growth marketing organizations start with a set of diagnostic questions. These questions function as decision filters, helping leaders separate marketing activity from actual business impact. They come directly from how to measure marketing ROI in a way that connects to causal outcomes rather than credited touchpoints.
What is the incremental conversion lift? This asks not how many conversions occurred, but how many would not have occurred without the marketing spend. The gap between attributed conversions and incremental ones reveals how much of reported performance reflects demand capture rather than demand creation.
What is the incremental search impact? If branded search volume rises following a campaign, what created that lift? Upstream video, social, or content investment often generates the demand that search later captures. Understanding this connection changes how upper-funnel spend gets evaluated.
What attribution redistribution is occurring? Referral traffic spikes or conversion rate improvements in one channel sometimes reflect credit shifting between paths rather than genuine growth. Identifying redistribution separates real gains from accounting changes.
Where is attributed alienation occurring? At what point does frequency, promotional dependency, or margin compression start producing negative incremental lift? Channels that look efficient in aggregate can be actively eroding value at the margin.
These questions are not new KPIs to add to a dashboard. They are the lens through which marketing investment ROI gets evaluated honestly. For teams building this capability from scratch, tracking content marketing ROI using incremental rather than attributed signals is a practical place to start, since content often influences conversions across multiple subsequent touchpoints.
Matching Measurement Standards to Funnel Position
One of the most common errors in building a marketing measurement plan is applying the same standards of statistical rigor to every channel, regardless of where it sits in the funnel. Lower-funnel and upper-funnel activity operate on fundamentally different timescales and produce fundamentally different signal quality.
Lower-funnel channels, including branded search, retargeting, and conversion-focused paid campaigns, generate fast, measurable feedback. Requiring 95 percent statistical confidence before acting on their results is appropriate. The signal is clear, the data is abundant, and underperformance should be addressed quickly.
Upper-funnel channels work differently. Video, brand campaigns, content, and influencer partnerships create future demand. Their effects develop gradually, often appearing as increased branded search volume, improved conversion rates, or lower customer acquisition costs weeks or months later. Requiring the same level of statistical certainty from channels with 8- to 12-week lag times means cutting potentially effective strategies before they can prove themselves.
This creates a pattern NP Digital research consistently surfaces: teams reduce upper-funnel investment because it lacks immediate proof, then experience declining lower-funnel efficiency as the demand pipeline weakens. SEO ROI follows a similar curve. Organic search investment can take months to produce measurable returns, but teams that cut it during that window often see compounding downstream effects on paid efficiency.
The practical approach is tiered standards matched to funnel position. Lower-funnel channels require high confidence before spending continues or scales. Upper-funnel channels can be evaluated at 50 to 60 percent directional confidence, supported by leading indicators like branded search lift, engagement rate trends, and downstream conversion rate improvements.
The Payback Curve Problem
A related challenge in knowing how to measure marketing ROI is what happens when budgets shift toward channels with longer payback periods. Most organizations evaluate all marketing activity on the same weekly or monthly reporting cadence, regardless of how long each channel takes to deliver its full value. This creates a systematic bias against the investments that often produce the most long-term growth.
Direct-response channels like paid search and retargeting deliver 80 to 90 percent of their value within the first week. Email and owned media deliver 60 to 70 percent within the first two weeks. Paid social and display activity produces 50 to 60 percent of its value in the first three weeks, with a long tail extending to 8 to 12 weeks. Video and brand investment delivers only 30 to 40 percent of its value in the first month, with the majority accruing over three to six months.
When marketing spend shifts toward longer-payback channels, weekly performance declines by design. The scrutiny does not. Teams that understand their channel-level payback curves can model expected performance rather than reacting to short-term dips. Teams that do not understand them tend to cut upper-funnel investment at exactly the point where it would have begun producing downstream returns.
Building a dual-view reporting approach helps address this directly. Reporting what happened this week alongside what the model projects based on payback curves gives leadership the context to evaluate performance honestly. This is a core component of unified marketing measurement, where multiple methods and timeframes are combined into a single coherent view of marketing performance rather than a collection of disconnected channel reports.
Why Directional Confidence Often Beats Perfect Precision
Waiting for certainty before acting is one of the most reliable ways to lose ground in modern marketing. How to measure marketing ROI is partly a question of marketing investment ROI, but it is equally a question of decision speed. A model with 60 percent directional confidence, acted on quickly and iterated frequently, consistently outperforms a perfect answer that arrives a quarter too late.
Incrementality testing and geo experiments are the most reliable ways to build directional confidence without waiting for statistical perfection. A well-designed geo holdout can validate whether a channel is generating causal lift within a matter of weeks. The result may not be 95 percent certain, but it is far more useful for a budget decision than months of attribution reporting that cannot establish causality at all.
Rapid iteration compounds this advantage. Organizations that run frequent, smaller experiments build measurement capability faster than those waiting to design the perfect study. Each test produces a documented methodology that makes the next one cheaper and faster. Over 12 to 18 months, this creates a meaningful gap in decision quality between organizations that have built this muscle and those still relying primarily on attribution.
Learning velocity, the rate at which an organization converts experiments into better decisions, matters as much as the precision of any individual measurement. The teams gaining ground are the ones that have made experimentation a routine part of how they allocate budget, not a special project triggered by a performance crisis.
What This Means for Modern Marketing Leaders
The shift in how to measure marketing ROI comes down to three practical changes in how marketing leaders operate. Each one moves measurement closer to the capital allocation decisions that actually matter.
