Most brands still don’t have a clear picture of how much traffic AI Overviews are sending them. Google hasn’t given us a clean signal for AI Overview traffic in Search Console, making it difficult to know how much organic traffic is coming from AI Overviews, which content is driving it, and how accurately that traffic is being reported.
So we built our own tracking.
Since September 2025, I’ve been capturing AI Overview referral data for one of the brands I manage in the transportation industry. From September 2025 to June 2026, we recorded 51,200 tracked events across 1,661 cited snippets.
The data shows that AI Overview prominence in the SERP isn’t stable. It also reveals clear patterns in the types of content Google cites, how those citations change over time, and how much of that traffic gets misattributed in analytics.
Here’s what nine months of tracking revealed — and what it means for SEO reporting, content prioritization, and GEO.
The tracking setup — and why it works
The methodology is straightforward, but most teams haven’t implemented it.
When a user clicks on a cited snippet inside a Google AI Overview, Google sometimes appends a #:~:text= fragment to the destination URL. We created a custom dimension in GA4 that fires whenever a session lands with that fragment present.
It’s not a perfect signal, but it’s the most reliable one available without waiting for Google to expose this natively in Search Console. The fragment is already sitting in your GA4 data. You just need to surface it.
Once we had it captured, we grouped snippets into thematic categories and started analyzing patterns. A few things stood out immediately.
Concentration is high: The top-performing snippet alone drove 2,276 events. The average across all 1,661 snippets is 31. Like most things in SEO, a small number of pages are doing the bulk of the work.
Snippets have lifecycles: Some peak during a specific period and then fade — usually when query intent shifts seasonally or when the content becomes stale relative to what Google is now preferring to cite. Others emerge months after publication and keep climbing. This makes freshness and specificity more important than you’d initially assume.
Content prioritization gets sharper: The most useful output of having this data is that it makes decisions more grounded. When you map snippet volume and growth trajectory against your current content investment, the gaps become visible.
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Certain topic types got cited
In our case:
Transfer time content and pricing content are already being cited frequently with clear upward momentum. The play there is to refresh and expand the existing content.
Destination guides are underperforming relative to what the data suggests they could be driving.
Structured transport comparison tables, formatted as actual HTML tables, are punching well above their weight in terms of citation frequency — which tells you something about how AI Overviews are selecting content to surface.
The broader pattern is consistent with what most people working in GEO are finding: AI Overview citations favor content that is specific, structured, and directly answers a well-defined question. Times, prices, named routes, comparison formats. Not vague editorial.
22.4% of AI Overview traffic is being misattributed to Direct
This was the most striking finding.
When we started plotting the dataset on a GA4 exploration report, we noticed that a meaningful portion of traffic arriving via AI Overviews was being attributed to the Direct channel rather than Organic Search.
We know AI Overviews only exist within Google Search. Traffic from there should be attributed to organic. So we built misattribution tracking into our AI Overview dashboard and started measuring it on a monthly and weekly basis.
Across the full dataset — more than 50,000 AI Overview events over nine months — the average misattribution rate was 22.4%.
That’s 11,468 AI Overview events attributed to Direct instead of Organic. The range varied by month:
Worst month: May 2026 – 29.3% of AI Overview sessions were misattributed to Direct.
Best month: April 2026 – 16.8% were misattributed.
The implications for SEO reporting are significant. Depending on your total organic traffic volume and how much AI Overview referral traffic you’re generating, you could be materially underreporting organic performance without realizing it.
AI Overviews are driving 7.53% of organic sessions — but it’s volatile
Using the same first-party dataset, we built a percentage metric to track what share of our total organic sessions came from AI Overviews each period.
From September 2025 to June 2026, 7.53% of organic sessions came from AI Overviews.
But the distribution is not flat:
At its peak in February-March 2026, it reached 16-17% — meaning nearly one in six organic visitors arrived via an AI Overview.
More recently (as of writing), it has trended down to around 2-4%.
That volatility is notable. It suggests AI Overview prominence in the SERP is not stable — it fluctuates based on query type, Google’s confidence in available content, and possibly broader algorithmic changes. Brands treating AI Overview traffic as a fixed percentage of organic are likely miscalibrating their models.
The caveats you should know
Two important limitations with this approach:
The #:~:text= identifier isn’t exclusive to AI Overviews
The same fragment is used by Featured Snippets and People Also Ask results. So some portion of what we’re capturing may bleed from those formats.
We did run the exercise in Ahrefs to check our Featured Snippet exposure, and at the time of checking, we were included in fewer than 30 — largely because we don’t target terms that typically trigger Featured Snippets.
In practice, we believe AI Overviews are the dominant driver given the volume and the period of data collection, but it’s not a perfectly clean signal.
The metric compares events to sessions
Our custom dimension in GA4 is event-scoped, not session-scoped. The AI Overview share percentage therefore compares events against sessions, which isn’t an ideal comparison.
It’s better than nothing — and it’s directionally accurate — but worth knowing if you replicate this.
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What this means practically
If you’re not tracking this yet, start. The setup isn’t complex, and the signal — imperfect as it is — is the best first-party data you’ll get until Google decides to surface this natively.
A few things to take away from nine months of data:
Structured, specific content wins. Times, prices, routes, named comparisons. Not editorial padding.
Freshness matters more than you think. Snippets have lifecycles. Stale content loses citation share.
Your organic traffic is likely underreported. If ~22% of AI Overview events are going to Direct, your channel reporting has a systematic gap.
The share is volatile. Don’t treat a peak month as a new baseline.
The #:~:text= fragment is sitting there in your GA4 data right now. You just need to surface it.
Special thanks to Simant Sah for helping collate the data and put together the report where the screenshots come from.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/08/image-61-WW0DkN.png?fit=2048%2C812&ssl=18122048Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-08-18 15:00:002026-08-18 15:00:00What 9 months of AI Overview data and 51,000+ tracked events reveal
Microsoft is giving advertisers a new server-side option for sending conversion data directly to its advertising platform, reducing their dependence on browser-based tracking.
What’s happening. Microsoft Advertising has published new documentation for its Conversions API (CAPI), a server-side measurement solution currently in beta.
CAPI lets advertisers send conversion and customer interaction data directly from their own systems to Microsoft Advertising through a single connection.
The details. Microsoft says the Conversions API can support multiple measurement scenarios, including online and offline events.
The potential benefits include:
Improving conversion measurement accuracy.
Increasing attribution coverage.
Sending both online and offline conversion events.
Reducing reliance on browser-only tracking methods.
Supporting multiple measurement use cases through one server-side integration.
How it works. Microsoft recommends using CAPI alongside Universal Event Tracking (UET) rather than treating it as a replacement.
UET continues to capture activity in the browser, while CAPI provides a server-to-server connection for sending event data directly to Microsoft Advertising.
Using the two together can give advertisers more complete measurement coverage when browser signals are unavailable or limited.
Why we care. CAPI gives Microsoft advertisers another way to preserve conversion signals and connect online advertising with actions that happen outside the browser, including offline events. For advertisers already investing in server-side measurement across other ad platforms, Microsoft’s solution could also make it easier to build a more consistent measurement strategy across channels.
Yes, but. CAPI remains in beta and isn’t available to every Microsoft Advertising account yet.
