Most brands approaching AI visibility ask the wrong question: How do we get stronger as an entity so that LLMs recommend us more?
In entity SEO, we tend to say, “Build the Knowledge Graph, add schema, and get more press.” But that logic assumes the LLM is evaluating the brand and deciding whether it’s good enough to recommend for any query related to what the brand sells. The LLM evaluates the query and matches it against whatever category associations it has built for the brand from third-party content.
What matters is whether the category your customers are using to search for you matches the category the LLM has coded you into.
What the data showed
João da Silva and I conducted a study of 12 athletic apparel brands in the U.K. over seven days, with 14,140 API runs across ChatGPT, Gemini, Perplexity, Claude, and Google AI Overviews. We tested the same brands using two different category framings: athleisure and athletic footwear.
After looking at the results derived from co-mentions and putting numbers on the impact of framing on category recognition for LLMs, we took the test one step further and changed the category register in the prompt.
The results were symmetric to a degree that rules out noise:
Brand
Knowledge Graph (KG) score
Athleisure rate
Footwear rate
Δ
Verdict
New Balance
64,235
1%
90%
+89
Jumped (footwear-coded)
Nike
25,996
77%
90%
+13
Small shift (footwear-coded with strong athleisure co-mentions)
Alo Yoga
3,062
63%
0%
-63
Dropped (athleisure-coded)
lululemon
810
90%
0%
-90
Dropped (athleisure-coded)
Sweaty Betty
751
9%
0%
-9
Stable
Reebok
665
1%
20%
+19
Small shift
Outdoor Voices
455
26%
0%
-26
Small shift
Rhone Apparel
400
5%
0%
-5
Stable
Varley
381
6%
0%
-6
Stable
TALA
356
5%
0%
-5
Stable
Gymshark
277
37%
0%
-37
Dropped (athleisure-coded)
LNDR
2
0%
0%
0
Stable
Notes:
New Balance goes from 1% to 90%.
lululemon goes from 90% to 0%.
The variation is approximately 0.9 points in both directions simultaneously.
We’re not talking about correlation here, but a controlled observation: what happens when we change only one variable — the category word in the prompt.
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Why this happens: Category coding
Nike, New Balance, and Reebok share the exact same Google Knowledge Graph (KG) description: “Footwear company,” so all three are recognized perfectly by every LLM we tested. From an entity standpoint (recognition), they start from an identical position. However, their behavior under different category framings isn’t identical at all.
The reason is what the paper formalizes as category coding: the combination of the KG description field and the third-party content corpus that has accumulated around a brand in a given category.
The KG description anchors a brand to a category in the model’s representation (impacts recognition).
The third-party corpus — articles, reviews, editorial comparisons, and roundups — fills in the detail of what that category association actually looks like (impacts recommendation).
Looking at the example from New Balance
New Balance’s KG description says “Footwear company,” and the third-party corpus that has accumulated around it corroborates the category by focusing on topics related to running shoes, performance footwear, and athletic training.
When a user asks about athleisure brands, the model doesn’t find New Balance in that corpus because there is no third-party association. But it does find lululemon, Alo Yoga, and Gymshark: all brands whose corpus is built from fashion publications, lifestyle editorial, and activewear roundups.
When we changed the query to athletic footwear, the retrieval flipped: New Balance is suddenly in the right corpus, and lululemon is not.
The model itself can’t and isn’t making a judgment about brand quality or belonging. What an LLM does is pattern-match a query category against a content category. If those two things align, the brand surfaces. If they don’t, it doesn’t, regardless of how established the brand is.
So, can you just recode your KG description?
Some brands reading this will consider the obvious shortcut: Change the KG description. If “Footwear company” is anchoring you to the wrong category, recode it to “Apparel company,” and the problem is solved.
However, the KG description is only half of what determines category coding. The other half is the third-party content corpus that has accumulated around your brand, and that doesn’t change because you updated a field in the Knowledge Graph. If your entire external content history is performance footwear, running, and athletic training, changing the description gives the model a new anchor with nothing attached to it. The corpus still says what it always said.
The corrective lever is third-party content investment in the specific category framing your customers are using: in the publications the model retrieves from, alongside the brands that already define that space. The KG description can support that work once the corpus exists.
The standard GEO advice is to strengthen your entity: a consistent name, clean schema, a strong About page, and more press coverage. That advice is correct for getting recognized and even recommended within the brand’s coded category, but it isn’t sufficient for getting recommended in adjacent category queries.
What determines recommendation in adjacent categories is whether the third-party content corpus around your brand matches the category framing your customers are actually using.
The questions worth asking about any brand are:
Are we visible in AI?
What category has the LLM coded us into?
Is that the category our customers are querying?
If a brand is strong in one category, but its customers are increasingly using adjacent category language to search (for example, athleisure instead of sportswear, or performance wellness instead of fitness), and the brand’s third-party corpus hasn’t kept pace with that language shift, the brand will be invisible in exactly the queries customers are using.
Nike is the study’s clearest positive case, surfacing in both athleisure (77%) and athletic footwear (90%) queries, despite being KG-coded as a footwear brand.
The reason is that Nike has accumulated enough athleisure-coded third-party content, including editorial coverage in fashion publications, inclusion in activewear roundups, and co-mentions with other athleisure brands, to register as category-eligible in both framings. It built a sub-stream in the adjacent category that New Balance didn’t.
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What is the audit question everyone should be asking?
Before investing further in entity optimization, it’s worth running a simple diagnostic: Take the five or six different ways your customers might phrase a category query for what you do, and test each one across two or three LLMs. Note which formulations surface your brand and which don’t.
For the ones that don’t, the questions to ask are:
Does third-party content about your brand actually use that language?
Are you being written about in publications that cover that category?
Are you appearing in editorial roundups that use that phrasing?
If the answer is no, you know where to start: getting into the external conversations that speak the language of that query.
Closing that gap means becoming a participant in the category comparison content that defines who belongs in that space.
