Google updated the Data Manager API with new audience management tools, more flexible data ingestion and expanded support for user-provided address data, making it easier for developers to maintain Customer Match lists and improve data quality.
The release is aimed at reducing manual work while providing better visibility into data issues that don’t require an ingestion request to fail.
What’s new. The headline addition is a new RemoveAllAudienceMembers method, which allows developers to clear an entire audience list in a single operation. An optional timestamp parameter also lets advertisers remove only members added before a specified date, making full audience refreshes much easier.
Google has also introduced field-level ingestion warnings. Rather than failing an entire request when optional fields contain invalid data, the API now processes valid records while returning detailed warnings that identify the problematic fields and explain why they failed validation.
The update also expands the address information that can be sent to Google Analytics destinations. Developers can now include street address, city and state or province alongside existing fields such as name, postal code and region, while user-provided data can also satisfy identifier requirements for certain multi-source events when other identifiers aren’t available.
Google has additionally released new AI agent skills in its Google Skills GitHub repository to help developers build Data Manager API integrations more efficiently within AI-assisted coding environments.
Why we care. The update removes several friction points for developers managing first-party data. Audience refreshes become simpler, ingestion issues are easier to troubleshoot without interrupting workflows, and the expanded address fields provide more flexibility when sending user data to Google Analytics.
Bottom line. The latest Data Manager API release streamlines audience management, improves error handling and expands data collection capabilities, giving developers more efficient tools for managing customer data across Google Ads, Display & Video 360 and Google Analytics.
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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Google launched dedicated Generative AI performance reports in Search Console on June 3, 2026, initially rolled out to a subset of UK-based sites.
The report tracks impressions across AI Overviews, AI Mode, and AI features in Discover, broken down by pages, countries, devices, and dates.
Data begins from May 18, 2026; there is no historical backfill.
Click data is not included in the current version, which is the most significant limitation.
Google also introduced an opt-out toggle allowing sites to block content from AI features without affecting traditional organic rankings.
AI visibility and traditional organic visibility are now two distinct, separately measurable channels.
For two years, AI search performance has operated as a black box. Brands could see that AI Overviews were growing, that click-through rates were falling, and that something had changed in how Google surfaced content. But first-party data on what was actually happening inside AI features was not available.
Google changed that on June 3, 2026.
What Google Actually Launched
Google’s Search Generative AI performance reports give site owners a dedicated view inside Search Console showing how their content appears within generative AI features. Previously, any traffic or impressions from AI Overviews and AI Mode were folded into the standard Performance report with no way to isolate them. That separation is now possible.
The report covers five dimensions: impressions (how often your URLs appeared in AI features), pages (which specific URLs were cited), countries (geographic breakdown of AI visibility), devices (for Search results), and dates (with hourly through monthly granularity).
The rollout launched June 3, 2026, initially for a subset of UK-based site owners. This is a direct response to the UK Competition and Markets Authority mandate, which requires Google to share this data under the Digital Markets Act. A global rollout is planned but no timeline has been confirmed. If your Search Console does not yet show the report, it is coming.
Alongside the reporting launch, Google introduced an opt-out toggle that allows site owners to exclude their content from AI Overviews, AI Mode, and Discover AI features entirely. Critically, opting out carries no organic ranking penalty. Google has confirmed the toggle will not be used as a ranking signal for traditional search results. The toggle takes effect from June 17, 2026.
The most significant limitation of the current report is that it tracks impressions only. There is no click data, no CTR, no average position, and no query-level breakdown. You can see that your content appeared in an AI feature, but not whether anyone clicked through to your site as a result.
For performance marketers accustomed to building decisions around clicks and conversion, this creates a measurement gap. AI systems are designed to answer questions directly, which means click rates from AI features are likely structurally lower than click rates from traditional results. An impression inside an AI Overview does not confirm a visit, and given how AI Overviews behave, it may not produce one at all.
The right way to read this data, at least in its current form, is as a resonance signal. A page that earns high AI impressions is content the model finds worth citing. Cross-reference AI impressions against the organic clicks that same URL earns in the standard Performance report, and you get a genuinely useful picture: pages that perform strongly in both channels are your highest-value content assets. As one analysis put it, high AI impressions and high organic clicks on the same page is the clearest signal of what non-commodity content actually looks like. For a deeper look at how to interpret and act on AI brand visibility data, see our recent breakdown.
The data also starts from May 18, 2026, which sits inside the May 2026 core update window. Early trend interpretation should account for that overlap, since ranking shifts from the update will be mixed into the initial AI impression patterns.
Reading AI Impressions as a Strategy Signal
The absence of click data in the current report does not make AI impressions useless. It changes how they should be read.
An impression inside an AI Overview or AI Mode response means Google’s system selected your content as a grounding source for its answer. That selection reflects the same quality signals that drive strong traditional rankings: factual accuracy, topical authority, clear structure, and relevance to the specific question being answered. Pages that consistently earn AI impressions are your content assets that the model finds most citable.
Cross-referencing AI impressions with organic clicks from the standard Performance report creates a genuinely useful diagnostic. Pages with high organic clicks and high AI impressions are performing in both paradigms simultaneously.
These are your non-commodity pages: content that earns traffic from users who click through and citation from AI systems answering related questions. Pages with high AI impressions but low organic clicks may indicate content that answers queries well enough to be cited but does not generate sufficient click intent on its own. These are worth examining for conversion optimization. Pages with low AI impressions and high organic clicks may represent ranking-driven traffic that is increasingly vulnerable as AI Overviews expand into their query categories.
Should You Opt Out of AI Features?
Almost certainly not, for most brands.
The business case for opting out is narrow. Publishers with content-licensing models, paywalled material, or specific legal reasons to restrict AI citation may have legitimate reasons to consider it. For the vast majority of brands, opting out means losing a growing visibility channel, with no ranking benefit to offset it.