Advertisers will also need the technical resources to establish and maintain a server-side integration, making implementation more involved than simply installing a browser tag.
The big picture. Microsoft is following the broader advertising industry’s shift toward combining browser and server-side signals rather than relying exclusively on tracking pixels.
The new documentation shows that server-side measurement is moving from an advanced implementation option toward a standard part of advertisers’ measurement stacks.
Google is giving advertisers clearer rules for measuring whether YouTube and Demand Gen ads prompt people to search for their brands — while making several notable changes from the metric’s original rollout.
What’s happening. Google has updated its documentation for Branded Searches, a conversion type that measures when someone searches for an advertiser’s brand on Google or YouTube after seeing an ad.
What’s changed is how Google now defines its availability, attribution and reporting.
The details: Google’s updated guidance introduces or clarifies several important points:
7-day default window: Branded Searches now uses a seven-day default conversion window, compared with the 30-day view-through window described when the feature originally launched. Advertisers can adjust the window between one and 30 days.
YouTube and Demand Gen: Google’s current documentation lists YouTube and Demand Gen as eligible campaign types. Performance Max, which was included when Branded Searches was announced in 2025, is no longer listed.
Consideration goal: Google now formally categorizes Branded Searches under the Consideration goal.
Reporting, not bidding: Branded Searches is treated as a primary conversion action but isn’t available as a bidding optimization goal. The data appears in Results and All Conversions rather than the standard Conversions column.
Brand mapping still matters: Advertisers don’t need to set up a Search Lift experiment, but Google says brand mapping must be configured for measurement to work.
Google’s documentation says Branded Searches can be viewed at the campaign, ad group and asset levels, as well as through Report Editor.
Why we care. Branded search behavior can provide advertisers with a useful signal between an ad impression and a traditional conversion.
Someone may see a YouTube ad, remember the company and search for the brand days later without clicking the original ad. Branded Searches is designed to make that influence more visible inside Google Ads.
That can help advertisers assess whether upper-funnel campaigns are generating genuine brand interest instead of evaluating them solely on clicks and direct conversions.
The bottom line. This isn’t a new conversion type or a new seven-day attribution window. The noteworthy update is that Google’s current Branded Searches documentation lists YouTube and Demand Gen — but no longer Performance Max.
First spotted. This update was spotted by Hana Kobzova, founder of PPC News Feed.
For the entirety of Google Search Console’s life, it has spoken one simple language: your website. Pages, queries, clicks, impressions, all tied to a domain you own and verify.
On July 7, that changed. Google shipped Platform properties, a new property type that lets you verify a social or video account and get the same first-party search performance data GSC has always given website owners. Think clicks, impressions, CTR, average position, and the actual queries behind them — just pointed at your social presence instead.
Worth noting up front: This covers Search, Discover, and News. So a TikTok clip or a YouTube Short can now be tracked across all three surfaces the same way you’d track a blog post.
The mechanics of the launch — which platforms are supported, how verification works, and how this differs from Google’s separate Search profiles feature — are already well covered.
However, one detail deserves repeating because it’s time-sensitive: there’s no historical backfill. Data collection starts the moment you verify. That means every week you spend deciding whether this is useful is a week of query data you can’t get back.
Let’s take a look at what this means for brands and SEOs, some examples of what it looks like in practice, and what it means for marketing going forward.
What GSC Platform properties actually changes for brands
The obvious win is a single first-party lens that gives brands access to both owned and earned content, instead of having to stitch together GA4 and native platform dashboards with a hefty dollop of guesswork.
But Google’s own guidance points at something more specific than better reporting. It points at a workflow. Three parts of it are worth knowing about:
Query groups: The Insights report clusters the search terms driving traffic into top, trending-up, and trending-down groups. That’s a real input for planning captions, hashtags and next topics. Ultimately, you’re working from demand that already exists rather than guessing at it.
The 24-hour filter: If a post starts picking up search traffic within a day of going live, you can catch it while it’s still live and act by cross-promoting to another platform or timing a follow-up.
Format comparisons: Using URL-based comparison filters, you can put Shorts against long-form, or Reels against static posts, and get an actual answer to “should we be putting more into short-form?”
These same three mechanics also change how you should work with creators. None of those three workflow parts are limited to owned content decisions, despite the signals coming from owned data.
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How GSC Platform properties can reshape creator partnerships
Smart brands could already be considering running them through a creator program to answer the questions brands have historically had to take on faith.
Query groups show you which conversations you already have traction in, but that also means, by omission, they tell you which conversations you don’t have a presence in. These insights become the foundations of an honest brief for creator activity.
Rather than simply picking creators based on reach and hoping the relevance follows, you can point to specific query territory you’re absent from and brief against it — developing your presence within new conversations. Alternatively, you could double down within a conversation by partnering with creators who can champion your brand, further developing your presence and preference.
This thinking reframes creator spend as buying coverage of conversations you don’t own yet or want to show up more within, rather than taking a risk on buying an audience you hope overlaps with yours.
Format comparisons can also feed straight into the brief itself. For example, if your own data shows Shorts consistently earning search visibility while long-form doesn’t for your category, that’s a concrete production instruction for a creator rather than a stylistic preference.
And the 24-hour filter gives campaign measurement a dimension it didn’t have before. During a live campaign, you can see whether creator content is earning genuine search visibility or just in-app impressions. This could provide a better read on whether the work is generating durable discovery or a spike that vanishes with feed changes.
There’s a neat symmetry here, too. This data lets creators show that their content genuinely ranks for specific searches, rather than simply racking up views. Platform properties give both sides of the negotiation the same evidence base and metrics.
Creator conversations that once centered on follower counts can now focus on which queries each party actually shows up for. That’s a far stronger basis for partnerships and can strengthen search marketing across the social-search landscape.
An example of how GSC Platform properties could work in the wild
Picture a hypothetical running shoe brand called Tiger Feet. They connect their Instagram and YouTube properties to the GSC Platform properties, and a few weeks later, the Performance report shows something nobody expected: A Reel about lacing techniques for wide feet is pulling steady clicks from Google for variations of “how to lace running shoes wide feet,” while the website has no page targeting that query.
That’s three decisions in one data point:
There’s proven demand: You’re seeing real-world results for a topic nobody on the Tiger Feet content team had previously flagged, and you know it’s real because people are searching it and landing on your content.
You’re capturing it in the wrong format for conversion. A Reel earns the click but sells nothing. A product-linked guide on your site, with the video embedded, could.
You now know what to write and roughly how to angle it. Because Platform properties give you the exact query language, rather than a keyword tool’s approximation of it, you have a road map to where to focus your content next.
Run the same logic in reverse, and it’s just as useful: If a social post is already ranking well for a query, that’s a case for not commissioning a page that could end up competing with it.
That’s the shift. Social content stops being measured purely on engagement and starts being measured on demand, which is a completely different question and a much more commercially useful one.
Platform properties give you first-party data, not competitive visibility
Platform properties only work on accounts you can verify, which means you get your own data and nothing else. No competitor view. No share of voice. No category benchmark.
Every other discipline in search has some form of competitive visibility. You can see who’s outranking you organically, estimate what competitors are bidding on, audit their backlinks, and track their positions.