Ask a performance marketer which numbers they check every morning and you’ll get the same list: CPM, CTR, CVR, ROAS. Ask them their match rate on Meta or Google, the share of an audience they uploaded that the platform could actually recognize and target, and you’ll usually get a pause. Most teams don’t track it. Plenty don’t know it’s a number at all.
That pause is expensive. Build an audience of 100,000 customers, upload it to a platform that matches 55% of them, and the campaign is running against 55,000 people. The other 45,000 are invisible to it, no matter how good the targeting or the creative is. And match rate sits upstream of every metric that teams do track.
When a platform recognizes only part of the audience you built, every number that follows (reach, frequency, conversions, return on spend) is quietly computed against that smaller matched audience. You can rework creative, retune bids, and rebuild your conversion model forever, and none of it touches the slice of your audience the platform never saw. The part worth examining is where matching breaks, why it’s getting harder, and how much reach teams lose without ever seeing it on a dashboard.
The gap between the audience you build and the audience you reach
Here’s the mechanic most teams never examine. When you push a first-party audience to a paid platform, the platform doesn’t target “your customers.” It targets the subset of your list it can resolve to its own logged-in users, usually by matching hashed emails and phone numbers against the identifiers on its accounts. Every record it can’t resolve simply falls out, with no error and no warning. The campaign runs against whoever survived.
Every privacy shift of the past few years has widened that gap. Third-party cookie deprecation removed the connective tissue that bridged identities across sites. Apple’s App Tracking Transparency cut off device identifiers. The walled gardens keep tightening their matching logic. And the mundane failure modes never went away: the customer who signs up with a work email but uses a personal one on social, the phone number formatted differently on each side, the record that’s three years stale. Identifiers are splintering faster than most CRMs and CDPs can consolidate them, so the distance between the audience you build and the audience you can reach is growing, not shrinking, no matter how clean your data is.
The insidious part: platforms report performance against the matched portion. So the campaign looks fine. You’re measuring the efficiency of the audience the platform found, not the audience you built, and the difference between the two never shows up in any report you open.
Four places it’s costing you right now
Most marketers who have thought about match rate file it under “retargeting problem.” It’s far broader than that.
Acquisition. Seed and exclusion lists that only partially match make prospecting less precise, and a platform training on partial signal has to guess more. That usually surfaces as inflated CAC, and never gets traced back to matching.
Retargeting. The obvious one, but state the math plainly: if your CRM list matches at 45%, more than half the customers you meant to re-engage never see the campaign. The program runs at less than half capacity, and its reported numbers say nothing about the people it never reached.
Suppression. The sneakiest. Suppression lists only suppress the customers a platform recognizes. Every existing customer who doesn’t match is invisible to your exclusions, so you pay acquisition prices to re-buy people you already have, and some of them get served the new-customer discount your loyal buyers never see. Low match rates don’t just waste budget; they fund your own margin erosion.
Lookalike seeding. Lookalike models expand from the matched portion of your seed, not the seed you uploaded. A weak match rate means the model learns from a skewed sub-sample of your best customers, and that error compounds as the platform extrapolates across millions of impressions.
Add it up, and match rate isn’t a data team curiosity. It’s a tax on every dollar of paid spend, and almost nobody has measured how big it is.
What happens when you close the gap
This isn’t just theoretical. CKE Restaurants, the company behind Carl’s Jr. and Hardee’s, ran their audiences through Rokt mParticle’s Match Boost to enrich identifiers for ad platforms. Match rates rose up to 117% on Google Ads and 29% on Meta.
Note what didn’t change: the budget, the creative, the campaign structure. The same spend simply reached more of the audience the brands had already built, and ROAS improved on that same spend. That’s the signature of a match-rate problem. When recognition goes up, efficiency follows, because the waste you’re removing was never visible to begin with.
This used to be a procurement project. Now it’s a setting.
If match rate has been ignored, part of the reason is that fixing it used to be genuinely painful. Improving recognition meant licensing third-party data: vendor evaluations, procurement cycles, legal review, an integration build, and months before you could measure anything. The cost of the fix outweighed an upside most teams weren’t even quantifying.
That’s no longer the shape of the problem. Enrichment increasingly happens at the point where audiences leave your customer-data infrastructure for the ad platform, a setting on the connection rather than a system you build.
Done well, it inherits the governance you already have: identifiers you’ve deliberately excluded for privacy or compliance stay excluded, and the enriched data is used only to sharpen the match in flight, never written back into your profiles or stored in the destination platform. Closing the gap has become a configuration decision, not a data-strategy overhaul. That doesn’t mean it’s solved for everyone. It means the excuse for not looking is gone.
How to check your own match rate
Measure the gap. It takes about thirty minutes.
Pick your top three paid destinations by spend.
For each, compare the size of the list you uploaded against what the platform actually matched. Google Ads reports a match rate on Customer Match uploads (bucketed, but close enough); Meta shows the resulting audience size, which you can hold against the list you sent. Most brands land somewhere in the 40–60% range for email-only lists, well below what most teams assume.
Run the same check on your largest suppression list. That’s the one that will sting.
If your numbers come back north of 70%, go back to optimizing creative. If you’re like most brands, you’ll find you’ve been paying full price to reach a fraction of your audience. Every metric you already track is downstream of that one number, and most teams have never looked at it.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/07/20260721_rokt-1-VE9egI.png?fit=1920%2C1080&ssl=110801920Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-07-21 11:00:002026-07-21 11:00:00Why your match rate is the most important number you’re not tracking by Rokt mParticle
The Yoast SEO Bulk Editor already gave you one place to update titles and meta descriptions across your whole site. Now it does much more. This release adds focus keyphrases to the editor, new search and status filters, a dedicated view for every content type, and AI-drafted metadata with Yoast SEO Premium. In short: you can find what’s broken, fix it in bulk, and let AI handle the heavy lifting while you keep final say.
