Google Analytics is a powerful tool that tracks and analyzes website traffic for informed marketing decisions.

Service URL: policies.google.com (opens in a new window)

The Interactive Advertising Bureau has released new findings that outline a sharp rise in creator-driven advertising. The report signals a shift in how brands plan budgets, assess channels, and measure outcomes. The numbers reveal that creator spending has moved far beyond novelty status. It now sits solidly in the top tier of paid media priorities, right behind social media and paid search.

Spending in this sector has grown from $13.9 billion in 2021 to $29.5 billion in 2024. IAB expects that figure to reach $37 billion next year. This projected 26% jump places creator advertising at roughly four times the growth pace of total media spending. That pace is difficult to ignore, especially for marketers who once treated creator programs as side projects.

Brands Now Treat Creators as a Distinct Channel

A key detail in the study is the shift from tactical use to channel-level investment. Nearly half of all surveyed advertisers classify creators as a “must buy,” a label usually reserved for foundational channels. This ranking reflects real budget shifts. It also shows how creator partnerships have become predictable enough for annual planning, not just campaign-level experimentation.

IAB’s CEO David Cohen described the trend as a sign of commitment. He noted that while brands are investing more heavily, they also expect better structure and clearer measurement. This points to a market that is still maturing. It also reflects a familiar theme: high spend often outpaces the operational frameworks required to support it.

Brands Are Using Creator Content Across the Funnel

The report outlines how marketers are using creator content to reach audiences, influence perception, and support direct sales activity. The primary goals include:

Top Campaign Objectives

Building brand awareness (43%) Reaching new audiences (41%) Improving brand reputation and trust (35%) Driving online sales and conversions (32%)

The presence of sales in the top four objectives shows a broader shift. Brands see creators as credible partners across the entire buyer path. It also reinforces one of the report’s central themes: creative storytelling still matters, but performance metrics are now part of the same conversation.

Nearly 40% of buyers list ROI as their primary KPI for creator campaigns. This aligns creator programs more closely with the accountability standards applied to paid search and digital display.

Finding the Right Creator Remains a Sticking Point

One of the most challenging pieces of the creator economy is creator selection. A third of advertisers say identifying the right partner is the biggest hurdle. This makes sense. The creator space is crowded, fragmented, and full of inconsistent signals.

Brands report that creator reputation and audience alignment are the top deciding factors. More than half of respondents prioritize both. These criteria reinforce that relevance and trustworthiness continue to drive partnerships. The report notes that many marketers still rely heavily on manual evaluation, which slows programs and increases internal workload.

IAB’s Zoe Soon stated that the current ecosystem feels more like matchmaking than structured buying. She pointed to the need for better discovery tools. Anyone who has tried to manually assess audience authenticity across a spreadsheet of creator profiles will likely agree.

AI Brings Efficiency, but Brands Voice Clear Concerns

Nearly three-quarters of buyers already use or plan to use AI within a year. The focus is on speed and quality control, not replacement. Current AI use cases include:

AI Applications in Creator Workflows

Content editing (49%) Creator briefs (46%) Content personalization (45%)

Even with these advantages, 95% of advertisers express concerns about AI’s role in creator campaigns. The biggest concern is the loss of human connection. Marketers invest in creators because of their ability to speak to audiences in relatable ways. Removing that connection undermines why the channel works in the first place.

Measurement, Standards, and Transparency Are the New Priorities

The report highlights a clear gap between rising spend and the infrastructure needed to support it. Brands want stronger attribution models. They also ask for consistent reporting frameworks and better fraud prevention. The goal is the same across all requests: clearer links between creator investment and actual business outcomes.

The demand for standardization mirrors what we’ve seen across other maturing digital channels. Once budgets hit scale, leadership wants cleaner reporting, predictable performance, and auditable metrics. The creator economy now sits at that transition point.

IAB’s Methodology Paints a Focused Picture

The study limits its projection to brand-directed creator advertising. It does not combine affiliate income, merchandise sales, fan tips, or other revenue sources that often appear in broader market estimates. This narrower view helps isolate ad dollars that brands intentionally allocate to creators. For marketers, this makes the forecast more relevant to budgeting and planning.

This approach matches the way many brands already structure their accounting. It draws a clear line between influencer spend and generalized creator commerce.

The growth curve described in this report feels aligned with what marketers are seeing across campaigns. Brands want scale. They want repeatable outcomes. They want creator content that matches business goals. And they want clear proof that the investment is paying off.

The report makes one thing obvious: creator advertising is now a central pillar of media strategy, not an optional add-on. With higher spending comes higher expectations. Brands want stronger reporting, smarter selection tools, and better operational frameworks. As the market evolves, those who succeed will be the ones who treat creator programs with the same seriousness as their other core channels.