Here, you get a perfectly clear view of your own performance and no visibility elsewhere — so this data should be combined with SERP analysis and manual research rather than relying on it in isolation.
What are the practical consequences of this limitation? It means:
You can’t tell good from great. If an article pulls 400 clicks from Google in a month, is that strong for your category or embarrassing? There’s no external reference point, so you’re benchmarking against your own history and nothing more.
You’ll spot demand, not competition: Query groups will tell you people are searching a topic and finding you. They won’t tell you that three competitors are already better positioned for it, or that the space is wide open. That still needs conventional keyword and SERP research alongside it.
Absence of data isn’t absence of opportunity: A query showing nothing in your reports might mean no demand, or it might mean healthy demand that’s all going to someone else. The report can’t distinguish between the two, and it’s an easy mistake to make.
The useful framing here isn’t that it’s a new report for SEOs to get lost within. The way to approach this development is as if it’s a new content inventory that behaves like your existing one.
That’s because Platform properties speak the vocabulary SEOs already work in, which means social content can easily fold into existing workflows instead of sitting in a silo owned by another team — further affording brands the opportunity to break down silos at a time when discovery of brands relies on collaboration across organic media strategies.
What can you do as an SEO with Platform properties? Turns out, a lot:
Gap and overlap analysis: You can check whether a query is already being answered by the brand’s own social content (or ask creator partners to share insights on their answers on your behalf) before commissioning a page for it. You can also spot cases where a page and an article are quietly competing and determine whether or not this is a good or bad thing.
Test captions and titles like meta titles: You can annotate the date that you rewrote a TikTok caption or YouTube title and then compare performance on either side of it. Same discipline you’d apply to standard optimizations.
Apply structural thinking off-site: Comparing things like playlist performance or formats within a platform allows you to think about grouping and cannibalization for content that doesn’t even live on your domain at all.
There are two caveats worth bringing up with clients before this lands in a report.
Reporting carries the usual Search Console delays, so this isn’t a real-time dashboard despite the 24-hour view.
Coverage will vary by platform and content type while the rollout matures, so early numbers need to be treated as directional rather than definitive.
Social strategy has traditionally sat with the social team and been judged on engagement. Platform properties give SEOs a legitimate reason to be in that conversation. The content is now visible in the same tool, in the same terms they already report in and understand, and that they can develop effective strategies around.
What GSC Platform properties change for search marketing, generally
If you step back and look at this new rollout, it confirms something Google has been building toward for years: The results page stopped being a list of blue links a long time ago.
Google’s own documentation shows exactly where this content appears — short-video carousels, “latest posts” carousels, and “what people are saying” SERP features — all within Search and Discover results.
Platform properties are Google handing marketers an instrument for a part of the SERP that was already there and simply going unmeasured. This points to three things worth thinking about:
The search universe is expanding, and the SEO/social divide keeps dissolving: When both are measured in the same tool with the same metrics, it gets harder to defend treating them as separate disciplines with separate owners and separate budgets.
Feed-based discovery is becoming as strategically important as query-based search: In terms of the role search plays in a brand’s discovery, that shift has been visible for a while, and this is the first proper measurement tool for it.
Google is now building on the assumption that search behavior is cross-platform: Google isn’t just tolerating the fact that people search for things they saw on TikTok anymore. Instead, it’s actively shipping insights that acknowledge that it’s happening.
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What you can do this week to take advantage of Platform properties
You should connect every active social account now. Don’t wait for a question that needs answering. With no backfill, any account you haven’t connected to is a question you won’t be able to answer later. Take advantage.
If you’re an SEO, rather than a social manager, get yourself into that reporting. Query groups and format comparisons are exactly the kind of data that should be shaping a content calendar. For most brands, that decision-making is happening without SEO as part of the conversation — the best brands will ensure that that changes.
I’ll admit that this isn’t a dramatic feature, on the face of it, although I, for one, am extremely excited about the potential this affords “search everywhere” strategy considerations. The new platform properties are Google quietly conceding that “search performance” was never really about the website. It was always about wherever people go looking for answers.
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Your brand could be ranking #1 on Google, but still be invisible to AI.
Absent from conversations your customers are having with large language models (LLMs) about your category.
Or worse, showing up inaccurately, with outdated or incorrect information that’ll hurt your sales.
Without prompt tracking, you’d never know.
Prompt tracking (sometimes called LLM visibility tracking) is the practice of monitoring how your brand shows up in AI answers over time, through mentions or citations.
It’s different from traditional SEO rank tracking, which tells you where your URLs appear on search engine results pages (SERPS) for specific keywords.
With rank tracking, you ask, “How close are we to position 1?”
But LLMs don’t answer questions with a static list of 10 blue links.
They pull from massive amounts of information to generate a unique response every time, tailored to the user and the context of the conversation.
You can even ask the same question twice and get two different answers.
In this search experience, it matters less whether your brand is mentioned first, and more that it says true positive things about you to the right people — consistently, across many runs of similar prompts.
But without a system to monitor it, you’re flying blind.
Prompt tracking gives you the directional intelligence to spot AI visibility gaps — the queries you’re consistently not showing up for — and close them.
This guide shows you exactly how. You’ll walk away with a free tracking template, a step-by-step system, and two real-world expert setups you can steal.
Free template: Download our prompt tracking spreadsheet to start understanding your brand’s AI visibility across LLMs ASAP.
Why Brands Need to Track Prompts
According to a study from Orbit Media, 55% of US internet users rely on AI as their primary or frequent research tool. Thirty-two percent use it for product recommendations.
Translation: A growing share of buyers are learning about you in AI tools. Without ever visiting your website.
Overall AI visibility scores tell you whether you’re showing up. Prompt tracking tells you where and how.
It can help you understand:
The types of questions you’re showing up for and where they fall on the customer journey (ToFu, MoFu, or BoFu?)
The questions your competitors are pushing you out of (and your share of voice on important topics)
The sentiment around your brand mentions
The questions you’re getting cited for, but not recommended (sometimes called ghost ranking)
This level of data helps you spot specific trends and gaps in your AI visibility over time.
They show up for prompts at all three stages of the funnel.
With prompt tracking, Gong can focus on conversations most likely to drive revenue and stop spending time and money tracking ones that won’t.
If their mention rate stays low on key BoFu topics over time, that’s a signal to update on-site or third-party content.
They can also see which relevant topics competitors are owning while they’re absent.
For example, Gong’s Engage product helps with lead generation.
But Salesforce and Hubspot consistently own these prompts.
This tells Gong two things:
Their audience isn’t aware of their lead generation use case
They need more content around it to train AI platforms to mention them
Sentiment prompts like ‘Is Gong worth the price?’ and prompts that surface ghost ranking can reveal similar trends and gaps.
The important thing is to track data over time.
AI answers are non-deterministic. The same prompt can return different brands across different runs.
Regular, repeated tracking is how you get meaningful signals you can act on.
When LLM Prompt Tracking Isn’t Worth It (and When It Is)
Prompt tracking isn’t for everyone.
Plenty of businesses spend time and budget on it and still walk away with data they can’t use.