The Bulk Editor now shows only the fields that matter: your titles, meta descriptions, and, new in this version, your focus keyphrases. Everything is searchable, filterable, and organized per content type. As a result, posts, pages, products, and custom post types each get their own editor, which is ideal for publishers and stores alike.
Whether you manage 50 pages or 5,000, you can still move through them quickly and systematically. Just inherited a site? The Bulk Editor remains the fastest way to get it to a solid SEO baseline without starting from scratch.
New: search and status filters
You can’t fix what you can’t see all at once. The new filters get you straight to the pages that need work. For example, you can pull up every page missing a description, every draft, or every post targeting a keyphrase in seconds instead of scrolling. From there, you review, update, and move on without opening a single page.
Easily search for the page, product or post you are looking to edit
New: AI drafts it, you approve it (selected plans*)
Even when you can see what’s missing, writing hundreds of titles and descriptions by hand is a project nobody has time for. With paid plans like Yoast SEO Premium, you can now select the content that needs work and let AI draft the metadata for you. You then review each suggestion, apply what you like, and tweak or discard the rest.
Crucially, nothing saves without your permission. Per-row and batch apply or discard controls, plus unsaved-changes warnings, give you bulk editing without bulk mistakes. That means you can work through hundreds of pages with confidence, not caution.
Working with the AI powered metadata generator
*AI drafting is available with Yoast SEO Premium. WooCommerce and Yoast SEO AI+ plans extend it to products and categories.
New for stores: catalog metadata at scale
Keeping product metadata complete and unique across a large catalog is one of the most tedious jobs in e-commerce. This release helps in two ways. First, products get their own view in the Bulk Editor, so you can filter your catalog and spot the gaps fast. Second, Yoast WooCommerce SEO and Yoast SEO AI+ plans extend AI drafting to products and categories. Your store can therefore generate, review, and approve catalog metadata at scale, right in time for the next seasonal peak.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-07-21 07:30:462026-07-21 07:30:46New in the Yoast SEO Bulk Editor: filters, focus keyphrases, and AI drafts
After notifying advertisers about the upcoming change last week, Google has now officially confirmed on the Google Ads Developer Blog that Local Inventory Ads (LIA) will be enabled by default for all Shopping campaigns beginning August 31st.
Why we care. Advertisers using Shopping campaigns will no longer be able to disable Local Inventory Ads through the Google Ads API. If your workflows or campaign structure rely on keeping local inventory separate from online inventory, you’ll need to update both your code and campaign configuration before the change takes effect.
What’s changing. Starting Aug. 31, Google will automatically enable the “Local products” setting for every Shopping campaign.
Previously, advertisers had to explicitly set the enable_local field to true in a campaign’s ShoppingSetting to serve products from their Local Inventory Ads feed.
After the update:
Shopping campaigns will always have Local Inventory Ads enabled.
The Campaign.ShoppingSetting.enable_local field will be ignored.
Google will automatically treat the setting as true, regardless of the value submitted.
The change aligns Shopping campaigns with Performance Max for Retail, where Local Inventory Ads are already enabled by default.
What developers need to do. Developers using Google Ads API v25.1 or later should stop attempting to set enable_local to false.
Doing so will return:
ContextError.OPERATION_NOT_PERMITTED_FOR_CONTEXT
For API versions earlier than v25.1, existing code will continue to work, but any attempts to disable Local Inventory Ads will simply be ignored, with Google treating the value as true.
How to prevent local products from serving. Advertisers that previously disabled Local Inventory Ads for specific Shopping campaigns now have two alternatives:
Create a listing scope using CampaignCriterionService with product_channel set to ONLINE.
Use the Inventory filter in the Google Ads campaign settings to exclude local inventory and separate budgets between online and local products.
In other words, Google is shifting inventory control away from a simple campaign setting and toward inventory filtering.
Not changing. The update applies only to Shopping campaigns.
The enable_local field will continue to function normally for other supported campaign types, including:
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/07/Locoal-Inventory-Ads-shopping-7V3gyH.png?fit=1672%2C941&ssl=19411672Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-07-20 21:33:432026-07-20 21:33:43Google confirms Local Inventory Ads will be enabled by default
Google has quietly updated its documentation for Lead Form assets, removing one of the biggest barriers that prevented smaller advertisers from using the format.
Why we care. The long-standing requirement for advertisers to spend more than $50,000 in Google Ads before becoming eligible for certain Lead Form experiences has disappeared from the Help documentation. If this reflects a product change—and not just a documentation update—it could make Google-hosted lead generation available to significantly more advertisers.
What’s changed. The $50,000 spend threshold is gone.
Previously, advertisers needed to meet one of two requirements to use Lead Forms in certain formats:
Spend more than $50,000 USD in Google Ads.
Or qualify as a reputable advertiser spending more than $1,000 per account (or $15,000 across accounts) and complete Advertiser Verification.
The updated documentation removes the $50,000 lifetime spend requirement entirely. The only eligibility path now mentioned is the advertiser reputation and verification requirement.
That is the most significant change in the update and substantially lowers the documented barrier to entry.
Campaign support has changed. Google has also simplified where Lead Form assets can be used.
Previously:
Search
Performance Max
Display
Video (beta)
Now:
Search
Performance Max
References to Video campaigns have been removed from the overview, while Display is no longer listed as a supported campaign type. Interestingly, the requirements section still references Display campaigns, suggesting the documentation may not yet be fully aligned across all Help pages.
More lead delivery options. Google expanded the documented methods for receiving leads.
Newly documented options include:
Email notifications
Zapier integration
These join existing delivery methods:
CSV download
Webhooks
Google Ads API
The addition of Zapier gives advertisers a no-code way to automatically send leads into thousands of CRM platforms and business applications.
Better clarification on lead retention. Google also clarified how long lead data is stored.
The updated documentation now explicitly states:
Manual CSV downloads are available for 30 days.
Google stores lead data for 60 days.
API exports can access up to 60 days of lead data.
Previously, the 60-day retention period wasn’t clearly explained alongside manual downloads.
