AI Max is Google’s latest foray into semi-keywordless targeting.
While you need keywords for the system to have a starting place, Google uses signals beyond keywords in deciding how to show ads to searchers.
In accounts with a strong history of broad match success, AI Max can be highly effective at finding new conversions.
If accounts are not well-optimized or have not been successful with broad match, AI Max can be a huge money pit.
To clear up a rumor before we get into the data: you do not have to use AI Max to have ads appear in AI Overviews.
Broad match keywords can show ads in AI Overviews regardless of your AI Max usage.
We’re looking at AI Max as a conversion expansion option, not just an option to show in AI Overviews.
This article examines the review steps you should take before you decide to test AI Max.
What to check before enabling AI Max
Accurate conversion tracking
Your conversion tracking must be accurate, deduplicated, and focused on business outcomes. AI Max optimizes toward what you have defined as success.
If you aren’t tracking all your conversions, or if your conversions are inflated, AI Max will be working from inaccurate data and making poor decisions.
Automated bidding with a conversion-focused strategy
Broad match only works well when you have a bid strategy that is focused on conversions, such as:
Our experiments with AI Max have shown that it is much more predictable with one of the target options (Target CPA or Target ROAS) than with the max bid options (Maximize conversion value or Maximize conversions).
Since the Max conversion options are meant to get you the most possible, regardless of the CPA or ROAS, they will often continue to spend your budget when the next set of conversions could have exceptionally high CPAs or very low ROAS.
If you use AI Max with one of the max bid options, pay close attention to your budget and the AI Max data.
Conversion volume
Technically, you can enable AI Max without any conversions for a campaign.
However, with under 30 conversions per month, AI Max has been highly erratic.
At over 100 conversions per month, it has done well more often than not, assuming you have had success with broad match in the past.
In general, you will want to test AI Max in campaigns that have at least 30 conversions per month.
If you are going to test AI Max, starting with non-brand campaigns that have a high conversion volume will usually give you a better introduction to AI Max’s possibilities for your account.
No impression share lost due to budget
If you’re already losing impressions due to your budget, your handpicked keywords will receive even less budget if you enable AI Max.
The goal is to spend as much as you can on your top keywords, and then have AI Max experiment with the budget we can’t spend.
If you are already losing impressions due to your budget, then enabling AI Max usually results in poorer performance.
Have proven broad match success
AI Max will treat all of your keywords as broad match, and then expand even further than your broad match keywords.
If you haven’t successfully used broad match, then enabling AI Max will be a waste of money.
You should first ensure that broad match can work for you, which might require reorganizing ad groups, testing new ads, and optimizing your landing pages.
Only after you have consistently seen good results with broad match should you try AI Max.
When you enable AI Max, you can expand URLs to other pages on your website.
This means that Google can pick any page of your website to use as a landing page when AI Max triggers an ad.
Google allows you to exclude URLs. Most sites should exclude:
Help files and support pages.
Pages not built for conversions.
Pages that do not have conversion tracking enabled.
FAQs.
Blogs.
Old landing page tests that are still live.
Old website designs that are still live.
A few people have found success with using AI Max with blogs and support pages. However, these seem to be exceptions more often than the standard result.
AI Max has struggled when there are many geographic landing pages.
We’ve seen accounts that target different geographies by campaign, and each campaign has its own set of landing pages.
AI Max has routinely mismatched the campaign’s geographic target with landing pages intended for other geographies.
For example, your California campaigns are sending all of their traffic to landing pages dedicated to Texas traffic.
If you want to use AI Max URL expansion, and you have landing pages dedicated to various geographies, you will need to exclude all the landing pages that are irrelevant to the geography of your campaign.
For companies that create dedicated landing pages for each campaign or ad group, I have yet to see an example of AI Max finding better landing pages.
In every example, AI Max’s URL expansion has needed to be turned off. Eventually, this option might work for advertisers, but I have yet to see that happen.
You can review the URLs that Google is using and exclude them. If you turn on URL expansion, you will want to regularly review these URLs.
My great hope for AI Max is the automatically created assets.
I wish I could enable this only for extensions. AI Max can help you scale messaging tremendously.
It can go through all of your ad groups and automatically create sitelinks and callouts at the ad group level.
This level of customization is one that many advertisers never have time to fully explore.
We had a client who enabled this feature, and suddenly, all their sitelinks linked to pages that were irrelevant to the keywords.
We’ve seen other clients use this feature, and their callouts improved dramatically.