Prompt tracking is NOT worth it when:
Your audience isn’t using AI to find solutions in your category
Your site isn’t set up to be crawled by AI
You don’t publish content regularly
You’re not looking for competitive or brand narrative insights
You need a single KPI to report upward
You don’t have bandwidth to act on insights
TL;DR: Prompt tracking yields valuable insights — but only if you’re set up to act on them.
If you are set up, the returns can be significant.
Take Gong, the sales call intelligence tool. It’s a great candidate for prompt tracking.
It has a full content engine that publishes across multiple channels (blog, reports, video, media coverage, audio, and more).
They compete in a crowded category where comparison prompts are common.
And they serve a buyer (sales leaders) who increasingly uses AI to evaluate software.
(Seventy-one percent of B2B software buyers now rely on AI chatbots for product research, according to G2, up from 60% in 2025.)
On the other hand, a local HVAC company that gets all its leads from Google Business Profile (GBP) and word-of-mouth doesn’t need prompt tracking. At least not yet.
Even if AI is overlooking them, building a content engine from scratch just to fix that isn’t a realistic investment.
Building Your Prompt Set: What to Include and Why
Don’t try to track every possible prompt your customers could be using.
Instead, focus on prompts you actually care about getting mentioned or cited in.
They should map directly to your product offering, audience pain points, and moments close to purchase.
Types of Prompts to Include
Tracking these four types of prompts over time will yield the most helpful insights:
Evaluation prompts: “Best tool for x use case” and specific feature queries
Reputation prompts: “Is x product worth the price?”
Comparison prompts: “Alternatives to x product,” “x tool vs. x tool,” “best x tools”
Gap prompts: Priority topics your competitors are pushing you out of
The first three are focused on understanding how your product is being recommended in buying conversations.
The last one is about understanding your competitive landscape and where you could improve.
Pro tip: Don’t just track one prompt for each type. Looking at answers for a single prompt is just noise. Reviewing a cluster of prompts over time is a real signal.
Margaret Kapitany, Offsite SEO Lead at Hootsuite, shares how she focuses her prompt set:
The prompts worth tracking are the ones that most closely mirror how a potential buyer would actually ask their AI for help, especially close to a purchase decision. For me at Hootsuite, that means prompts that cover comparison, evaluation, and recommendation queries from a social media manager or CMO, phrased the way they’d talk to a colleague or trusted industry peer.
What that looks like in practice at Hootsuite:
Prompts worth tracking
Prompts not worth tracking
“How does Hootsuite compare to [competitor]?” (Comparison)
“What is social media management?” (Pure definition — won’t convert)
“Which social media management platforms integrate with Salesforce?” (Evaluation)
“Is Hootsuite a good company?” (Vanity — brand mention is baked in, nothing actionable)
Where to Find Prompts Worth Tracking
Find prompts wherever you normally go to learn about your audience.
To find prompts worth tracking, look at:
Keyword research: Commercial and Transactional intent queries like “best x software”
Google’s “People also ask” (PAA) boxes: Comparison and evaluation questions like “Best alternative to x” or “does x integrate with y”
Perplexity’s related questions: Similar to PAA, comparison and evaluation questions
Reddit, Quora, Facebook Groups in your industry: Repeated questions, especially ones that compare options or express frustration
Sales call transcripts: Repeated questions asked right before or during a purchase decision
Semrush prompt suggestions for your brand: Queries tied to buying decisions that you or your competitors are showing up for
Pro tip: For every question you uncover, do a quick gut check: “If someone asked an LLM this, would I want to see my brand show up? Would I be upset if it didn’t?” If the answer is “Yes, and yes,” keep it.
Let’s return to Gong as an example of how to find prompts.
Keyword research shows me the questions people are asking Google about my category, how popular they are, and the language they use.
If I use a tool like Semrush, I can filter to Commercial or Transactional intent keywords (the ones labeled “C” or “T”). And add the most popular and relevant ones to my prompt tracker.
Then, I can dig through Reddit forums my audience frequents to find repeated frustrations and buyer queries.
I would take “Sales enablement tech stack suggestions,” “AI tools for sales enablement.” I’d ignore the queries about SMBs or startups because those aren’t Gong’s target audience.
Next, I’d extract the comparison or evaluative prompts Perplexity surfaces when I prompt it with terms related to my business.
For Gong, I used “best conversational insights tool for sales” to find some good candidates:
Gong vs. Chorus (or any other competitor on this list)
Which conversational insights tool has the best ROI for enterprises?
Pro tip: When writing prompts, don’t agonize over exact wording the way you would with keywords. LLMs cluster semantically similar queries together. Track a few natural variants, like “best sales enablement software” and “top sales enablement tools.”
These sources are solid, but you’re still inferring and collecting them manually is slow.
The exact prompts your audience runs in LLMs and how often they’re using them, ranked by frequency.
This grounds your prompt set in real demand that’s always up to date.
You can be sure you’re tracking questions your users are actually asking.
From this list for Gong, I’d choose to track prompts under “AI-Driven Sales Enablement” and “Sales Coaching and Enablement Tools,” as they’re BoFu queries related to my product.
Organize By Product or Use Case
To build your first prompt set, start small.
All you need is 20-30 prompts over 4-6 broad categories that align with your product offering or use cases.
Ensure every category includes a mix of your four types of high-value prompts and add them as tags.
For a B2B SaaS company like Asana, this prompt tracking setup could look like the following:
Project management
Task management
Workflow automation
Team reporting
Best project management software for marketing teams (Evaluation)
Best task tracking tools for cross-functional teams (Evaluation)
Best workflow automation software for ops teams (Evaluation)
Best project reporting tools for enterprise teams (Evaluation)
Asana vs. Monday.com for project management (Comparison)
Does Asana actually improve team productivity? (Reputation)
Asana vs. ClickUp for workflow automation (Comparison)
Is Asana’s reporting good enough for large teams? (Reputation)
Project management software with Slack integration (Evaluation)
Best task management software for small teams (Gap)
Asana vs. Notion for managing marketing workflows (Comparison)
Asana vs. Smartsheet for project visibility (Comparison)
Pro tip: Use branded prompts only for comparison and reputation tracking, as they can inflate your visibility score. Keep the rest of your prompt set unbranded so you actually learn where you’re getting found, not just where you’re already known.
This setup lets you easily see which categories you’re winning and losing in over time.
Or, what types of answers you need to do a better job of showing up in.
Example: Hootsuite Prompt Set
You may decide to add more tags or organize your prompts in a different way as you expand.
For example, Margaret uses multiple different tags — not just categories — for her prompt set for Hootsuite.
We’ve built out prompts in three ways:
Funnel stages, with most of our attention on conversion
Our target industries, with tailored terminology
Intent type: comparative, evaluative, integrative (e.g. “works with X tool”), and problem-led (“how do I solve Y”)
Almost all of our prompts carry multiple tags, e.g., BoFu + Healthcare + Evaluative. That tagging is what lets me slice the data later.
When leadership asks for numbers, she can report on which industries Hootsuite is most visible in, or cross-reference visibility for BoFu prompts with direct traffic trends.
Margaret’s setup shows an important lesson: However you build your prompt set, structure it so you can answer the questions you’ll want to ask later.
How to Track Prompts: Step-by-Step Process
All you need to get started with prompt tracking is a spreadsheet and 30 minutes a week.