New positioning. Google also refreshed how it describes Lead Form assets.
The previous documentation focused on generating leads and increasing conversions.
The updated version emphasizes:
Higher-quality leads
Higher lead volume
Easier implementation
OTP verification for improving lead quality
Expanded country availability. Google added more than two dozen additional countries where Lead Form assets are eligible, including Bahrain, Croatia, Estonia, Jordan, Kuwait, Morocco, Qatar, Serbia, Slovenia and Tunisia.
Bottom line. Google’s latest Lead Form documentation refresh includes several usability improvements, expanded integrations and broader geographic availability. But the standout update is the disappearance of the $50,000 Google Ads spend requirement—potentially opening one of Google’s most valuable lead-generation formats to far more advertisers.
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Early in my career, I was responsible for reporting metrics for a company’s homepage. A member of the usability team wanted to know how much traffic a particular widget was getting. When we pulled the numbers, the result was underwhelming: About 2.5% of visitors actually used it.
But that’s not the number that ended up in the report. Instead, the widget’s usage was reframed as “a couple thousand visits per month.” That was technically true. It also told a completely different story from 2.5%.
That moment taught me something I’ve carried throughout my paid search career: Data isn’t black and white, and the person presenting it has a responsibility to tell an accurate story, not just a flattering one.
Data doesn’t lie, but PPC practitioners sometimes do. In PPC, we have more opportunities to blur that line than most people realize. Here’s where it happens and how to make sure your reporting holds up so you stay honest and ethical.
Conversions aren’t just conversions
If there’s one number that gets flattened most often in paid search reporting, it’s conversions.
A “conversion” can mean something completely different depending on what’s actually being counted. A form fill isn’t a marketing qualified lead (MQL). An MQL isn’t a sale.
For example, a phone call, a chat initiation, and a user watching 50% of a video are all conversion actions I’ve seen tracked in the same account, sometimes rolled into the same headline number and reported as “conversions.”
When you tell a client or stakeholder, “We got an excellent number of conversions,” without specifying what that conversion actually was, that’s not reporting. That’s editorializing, and it’s not a solid foundation for PPC strategy decisions.
Before you present a conversion number, ask yourself:
What action is actually being counted? A form fill, a call, a chat, a video view, or a purchase?
How far is that action from an actual business outcome? A lead and a closed sale aren’t the same type of win.
Would the person reading this report make a different decision if they knew what was behind the number?
If the answer to the third question is yes, you owe them that context, and you should include it when you deliver the report.
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Your CTR benchmark is probably a decade out of date
I still hear practitioners say a campaign is performing well because its click-through rate is “over 2%.” That benchmark comes from an era of PPC that doesn’t exist anymore.
Today’s bidding algorithms are far more sophisticated at finding users who resemble your existing converters. That alone pushes click-through rate (CTR) up across the board, independent of anything you did strategically.
A 2% CTR benchmark from 10 years ago tells you almost nothing about whether a modern, algorithmically targeted campaign is actually healthy and meeting its goals.
Reporting that “CTR is above benchmark” without acknowledging what’s driving that lift, whether it’s better targeting, better creative, or simply a more capable algorithm finding easier audiences, is another way data gets presented as good news without earning that designation.
I don’t think there’s a legitimate universal benchmark left to point to. The algorithm has gotten too good at finding easy clicks for a single number to mean the same thing across accounts, industries, or even campaigns within the same account.
Stakeholders will always ask the fundamental question: “Are these numbers good or bad?”
As subject matter experts, our job isn’t to hand them a legacy benchmark to check off, especially one that doesn’t really exist in any meaningful form.
True expertise means redefining success and shifting the conversation away from vanity metrics that the algorithm inflates for us. It means anchoring our reports in the business outcomes we were hired to drive.
It’s also important to explain how modern bid strategies affect the metrics you’re reporting.
Raw numbers and percentages tell different stories — use both
The widget story I opened with is really a story about raw numbers versus percentages, and that same tension shows up constantly in paid search reporting.
When you’re breaking down conversions by type, for example, showing that phone calls make up 40% of conversions versus leads at 60% tells a very different story than saying “142 calls, 213 leads.” Neither version is wrong. But presenting only one of them, especially the one that happens to look better, is a choice. It isn’t neutral reporting.
It’s something we have to be conscious of whenever we present data.
The fix isn’t complicated: Show the data in more than one way.
By presenting raw counts and percentages together, you give whoever reads the report enough context to understand what actually happened, instead of what you want them to take away from the report. Percentages add context to the data.
What you choose to focus on in a report is itself a form of manipulation
This kind of manipulation by omission is something I think about most often because I’ve seen it cost advertisers real money and create confusion.
For example, I once took over an account from another practitioner who had been telling the business that its low cost per click (CPC) was a sign of success.
If a low CPC were actually the goal, it’d be easy to hit that number by running everything through the Display Network or another upper-funnel campaign. But that campaign type may not align with what the brand actually needs.
In my example, because the business had been told for months that a low CPC meant good performance, it had bought into the wrong metric entirely.
In reality, a higher CPC often drives better business outcomes and can even result in a lower cost per acquisition because you’re paying more to reach higher-intent, higher-value users instead of optimizing for cheap clicks that don’t convert. For my client, that turned out to be true.
Focusing a stakeholder’s attention on the metric that makes your work look best instead of the one that reflects their actual goals is one of the quietest ways data gets weaponized in this industry. We should be client-first when it comes to reporting data.
Attribution can hide whether your spend is doing anything at all
Even accurate reporting on conversions, CTR, and CPC can still mask a bigger question: Would those conversions have happened anyway?
Attribution models give credit for conversions across touchpoints, but it’s important to remember that credit isn’t causation. A branded search campaign can show a huge volume of “conversions” that would’ve happened through organic or direct traffic, regardless of whether the ad ever ran. The report looks great, but the incremental business impact may be close to zero.
This doesn’t mean we wouldn’t run a brand campaign. It means the data needs more context and nuance.