The opt-out toggle is most useful as a signal that Google has acknowledged the tension between feeding AI systems with publisher content and compensating those publishers for it. Having the control is meaningful. Using it defensively, without a clear strategic rationale, is not recommended.
What to Do Now
Even before your Search Console account gains access, prepare the reporting infrastructure now.
Define how AI impressions will feed into your performance reporting. AI visibility and traditional organic visibility are now distinct channels, and they need separate measurement frameworks. A page ranking well in traditional search is not the same as a page earning AI citations, even though both matter.
Identify which pages are likely candidates for AI citation in your current content mix. Content that directly answers specific questions, uses clear structure, and is factually accurate and current is more likely to surface in AI features. Those pages should be audited for completeness and optimized before the report gives you data to react to.
Set a baseline as soon as access arrives. The report currently has no historical data before May 18, 2026, which means the earlier you establish your first benchmarks, the more useful comparative data you will have going forward.
FAQs
When will I get access to the new AI report in Search Console?
The current rollout is limited to a subset of UK-based site owners. Google has said a global rollout is planned but has given no specific timeline. Monitor your Search Console account for the Generative AI section to appear.
Can I see which queries trigger AI impressions for my content?
Not in the current version. The report does not include query-level data. This is one of the most significant gaps and may be addressed in future updates.
Does opting out of AI features help my rankings?
No. Google has confirmed that the opt-out setting will not be used as a ranking signal for traditional organic search. Opting out affects AI Overviews, AI Mode, and Discover AI features only.
Should I treat AI impressions the same as organic impressions?
No. AI impressions indicate your content was cited inside a generative feature, not that a user was shown your link in a traditional results format. The downstream behavior is different, click rates from AI features are likely lower, and your measurement framework should reflect that distinction.
Conclusion
AI search visibility has been growing for two years with no way to measure it directly. The new GSC Generative AI performance report closes that gap, partially. Impressions data without clicks is an incomplete picture, but it is a meaningful start. The brands that build reporting frameworks around this data now, before a global rollout makes it standard practice, will be better positioned to interpret performance and make decisions as the reporting matures.
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On the latest episode of PPC Live the Podcast, Ana Kostic shared how one account restructure taught her a lesson that still shapes how she manages paid media today: successful PPC isn’t about building the perfect campaign structure—it’s about protecting the business behind it.
The mistake: A “perfect” account restructure
Early in her career, Kostic inherited a Google Ads account with a messy structure. Determined to apply PPC best practices, she rebuilt the account from the ground up, confident that cleaner campaigns and better keyword organisation would improve performance. Instead, traffic and sales dropped by around 40%.
The hidden cost of starting over
The issue wasn’t the new structure itself—it was wiping away years of historical performance data that Google’s systems had learned from. The account eventually recovered, but only after roughly two and a half months, with the full benefits taking closer to six months to materialise.
The lesson wasn’t about Google Ads
For Kostic, the biggest takeaway wasn’t technical. It was recognising that businesses can’t afford major revenue dips while platforms relearn campaign performance. “You have to think about the business first,” she said.
Ask business questions before platform questions
Today, Kostic starts every onboarding by understanding the business rather than the account. She asks about cash flow, margins, growth goals and how much short-term disruption the company can realistically absorb before recommending major structural changes.
Why slow beats perfect
Rather than replacing campaigns overnight, Kostic now introduces changes gradually, allowing new structures to learn while existing campaigns continue delivering results. Her philosophy is simple: “We like it slow and boring.”
Communication is part of optimisation
Kostic also credits her former manager for helping navigate the difficult conversations that followed. Instead of assigning blame, the agency focused on transparency, created a recovery plan and supported both the client and the team until performance stabilised.
PPC doesn’t stop inside Google Ads
One of Kostic’s biggest pieces of advice is to spend more time talking to sales teams. Those conversations often reveal the language customers actually use, helping advertisers build better campaigns than platform data alone can provide.
AI doesn’t change the fundamentals
While she’s a strong advocate for products like Performance Max, Kostic says AI shouldn’t replace good decision-making. Advertisers should still put guardrails in place, test gradually and prioritise business stability over chasing every new feature.
Bottom line
The biggest PPC mistakes aren’t always campaign mistakes—they’re business mistakes. For Kostic, rebuilding an account too aggressively became the experience that transformed her from a platform specialist into a business-first strategist.
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.
If you just logged into the new AnswerThePublic for the first time, there’s a lot more going on than the tool you might remember.
The version most people know was a keyword visualization tool. Type in a topic, get a wheel of questions people are searching. That version was useful. The new AnswerThePublic is a different category of product entirely.
It’s an AI content engine. You give it your website. It understands your business. It surfaces the keyword opportunities most likely to drive results for you specifically. It researches the competition. It writes the article. It publishes it to your WordPress site. And it repeats that process on whatever schedule you set.
This guide walks you through every part of the product in the order you’ll actually use it, so by the end, you’ll know where to start, what each feature does, and how to get your first published article live.
Two Ways to Start a Search
AnswerThePublic gives you two entry points, and the one you choose shapes everything that comes after.
The first is Analyze My Website. You drop in your URL, and AnswerThePublic reads your site to understand your business before you search for anything. The second is Search Keywords, the classic mode. Type in a keyword, get insights.
Both work. But if you use your website URL, every keyword idea, every content recommendation, and every article the tool generates gets filtered through your actual business context. Instead of generic volume-ranked results, you see the opportunities that actually move the needle for your site specifically.
For most users, starting with Analyze My Website is the right call.
The Business Summary: The Brain Behind Everything
When you analyze your URL, AnswerThePublic builds a Business Summary for you automatically. It includes your business name and type, your target customers, your key features and unique value proposition, your geographic focus, and the topics you should focus on for content.
You can view and edit this anytime, from the Business Summary button at the top right of the Suggested For You page, or inside Settings. This profile is what makes every recommendation specific to you rather than generic. If something’s off about how we characterize your business, fix it. Everything downstream improves when you do.