With our tracker, you can log the following for each prompt:
The prompt itself
Category
Tags (e.g., type, industry)
The LLM you’re testing it in
Whether your brand was mentioned (yes/no)
Whether you were cited (yes/no)
Sentiment of the mention (positive, neutral, negative)
Competitors mentioned
The date
Customize it to whatever makes sense for your business.
Step 2. Run Each Prompt Across Multiple LLMs
Different LLMs pull from different sources, so answers will be different across all of them.
Tracking prompts from only one LLM won’t give you a full picture of your brand’s presence in AI search.
Ideally track prompts in all major LLMs, including:
ChatGPT
Gemini
Perplexity
Claude
If you need to save time, review Presenc AI’s 2026 platform demographics report to identify which LLMs your audience actually uses and focus on those.
Pro tip: Make sure you’re using a temporary chat to run your prompts. Your regular chat window will serve you an answer that takes into account everything it knows about you from past conversations. You want to track what an LLM might recommend to anyone, not just you specifically.
Step 3. Run Each Prompt at Least Twice Per Session (Optional)
Answers will vary run to run. So if you have the time, run each prompt 2-3 times per session for more reliable data.
You’ll catch when your brand shows up one out of three times (a 33% mention rate).
If you don’t have time, don’t worry. You’ll still see trends over time.
Step 4. Log Competitor Mentions, Too
Competitor data is half the value of prompt tracking.
Seeing a competitor get mentioned consistently in a category you’re performing less well in is a signal.
Maybe they published a new comparison page. Or were included in an influential report.
Look into their strategy to see what they’ve done to improve and learn from them.
Step 5. Track Weekly. Action on Data Monthly.
Regular prompt monitoring on a weekly basis is often enough to catch shifts.
It gives LLMs enough time to crawl and learn from new or updated content.
But don’t make any decisions with only one week of data.
A single week of low visibility in a category could be a fluke. Four weeks of it is a trend worth acting on.
(We’ll cover what to do when you spot one in the “How to Read and Act on Prompt Data” section below).
In this example, Asana shows up with a low citation rate for two LLMs only one week out of four.
Rushing to fix that ASAP could turn out to be a waste of time.
Step 6. Upgrade to an Automated Prompt Tracking Tool
Manual LLM visibility tracking is a great way to validate that you can actually get some useful insights from the practice.
But if you want to grow your prompt set beyond 20-30 prompts, it’s going to start taking much longer.
A tool like Semrush can help you move faster and suggest actionable opportunities based on your data.
To get started, open the Visibility Overview dashboard, enter your domain, and click “Check AI Visibility.”
You’ll see a summary of how often LLMs mention your brand, which competitors are mentioned alongside you, and a breakdown across each LLM.
Scroll down for a list of prompts where you’re already getting mentioned. Click “Opportunities” to see your gap prompts.
Click “Monitor” on the prompts you want to include in your prompt set.
Pick the LLMs you want to monitor, paste in your prompts, and hit “Start Tracking.”
You can add tags by intent, topic, or campaign so you can slice the data later.
Further reading: See how the top AI visibility tools stack up on pricing, LLM coverage, and reporting features.
Example: How an Agency Tracks Prompts for E-Commerce Brands
When you’re tracking hundreds of prompts across multiple clients, a basic spreadsheet won’t be enough.
Jonny Nastor, Founder & Head of Strategy at Digital Commerce Partners, knows this firsthand.
He built a prompt tracking map based on the theory that most buyers ask LLMs about a specific job they need done or task to accomplish. Then filter by their specific situation (a.k.a. constraints).
For example, when shopping for smart doorbells, a buyer might search “best video doorbell with no monthly subscription fees.”
The job to be done is “best doorbell.” The constraint is cost (low fees or no subscription).
He calls it a “Constraint Map.” Every intersection of job and constraint in the map becomes a prompt.
He gets ideas for constraints from keyword modifier data. In Semrush, you can see these in the Keyword Magic Tool.
He also pairs each prompt with search volume data to roughly understand its popularity and prioritize accordingly.
He runs his prompts across ChatGPT, Perplexity, and Gemini automatically through their APIs to log how each one responds.
Depending on the client’s needs, he tracks one or all of the following AI visibility metrics for each prompt:
How many times the brand was cited by LLMs
The brand’s recommendation (or mention) rate
Instances of ghost ranking
For this client, it was only citations on Bing’s AI search.
For each metric, he watches for trends over time, not drawing any conclusions based on one week of data.
He also audits content readiness with a mix of the following (again, depending on client needs):
If a page exists to address the prompt
If that page links directly to the specific products that answer the prompt
If product details (attributes) like price, dimensions, compatibility, etc., are listed on the page
If the content on the page is extractable by an LLM (e.g., no bot-blocking settings or JavaScript-heavy rendering that prevent AI crawlers from accessing the page)
This tells him exactly what to fix so AI is more likely to recommend the brand.
This system surfaced ~52,000 monthly searches with zero AI coverage for one of Jonny’s clients. And a content strategy for the next quarter.
How to Read and Act on Prompt Data
Generative AI prompt tracking data might seem hard to trust at face value.
LLMs give variable answers even in temporary chats.
Platforms push silent model updates that tweak source weighting.
And training data bias is real. One study found LLMs often favor global brands over local ones, meaning you might be invisible in your category because of the model’s defaults rather than your content.
Margaret at Hootsuite found the changing outputs of LLMs surprising when she first started prompt tracking:
It was way more chaotic than I expected. The same prompt can return a totally different brand list on ChatGPT vs Gemini vs Perplexity. “AI visibility” isn’t just a single thing to optimize for: you’re effectively running parallel strategies, and they don’t transfer cleanly, and answers will fluctuate constantly.
This variance doesn’t mean prompt tracking data is useless.
But it is the reason you need to track trends over time instead of getting hung up on moment-specific snapshots.
Here are a few meaningful signals to watch for.
Increased (or Decreased) Frequency Over Time
A consistent rise in mentions or citations usually means your content efforts are working.
A consistent drop could mean a competitor is gaining ground or a key piece of content has gone stale.
Action to take: If the trend is up, note what you published in the weeks before the lift. That’s your playbook. Keep doing it.
If it’s down for four or more consecutive weeks, pick one fix for that category:
Refresh a stale piece with updated data
Publish a new comparison page targeting the prompts you’re losing
Pitch a third-party source that’s being cited in that space
Consistent Source Inclusion
If you repeatedly see the same third-party sources cited in your LLM visibility tracking data, stop and take note.
It means the LLMs trust these sources.
And third-party sources are powerful for AI visibility. Airops found 85% of brand mentions come from third-party pages.
For example, if G2, TechRadar, and Capterra keep appearing across your evaluation prompts, those are your priority pitches.
You can see your top “Cited Sources” and “Source Opportunities” (where you’re not being mentioned but your competitors are).
Action to take: Make a list of the sources that keep showing up. For each one, check if you’re already featured. If so, is your listing current and accurate?
Then, pick one you’re missing from and pitch it. A contributed piece, product review campaign, or quote request can all work.
Unless you’re correcting a mention, don’t try to pitch competitors’ sites. They’re not likely to accept.
And don’t only focus on categories you’re losing. Reinforcing visibility in categories you’re already winning is valuable too.