Incrementality testing — whether that’s using a holdout group, running a geo experiment, or conducting a conversion lift study — is the only real way to answer whether your media spend is creating new business or simply claiming credit for outcomes that would’ve happened anyway.
Reporting conversion volume without ever addressing incrementality is one of the most common and most defensible-sounding ways paid search data tells an incomplete story.
Most of the metric issues above happen without anyone intending to mislead. But a few specific patterns are worth calling out because once you see them, you can’t unsee them in your own reporting.
Conversion stacking: Counting multiple actions from a single user journey — say, a chat, then a call, then a form fill from the same person — as three separate wins instead of one.
Cherry-picked date ranges: Comparing this month to a deliberately slow month last quarter, or quietly excluding the week your account had an outage or tracking issue. A date range chosen against a weak baseline can make almost any account look like it’s improving.
Vanity metric substitution: Leading with a metric that looks good — perhaps impressions, clicks, or “reach” – when the metric that actually matters — qualified leads, revenue, or CPA – tells a less flattering story.
I’m not saying every practitioner who uses these patterns is acting in bad faith. Most of the time, it’s habit, not deception. But habits are exactly what need to be questioned if we’re serious about reporting data honestly.
Paid search doesn’t have a governing board. That’s exactly why this matters
Unlike many licensed professions, paid search practitioners don’t answer to a regulatory body. We have platform certifications, not an ethics board. That means the standard for how honestly we present data to clients and stakeholders is largely self-imposed.
I don’t think that gets talked about enough in our industry. It’s easy to frame a number in the best possible light, especially when your job security or a client relationship depends on the story that number tells.
But contextualizing conversions accurately, using current benchmarks, showing both raw numbers and percentages, and focusing on the metrics that actually reflect business outcomes isn’t just good practice. It’s the ethical baseline for PPC.
If we don’t hold ourselves to that standard, no one else will.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/07/How-to-report-PPC-performance-without-lying-to-yourself-or-your-boss-ZfLypS.png?fit=1920%2C1080&ssl=110801920Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-07-20 15:00:002026-07-20 15:00:00How to report PPC performance without lying to yourself (or your boss)
Google is making it easier to implement Google Ads purchase conversion tracking by introducing a Guided Setup experience in Google Tag Manager, reducing the manual work required to configure tags.
What’s happening. A new “Guided Setup” card is appearing in Google Tag Manager for Google Ads Purchase Conversions.
The beta feature automatically creates the required:
Tags
Triggers
Variables
Instead of requiring users to configure each component manually.
Why we care. Conversion tracking is essential for campaign optimisation, but implementation can be complex—particularly for advertisers new to Google Tag Manager. Guided Setup aims to simplify the process, reducing setup errors and helping advertisers start measuring purchases more quickly.
Between the lines. The update reflects Google’s broader push to lower the technical barriers to measurement as advertisers increasingly rely on automated bidding strategies that depend on accurate conversion data. By automating much of the configuration, Google could improve tracking adoption and data quality across Google Ads accounts.
What we’re watching. The feature is currently in beta, meaning it isn’t yet available to all Google Tag Manager users. Google has not announced when a wider rollout will begin.
The bottom line. Google Tag Manager’s new Guided Setup streamlines Google Ads purchase conversion tracking by automatically configuring key measurement components, making implementation easier for both new and experienced advertisers.
First spotted. The beta feature was shared by Paid Search expert,Vivek Gupta on LinkedIn.
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.
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Digital PR is the single most important strategy to win in AI search.
A study by Muck Rack found that 84% of AI citations come from earned media, third-party sources like editorial coverage, reviews, and forums.
In AI search, third-party validation beats self-promotion.
Publishing onsite content is still important, no doubt. But your AI visibility is mostly shaped by what happens outside your website.
The more frequently your brand name appears across the web, the more likely LLMs are to recognize, trust, and eventually recommend you.
In this guide, I’ll show you six proven digital PR strategies to earn more backlinks and brand mentions, strengthen your offsite authority, and increase your visibility in AI-generated answers.
Why Digital PR Now Shapes AI Visibility
AI systems go well beyond reading your website.
They pull information from dozens of sources across the web, then combine it into a single answer.
The brands that win in AI search are those that appear consistently across those sources.
The top two metrics that impact AI visibility are domain authority and high-quality backlinks (from DA 60+ sites).
Data-led content is how you earn those quality backlinks. People love citing fresh numbers.
I helped one of my clients, Resource Guru, put together the “Agency Overworking Report 2025.”
Since it was published, this report has generated 21 backlinks naturally, including coverage in Forbes.
It’s also consistently being cited in AI answers.
How to Do It
First, publish your data-led content.
You have several options:
Conduct new research nobody has done before: This is an excellent choice if you’re in a growing field like GEO, where data is scarce, and any credible study will attract attention.
Update existing studies: Find stats that haven’t been refreshed in over two years. Outdated data is everywhere, and writers actively look for newer numbers to replace them. If you can be the source that fills that gap, you’ll earn the citation.
Compile existing statistics: You don’t always need to generate new data. Aggregating hard-to-find stats into one well-organized resource can be just as link-worthy.
Once your data is live, pitch it proactively.
Find articles in your niche that reference outdated statistics or cite sources that no longer exist. Reach out to the authors and offer your fresher data as a replacement.
Also, pitch your new data to existing statistical roundups in your niche. These pages are usually updated frequently, so the author is more open to new additions.
Here’s an example of a good pitch that I received for my AI SEO stat roundup:
Strategy 2: AI Citation Outreach
AI citation outreach is the practice of securing placements in the specific sources AI models already pull from.
In my experience, it’s the quickest way to start earning brand mentions in AI responses.
How It Boosts AI Visibility
If you can get into the content AI cites, you can influence the answers AI gives.
For example, my agency Position Digital was included in Exposure Ninja’s listicle on “The Best AI Search Optimisation Agencies in 2026.”