The Left Sidebar: Your Four Main Surfaces
Home is your search history. Every keyword you’ve ever searched, across every source: Google, Bing, ChatGPT, Gemini, YouTube, Amazon, and Instagram. Filter by region, language, provider, or date to find anything fast.
Suggested For You is your content command center. It’s where most users should spend most of their time.
Content Schedule is your publishing calendar. Set how many articles you want generated per week or per day, drag and drop to reorganize, and see status, ranking position, and search volume next to each item so you’re scheduling with context, not guessing.
All Content is your full article library. Every article generated, published, or sitting in queue. Searchable and sortable.
Suggested For You: Where Most Users Should Spend Most of Their Time
This page is designed to eliminate the blank-page problem. When you land here, you see three things.
At the top: AI-generated content ideas, each tagged with one of three opportunity signals: Best for AI Visibility, Best Short-tail Opportunity, or Best Long-tail Opportunity. Each idea includes a reason why it’s a good opportunity for you specifically, based on your Business Summary. These are often the hidden gems that most keyword tools would never surface because they require understanding your business, not just your niche.
Below the suggestions: The classic AnswerThePublic keyword wheel, now grouped by topic cluster so related ideas stay together visually.
Below the wheel: A full table of your next 50 content ideas, ranked, categorized, and ready to generate. Click any one to start the content process.
Clicking Any Keyword Opens the Full Research View
When you click into a specific keyword, you get a complete research page in a single view: search volume, CPC, and your Content Studio rank for that keyword.
Then the Top Ideas wheel, now with two layers. The inner ring shows what people are asking AI models about this topic. The outer ring shows what they’re typing into search engines.
Below that, the AI Prompts table: every AI prompt related to your topic, paired with three signals new to this version:
Intent: what the user is trying to accomplish with this query
Sentiment: the emotional tone of how AI models are answering this question
Brands: which companies are being mentioned in AI responses for this query
Keep scrolling and you’ll see Organic Searches from Google and Bing, a People Also Ask mindmap you can expand into a full tree view, Social Media results from YouTube, TikTok, and Instagram, and Shopping data from Amazon. Every source. One view. No tab-switching.
One-Click Content Generation from Anywhere
You can generate content with one click from anywhere on Suggested For You, and from anywhere on a keyword detail page. From the top suggestions. From the keyword wheel. From the table. From a People Also Ask node.
Before you hit generate, you get a review screen to check the target keyword, edit the content idea, pick a title, and see why this topic is relevant to your business.
When you click Generate Now, Content Studio runs a five-step editorial pipeline:
Researches the top-ranking pages for your keyword
Pulls the facts and statistics that matter
Builds an outline based on what’s already winning in the SERP
Writes the article in your brand voice
Refines it for natural language and flow
This isn’t a raw AI dump. It’s a structured editorial process trained on real ranking data. What used to take an SEO team days, Content Studio produces in minutes.
Full Control Inside the Article Editor
Once your article is generated, you have full control. Add images. Replace the cover image with the AI generator or upload your own. Rewrite any paragraph by hand.
And this is the capability most new users don’t discover: select any section and ask the AI to regenerate just that part. Not the whole article. Just the paragraph or sentence that needs a different angle. That single feature alone saves hours of editing in a typical workflow.
A plagiarism score runs in the background the entire time. By the time you hit publish, you know the content is uniquely yours.
The Right-Panel Tabs
Overview shows your target keyword, monthly searches, difficulty, plagiarism score, word count, and all internal and external links in one place.
SERP shows the top Google results currently ranking for your keyword, without opening a new tab.
Research shows every fact the article cited, with numbered sources and clickable links. Verify any claim before you publish.
Exporting, Saving, and Publishing
From the top of the editor: export to PDF, export to Markdown, save a draft, or publish directly. The Share button auto-generates captions for Twitter/X, LinkedIn, Instagram, and email, following best practices and character limits for each platform. Write once, distribute everywhere.
Settings: Set It and Forget It
Publish Controls connects your WordPress site. Choose Draft or Published as your default, or enable Auto Publish to send new articles live without manual review. For teams running a daily content cadence, this is transformative.
Image Styles gives you sixteen visual templates (corporate illustration, cartoon, photographic, and more), or let the AI Style Picker choose the best style for each article based on the topic.
Brand Voice lets you pick from templates like The Straight Shooter, The Grounded Guide, or The Curious Storyteller, or build a fully custom voice profile. This is what makes generated articles sound like your brand instead of a generic AI output.
Article Generation controls structure: set length from 500 to 5,000 words, toggle external links, add your sitemap for automatic internal linking, and schedule daily generation at a specific time.
Business Summary is the editable brand profile. Come back here anytime your business evolves: a product launch, a new service, a new audience segment.
Localization sets your content language and target country. Content quality improves significantly when these match your actual audience.
The Fastest Path to Your First Published Article
Four steps in order:
Add your website on first search, so the Business Summary gets built automatically.
Go to Suggested For You and pick an idea tagged Best for AI Visibility or Best Long-tail Opportunity.
Generate your first article with one click. Review the title and keyword, then hit Generate Now.
Connect WordPress in Settings under Publish Controls and publish.
The Bigger Picture
The thesis behind the new AnswerThePublic is simple: your website already tells us what your business is, what you sell, and who you’re selling to. Your customers are already searching for it, in Google, in AI tools, on YouTube, on Amazon.
The gap between what your customers are searching for and what you’ve published is your content opportunity. AnswerThePublic closes that gap automatically.
You just needed a tool smart enough to connect the dots.
Frequently Asked Questions
What’s different about the new AnswerThePublic vs the original?
The original was a keyword visualization tool. The new version adds AI content generation, a Business Summary that personalizes every recommendation to your site, a publishing calendar, WordPress auto-publish integration, and a full article editor with plagiarism scoring. It’s a complete content production system built on top of the original keyword research foundation.
Does AnswerThePublic write the full article, or just an outline?