Google Analytics is a powerful tool that tracks and analyzes website traffic for informed marketing decisions.

Service URL: policies.google.com (opens in a new window)

Google Analytics is a powerful tool that tracks and analyzes website traffic for informed marketing decisions.

Service URL: policies.google.com (opens in a new window)

Google Analytics is a powerful tool that tracks and analyzes website traffic for informed marketing decisions.

Service URL: policies.google.com (opens in a new window)

Brandwatch has announced a significant upgrade to its platform, introducing new AI features and expanded data coverage aimed at helping marketers understand trends faster and make stronger decisions. The update centers around Iris AI, the intelligence layer woven throughout the Brandwatch system. Iris now interprets larger data sets, responds to questions conversationally, and supports deeper analysis across an even broader range of digital signals.

The company describes this wave of innovation as a shift in how marketing and communications teams work with data. Instead of long manual searches, complicated dashboards, or guesswork, Brandwatch is pushing for quicker interpretation, clearer summaries, and smoother workflows across teams that depend on accurate insight.

Iris AI Takes a Bigger Role Across Brandwatch

Iris AI has long served as Brandwatch’s intelligence engine. With this update, it receives a sharper set of tools and a more conversational interface. The goal is simple: give marketers a way to ask questions the same way they would ask a colleague and receive answers grounded in data, not assumptions.

Ask Iris: AI Assistant for Instant Answers

The new Ask Iris feature lets users type natural-language questions and receive direct responses. No queries. No custom dashboards. Iris searches and analyzes data automatically, builds charts, and provides summaries that highlight the points that matter. Users can continue the conversation, refine the question, or pull in more context without needing extra steps.

AI Query Writer for Boolean Searches

Building Boolean queries has often slowed analysts down. The AI Query Writer now generates complete queries from the ground up. It suggests keywords, hashtags, and subreddits based on the topic at hand, helping teams reduce trial-and-error work.

AI Dashboards for Faster Clarity

Data dashboards can become overwhelming without context. Iris can now summarize dashboard findings in plain language. These summaries explain what the numbers mean and point out shifts worth paying attention to. It removes the friction involved in translating charts into decisions.

Broader Data Sources Strengthen the Insights

Brandwatch’s data foundation has long been one of the largest in its category. The updated platform widens that base further. Marketers now gain access to data signals from Threads, YouTube, TikTok, LinkedIn, Trajaan search data, and more than 70,000 trending podcasts. Each podcast also includes transcript analysis, giving teams full visibility into spoken discussions tied to their brand or market.

The platform’s social media management features now include influencer summaries, translated content powered by Iris, and AI-driven industry benchmarks. These additions give users a stronger view of how their content and competitors stack up in real time.

What’s Coming in 2026

Brandwatch is already outlining its 2026 roadmap. Future plans include more APAC data sources, new integrations for customers who want to bring their own datasets into Brandwatch, and deeper analysis of video and image content. A new mobile app experience is also planned, which points to a growing focus on insight access from anywhere.

Jim Daxner, Cision’s Chief Product Officer, described Iris as a digital partner for marketing teams. His point underscores a growing trend across the industry: teams want AI that explains what is happening, provides context, and supports decisions without overwhelming them with noise. Brandwatch is betting that transparent models, clear summaries, and broad data coverage can deliver exactly that.

This update reflects a market where speed matters and teams cannot afford to wait for long research cycles. Marketers want clear signals, faster analysis, and tools that can help them respond to cultural moments before they fade. Iris AI seems positioned to give them that edge, especially as digital signals multiply across new platforms.

The latest release reinforces Brandwatch’s role as a central intelligence hub for marketing and communication teams. With wider data coverage, more automation, and smarter analysis tools, the platform helps users turn constant digital noise into meaningful patterns. It also gives teams the structure they need to act with confidence, even as consumer conversations shift by the hour.

As 2026 approaches, Brandwatch appears committed to building AI that works alongside humans instead of replacing them. The updates announced today show that direction clearly. The platform continues to expand, and marketers gain more ways to read signals, interpret change, and act quickly—with clarity guiding every step.

AI has sparked a revolution that is transforming everything from how products are made and marketed to how people discover and choose what to buy.

Instead of scrolling through lists of blue links, people now explore through conversations, ask follow-up questions, and build confidence long before they reach a website. This new path to conversion means brands and content owners must stop chasing clicks and focus on the signals that truly drive outcomes.

But how do we know which conversion signals are most important?

While the specific goals of every business or publisher differ, AI search is making one thing clear: the most valuable signals are the ones connected to visibility. By tracking impressions, placement in AI answers, and citations, brands can see where content is being surfaced, trusted, and considered, even before a visit occurs. More importantly, these signals reveal where interest is forming and where optimization can create lift, helping teams double down on what works to improve visibility in the moments when decisions are being shaped.