And since that listicle was cited by ChatGPT, my agency ended up being recommended in one of the answers.
How to Do It
LLMs cite content differently. Each model has its own preferences, tendencies, and patterns.
For example, I asked AI Mode and ChatGPT to recommend the best SEO tools.
Here’s what ChatGPT said:
And here’s the AI Mode answer:
Similar answers. But totally different citations.
So, you need to tailor your strategy to each AI model you want to target.
1. Research user prompts
Start by mapping out the prompts your target audience is likely to type into AI tools.
AI companies haven’t given us prompt data yet, so we’ll have to settle for educated guesses.
Here are some good places to start:
Source
Tips
User questions in sales calls
Note the exact language prospects use when describing their problem
Queries in Google Search Console
Look for ultra-long queries in your performance report
Filter for question keywords starting with who, what, how, why, which
Alternatively, you can use Semrush’s AI Visibility Toolkit to find out exactly what your audience is typing into LLMs.
2. Monitor the cited pages
Once you have your prompt list, run each prompt through each AI model and note which pages are cited.
If you don’t want to do it manually, Semrush’s AI Visibility Toolkit shows you the sources for each prompt.
You can also use ListBrew to surface “best X” listicles and comparison pages that already get cited in AI answers for your target prompts.
3. Find the contacts
Find the contact information for each opportunity using tools like Hunter and Apollo.
Tip: Prioritize someone who has editorial control over the content, like the author or content editor, rather than generic email addresses like hello@domain.com.
4. Send personalized pitches
Reach out to each prospect and ask to be included in the content.
Keep the pitch short and specific to their piece. To increase your chances, offer something in return, such as:
A free trial or demo of your product
A reciprocal mention in one of your own high-traffic pieces
Also, prepare a short blurb that the author can easily copy and paste into the content. It seems simple, but it makes a big difference.
Here’s an example of a pitch I sent during one of my listicle outreach campaigns:
Strategy 3: Reactive PR
Most digital PR campaigns take weeks or even months to prepare and execute.
Reactive PR is about speed — being the first one to respond to breaking news, viral trends, and emerging topics.
How It Boosts AI Visibility
LLM training data has a cutoff date.
When someone asks about a current event, the model can’t rely on what it already knows. It has to search the web for fresh sources.
That’s your window.
When a story first breaks, there are usually only a few credible sources covering it.
If your brand is among the first to publish useful commentary, analysis, or original reporting, your content has a much higher chance of being surfaced and cited by AI systems.
The piece has since gained over 16,000 readers, 500 backlinks, and several AI citations.
How to Do It
The biggest challenge with reactive PR is spotting trending stories before they become a trend.
By the time a topic is dominating headlines, you’re already too late.
Success comes from identifying emerging conversations while they’re still gaining traction.
A few ways to do that:
Join online communities: Many trends gain momentum on Reddit, X, and Slack before they receive mainstream coverage.
Follow industry leaders on LinkedIn: Experts often share breaking news, observations, and predictions here before publishing full articles or reports.
Follow the major players in your industry: Product launches, partnerships, acquisitions, funding rounds, and policy changes often create PR opportunities. The sooner you spot these developments, the faster you can react.
Track journalist requests: Platforms like Qwoted, Featured, and JournoRequests can reveal stories that journalists are actively working on before they’re published.
Use Google Trends and Exploding Topics: These tools help you find emerging topics that your audience is searching for right now.
A secondary layer of this process is monitoring how stories spread across publications.
Many trends don’t appear out of nowhere in major media — they first circulate through smaller blogs and niche industry sites.
Tracking this ripple effect helps you identify narratives early and position your response while the topic is still evolving.
Set up Google Alerts: Create alerts for important keywords, competitors, and industry topics so you get notified as soon as new stories are published.
Monitor industry newsletters: Curated newsletters are often one of the fastest ways to discover emerging trends and conversations.
Once you spot an opportunity, move quickly.
Write a report or a commentary piece on your website and social media, and pitch the story to major news outlets in your field.
Strategy 4: Ego Bait
Ego bait involves creating content that features industry experts and influencers.
The goal is to stroke their ego, making them more likely to share your content, mention your brand, or link back to your site.
How It Boosts AI Visibility
Featuring recognized experts can improve AI visibility in two ways.
First, it increases the credibility of your content.
An AI SEO study found that pages containing expert quotes receive 4.1 citations in ChatGPT on average, compared to 2.4 for pages without them.
And we’ve seen it firsthand.
Our article on SEO competitor analysis features insights from 20 experts, and it’s cited by both Google’s AI Overviews and AI Mode.
Expert commentary acts as a trust signal, making the content more authoritative and cite-worthy.
Second, it creates a distribution channel.
When experts are featured in your content, many will share it with their audience, mention it on social media, link to it from their websites, or include it in newsletters.
As those mentions and backlinks accumulate, your brand becomes more visible across the web.
There are three types of ego-bait content you can create:
1. Expert roundups
Use journalist outreach platforms like MentionMatch, Featured.com, and Qwoted to gather expert insights for your content.
Choose the best quotes and feature them on your blog post.
Once it’s live, tag every contributor in your LinkedIn post.
Most will reshare it, comment, or at minimum engage with it, which extends your reach well beyond your own following.
2. Case studies and success stories
Highlight real results from customers, partners, or collaborators.
A well-written case study flatters the subject, gives them something worth sharing, and adds a layer of credibility that generic content can’t replicate.
3. Top experts or influencers lists
Curate lists of respected people, companies, or voices in your category.
Examples include:
Top SEO Experts in 2026
Most Influential AI Search Voices
Leading Growth Marketers in SaaS
Being included in a curated list is often incentive enough for people to share it, especially if the selection feels credible and relevant.
This type of structured content is also highly citable by AI systems.
Strategy 5: Thought Leadership
SEO content is built to rank.
Thought leadership content is designed to establish you as a recognizable voice — both for people and LLMs.
And when you already have an established presence, it’s much easier to market your business.