It writes the full article following a five-step editorial pipeline: competitor research, fact gathering, outline, drafting, and refinement. You can edit any section manually or use selective regenerate to rewrite specific paragraphs.
Can I use AnswerThePublic if I don’t have a WordPress site?
Yes. Export articles as PDF or Markdown, or copy and paste the content directly to any platform.
Does it work in languages other than English?
Yes. AnswerThePublic supports multiple languages and regions. Set your content language and target country under Localization in Settings for best results.
The new AnswerThePublic is live, and you can try it free, no credit card required. Try the new AnswerThePublic.
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TikTok now allows creators and brands to manage the keywords associated with their video metadata.
Keyword suggestions will still be reviewed by TikTok to help prevent spam and misleading tagging.
The update reflects TikTok’s continued evolution into a search-first discovery platform.
Brands should incorporate metadata optimization into their broader TikTok SEO and content strategies.
As social search continues to grow, keep a close eye on keyword performance and audience relevance.
TikTok has introduced a new way for creators and brands to improve how their content is discovered.
The platform now allows users to manage the TikTok keyword metadata associated with their videos, including removing irrelevant keywords and suggesting new ones that better match the content. TikTok will continue reviewing these changes before they are applied, helping maintain the quality and accuracy of search results.
On the surface, this looks like a minor product tweak. It actually points to a broader shift in how content gets discovered on the platform.
As more users turn to TikTok search for product recommendations, tutorials, reviews, and local businesses, the keywords attached to a video play a bigger role in determining who sees it. For brands, this creates another opportunity to improve content relevance while supporting broader social search optimization and AI visibility efforts.
Here’s what changed, why it matters, and how marketers should respond.
What Changed With TikTok Keyword Metadata?
TikTok’s latest update gives creators more influence over the metadata connected to their videos. Rather than relying solely on automated systems, creators can now help ensure the keywords associated with their content better reflect what viewers will actually find.
Creators and brands can now review the keywords attached to their videos, remove terms that don’t accurately describe the content, and suggest new keywords that provide better context.
This added control allows creators to improve the accuracy of their TikTok metadata, making it easier for the platform to understand what each video is about. Better metadata also helps align videos with the searches users are actively performing on TikTok.
While creators can recommend changes, TikTok isn’t handing over complete control.
The platform will review suggested keywords before applying them to prevent spam, keyword stuffing, or misleading descriptions. This review process helps maintain the quality of search results while giving creators more input into how their content is categorized.
The result is a balance between creator flexibility and platform integrity, ensuring that TikTok keyword metadata remains useful for both users and the recommendation system.
Why This Update Matters
Keyword metadata now plays a central role as TikTok expands beyond entertainment into a destination for search and discovery.
This latest update reinforces that shift by giving creators more opportunities to improve how their content connects with user intent.
Search Is Becoming a Bigger Part of the TikTok Experience
People are no longer using TikTok only to scroll through their For You feed. Many now use the platform as a search engine to find recipes, product reviews, travel tips, local recommendations, and answers to everyday questions.
As TikTok search continues to grow, accurate keyword metadata helps the platform understand which videos are most relevant for a given query. That makes search optimization a core part of content strategy, not an afterthought.
More accurate metadata doesn’t guarantee more views, but it can improve the quality of visibility by helping the right audience discover the right content.
For brands, this creates stronger alignment between content and user intent while supporting long-term TikTok discoverability. Rather than optimizing only for reach, marketers can focus on attracting users who are actively searching for information related to their products or services.
Brands that embrace these changes early will be better positioned as search continues to evolve. On-platform and off-platform search visibility is being shaped more and more by social search keyword performance, so brands that move quickly on new visibility tools will have an edge.
What This Means for Your Social SEO Strategy
TikTok’s latest update signals a broader shift in how brands should approach content discovery across digital platforms.
Treat TikTok Search as Part of Your SEO Strategy
Social platforms and search engines continue to influence one another, making TikTok SEO a core part of a broader digital strategy.
Keyword research shouldn’t stop with Google. Understanding how audiences search on TikTok can help brands create content that performs across multiple discovery channels while supporting broader AI visibility initiatives.
Monitor Performance Beyond Views
Views remain an important metric, but they don’t tell the whole story.
As metadata optimization becomes part of the publishing workflow, brands should also monitor search placement, engagement quality, audience relevance, and the keywords driving discovery. These insights provide a clearer picture of whether content is reaching the users it was designed for.
Social Search and SEO Are Becoming More Connected
The way people discover information continues to change.
Users move between traditional search engines, AI-powered assistants, and social platforms depending on what they’re looking for. TikTok’s latest metadata update reflects this shift by placing greater emphasis on keyword relevance and search intent.
For marketers, that means social search optimization should no longer exist in its own silo. Keyword strategies developed for search engines can inform content on social platforms, while insights from TikTok search can uncover new opportunities for SEO and content marketing.
As search behavior evolves, the brands that create content around how people actually search, regardless of platform, will be in the strongest position to earn visibility.
FAQs
What is TikTok keyword metadata?
TikTok keyword metadata refers to the keywords associated with a video that help TikTok understand its content and determine when it should appear in search results or recommendations.
Can creators edit TikTok metadata?
Creators can now suggest changes to the keywords attached to their videos, including removing irrelevant terms and recommending more accurate ones. TikTok reviews these suggestions before applying them.
Why is TikTok focusing on search?
More users are relying on TikTok search to discover products, businesses, tutorials, and recommendations. Improving keyword metadata helps the platform deliver more relevant search results.
How does TikTok keyword metadata affect visibility?
Accurate TikTok metadata helps connect videos with relevant searches, improving discoverability and helping brands reach audiences that are actively looking for related content.
Conclusion
TikTok’s new keyword management tools might look like a small feature update. The bigger story is what they signal about where the platform is headed.
The platform continues to invest in search, giving creators more opportunities to improve how their content is understood and discovered. For brands, this means TikTok keyword metadata should become part of a broader content strategy rather than an isolated platform feature.