It’s not that people are no longer clicking. They’re just clicking at later stages in the journey, and with far stronger intent. In fact, current industry research shows that visits from AI-search environments are not just matching but often exceeding traditional conversion rates, reinforcing a simple truth: quality matters more than volume. The priority now is to understand user actions and guide people toward high-value outcomes, whether that is a subscription, an inquiry, a demo request, a purchase, or other meaningful engagement.

This shift applies not only to commercial sites but also to news and information publishers, where visiblity in AI answers can shape deeper forms of engagement such as read-depth, recirculation, or return visits rather than immediate clicks.

The objective for publishers is to align with user intent and deliver clarity, credibility, and value at every stage of discovery, so that when someone is ready to act, your brand or content becomes the natural choice.

How the Path to Conversion has Changed

Before AI search, a typical customer might start with a broad query like “best dishwashers for small spaces,” then scroll through pages of blue links, piecing together details from reviews, spec sheets, and reseller sites. It was a manual, time-consuming process.

Today, AI-powered search experiences understand intent and surface richer, more relevant results instantly.  Instead of sending users through multiple clicks and sources, the system embeds high-quality content within answers, summaries, and citations, highlighting key details like energy efficiency, noise level, and smart home compatibility. This creates clarity faster and builds confidence earlier in the journey, leading to stronger engagement with less friction.

In the dishwasher example, the customer quickly realizes that a quiet, energy-efficient model with smart features best suits their open-concept kitchen. They refine their query and AI search responds with a more detailed summary drawn from brand sites, reviews, and resellers, surfacing purchase options and rebates alongside recommendations with more relevant links. 

In fewer steps than ever, the customer reaches a confident decision, guided by intent-aligned, multi-source content that reflects brand and third-party perspectives. This behavior shift, where discovery, research, and decision happen continuously within one environment, is redefining how site owners understand conversion. 

With journeys becoming shorter and more connected, content clarity is critical. When information is not easily interpreted by LLMs, key details may be missed, reducing opportunities to appear in influential moments.

As these shifts play out, the signals generated inside AI experiences become even more valuable, helping content teams see what resonates and where improvements can strengthen visibility.

How AI Search Understands and Responds to Evolving Intent 

AI systems refine their understanding of user intent across each follow-up question, chat turn, and contextual signal, including proactive moments where the system surfaces relevant content based on context or inferred needs.

 According to Microsoft internal research, data from Microsoft Copilot sessions show a 22% increase in unique chat turns per session¹, reflecting that search has evolved into an ongoing conversation rather than a single query. Each follow-up clarifies intent and brings the user closer to conversion, whether that means clicking a product link, making a purchase, or taking another high-value action.

Microsoft Advertising reports that Copilot-assisted customer journeys are 33% shorter on average than traditional search, and high intent conversion rates (users showing clear readiness to act) are 76% higher for AI-powered experiences compared to traditional search surfaces².

For marketers, publishers, and SEOs, this means moving beyond traditional metrics to track new engagement signals that reflect influence, trust, and readiness to convert.  

AI-powered search builds on retrieval to generate contextual, conversational answers. Structured content, like schema-marked product pages, FAQs, and comparison tables, helps AI systems interpret and summarize your content more effectively. This increases the chances of your being cited, clicked, and engaged with in multi-source answers.

The New Conversion Journey in AI Search 

As AI-powered search reshapes how people explore information, more of the journey now happens inside the experience itself.

 Across the industry, AI-powered search is changing how users interact with search results. Users now spend more of the journey inside AI experiences, shaping visibility and engagement in new ways. As a result, engagement is shifting upstream (pre-click) within summaries, comparisons, and conversational refinements, rather than through multiple outbound clicks.

 Zero-click visibility is one clear example of this change. When structured content such as FAQs, product specs, or schema-marked reviews appear in AI summaries or side-by-side comparisons, it can help shape brand awareness and preference before a user ever clicks through. A well-structured product page might have its key features surfaced in an AI-generated summary, while a retailer’s support content could appear in an answer box explaining setup or compatibility. 

 But zero-click outcomes are only part of the picture. In AI search, users also refine and narrow intent within a single session, asking follow-up questions, comparing brands, or filtering by features like “quietest dishwasher with smart home integration.” Each interaction creates engagement that moves the user closer to purchase, subscription, signup, or other meaningful action, even if the final action happens later or on another device.

The strategic challenge is that many of these signals, including visibility in summaries, cited content, or follow-up query engagement, are not captured in traditional analytics as it exists today. Yet they strongly influence trust, familiarity, and downstream conversions (on-site actions). 

For marketers, visibility itself is becoming a form of currency. If your content is surfaced in summaries or comparisons, you’re shaping preference before a click ever happens. 