How It Boosts AI Visibility
Most content gets ignored because it fails to give people a reason to engage.
Thought-provoking content, on the other hand, is designed to challenge assumptions, introduce new perspectives, and spark discussion.
Just look at this “controversial take” from Khanh Linh Le.
This post generated an unusually high number of comments relative to its likes because it challenged conventional thinking and encouraged debate.
Some people agreed. Others pushed back. But everyone had something to say.
That’s the goal: to drive engagement.
Engagement drives shares. Shares drive mentions across blogs, forums, social platforms, and publications.
And when a lot of people talk about you, AI pays attention.
How to Do It
1. Build a LinkedIn presence
The first step is to start posting on LinkedIn.
Why?
Because at the time of Semrush’s study, LinkedIn was the second-most-cited domain in ChatGPT, AI Mode, and Perplexity.
Some tips:
Publish consistently: AI citations reward relevance and consistency more than virality. The Semrush study found that 75% of cited authors post at least one post per week.
Take a stance: Share opinions, challenge assumptions, and weigh in on debates happening in your industry. Posts that take a clear stance generate far more engagement than informational updates.
Use your personal and company pages: Some LLMs like Perplexity gravitate more towards company pages, while others like ChatGPT and AI Mode like citing personal pages.
2. Write guest posts for major publications
Guest blogging is a great way to distribute your ideas and share your expertise in high-authority publications.
When multiple authoritative sources cover the same topic and all point back to you as the originating voice, AI models start to recognize you as the go-to authority on that subject.
I then publish variations of that post on other publications, ideally those with a high website authority.
As an example, I wrote about “Content Refreshes” on the Position Digital blog.
I then wrote guest posts on the same topic on Sitebulb and Surfer SEO.
All three articles are now cited by AI Overviews.
3. Appear as podcast guests
Podcasts are one of the most underutilized thought leadership channels.
A single appearance can generate:
Mentions on the host’s website
Show notes and transcripts
Social media clips
Newsletter features
Citations from future content creators
If you’re just getting started, don’t chase after big, established shows.
Start small. Build relationships with newer podcasts in your niche.
Smaller shows are easier to get on, their hosts are often more engaged, and the content still gets indexed and cited.
As your reputation grows, the bigger opportunities follow naturally.
Strategy 6: Community Building
Community building is about establishing your brand’s presence in third-party forums, review sites, and customer rating platforms that AI models treat as trusted, independent sources.
This way, your brand is validated not just by what you say about yourself, but by what neutral platforms say about you.
How It Boosts AI Visibility
AI models trust neutral, community-driven sources over brand-owned marketing.
It’s not an opinion. It’s backed by actual data.
According to another AI visibility study by Semrush, LLMs cite Reddit threads about Microsoft products much more frequently than Microsoft’s own blog.
Let that sink in.
And it’s not just forums like Reddit. Review platforms like Trustpilot and G2, where users can share authentic reviews and ratings, carry significant weight too.
Seer Interactive also did a study of 800k AI responses and found that brands with no Trustpilot profile have a median AI citation rate of just 1%.
Brands with even a minimal profile, as few as 1 to 13 reviews, jump to 53.5%.
The implication is clear:
If AI has to choose between what a brand says about itself and what hundreds of independent users say about that brand, it will often favor the latter.
How to Do It
1. Contribute insights on Q&A platforms like Reddit and Quora
These communities are scraped heavily by AI models and frequently cited in responses.
But be careful, Reddit and Quora communities have a low tolerance for overt self-promotion.
Accounts that exist purely to promote a product get flagged, downvoted, or banned.
The key is to participate as a real contributor, not as a marketer.
Be transparent about who you are and what company you represent, but focus primarily on being helpful.
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Do you want to start a blog or online store? Then you’ll need a website. But did you know you’ll also need web hosting to get your website online? There are many aspects to web hosting, and you might not be sure what it entails. You have to choose a host, a hosting type, and many other things. That’s why we wrote this guide, to help you understand what it is and pick the right host for your site!
Web hosting makes your website accessible online, requiring you to choose a host and type of hosting.
Decide between fully-hosted and self-hosted options based on your control and data ownership preferences.
When picking a host, consider server location, storage space, audience size, and additional services offered.
Understand the different types of hosting: shared, dedicated, and VPS hosting to find what suits your needs.
Specialized hosting plans exist for WordPress.org sites, which come optimized and may include support for security and updates.
What is web hosting?
Let’s start with the basics. Web hosting is a service that makes sure your website is accessible through the internet. The pages and files that your website consists of live on a server. Whenever someone visits your website, that server sends over the necessary ‘files’ to display your site to the site visitors. This server is owned by a web hosting company, and you can rent space to store your website files on a server that meets your needs. Without web hosting, your website simply can not exist.
It’s also good to know that, although it doesn’t have a direct effect, your choice of hosting impacts your SEO. Reliable hosting improves the security, speed and uptime of your website. All factors that tell search engines that your website is trustworthy and a good option to show to their users. Upgrading your hosting plan or switching to a new hosting company can therefore improve your site performance, just make sure to choose reliability and your website’s needs over low prices.
So, where do you start? By looking at web hosts or hosting companies. These companies provide servers and internet connectivity in a data center. In other words, they provide space for your website’s data, so it can exist and other people can find it.
Fully-hosted versus self-hosted
The first choice you have to make when picking the right host for your site is whether you want to create a fully-hosted or self-hosted website. The difference is pretty self-explanatory. Fully hosted means every site on the platform is hosted on its own servers. These companies take care of all the hosting stuff for you, such as Shopify and WordPress.com.
Self-hosted, on the other hand, means you’ll have to arrange hosting yourself. A good example is WordPress.org. Now, you might be thinking: why would I want to do everything myself? And while it’s true that self-hosting is extra work, it also means you own all the data yourself. Plus, you get access to the full codebase and can choose your own hosting company.