As TikTok SEO, social search optimization, and AI visibility become more connected, optimizing metadata is another way to ensure your content reaches the audiences searching for it. Brands that begin testing these features now will be better prepared as social platforms continue expanding their role in how people discover information.
If you want to stay ahead of TikTok’s latest search developments, reach out to the NP Digital team to learn how emerging search experiences can fit into your long-term marketing strategy.
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Earlier this month, we announced platform properties for Search Console,
allowing you to track how your social and video posts on Instagram, TikTok, X, and YouTube perform on Google Search,
Discover, and Google News. Today, platform properties are globally available to everyone.
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Incrementality and attribution are two approaches to measuring marketing performance that are frequently discussed as though they are competing lenses viewing the same data. But they’re actually designed to answer very different questions, using different forms of evidence.
Attribution asks which observed marketing touchpoints should receive credit for a conversion. Incrementality asks whether the marketing activity caused additional conversions that wouldn’t have occurred without it.
A refresher on attribution
Attribution is the favored child of marketing analytics teams everywhere, circa 2015. Marketers discovered that some conversion paths contained multiple touchpoints across the digital landscape, like this:
That raised questions about which channel should get what “credit”:
Should the display ad get the most credit for the conversion because it was the first exposure?
Or should the email, because that’s the touchpoint that finally convinced the user to buy?
And what about the social ad and the organic presence in the middle?
That’s where attribution modeling came in. Attribution modeling provided frameworks for deciding how that credit should be distributed. Some models assigned the entire conversion to a single touchpoint. Others divided it among multiple interactions.
So if the final value of the conversion is $100, an attribution model tells marketers that display can take credit for $30, email for $30, and the remaining $40 is split between paid social and organic.
Then, when you’re evaluating the success of your channels, you have a more nuanced framework for distributing revenue credit. And when you’re deciding on what channels get what budget for the next fiscal year, you have a way to compare and contrast.
Example: How a $100 conversion might be distributed across four marketing touchpoints.
Attribution model
Display
Paid Social
Organic Search
Email
How credit is assigned
First-touch
$100
$0
$0
$0
All credit goes to the first observed interaction.
Last-touch
$0
$0
$0
$100
All credit goes to the final observed interaction before purchase.
Linear
$25
$25
$25
$25
Credit is divided equally among every observed touchpoint.
Position- based
$40
$10
$10
$40
The first and last interactions receive the most credit, while the middle interactions split the remainder.
Time-decay
$10
$20
$30
$40
Touchpoints receive progressively more credit as they occur closer to the conversion.
Data-driven
$30
$20
$20
$30
Credit is distributed according to each touchpoint’s estimated contribution to the conversion.
Note: These are simplified examples. Position-based models can use different weighting rules, time-decay allocations depend on timing, and actual data-driven models vary.
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Incrementality 101
Incrementality began to gain renewed interest from marketers around 2020.
Rather than dole out credit for a sale to different touchpoints and channels based on a mathematical equation, incrementality relies on carefully guardrailed tests of real, live sales data that attempt to prove the “true” impact of a marketing activity rather than its correlation. Incrementality tries to answer the question:
How many of these sales were actually caused by this campaign, without counting how many would have happened regardless?
The answer to that question is what marketers call lift. And through tightly controlled tests leaning on the scientific method, marketers were able to isolate the difference in sales between a group exposed to the marketing activity and an equivalent group that wasn’t exposed to marketing materials.
Incrementality is best explained through an example.
Let’s say you want to discover the lift of a given marketing campaign. So you divide your audience into two groups: a control group of folks who won’t be exposed to the campaign and an exposed group that does see the campaign.
You run your campaign for 30 days, then look at the results. While the exposed group completed 1,000 purchases, the control group completed 800 purchases. The incremental lift of the campaign would be 200 purchases.
An attribution model could associate many or all 1,000 purchases with the campaign. It would allocate the value across the platforms and touchpoints involved according to the model you choose.
Incrementality, on the other hand, would conclude that only those 200 additional purchases were actually caused by the campaign.
Where marketers go wrong is when they go all in on either framework. The two concepts can play nicely together (provided you’re using the right one to answer the right question). If you’re looking to optimize your campaigns or deep dive into the user journey of your customer, attribution is going to be your best friend, helping you evaluate platforms and touchpoints by giving you a shared success metric with which to compare them.
If, on the other hand, you’re defending your budget from a proposed cut, incrementality is going to be your strongest source of evidence regarding which channels actually create additional business with their budget, rather than capturing business that would have happened anyway.
Attribution
Incrementality
Primary question
Which observed marketing touchpoints should receive credit for a conversion?
How many additional conversions occurred because of the marketing activity?
Best use case
Ongoing campaign optimization, understanding customer journeys, and allocating credit across measurable channels.
Validating whether an investment creates additional business value and informing higher-level budget decisions.
Main blind spot
Correlation is not causation: a touchpoint may receive credit for a conversion it did not actually create.
Tests can be expensive, slow, or difficult to design, and results may not explain which individual touchpoints influenced the customer.
Most likely stakeholder
Channel managers, performance marketers, platform teams, and marketing analytics teams.
Marketing leadership, finance, data science, growth strategy, and budget owners.
Where platforms get confused by attribution and incrementality
Most often, it’s asked when a channel platform’s reported revenue or conversions differ from the numbers found in the client’s CRM, web analytics, or other source of truth. They almost never line up perfectly.
What can be tough to explain succinctly to a client is this: The fact that they differ doesn’t necessarily mean either is incorrect. Each system applies its own logic based on the interactions it can observe, which conversions should qualify for credit, and how long after an interaction credit can still be attributed. But neither is “wrong.”
An advertising platform may correctly observe and report that a customer viewed or clicked on an ad before purchasing. But evidence that an ad was seen before a purchase isn’t necessarily proof that the ad caused it, nor is it proof that the ad didn’t cause it.