Does AI Search Traffic Convert?  

The latest research from multiple independent sources points to the same trend: AI-search visits may be smaller in volume but are producing more qualified traffic and higher conversion rates than traditional organic search.

Amsive found that 56% of sites saw higher conversions from AI-driven sessions, with high-traffic sites converting at 7.05% compared to 5.81% for organic³. Similarweb reported a similar pattern, with AI referrals converting at 11.4% versus 5.3% for organic across global ecommerce⁴.

Additional industry data supports the same direction. Adobe Digital Insights shows AI-referred retail conversions improving steadily year-over-year⁵, while Microsoft Advertising reports that Copilot-powered journeys are 33% shorter and 76% more likely to lead to lower-funnel conversions⁶. And though BrightEdge notes that AI search still represents less than 1% of referral traffic, each visit carries notably higher intent and stronger engagement⁷.

Microsoft Clarity’s latest platform data confirms this pattern. Analyzing traffic across 1,200 publisher and news sites, Clarity reports that AI-driven referrals grew 155% over eight months and converted at up to three times the rate of traditional channels such as search and social. Even though AI referrals still represent under 1% of total visits, these users consistently display higher intent and engagement, signing up, subscribing, or purchasing at far greater rates8. Clarity now tracks AI referrals directly, distinguishing between organic AI platform visits and paid AI placements to help publishers see where their content performs best within AI experiences.

These findings suggest that AI search is not replacing organic traffic but improving the quality and outcome of each visit. Users arrive with clearer intent, encounter content that meets their needs faster, and move through the conversion path in fewer steps.

For marketers, publishers, and content owners this shift means success should be measured not just by visit volume but by quality of engagement, visibility, and downstream conversion readiness.

As more of the journey happens upstream inside AI experiences, understanding those early signals has become essential. Brands need visibility into where content is being surfaced, how users interact with it, and how those interactions translate into on-site behavior.

What This Means for News and Information Publishers

News and information publishers often measure value differently than commercial sites. Rather than focusing on product-driven actions, success may depend on signals such as read depth, article completion, returning reader patterns, recirculation into related stories, and newsletter sign-ups or registrations.

AI search can surface authoritative reporting earlier in the journey, bringing in readers who are more inclined to engage deeply with coverage or return for follow-up stories. As these upstream interactions grow, publishers benefit from visibility into how their work appears across AI answers, summaries, and comparisons, even when user journeys are shorter or involve fewer clicks.

Connecting Visibility to Real Engagement

Bing Webmaster Tools continues to evolve to help site owners, publishers, and SEOs understand how content is discovered and where it appears across traditional search results and emerging AI-driven experiences. Paired with Microsoft Clarity’s AI referral insights, these tools connect upstream visibility with on-site behavior, helping teams see how discovery inside summaries, answers, and comparisons translates into real engagement. As user journeys shift toward more conversational, zero-UI-style interactions, these combined signals give a clearer view of influence, readiness, and conversion potential.

Rethinking Success Metrics in the Age of AI Search 

AI search is changing not only how users discover content but also how and when they demonstrate intent to act.  Conversion is now a distributed journey influenced by every touchpoint, from summaries and comparisons to high-intent clicks. 

Success depends on connecting traditional key performance indicators (KPIs) with emerging AI search visibility signals. Rather than only tracking clicks or last-touch conversions, brands and content owners should align their analytics with signals like impressions, citations, query refinements, and answer inclusion that reflect how influence builds throughout the journey.  

Ultimately, AI search provides new opportunities to connect more directly with user intent and deliver solutions that feel immediate, relevant, and personalized, whether the goal is commercial or informational. 

 Just as the dishwasher shopper formed confidence through AI-generated summaries, refinements, and comparisons before ever clicking, today’s conversions are increasingly shaped upstream inside the AI experience.  Those early evaluation moments, before a click ever happens, are where preference forms and intent solidifies.

To adapt, marketers should define which new visibility and engagement signals matter most for their business and map them to existing performance metrics. Tools like Bing Webmaster Tools and Microsoft Clarity can help surface insights into how users engage with your site, navigate to key content, and interact after discovery. By optimizing for intent, structure, and responsiveness, site owners can better capture and act on the most meaningful results that show how visibility and engagement now drive performance in AI search.

Fabrice Canel and Krishna Madhavan 

Principal Product Managers 

Microsoft Bing 

To stay updated on the latest AI search strategies, continue to follow the Bing Webmaster Blog.   