First, prioritize causal insight over attribution reports for strategic decisions. Attribution has a role in day-to-day optimization, but it should not be the primary input when deciding where to increase or decrease investment at a channel level. Incrementality testing and marketing measurement tools that surface marginal returns give a more reliable picture of where the next dollar will produce incremental growth.
Second, allocate budget based on marginal impact rather than blended performance. A channel running at strong average ROAS may be saturated at the margin. A channel with weaker blended numbers may have significant headroom. Understanding where diminishing returns begin is what separates organizations optimizing toward real growth from those optimizing toward the appearance of it. This is the core of unified marketing measurement: combining MMM, incrementality, and attribution signals to see the full picture rather than any single view in isolation.
Third, build experimentation into the operating rhythm rather than treating it as a special project. Weekly budget decisions based on directional evidence outperform quarterly reallocations based on attribution. Organizations that run incrementality tests regularly, document the results, and apply those learnings to subsequent decisions accumulate a structural advantage that compounds over time.
FAQs
What Is ROI in Marketing?
Marketing ROI, or return on investment, measures the revenue generated relative to what was spent on marketing. The basic formula is (revenue attributed to marketing minus marketing cost) divided by marketing cost. In practice, meaningful marketing investment ROI analysis goes beyond this formula to account for which revenue was incremental, what the margin on that revenue was, and how long it took to recover the initial spend.
How Do You Measure Marketing Success?
Measuring marketing success depends on which question you need to answer. For operational performance, platform metrics and attribution data provide fast feedback. For strategic decisions about where to invest, incrementality testing and marketing mix modeling give more reliable signals. A complete marketing measurement plan uses both, matched to the type of decision being made.
What Is a Good Marketing ROI?
There is no universal benchmark for good marketing ROI because it depends heavily on margins, customer lifetime value, and payback period. A channel delivering 3x ROAS with strong retention and high margins may outperform a channel at 6x ROAS where customers churn quickly and margins are thin. Evaluating ROI in the context of customer value and payback period gives a more accurate picture than any single ratio.
How Do You Improve Marketing ROI?
Improving marketing investment ROI typically comes from three places: identifying and cutting spend in channels that are capturing existing demand rather than creating new demand; reallocating toward channels with demonstrated incremental lift; and building upper-funnel investment that reduces customer acquisition costs downstream. Incrementality testing is the most reliable tool for identifying which of these opportunities exists in your specific channel mix.
Conclusion
Knowing how to measure marketing ROI has always required judgment alongside data. What has changed is that the data itself has become less reliable as a standalone guide. Attribution models over-credit demand capture. Platform dashboards optimize within closed ecosystems. Blended ROAS hides where spending stops working. And the channels doing the most to build future demand are often the ones that look weakest in a standard report.
The organizations closing this gap are building unified marketing measurement approaches that combine causal proof with directional confidence, match standards to funnel position, and make budget decisions at a cadence that reflects how fast markets actually move.
If you are building this capability, start with the questions before the tools. Identifying which decisions your current stack cannot support is more valuable than adopting new marketing measurement tools before you know what gaps they need to fill. And for teams beginning with organic and content investment, this breakdown of content marketing ROI applies the same incremental thinking to channels that are often the hardest to measure and the most underfunded as a result.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-04-15 07:00:002026-04-15 07:00:00How Smart CMOs Decide Where the Next Marketing Dollar Goes
A major shift is underway in digital advertising: Meta Platforms is projected to generate more ad revenue than Google in 2026, signaling how marketers are increasingly favoring automated, performance-driven platforms.
Driving the news. According to Emarketer, Meta is expected to bring in $243.46 billion in global ad revenue this year, narrowly topping Google’s projected $239.54 billion.
Meta is forecast to capture 26.8% of global ad spend.
Google is projected to take 26.4%.
It would be the first time Google has lost the top spot in digital ad revenue.
Why we care. Meta’s growth suggests brands are getting more value from automated, performance-focused tools, which could influence how they split budgets between Meta and Google. It’s also a reminder that platform dynamics are changing fast, so media strategies need to stay flexible.
Catch up quick: Google has long dominated digital advertising through Search ads, Display ads across the web, and YouTube.
But its core ad business is growing more slowly than in previous years.
Meanwhile, Meta has benefited from AI-powered ad automation, stronger performance measurement tools, and continued scale across Facebook, Instagram, and WhatsApp.
Why Meta is winning now. Advertisers are increasingly prioritizing platforms that can deliver both reach and measurable return.
Meta’s advantage has been its ability to automate creative and targeting faster, optimize campaigns with less manual input, and make it easier for brands to prove ROI.
That’s especially appealing in a tighter economic environment where marketers are under pressure to do more with less.
Yes, but. Google is still enormous — and still growing.
Its search business remains one of the most profitable ad engines in the world, and YouTube continues to attract brand budgets. But the company faces more pressure from, AI search disruption, antitrust scrutiny, and slowing growth in traditional search advertising.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/04/Inside-Metas-AI-driven-advertising-system-How-Andromeda-and-GEM-work-together-zLESHi.jpg?fit=1920%2C1080&ssl=110801920Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-04-14 17:48:032026-04-14 17:48:03Meta is on track to overtake Google in global ad revenue for the first time
![Alt text: Incrementality testing diagram showing a test and control group methodology, with three diagnostic questions that determine when to use incrementality testing instead of relying on correlation-based attribution.]](https://i0.wp.com/neilpatel.com/wp-content/uploads/2026/04/how-to-measure-marketing-roi-001-700x324.webp?resize=700%2C324&ssl=1)