Movement from Mention to Citation
If you used to get mentioned and now only get cited as a source, you’ve slipped into what Jonny calls “ghost ranking” territory:
“Your content shows up in the citation panel, but the AI recommends a competitor.”
Like this example from Teva.
An increase in your “ghost ranking count” over weeks means it’s time to investigate.
Jonny knows this first hand:
Our prompt tracking showed our agency was being cited on agency-directory pages (Trustpilot, Clutch, Semrush) but ghost-ranked at an 83% rate. AI was using directory content as the source of truth, then recommending whichever agency had the densest, most-named presence.
Action to take: Find the sources showing up in the citation panel and audit your presence on each one. Fuller profiles, more reviews, and accurate product details all help convert a citation into a recommendation.
For Teva, this means pitching to be included in the cited articles by REI, backpacker.com, and Outdoor Gear Lab.
It also means updating their own pages (including the ones being cited) with more or newer details.
Then, watching to see if they get less ghost rankings over time.
Common Misreads to Watch for
When you first start generative AI prompt tracking, try to avoid getting tripped up by the following false conclusions.
“Our AI visibility dropped this week. Something’s wrong.”
One week of low visibility or mentions is likely natural variability.
Wait for four consecutive weeks before treating it as a trend worth acting on.
“Our score looks low. We need more content.”
A score can be low for multiple reasons, and the fix isn’t always new content.
Sometimes it’s getting included in more third-party sources or forum threads. Or getting more customer reviews.
If you’re just starting out, you may need to go back to your prompts and make sure they’re not too vague or top-of-funnel.
For example, a broad query like “mesh wifi” may return a definition answer rather than a list of brands.
“Our overall visibility improved after adding more prompts to our tracker. We’re doing something right.”
Adding more prompts to your prompt set will usually make it look like your AI visibility has increased. You’re getting mentioned in more prompts.
“Our overall visibility improved after adding more branded queries. We’re doing something right.”
A branded query is one that mentions your brand name. Of course you get mentioned in the answer.
That doesn’t tell you anything useful.
Limit tracking branded prompts to comparison and reputation prompts. And keep them in a separate cluster so you can filter them when measuring your overall AI visibility.
Weekly Prompt Tracking Workflow
Turning prompt tracking data into a strategy is where you prove the value.
Margaret starts by looking at topics where Hootsuite has low visibility.
Then I look at individual prompt answers to see “What does the internet think about us in this category, and how do we change that?” This usually translates into a few concrete questions for further research:
Where are the third-party listicles, comparison posts, and analyst write-ups that the model is pulling from, and are we represented accurately on them?
Do we have a first-party comparison or evaluation page that an LLM can confidently cite, written for the actual buyer in that vertical?
Do we have our customers (ex. case studies, reviews) reinforcing the same message?
From here, she can recommend actions like updating a case study or getting a mention in a third-party listicle.
Here’s a simple workflow you can use to turn your prompt monitoring routine into AI visibility gains over time.
Pro tip: Don’t expect overnight wins. LLMs take time to reflect new content. Watch for directional improvement over weeks and months. You’re not chasing a score; you’re watching whether your gaps are closing over time.
Step 1. Review prompt cluster trends weekly: What’s the visibility score for your BOFU prompts? Has it fallen for your healthcare cluster? Or a specific use case category?
Step 2. Spot recurring gaps: Note any clusters, categories, or topics that have been underperforming for four weeks or more.
Step 2a. Plan one fix per cluster: Use your content strategy brain to determine the most impactful fix.
That might be:
Publishing a new comparison page to improve a BOFU prompt cluster’s score
Updating an existing article with fresher data
Publishing a type of content you haven’t tried yet on this topic (e.g., video, podcast, social post)
Step 3. Identify recurring third-party sources: Note sources AI consistently cites across your categories. Reddit? LinkedIn? G2? YouTube creators? A trade publication?
Step 3a. Pitch one source you’re missing from: If accepted, you’ll build more off-site authority and increase your chances of being mentioned in the answers you care about.
Bonus resource: Pitching a journalist or news outlet? Use our Journalist Pitch Template, designed by PR experts, to get started quickly.
Start Winning AI Visibility with Prompt Tracking
Prompt tracking isn’t a scoreboard. It’s a compass.
The brands that get real value out of prompt tracking aren’t monitoring every possible prompt.
And they aren’t reacting to one bad week.
They focus on bottom-of-funnel prompts and follow the direction of the graph, not the dot.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-08-13 21:23:042026-08-13 21:23:04Prompt Tracking: How to Find (and Fix) Your AI Visibility Gaps
Nearly 300 French newspapers filed a complaint today with France’s competition authority. They accuse Google of violating a compensation agreement by launching AI-generated search summaries without publishers’ consent.
What’s happening. APIG, the Alliance de la Presse d’Information Générale, filed the complaint after Google launched AI Overviews (the AI-generated summaries that typically appear above traditional organic search results) in France in late July, France 24 with AFP reported.
Why it’s happening. The publishers say the feature further reduces traffic to their websites. Instead of sending users to original news articles, Google answers their questions directly in Search.
What they’re saying. APIG says Google introduced AI Overviews without publishers’ approval. The group argues this breached commitments Google made under a 2022 agreement on compensation for news content.
Google says AI Overviews help people ask more complex questions, discover new content, and give publishers tools to manage how their content appears.
“The editors are not seeking to halt innovation. They are asking for this value to be shared, and for compensation of the use of their content,” said APIG President Marc Feuillee.
How we got here. The complaint revives a dispute that has already led to regulatory action.
In 2024, France’s competition authority fined Google €250 million for failing to comply with parts of the 2022 agreement.
Separately, the European Union opened an investigation in December into whether Google used publishers’ content to train, and provide AI services without appropriate compensation.
Why we care. Publishers argue that AI-generated search answers reduce referral traffic while citing their own reporting. The latest complaint adds to growing regulatory scrutiny over whether AI-powered search can use publisher content without violating licensing agreements or competition rules.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/08/google-dam-ID8ETM.webp?fit=1920%2C1080&ssl=110801920Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-08-11 17:21:282026-08-11 17:21:28300 French newspapers file complaint over Google AI Overviews
Google is testing a new mobile Search ad layout that surfaces the advertisers in a sponsored results block before displaying the ads themselves, potentially giving users more context before they click.
If rolled out more broadly, the change could influence how users scan and interact with paid search results.
What’s happening. The experiment places a list of advertisers—including each advertiser’s favicon and domain name—at the top of a group of sponsored results on mobile Search.
Instead of seeing the first ad immediately below the “Sponsored results” label, users first see the participating advertisers, followed by the individual ads.
Google has not commented on the experiment, and it remains unclear whether the feature will expand beyond a limited test.
Why we care. Highlighting advertisers before the ads themselves could increase brand visibility and help users identify the companies featured in the sponsored results block before reading individual ad copy.
For advertisers with strong brand recognition, the format may improve trust and encourage clicks. Less familiar brands, however, may need to work harder to stand out.
First spotted. This update was spotted by Digital Marketer Sachin Patel, who shared his discovery on X.
Google has introduced a Campaign Data Import Validation Report, giving advertisers a new way to check the quality of imported campaign data before it impacts reporting.