1. Microsoft. (2024). First-Party Data – Copilot Chat Turns (Aug 2023–Aug 2024). Internal Microsoft dataset, cited in The New Search Advertising Landscape: How to Win When AI Is Changing Everything. https://about.ads.microsoft.com/en/resources/discover/insights/the-new-search-advertising-landscape-how-to-win-when-ai-is-changing-everything
2. Microsoft. (2025). 73% Higher CTRs and 33% Shorter Journeys in Copilot Search. Microsoft Advertising Blog. https://about.ads.microsoft.com/en/blog/post/august-2025/73-higher-ctrs-why-advertisers-need-to-pay-attention-to-conversational-ai
3. Amsive. (2025). LLM-Driven Traffic Performance vs. Organic Sessions. https://www.amsive.com/insights/marketing-strategy/ai-search-conversion-performance/
4. Similarweb. (2025). Global Ecommerce Report: Growth Shifts to Apps and AI. https://ir.similarweb.com/news-events/press-releases/detail/132/similarwebs-3rd-annual-global-ecommerce-report-growth-shifts-to-apps-and-ai
5. Adobe Digital Insights. (2025). AI-Driven Traffic Surges Ahead in Q2. https://business.adobe.com/blog/ai-driven-traffic-surges-ahead-in-q2
6. Microsoft. (2025). Copilot Conversion Rates Higher for Lower-Funnel Ad Types. Microsoft Advertising Blog. https://about.ads.microsoft.com/en/blog/post/april-2025/psst-the-valuable-consumers-are-over-at-microsoft-medias-ai-powered-experiences
7. BrightEdge. (2025). AI Search Visits Surging in 2025. AI Search Visits Surging in 2025—But Organic Search Remains the Cornerstone of Digital Growth. https://www.brightedge.com/resources/research-reports/ai-search-visits-in-surging-2025
8. Microsoft Clarity. (2025). AI Traffic Converts at 3× the Rate of Other Channels [Study]. https://clarity.microsoft.com/blog/ai-traffic-converts-at-3x-the-rate-of-other-channels-study/

]]> https://seonews.info/how-to-monitor-your-youtube-video-mentions/ Thu, 20 Nov 2025 15:48:09 +0000 https://seonews.info/how-to-monitor-your-youtube-video-mentions/

Your brand is being talked about on YouTube right now—and you probably have no idea what’s being said.

YouTube is the second-largest search engine, where users spend roughly 50 minutes daily (and growing).

It’s also the 2nd most cited source by LLMs, meaning video mentions don’t just reach human audiences—they teach AI how to recommend your brand.

YouTube creators are shaping brand narratives in front of millions of viewers.

Take Canva: it picked up around 460 YouTube mentions this month (according to the YouTube report in Ahrefs Brand Radar).

That might not sound like much, until you realize those 460 mentions translate to 3.4 million views.

If you’re not monitoring your YouTube mentions, you’re leaving your reputation in someone else’s hands.

YouTube mentions are any instance where your brand name appears in a channel name, title, description, transcript, or link.

There are two distinct types:

Tagged mentions

Tagged mentions work like social media tags (e.g. @channelname). They link to your channel, and trigger notifications in YouTube whenever you’re mentioned in titles, descriptions, or comments.

One thing to remember about tagged mentions is that the creator has to intentionally tag you—they aren’t just casual passing mentions.

Untagged mentions

Untagged mentions, on the other hand, are organic mentions. They don’t link back to your channel and you won’t get notified when you land one. Untagged mentions can appear anywhere: in channel names, thumbnails, titles, descriptions, transcripts, and links.

YouTube mentions can take the form of:

• References to your product/service even without using the exact brand name
• Comparisons or reviews that feature your brand alongside others
• Casual name-drops in tutorials, vlogs, or commentary content

You can monitor mentions in tagged posts natively within YouTube Studio.

Just head to “Community” in the menu on the left, and look for the “Mentions” tab on the dashboard.

But for untagged mentions, you’ll need a social media monitoring tool.

Brand Radar’s YouTube monitoring tool does exactly this—tracking your brand across ~15M videos (and growing) by scanning titles, descriptions, and transcripts.

In this guide, I’ll walk you through monitoring YouTube mentions with this tool, using real examples from Ahrefs.

Track brand name variations

Check transcripts to find spelling variations based on how your brand name is pronounced. Luckily for us, we have a video on “how to pronounce Ahrefs”, where our global community of users have shared how they say our brand name. I mined the transcript, and found several “Ahrefs” variations (like “Arafs” and “Ah rifs”), to help me track down more YouTube mentions of our brand.

If your brand name is also tricky to pronounce, think about tracking for mentions of your brand’s features, products, or services.

For example, “Keyword difficulty” and “Domain rating” are both uniquely developed Ahrefs scores, so we might want to think about tracking those to get a better sense of where our brand is referenced.

You can save these variations by hitting the “Save report” button on the main Brand Radar dashboard:

In return, you’ll get a monthly view of your YouTube brand mentions, which you can track periodically to assess growth or decline.