This becomes particularly important with automated campaigns, especially as platforms continue to push these automated solutions on marketers. Automated systems are designed to maximize performance based on the conversion signals defined inside the platform. They’re simply not designed to maximize performance based on your carefully calculated incremental lift test results.
As a result, automated campaigns target audiences, placements, and queries already associated with users likely to convert, such as existing customers, branded searchers, and remarketing audiences. Those conversions may be entirely valid according to the platform’s attribution model, while creating less additional revenue than the campaign report implies.
In other words, automated campaigns can increase the number of conversions credited to a given campaign without actually causing an equal increase in total sales.
It’s true, platforms are increasingly offering lift studies and other incrementality-focused tools. But it’s a mistake to assume that incremental value is automatically incorporated into automated campaign optimization.
A platform like Google Ads may provide a controlled lift experiment, but unless the advertiser applies the test findings — or selects a campaign setting explicitly designed to optimize for incrementality — the measurement system and the delivery system are still going to be working toward two different definitions of success.
Some platforms are starting to address this. Meta, for example, now offers a very promising incremental attribution model intended to optimize delivery toward conversions it predicts were directly caused by advertising. For now, though, that’s a specific optimization choice that, again, requires action by the advertiser and isn’t an inherent feature of every automated campaign.
Every click they win is a customer you lose.
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Know which question you’re trying to answer
In sum, attribution and incrementality aren’t competing methods for finding one definitive metric. They’re different tools designed to answer different questions. And like most tools, they perform best when they’re doing the job they’re designed for. You wouldn’t try to use your Allen wrench as a hammer, would you?
Attribution helps us marketers understand which touchpoints contributed to a conversion and provides a shared basis for comparing channels. Incrementality helps businesses understand whether their marketing investment generated additional conversions that wouldn’t have occurred otherwise.
The best marketers need both. While attribution provides the ongoing signals needed to optimize campaigns and understand customer journeys, incrementality helps validate whether those optimizations are creating new business value or simply capturing demand that already existed.
As automated campaigns take greater control over targeting, placements, bidding, and budget allocation, understanding both sides will only become more important. A system can become exceptionally efficient at maximizing attributed conversions without becoming equally effective at producing incremental growth.
So the next time a platform report contradicts your CRM, don’t assume either number is wrong — ask which question each number was designed to answer.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/07/image-378-7F3YrD.webp?fit=1491%2C1055&ssl=110551491Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-07-28 15:00:002026-07-28 15:00:00Attribution vs. incrementality: Why you need both
One of AI Max’s capabilities is creating assets for you. This can take the strain off the PPC team by reducing the need to customize ads for every single ad group.
We wanted to quantify how well the text customization feature worked for various companies, so we ran tests with three different companies to understand how much we should or shouldn’t be using text customization.
As we’re a software company that helps companies manage their PPC accounts, not the agency doing the work, we worked with the companies to guide them on how to set up and analyze the tests.
We’ll start by examining the process we used to help the companies set up the tests so you can run this experiment yourself, and then we’ll examine the test results.
Text customization and messaging restrictions
Before you run this test, you need to understand the features involved.
First, you need to turn on AI Max and the text customization feature.
Since this is AI working in the background for you, it can tailor the assets in every single ad group to the keywords in that group. While this sounds nice, the assets can sometimes be used to run promotions or advertise products and services you don’t offer.
To help guide the system, you should apply messaging restrictions when using auto-created assets.
Messaging restrictions can help guide the system on what the ads should and shouldn’t say. In addition, you can give it rules around your brand guidelines.
The overall process for creating good messaging restrictions is fairly simple and only takes an hour or two:
Use a prompt in Gemini to create your initial assets.
Then use a prompt to make the ads overly promotional, create promises you don’t like, etc. Essentially, you’re having the system write ads you don’t approve of and want to ensure Google doesn’t create assets like these on your behalf.
Create messaging restrictions to stop the assets you don’t like from being generated.
Use a prompt to create new, highly promotional ads with your messaging restrictions until all created assets fit your company’s messaging guidelines.
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
Choosing the campaigns to test
We wanted to see how these assets performed across different types of companies and different levels of campaign optimization.
Therefore, we first chose three business types:
Ecommerce.
B2B lead gen.
B2C lead gen.
In each account, we only wanted campaigns that met a specific set of criteria:
Did not use brand keywords.
Spent at least $20,000 per month.
Had at least 100 ad groups.
Most companies have some campaigns that are their best performers, where the team spends a lot of time optimizing the campaigns. Then you have your campaigns that are long-tail, do well in aggregate, but receive less attention.
To understand how well text customization was going to perform, we used two campaigns that were highly watched and two that were somewhat neglected from each account.
We also wanted to see how the new assets performed, so we chose campaigns that didn’t rely heavily on pinning, which excluded many of the highest-spending campaigns, since most large and enterprise companies extensively use pinning in their top campaigns.
Finally, we wanted to test only the assets, not URL expansion, so none of the campaigns used the final URL expansion feature.
Asset review
As you run these tests, you’ll want to monitor the auto-created assets and remove any that don’t align with your brand messaging or offers.
When looking for AI-generated assets, it’s essential that you change the default filters to include the ad, as this filter isn’t chosen by default.
The companies in the test monitored these assets as they were created and removed them before they received many impressions. Ignoring the B2B results (more on that later), approximately 19% of the auto-created assets were removed.
This ecommerce company sells over 100,000 SKUs, so it has a lot of products, and many users are accustomed to visiting the website and searching again if the landing page doesn’t have the specific product they’re looking for.
At first glance, it appeared that both AI Max and text customization were incredibly successful.
However, after further analysis, we found that AI Max was poaching impressions, clicks, and conversions from other campaigns and that the overall revenue for the account declined.
The company added many search terms as keywords to help Google prioritize the correct ad group and campaign. Then it added more negative keywords and audience lists to slow cannibalization and reran the tests.