The report is designed to help identify missing or incomplete data from non-Google advertising platforms.
What’s happening. The new validation report reviews both non-Google campaign data and previously imported campaign data, highlighting campaigns that may be missing key performance metrics.
Specifically, it helps advertisers identify imports that lack essential reporting fields such as cost, clicks and impressions, making it easier to spot gaps before relying on the data for analysis.
Why we care. Missing or incomplete data can lead to inaccurate reporting, making it harder to compare channels or evaluate marketing performance. The new validation report provides an additional layer of quality control, helping you ensure imported data is complete and reliable.
Channels outside of Google and Meta have struggled to get recent headlines in the paid advertising world. But my agency’s performance marketing team has seen one under-the-radar channel — Reddit — introduce a ton of new features and ad types in 2026, making it more viable for our clients.
Between new ad types and AI-powered functions, powerful integrations for shopping, video ad enhancements from late in 2025, and Reddit’s AEO and SEO clout making marketers more aware of the need to engage on the platform, we’ve got more clients active on, or curious about, Reddit than I’ve ever seen.
That said, Reddit’s ad platform — though much further along than it was a couple of years ago — still needs more features and improvements to existing ones to draw a bigger share of advertising budgets.
My Reddit targeting and audience wishlist
A huge part of Reddit’s influence comes from the extremely candid and opinionated content its users provide — and users are enabled to be exactly that, in large part, because accounts use usernames rather than real names.
Redditors can be as anonymous as they want, and I don’t wish to change that. Instead, I’d like Reddit to offer the following.
1. Expand reach without sacrificing privacy
Better identity targeting for anonymous or logged-out users in a privacy-safe way would expand our targetable reach, which is a big deal given Reddit’s niche status compared to Google and Meta.
See exactly how your competitors win.
Uncover the keywords, ads, landing pages, and strategies driving your competitors’ paid search success—and find your next opportunity to outperform them.
Analyze your competitors
2. Bring B2B intent to Reddit
B2B and firmographic targeting that includes:
Job title, function, and seniority.
Industry and company size.
Named-company and account-list targeting.
Tech usage and/or professional-interest segments.
Decision-maker and buying-committee audiences.
Reddit offers communities, interests, keywords, customer lists, and lookalikes. Adding privacy-safe firmographic data would make the platform more competitive with LinkedIn while preserving Reddit’s differentiated behavioral signals.
3. Make purchase intent easier to target
Reddit should turn its strongest advantage — people actively researching and discussing decisions — into understandable, scalable “in-market” and journey-stage audiences, including:
Recently researched a category.
Asked a question about a product or problem.
Engaged with comparison or recommendation threads.
Evaluated alternatives.
Repeated recent engagement.
4. Make existing audience signals more reliable
As for what we can do on Reddit with the reach we have, I’d like to request stronger retargeting depth, lookalike audiences, CRM integrations, and cross-subreddit behavioral signals. Reddit introduced all those targeting options, but they’re still patchy and unreliable, further eroding scale and performance.
5. Give advertisers more control over who they reach
Reddit advertisers need greater confidence that their strategies are hitting the right people. Adding exclusions would help ensure we can eliminate some irrelevant eyeballs, so I’d like to ask for:
Negative keyword and community exclusions.
Current-customer and existing-lead suppression.
Clear control over AND versus OR targeting logic.
6. Make audience planning more predictable
Last in the audience and targeting category, advertisers need better audience planning and forecasting. Before spending budget, they need to see:
Realistic audience sizes (Reddit currently has ranges that can seem wildly unrealistic).
Demographic and device composition.
Top associated communities and topics.
Estimated CPM, conversion volume, and audience saturation based on budget.
With the releases of Brand Lift, Conversion Lift, and CAPI, Reddit measurement took a huge step forward in 2023.
Today, Reddit’s role in the user research and consideration phase is increasingly important as AI search minimizes clicks and pushes performance marketers to focus further up the funnel.
That means Reddit’s value is increasingly measured through analyses like MMM and MTA rather than through a persistent reliance on last-click attribution.
All of that is to say that it’s easier to measure Reddit ads’ impact than it was a few years ago. Yet, indicative of a platform that’s still more of a challenger than a powerhouse, it’s falling short on some basics.
There’s one specific (and persistent) gap that should be table stakes for a serious ad platform: more third-party integrations for dynamic UTM tracking. Beyond that, I’d love to see Reddit introduce the following.
7. Prove incremental value without leaving the platform
Reddit is generally considered a test channel, so self-serve incrementality testing — which larger competitors offer — would help demonstrate the incremental value it drives. Specifically, I’d like to see Reddit provide:
Conversion-lift studies.
Randomized audience holdouts.
Geo experiments.
Self-serve brand-lift studies.
Incremental reach and frequency measurement.
In aggregate, these tests would help Reddit advertisers defend upper- and mid-funnel investment when last-click reporting underrepresents its influence.
8. Connect ad delivery to revenue outcomes
Reddit has Pixel, CAPI, and omnichannel attribution capabilities. The larger opportunity is to make it easier to pull in offline data from CRMs and other sources, so advertisers can lean into down-funnel and revenue-based optimization.
Native HubSpot, Salesforce, and other CRM integrations would be particularly valuable. Advertisers need to tell Reddit which leads became valuable customers, then allow its delivery system to find more people with similar signals.
My Reddit ad formats and creatives wishlist
More than on most platforms, it’s critical to make sure your ads appear as native as possible to their context. Redditors turn up their noses at anything inauthentic.
In 2024, Reddit made some updates to Conversation ads that gave advertisers a useful option in a native setting, but there are many more native formats they could introduce. I’ll list some of my recommendations below.
9. Give advertisers more ways to fit into the conversation
Polls, product carousels, and Q&A ad units designed to fit within the Reddit environment would all be good formats for advertisers to test.
10. Bring short-form video up to speed
Another area of opportunity for Reddit is more competitive short-form video. Yes, they do have short-form video, but introducing vertical formats and optimizing for autoplay would make that format a lot more viable for advertisers (this is kind of in the “table stakes” category).
11. Put ads where high-intent Reddit research happens
Reddit Answers was integrated into the broader Reddit search experience in May 2026. Reddit Answers ads in high-intent searches and recommendation questions, with clear sponsorship labeling and strict relevance requirements, would give advertisers another way to reach users close to the purchase decision.
12. Let advertisers learn from past campaigns
The last big item on my creative list is an ad library that expands to show historical ads, much like Meta’s, where you can see both paused and active ads. That kind of archival reference would be super helpful to have on the back end.
Every click they win is a customer you lose.
See where competitors are investing, which keywords drive their results, and how to capture more of the market.
See who’s stealing your traffic
Reddit could earn more of the ad budget
If it’s not obvious, Reddit has taken some significant strides forward over the last few years, to the point where it can be surprising when it hasn’t checked some of the more basic boxes.
These requests shouldn’t be considered pipe dreams, either. The above list was compiled from situations where my team and I have butted up against limits when running real Reddit campaigns.
Given the way AI search engines have eaten into clicks and made awareness more of a paid media play, and given Google’s heavy-handed move away from control and toward mass automation, we’re very willing to divert budget to Reddit if we can make a solid business case for it.