Beneath that, you’ll see a list of videos organically mentioning your brand, ordered by “relevance”.

Results that are considered “relevant” are ranked based on a combination of mention frequency and view count.

Then I’ve ruled out certain elements our partners typically include in their video descriptions, like partner links containing yt., or specific products that we’ve been working with them to promote like awt (i.e. Ahrefs Webmaster Tools).

That filter takes us from 1,482 results to 229 word of mouth mentions. But we’re not done yet.

Some of these mentions are showcasing alternatives to Ahrefs, or pitting us against our competitors.

For now, we want to focus on UGC that is centered around our brand—not our competitors.

So, we need to add a Title > doesn’t contain > partial match > [competitor name]. This leaves us with 201 mentions.

If you’ve followed along so far, you can continue filtering even further to see which videos your brand is the primary focus of (e.g. Title > contains > partial match > [brand name])

Versus the videos that mention it in passing (e.g. Title > Doesn’t contain > partial match > [brand name])

This kind of analysis can show you how customers actually use your products and services.

For example, the video below shows that people particularly use our tool Brand Radar for AI competitor gap analysis…

While the next video shows that some users would like to see more actionable recommendations within the platform…

You can use these kinds of insights to shape your content, and pass them over to the product team to inform your roadmap.

There are a ton of benefits to monitoring influencer mentions:

• Find creator fans: Track down influencers already mentioning your brand organically, then send them early access to features or paid partnership offers they’re likely to accept
• Assess audience fit: Before reaching out, see if their viewers match your target market, so you only pitch to channels where your product actually resonates
• Prioritize high-impact creators: Sort by view counts and engagement, then allocate your sponsorship budget to the top performers
• Tailor your pitch: Pull specific quotes like “I love how [Brand] handles X,” then open your email with “Saw you praised our X feature in your recent video—interested in test-driving the upgrade?”
• Save time and budget: Build a shortlist of pre-vetted creators who actually align with your brand, instead of cold-pitching dozens and hoping something sticks

How to find relevant influencers to sponsor

Find creators worth partnering with by sorting organic mentions based on your goals, then zero in on videos and audiences that actually align with your brand.

Start with the report you’ve already configured to find organic word-of-mouth mentions (i.e. the one that filters out your brand channels and the partners you already sponsor).

The aim is to discover the most visible creators, with audiences that are relevant to your brand.

As I said earlier, your YouTube mentions are sorted by “relevance”—which is a combination of mention frequency and view count by default. 

That means you should be able to find quality influencer mentions right off the bat.

But some great opportunities may be trickier to track down.

For instance, you might land just one mention in a highly visible video that ends up being hyper-relevant.

That’s why it’s important to test out different sorting options—e.g. order “By views” vs. “By relevance”.

Then, when you come across a creator that looks interesting, you can read the transcript and watch the video to better understand the context of your brand mention.

Here’s an example of what I mean.

I set the chart in Brand Radar’s YouTube report to Impressions, and found one mention that bumped up our brand awareness by 115% overnight.

I tracked it down in the list view (by sorting “By views”), then clicked the transcript timecode to watch the video and understand the mention context.

Ultimately, the creator briefly recommended Ahrefs as a long-tail keyword tool for solopreneurs looking to start SEO-monetized blogs.

Understanding the context helps you get a better idea of whether an influencer is worth sponsoring. You can quickly assess:

• Is this an audience you want to reach?
• Is the recommended product/service valuable to your business?
• Does the channel or video topic align with your marketing goals?

In this example, the audience probably isn’t right for Ahrefs. The video primarily targets beginners who are more likely to be interested in our free keyword tools.

Our priority, on the other hand, is promoting our more advanced tools to mature SEOs and marketers in medium-sized to enterprise businesses.

Which brings me on to the next video…

While it has fewer views, it features Ahrefs in workflows that appeal to our actual target audience: mature marketers in corporate brands.

It also covers a topic that is a big product focus for us right now: AI.

Plus the creator shows off a use case that could be even better handled by our new ChatGPT SEO tool: Ahrefs MCP.

Diving even deeper, I can see the creator has ~98K subscribers, and that her channel aligns with our topic focus: Digital marketing, AI strategy, professional growth, tech & business.

So that’s an:

• Audience match
• Product match
• Marketing/topic match

To me, this looks like a much better partnership opportunity.

If you want to cast an even wider net, you can repeat this whole process, but remove your own brand mentions from the equation, and instead filter by the market/industry you’re aiming to get mentioned in.

For example, here’s a detailed filter I built to find relevant creators in the “AI marketing space”.

Use the filters to sort through your mentions until you find creators worth partnering with.