We observed that AI text customization wasn’t as effective as human management of assets for highly optimized campaigns. However, it was good at assisting the long-tail campaign.
When creating RSA assets for B2B companies, one of the most important considerations is how to properly prequalify your audience through your asset usage. You want your ads to be unattractive to B2C searchers and appeal to B2B searchers.
This company had previously used pinning quite extensively across its assets to ensure this qualification. However, it wanted to see how well Google could optimize its accounts, so it removed its pins during this test.
The nuance of prequalifying a B2B audience is one that text customization clearly doesn’t understand. The B2B accounts saw their CTRs skyrocket. However, the conversion rates declined significantly since the ads were attracting many B2C searchers.
The messaging restrictions included language to ensure the assets prequalified users as part of a B2B audience. While some of the assets met this criterion, the overall ads that were shown to users didn’t properly appeal to B2B buyers.
The other tests ran for over a month. However, after three weeks, the results were so poor that this company stopped the tests and went back to pinning its ads and removing the auto-created assets. Within a week, its results returned to their pretest levels.
B2C lead generation
Our last test was with a B2C lead generation company that localizes its ads through geographic ad copy or geographic insertion.
Its optimized campaigns had tailored ad copy for the keywords in every single ad group. Its long-tail campaign had a few headline assets in each ad group, tailored to the keywords, but most assets were reused across ad groups.
Seeing formulaic ad copy in lower-priority campaigns is quite common, and this is where we were hoping to see AI auto-created assets perform well, since there was a lot of opportunity for better ads.
AI Max auto-created assets didn’t disappoint in these low-priority campaigns. While these assets didn’t outperform the assets humans had spent a lot of time testing in their top campaigns, AI performed quite well for the long-tail campaign.
Every click they win is a customer you lose.
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Where AI Max automated assets work best
AI is a fantastic tool at your disposal. For ads where you’re spending a lot of time thinking through your messaging, it can be good at helping you generate ideas, but human-created assets still outperform AI-generated assets.
If you need specific types of assets, such as prequalifying users for B2B audiences, specific offers, or short-term promotions, then you should take control of the assets yourself and not turn them over to AI.
However, auto-created assets shine when you just don’t have enough time to fully optimize your creatives. Using AI to create your assets, assuming you have good messaging restrictions and regularly review these assets, can help your overall performance.
We’re still a long way from AI being a turn-on-and-forget setting. It needs babysitting and oversight. However, having AI do the heavy lifting in areas where you don’t have the time to fully optimize, and then spending your time reviewing and tweaking the outcomes, is the best use of AI in ad creation.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/07/Google-Ads-AI-Max-Asset-optimization-HuwqrY.webp?fit=1604%2C686&ssl=16861604Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-07-28 14:00:002026-07-28 14:00:00Putting Google Ads AI Max’s automated ad copy to the test
Sadly, every so often, you hear of content being indexed and exposed on Google and other search engines. Often the issue is not necessarily with the search engine but with the site that hosts and holds that information. Those sites often do not set the proper blocking mechanisms in place to communicate to Google and other search engines that the content should not be indexed and shown within the search results.
That is what happened recently with Anthropic’s Claude Chats showing up in Google, Bing and other search engines.
More details. Wired’s story named Private Claude Chats Exposed in Google and Bing Search Results explained how private chats from Claude were found on the web on Google, Bing and other search engines. The chats included politics, health discussions and more, all pretty sensitive chats. “Claude allows users to share with other people “snapshots” of chats by creating a public URL to a specific chatbot thread,” Wired explained.
“The reasons some of these URLs were indexed by major search engines comes down to the basic functions of websites, search engines, and the collision of the two when generative AI gets in the mix,” Wired added.
The primary issue is that if you block both using robots.txt directive and use noindex on that page, search engines like Google and Bing won’t be able to see the noindex tag since the crawlers won’t be able to access the page with the directive.
Doing a site command for [site:claude.ai/share] would return hundreds of chats from Claude over the weekend, but now those results have been removed.
Glenn Gabe said on X that he wished these journalists would have spoken to an SEO before covering the story. “It’s filled with bad information. If you block via robots.txt AND noindex the page, Google and Bing *cannot* see the noindex tag since they can’t crawl the page and see the tag in the HTML,” he explained correctly.
This is not new information; Google’s own documentation has a huge notice at the top of the page that says in bold and red highlights:
“Important: For the noindex rule to be effective, the page or resource must not be blocked by a robots.txt file, and it has to be otherwise accessible to the crawler. If the page is blocked by a robots.txt file or the crawler can’t access the page, the crawler will never see the noindex rule, and the page can still appear in search results, for example if other pages link to it.”
What Google said. Ned Adriance, a Google spokesperson from the Google Search side of the team, understands how this works. And he told Wired, “Neither Google nor any other search engine controls what pages are made public on the web, and these pages were indexed across many search engines.” He added, “We give site owners clear controls to decide whether pages can be crawled or indexed, and we always respect those directives.”
For some reason, Microsoft Bing and Anthropic did not provide any comment to Wired on the issue.
Why we care. This shows the importance of consulting with an SEO who understands how to ensure the right pages are indexed and visible in search and maybe more importantly, the pages you do not want to show up in Google or Bing Search are not discoverable and do not show up in the search results.
Controlling the visibility of your content is SEO 101, and sadly, we see this issue come up over and over again with sensitive information being open and available on the web.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/07/search-leak-exposed-1920-pUCVY7.jpg?fit=1920%2C1097&ssl=110971920Dubado Solutionshttp://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.pngDubado Solutions2026-07-28 13:34:212026-07-28 13:34:21Google indexed Claude Chats because Anthropic didn’t block your private chats from search engines
For every dollar you spend on SEO, how much do you get in return?
Impressions, clicks, rankings, and query growth can show the results of SEO activity. But they don’t tell executives what they need to understand: how much it costs to acquire a customer (CAC), how that cost changes as SEO efforts continue, and whether overall acquisition efficiency is improving.