How to choose a hosting company
These days, there are thousands of companies to choose from. They all have their own benefits and hosting plans. So, how do you pick the right host for your site? What should you focus on? First, it’s important to take a couple of things into account.
Server location
As those real estate TV shows always say: location is everything. It’s not necessarily everything when it comes to hosting, but it is important. Where your host’s servers are located is an important factor in your site’s speed. Why? The smaller the distance between your audience and your server, the faster your website is. For example, if your audience is located in the US but your host is based on the other side of the world, that could slow down your site for visitors, which is something you want to avoid.
Storage space
Second, consider what kind of website you want to create. This impacts how much storage space you need to acquire. For example, a small blog needs far fewer resources than a big online shop with thousands of products. Consider how many pages your site will have and how much content it will have. If you already know you want to build a large website with many large images and videos, you’ll need to look at hosting plans with more storage space.
How big is your audience?
We know this is a hard one to answer. But you might already have an idea of the number of page views you want to reach. Take this number into account when deciding on a hosting company and plan. Because every time someone views a page on your site, it adds to the amount of bandwidth you use. In other words, all the texts and images on that page need to be downloaded onto each visitor’s computer before they can be displayed in their web browser.
Other services offered
A final consideration is the need for other services related to your website. Many hosting companies offer additional services, such as email hosting, SSL certificates, automated website backups, and website builders. The website plan you choose should provide the services you need to get you up and running, preferably beyond basic hosting. For example, choosing a hosting plan that comes with a website builder helps you create your website right away.
Bluehost has recently launched an AI Website Builder. You answer a few questions, and it generates your website for you. It lets you make changes through chat, and you see changes in real time, no coding or design skills needed. Just describe what you want. Hosting and a domain are included, and human support is available if needed. Ideal if you’re setting up a website for your small business and want it to go live quickly. If your website is a bit more complex, for example, when you want to create a membership portal, this might not be the best option.
Extra questions to ask
In addition to server location, storage space, and the number of visitors, there are tons of other factors that determine your ideal hosting company. That’s why we compiled a couple of additional questions you could ask yourself (and a hosting company) when picking the right host for your site.
Do I need help or support setting things up?
Does the company offer live chat and phone support, and do they respond quickly?
What happens if my site gets hacked or goes down in the middle of the night? Will they help?
What is my budget?
Do I need the ability to add more websites?
Does this hosting company have good reviews?
And, for higher-end packages, what type of hardware, like SSD storage, CPUs, and memory chips, do I need for my site?
Different types of hosting
Finally, let’s look at the different types of hosting. In this guide, we’ll focus on the three most common ones: shared hosting, dedicated hosting, and VPS hosting. But there are lots of other options out there.
Shared hosting
Shared hosting means you share the same server resources with multiple customers. These server resources determine how much space, bandwidth, mailboxes, etc., you have available. It also means there will be resource limits on each hosting package. Don’t worry, sharing resources doesn’t mean other customers can see or access your data. Shared hosting is probably the cheapest option, but it might not be the best choice if you expect to need a lot of server space and bandwidth.
Dedicated hosting
Dedicated hosting does exactly what it says on the tin. You’ll get a dedicated, fully allocated hardware server for your use only. This means you’re not sharing the server with other customers. Instead, you’ll have your own personal server with its own processors, hard disks, and memory. This makes dedicated hosting an interesting choice for bigger sites, such as large online shops or corporate sites.
VPS hosting
VPS hosting stands for Virtual Private Server. A VPS is a virtual machine on a physical server that is divided into different sections. You get your own space on this server, with access to a certain amount of resources, like memory and processors. Unlike with shared hosting, these resources are for your use only. Additionally, you can optimize your VPS server to your own preferences. For example, you can determine which software runs on it and adjust the settings to your liking, just like with dedicated hosting.
Hosting plans for WordPress.org sites
If you’re planning to create a WordPress.org website, then we’ve got good news for you. There are tons of hosting companies that offer hosting plans specifically for WordPress.org sites. The benefit? The plans are completely optimized for WordPress-based websites. Usually, they already have WordPress installed, so you don’t have to take care of that anymore. Sometimes they also offer security and update services. This means you don’t have to worry about your site’s security or updating WordPress and the plugins you have installed.
Now you know all the basics about hosting companies, hosting plans, and the different types of hosting. Still, it’s good to consider which factors are important to you before choosing a hosting company and plan. Because your site’s hosting can impact factors like site speed and the amount of content you can host.
If you want more information about hosting, we recommend checking out our Technical SEO course. The course dives into considerations when choosing a host, as well as practical tips for making your site easy for search engines to find. And much more!
FAQ
After reading this post, I realized that I might need a better host. Is it possible to switch hosts?
It’s possible, but it’s going to depend on your setup and where you’re moving to. Luckily, most hosts offer great support. Typically, they’ll guide you through all the processes you need to go through. These processes are, for example, moving your files, your database, and maybe some configuration info.
When you’re choosing your host, it’s worth getting in touch with their support team to see what kinds of services they offer and how much they can help you with that.
Okay, but there are many hosting options out there. Is there a tool that lets me enter my data and help me choose?
Oh, that’s tricky! There are lots of tools on the internet to help you pick a hosting provider, but it’s a bit of a minefield. We recommend checking out yoast.com/hosting.
But really, do your own research. Get a feel for what your budget is, what kind of features you might need, and talk to the support people. Many of these companies and websites also offer live chat.
I’m just starting out with my site, and I don’t know how many visits I’ll get. I’m hoping for a lot, of course, but I might get only a few. So, when picking a host, what should I look for in terms of bandwidth?
It’s good that you’re thinking about bandwidth beforehand, but don’t let it dictate your choice. Instead, research how much different hosting companies charge for their different packages, and how the prices change as you need to scale. Maybe you can start off small and upgrade later. Again, shop around, review pricing and packages, and talk to support staff.
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Your firm has a website, a handful of blog posts, and maybe a paid listing on Avvo, yet the personal injury firm three blocks over keeps showing up first…
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