Before people open their wallets, they’re on YouTube watching reviews and checking if your product or service actually solves their problem.¹

These mentions reveal what draws buyers to your brand, or sends them to a competitor.

Tracking bottom-of-funnel YouTube mentions helps you:

• Address common objections: Spot recurring concerns in reviews, then publish blog posts, update your FAQ page, or ship product fixes that directly counter them
• Double down on differentiators: When creators say one of your features beats your competitors, spotlight it on your homepage, product pages, paid ads, and sales decks
• Train your sales team: Give sales reps responses to the exact objections, questions, and problems appearing in these videos
• Mine testimonials: Find authentic customer stories in video mentions, then screenshot quotes for your website, repurpose clips for social proof ads, or build case studies around them
• Sponsor strategic comparisons: Find creators who position you favorably against competitors, then reach out with sponsorship offers or new talking points to reinforce
• Correct misinformation: Catch inaccurate comparisons in videos, email the creator with accurate info, or offer to collaborate on an updated video
• Improve feature priorities: See which competitor features consistently win over buyers, then bump similar capabilities up your product roadmap or sunset features nobody mentions
• Optimize comparison pages: Note what buyers actually care about in video comparisons, then rewrite your “Brand vs Competitor” landing pages to lead with those exact pain points and features

How to find high purchase intent mentions on YouTube

The easiest way to find mentions of your brand in buyer-focused content is to search for comparison videos featuring your brand and competitors.

Keep your earlier “doesn’t contain” filters active (i.e. the ones excluding your own brand channel and partners), then reintroduce the competitors that you filtered out.

In this example, I’ve simply removed the Title > doesn’t contain > partial match > [competitor name] filter.

I’ve also excluded mentions of the word “free” in the video title, just so I can focus on creators reviewing our paid tools.

Now all of our competitors are back in the game.

If you have a larger brand with more mentions, you can add extra keyword modifiers to zero in on comparison content—e.g.

• versus/vs
• alternative
• compares/compared/comparison
• review
• pros and cons
• worth it
• best
• top

This wasn’t necessary for us, as our most popular mentions tend to come from head-to-head reviews, so they’re pretty easy to find.

Analyze your video mentions in NotebookLM

Take your YouTube video mentions over to NotebookLM if you don’t have time to watch every review. You can get a super quick summary, gauge the sentiment of your mention, and decide whether you need to actually do something about it.

After setting up my filters, I immediately found two reviews pitting Ahrefs against Semrush.

The first came from “Marketing Island”, published back in May 2024. To date, it’s been viewed 10.2K times.

The second was from “Jaume Ros”, published back in August 2024 and viewed 3.4K times.

When you run them through NotebookLM, it becomes clear one recommends Semrush, and the other Ahrefs.

But in both cases, the creators review Ahrefs negatively due to our old “credits” based payment system, and the fact that we lack a broader range of marketing tools.

Our platform and pricing has changed a lot since these videos were created.

We’ve done away with credits, and added much more versatile marketing features like Brand Radar for AI monitoring, Social Media Manager for scheduling social posts, and Web Analytics—our GA4 alternative for analyzing all-site traffic.

One action we might take here is to reach out to these creators, update them on Ahrefs’ product developments, and ask if they’d be interested in filming new reviews.

This way we can improve the accuracy of the information surrounding our brand, and hopefully encourage more conversions.

Sponsored content often represents a big chunk of marketing investment.

Tracking your sponsored mentions helps you confirm whether content went live and measure which partnerships drove the best engagement.

Specifically, you can:

• Check deliverables: Confirm creators actually published the content they were paid for, and follow up on any missing or delayed content
• Assess the quality of the mention: Evaluate how authentically and prominently they featured your brand—did they mention you in the title and thumbnail or just once at the 8-minute mark in a 20-minute video?
• Track longevity: See if sponsored videos continue driving mentions over time or fade quickly. For example, if a video still generates mentions six months down the line, that creator delivered lasting value and is worth working with again
• Competitive intelligence: Track which creators your competitors are sponsoring, then learn from their strategy. Does it seem to be working? Could you go after similar influencers? Or differentiate your brand with distinct niche influencers?

How to analyze the YouTube mentions you sponsor

Once you’ve secured sponsorships, use Brand Radar to monitor them and make sure your budget is going to good use.

Remember all of the “does not contain” parameters you set up earlier (to filter out sponsored mentions)?

Switch them over to “contains”.

Make sure you’ve accounted for all the phrases or links you’ve asked your sponsors to include in their descriptions.

This will help you keep track on output, assess the relevance of your sponsored mentions, and compare the engagement generated by different partners.

You can also study how the partners of your competitors highlight their sponsor relationships (e.g. semrush encourages creators to promote their free trial).

Then use those findings to build out a query, so you can track rival sponsorships.