Reddit’s unique community and research signals would be powerful if the platform figured out how to turn them into targetable audiences using ad formats that enable real conversations throughout the buying journey.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/08/12-things-Reddit-needs-to-make-its-ad-platform-more-competitive-5Nag4x.png?fit=1920%2C1080&ssl=110801920Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-08-11 15:00:002026-08-11 15:00:0012 things Reddit needs to make its ad platform more competitive
Introduced in 1998, the Digital Millennium Copyright Act (DMCA) was designed to solve a specific problem: protecting creators from digital piracy while granting online platforms a “safe harbor” from liability.
Under Section 512, search engines and host providers avoid expensive lawsuits as long as they act swiftly to remove content when a copyright holder flags an infringement.
That “act swiftly” requirement creates an unintended vulnerability. Because major platforms rely on automated processing to handle millions of removal requests every month, bad actors can trigger automatic deindexing without human review.
What was designed as a shield for intellectual property is now a weapon for corporate censorship, reputation management, and competitive sabotage.
A real-world example of DMCA abuse
In March, a clear demonstration of DMCA abuse played out in full public view. Investigative reporters at Press Gazette published a story examining how a digital media firm, Clickout Media, allegedly acquired established news domains to deploy AI-generated gambling content, a tactic known in SEO circles as site reputation abuse.
Within 48 hours, both articles vanished from Google search results worldwide.
An entity identifying itself as “US Webspam” filed a copyright complaint alleging that Search Engine Land had copied its material word-for-word, including proprietary images.
The claim was fraudulent:
Zero image match: The Search Engine Land article contained no images whatsoever.
No direct outreach: The filing claimed “multiple good-faith efforts” had been made to resolve the dispute, yet no communication was ever sent to the editorial team.
Fabricated source: The complainant pointed to irrelevant third-party domains as the “original source” of the reporting.
Despite these clear discrepancies, Google’s automated systems processed the request and deindexed the article.
It took public pushback from search industry analysts, widespread media attention, and a formal counter-notice before Google reinstated both Search Engine Land and Press Gazette in the search index several days later.
If an established publication with high domain authority can have its coverage muted overnight, individual creators, niche blogs, and small businesses face an even tougher challenge.
Be the brand customers find first.
Track, grow, and measure your visibility across Google, AI search, social, local, and every channel that influences buying decisions.
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The mechanics of bad-faith DMCA takedowns
Understanding why weaponized takedowns succeed requires looking at the incentives of online platforms.
Under U.S. copyright law, platforms like Google, Cloudflare, and major web hosts remain protected from copyright liability only if they act “expeditiously to remove, or disable access to” material claimed to be infringing.
Because taking down a page carries virtually zero legal risk for the platform, while ignoring a notice risks a lawsuit, platforms prioritize speed over verification.
Key structural flaw: Section 512 places the burden of proof on the accused rather than the claimant. The platform shoots first and asks questions later.
Malicious actors exploit this asymmetry using three primary techniques:
Scraper-and-backdate schemes: A bad actor scrapes your original article, pastes it onto a disposable site, alters the metadata or timestamp to make it appear published earlier, and files a DMCA claim alleging you stole their work.
Fabricated entities: Attackers submit takedown claims under fake organization names, such as “US Webspam,” or impersonate third-party blogs to hide their true identity.
Reputation suppression: Individuals or brands facing negative press file dubious claims against investigative pieces, product reviews, or customer complaints to suppress damaging search results during high-impact windows.
5 steps to take when your content is unfairly DMCA’d
Discovering that your URL has been de-indexed or pulled down by your host can be alarming. Following a structured procedure helps ensure your content is restored efficiently.
1. Identify and inspect the notice
When Google removes a URL due to a DMCA claim, you’ll receive a notification in Google Search Console under Security & Manual Actions, along with an email detailing the notice.
Locate the exact complaint in the Lumen Database (lumendatabase.org), a public repository that archives legal requests to remove web content. Examine the filing carefully:
Who is listed as the copyright owner?
What specific URL do they claim is the original source?
What specific text or media is alleged to be copied?
2. Gather incontrovertible proof of prior ownership
Before filing your response, assemble evidence establishing that your content predates the claimant’s alleged original version:
CMS timestamp logs: Export full publish logs from WordPress, Ghost, or your custom database showing creation dates and edit histories.
Wayback Machine archives: Pull historical snapshots from the Internet Archive showing your page live before the claimant’s alleged publication date.
RSS feed records: Retrieve original RSS distribution records, which index publication times down to the second.
Git commit history: If your site uses static site generators or version control, export commit hashes showing when the file was created.
To force the platform to restore your content, you must file a formal DMCA counter-notification. Under statutory guidelines, a counter-notice must include:
Identification of the material that was removed and its original location.
A statement under penalty of perjury that you have a good-faith belief the material was removed as a result of mistake or misidentification.
Your full name, physical address, phone number, and email address.
Consent to the jurisdiction of the federal district court for your judicial district (or the district where your host is located if outside the U.S.).
Your physical or electronic signature.
4. Understand the statutory waiting period
Once the platform receives your valid counter-notice, the law requires it to forward it to the original claimant. The claimant then has 10 to 14 business days to submit proof that they’ve filed a formal lawsuit seeking a court order to restrain you from publishing the content.
If the claimant fails to produce evidence of a court filing within that 10- to 14-day window, the platform is legally obligated to restore your content to its index or server.
5. Escalate publicly and pursue Section 512(f) remedies
In high-stakes situations, such as time-sensitive reporting or ecommerce launch campaigns, waiting two full weeks can cause serious financial damage.
If your case involves an obvious fraudulent claim (such as alleging image theft on an all-text page), publicize the details on social platforms like X and LinkedIn while tagging the official search representative handles. Search engine teams do occasionally fast-track obvious manual reviews when blatant system gaming is exposed publicly.
Section 512(f) of the DMCA explicitly holds that any person who knowingly materially misrepresents that material is infringing shall be liable for damages, including costs and attorneys’ fees incurred by the alleged infringer.
While litigation takes time, sending a formal litigation hold letter to a bad actor who used a real legal identity can prompt a swift withdrawal of their claim.
Proactive steps to safeguard your intellectual property
While you can’t stop an anonymous actor from filing a fraudulent notice, proactive site hygiene makes resolving disputes far simpler:
Auto-archive upon publication: Set up an automated webhook or API connection to archive new articles to the Internet Archive immediately upon publishing.
Maintain clear byline and copyright notices: Ensure every article includes schema structured data (NewsArticle or Article schema) complete with datePublished, dateModified, and author metadata.
Monitor Search Console alerts: Ensure Google Search Console notification emails route to a primary inbox that gets checked daily. Missing a notice delays your counter-filing window.
Keep original source files: Store raw video files, unedited photography, audio recordings, and rough draft revisions safely off-site to demonstrate your creation process.
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Defending your content against fraudulent takedowns
The Search Engine Land incident highlights a structural issue in modern web governance: automated copyright tools built to shield giant platforms can easily be turned against legitimate creators.
Until policy revisions require stronger identity verification or impose stiffer penalties for false claims, bad actors will continue to misuse takedown requests to silence competitors and critics. By understanding your rights, documenting your publishing pipeline, and acting quickly with formal counter-notifications, you can defend your work and restore your digital presence.
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