The challenge is that SEO rarely operates within the clean boundaries that a channel-level CAC calculation implies.
SEO creates entry points across the customer journey and influences other acquisition channels along the way. Its value, then, isn’t only in the customers directly attributed to organic search. It’s also in how SEO can make the broader acquisition system more efficient.
The reality of acquiring a customer
A user may first find a company through a nonbrand search, return through paid search, compare alternatives through content found in ChatGPT, sign up for a newsletter on the website, read through guides for a week, and then finally convert through the owned channel.
The final conversion might be attributed to email. Paid search may receive some credit for the return visit. The original organic discovery may disappear from the standard report entirely.
But SEO still influenced the acquisition and may have reduced its total cost.
CAC can be measured by individual channel or across channels as blended CAC. CAC expectations vary considerably by channel:
Paid search.
Paid social.
Email.
SEO.
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Paid search captures high-intent demand
Paid search may have the cleanest attribution. Users search for solutions to their problems, product terms, categories, or other related queries. You pay for the click, and a percentage of those clicks convert.
From there, you get spend / customers acquired = paid search CAC.
A user who clicks a search ad may already know the brand through social campaigns, podcast appearances, how-to guides, recommendations, or competitor research. The demand is high-intent, and paid search captures its final expression.
Paid social influences demand earlier
Paid social’s impact on CAC is often indirect because its primary strengths are creating awareness, warming audiences, building retargeting or feeder pools, and connecting users with problems they may want to solve before they’re ready to buy.
It’s unlikely that your users are scrolling on Instagram thinking, “I would like to spend some money right now.”
But seeing a product address their problem during their free time may make the brand more familiar when they later search for a solution at work, improving blended CAC efficiency.
If you looked only at paid social as spend / customers acquired = paid social CAC, you’d probably cut the budget. But then, if you look at what it does to paid or branded search CAC through holdout tests, you’d potentially restart the social budget after seeing the overall acquisition engine decline.
It’s a form of incrementality. Experiments compare exposed and unexposed groups to estimate how much additional activity a marketing investment produces instead of simply assigning credit to the last recorded touchpoint.
Email depends on other acquisition channels
Lifecycle channels like email work differently. If you own an audience through email capture and you look at converting them into paid users or continuous purchasers, you can think of email CAC along the lines of cost of email program / converted customer value.
But then you still have to run paid to capture the emails in the first place, or you need a strong SEO presence to do so. The apparent efficiency is highly dependent on other channels.
SEO touches all of these channels, and all of these channels can influence SEO in return. For example, a paid social campaign could generate 100 brand mentions that benefit your overall organic visibility.
Together, they create a connected acquisition system.
It sounds like the solution is a better attribution model, and then SEOs can speak about CAC more effectively and get more budget. But there are still limitations.
Last-click attribution would just give credit to the final measurable source. First-click would just give credit to the initial source. Linear or position-based models distribute credit, and data-driven attribution would observe data to estimate what contributed most.
Data-driven attribution may improve reporting, but it remains a model rather than a complete record of the customer journey.
Because how do you evaluate interactions that aren’t observed, identified, or connected to a user’s journey? There are deleted cookies, consent restrictions, cross-device behavior, long sales cycles depending on your niche, offline conversions, and that list of limitations could go on for a while.
So, regardless of your attribution model, it doesn’t always provide a complete record of causality and shouldn’t be treated as such. This further reinforces that acquisition is a system, not an isolated channel.
As the search ecosystem changes, even more of SEO’s influence is becoming difficult to observe.
Measuring CAC for SEO as an isolated channel is becoming increasingly difficult.
SparkToro’s analysis of Similarweb clickstream data found that 68.01% of U.S. Google searches ended without a click during the first four months of 2026. In 2024, the figure was 60.45%, representing an increase of roughly 7.6 percentage points in two years.
Users can still see a company in an AI Overview, read a search snippet, or engage in other behaviors, but fewer and fewer are measured through impression → click → conversion.
SEO still influences these interactions, but its impact may appear smaller in a dashboard.
There’s also significant overlap between SEO efforts and AI visibility, depending on which agency or existential hill you’re standing on.
SEO leans out blended CAC
SEO’s biggest advantage is that its financial returns can compound. A paid campaign stops sending traffic to the website when the budget stops, but a strong organic presence can continue creating entry points long after the initial investment.
If you build topical authority across a category with meaningful demand, the cost to maintain that visibility, including the costs of keeping up with competitors, is often lower than continuously buying the same demand through paid search.
That could include technical improvements, content production, digital PR, product pages, and ongoing optimization.
During the first few months, the program may appear inefficient from a CAC perspective because the investment occurs before returns materialize. Then, as visibility grows, that same work starts to increase customer volume while spend stabilizes at maintenance levels, and CAC decreases.
That’s one way SEO leans out blended CAC. It does this by:
Creating nonpaid entry points into the funnel.
Capturing demand that paid would otherwise have to buy.
Supporting paid search and paid social conversion.
Increasing branded and direct demand over time.
Educating buyers before sales conversations.
Improving conversion through comparison, use-case, and objection-handling content.
Feeding owned channels like email.
Reducing support and retention friction through product and help content.
When considering the impact, it can be true that SEO is among the more efficient levers for reducing a business’s blended CAC.
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Reframe the SEO investment conversation
The attribution model can point to various channels as the highest-performing, but the organic infrastructure may be contributing to that performance.
That’s where SEOs should point when reframing the conversation.
Instead of answering how much you get in return for every dollar spent on SEO, consider how much more money you’ll have to spend on other channels for every dollar not spent on SEO.
If SEO is doing its job, it’s part of a cohesive system, and its role is to increase volume while reducing blended costs.
SEO teams should still report channel CAC when the data allows, but executives should evaluate it alongside influenced pipeline, replacement costs, and changes in blended acquisition efficiency.