This kind of setup will help you find your competitors’ top creators and swoop in with better offers, or spot niches they’re ignoring, then own those spaces before they catch on.

YouTube is the 2nd most-cited source by LLMs—so video mentions directly influence how AI assistants position your brand.

Here’s what you gain from monitoring AI-cited content:

• Spot AI-driving mentions: See which video mentions lead to AI citations. For example, if a tutorial from “MarketingPro” generated 180 ChatGPT citations while your other sponsored video landed <20, prioritize “MarketingPro” for your next campaign—or replicate their tutorial format in your owned content
• Understand AI preferences: Learn which topics, formats, or creators AI favors most. Say AI cites 10-minute tutorial videos 5x more than 2-minute product demos, then you need to shift your video strategy to prioritize longer, educational formats
• Find the best AI partners: Target creators whose content is frequently cited in AI. For instance, if “TechReviewer101” appears in 70% of relevant AI responses, add them to your top-tier sponsorship list and consider allocating more budget to that partnership
• Create AI-led video content: Produce more of the video types AI references. For example, if AI consistently cites “How to do X with [Your Tool]” videos, script and produce five tutorial videos with that exact title format
• Measure dual impact: Track how YouTube presence boosts AI visibility. You can do this by monitoring your total AI citations post-sponsorship. If visibility jumps from 200 to 500 mentions, for example, then your partner is worth working with again

How to see whether your YouTube mentions drive up AI mentions

Whenever you find a mention of your brand in YouTube, drop the video URL into Brand Radar.

This will show you which of your YouTube mentions are also responsible for AI visibility across seven different AI assistants.

For instance, while analyzing how Ahrefs appears in recent “best tool” videos, I spotted this LearnWire mention:

I took the URL over to the Brand Radar dashboard, and dropped it into the “URL > contains” filter:

As it happens, that one video triggered 254 mentions of Ahrefs in AI Overviews and AI Mode.

You can scale this analysis to find out which topics, formats, and creators AI cites most often—then run the same check on competitor mentions.

Then it’s just a case of producing and sponsoring more of that content.

Wrapping up

Start with monthly brand monitoring in Ahrefs Brand Radar to see where you stand.

• AIOs reduce clicks to websites, which increases the value of being mentioned inside the answer box.
• These mentions reinforce topical authority and brand reputation.
• People trust what they see in AIOs, often before they scroll.

The importance of brand in AI-powered search

• Increase visibility
• Strengthen credibility
• Reinforce expertise
• Feed the signals AI systems use to determine authority

Impact on SEO Strategy

• Clear headers
• Skimmable sections
• Bullet lists
• Question and answer formatting
• High-value keywords used naturally

AI-powered Search and SEO: Building Brand Visibility beyond Blue Links

• Contextual relevance
• Brand visibility
• Webwide credibility
• Authoritative content ecosystems

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When Everyone’s Competing for Clicks, Email Quietly Wins

Turning Holiday Traffic into Conversions with Automation

The Essential Automations

• Welcome Flow: Reward new subscribers with a discount or exclusive offer. This first impression often determines whether they will open future emails.
• Browse Abandonment: Follow up with users who viewed products but did not add them to cart. Include recently viewed items, complementary products, or limited-time messages that re-spark interest.
• Cart Abandonment: Recover high-intent shoppers with a timely reminder, social proof, or free-shipping incentive. Even a single recovery per day can add significant revenue during the holiday rush.
• Post-Purchase Flow: Keep recent buyers engaged with recommendations or gift guides. Repeat purchases skyrocket when customers receive relevant, personalized follow-ups.

Best Practices for Holiday Email Personalization

1. Segment by intent and behavior. Send different messages to first-time visitors, repeat customers, and loyalty members.
2. Use dynamic content. Pull in recently viewed products, personalized gift recommendations, or location-based shipping reminders.
3. Keep branding consistent. Use branded templates, colors, and tone that match your website and social presence. The more cohesive your customer experience, the more trust you build.
4. Test and optimize. A/B test subject lines, incentives, and layouts early in Q4 so your highest-performing versions are ready for Cyber Week.

The ROI Math: Why Email Beats Every Other Channel

Avoiding the Most Common Email Mistakes

• No warm-up strategy. If you have not emailed your list recently, reintroduce engagement gradually to avoid deliverability issues.
• Ignoring mobile users. Over 70% of holiday emails are opened on mobile devices. Optimize your templates for smaller screens and faster load times.
• Over-discounting. Not every email needs a coupon. Rotate educational content, gift guides, and social proof to balance your offers.
• Sending too late. Many consumers start holiday shopping earlier each year. Launch your holiday automations by early November to maximize exposure.

How SMS Strengthens Your Holiday Email Strategy

Before You Increase Ad Spend, Strengthen Your Email Foundation