Google still has a ways to go in how they auto-create assets, but this is a feature I have high hopes for.
Unfortunately, you can’t enable this feature for only ad assets (extensions). If you enable automatically created assets, Google will create additional RSA assets for you.
These assets can cause customer confusion by:
Making promises your brand doesn’t meet.
Using messaging that isn’t compliant with the law for regulated industries or doesn’t follow your brand guidelines.
You can write guidelines for how you want your ads to appear and rules on what shouldn’t be used.
If you’re going to have Google automatically create assets, you’ll want to add guidance on how the ads should be created.
Note that term exclusions and text guidelines (Google’s official names for these features) don’t appear to be enabled in all accounts right now and may still be rolling out to advertisers.
Overall, Google’s auto-generated RSA assets have a poor track record, and if you enable them, you will want to regularly review what Google is creating on your behalf.
How to test AI Max
Since Google has a history of matching broad match keywords to other brands and generic keywords, AI Max has been very inconsistent with brand keywords.
I’d suggest starting with your top non-brand keywords to test AI Max.
For most brands, there are more conversions to be had in non-brand expansion than in finding more people who are already searching for your brand.
AI Max can be enabled at the campaign or ad group level.
One of the best ways to run a limited test with AI Max is to enable it only in a few ad groups that have a lot of conversion data and a successful history with broad match.
In the interface, enabling AI Max for only a few ad groups is painfully slow.
You have to enable AI Max at the campaign level, then go into every ad group and turn it off where you don’t want it enabled.
The Google Ads Editor lets you turn AI Max on or off at the ad group level.
If you want to test AI Max in only a few ad groups, then use the editor for your initial setup.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/01/Google-Ads-Add-URL-exclusions-ft946X.png?fit=1105%2C389&ssl=13891105http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-01-27 15:00:002026-01-27 15:00:00Is your account ready for Google AI Max? A pre-test checklist
Have you ever tried to find inspiration for ads by scrolling your own Facebook feed?
Then you know that most companies’ ads aren’t very compelling. Also, scrolling Facebook in this day and age is weirdly exhausting.
Here’s the truth: most high-performing ads in 2026 aren’t winning the day because they’re wildly original or uniquely “viral” (do we still call something that?).
They’re winning because they follow the same repeatable templates that smart marketers have been using for decades.
(Yes, even now. Even with AI. Even with “creative strategy” and words like “scrollable” being used non-ironically in business initiatives.)
This article goes back to basics, eschewing “inspiration” in favor of tried-and-true approaches.
Below are four Facebook ad templates you can use right now, regardless of what you’re selling, with real examples that show the strategy behind top brands’ creative.
1. Problem? Meet solution
Pain point → Relief → Simple next step
This is advertising 101. It worked in 1926, it works in 2026, and it’s still undefeated for a reason.
Despite what some business owners believe, customers don’t wake up thinking about your business.
They wake up thinking about their life:
“I spent too much money.”
“I don’t have time.”
“I feel stuck.”
“I’m overwhelmed.”
“I can’t stay consistent.”
That means you’ve got to meet them where they are.
If your customer doesn’t realize their situation is solvable, they won’t buy anything.
That means, even if you’re the best solution in the world, until they recognize the problem, they won’t look for an answer.
Example: ClickUp
ClickUp takes a modern pain point that most tech workers struggle with on a daily basis, and reframes it into something that can actually be solved:
Overwhelmed by multiple tools and apps? Stop switching between them and use one platform that does it all.
This ad isn’t just selling “project management.” It’s selling:
Mental relief.
A single source of truth.
Less context switching, more productivity.
Team alignment.
The promise (though some might say illusion) of control.
Plug-and-play copy starter
Still dealing with [problem]?
You’re not alone – and you don’t have to stay stuck.
[Product/service] helps you [benefit] without [common objection].
Unique selling point → Instant comparison → ‘Oh, hey’ moment
If you’re in a crowded industry fighting for market share (and in 2026, a lot of businesses are), the brands that stand out are the ones that make it easy for customers to answer one question:
Why should I choose you?
Let’s be clear: you don’t necessarily need a radical innovation or a show-stopping differentiator.
Sometimes it’s how you do things, what you prioritize, or who you’re for.
All that really matters is that you’re different in a way people can understand quickly and easily.
Example: The Woobles
Crocheting has been around forever. Beginner kits have existed for decades. Patterns have been sold in stores since before we were all born.
And yet, somehow, The Woobles managed to grab a huge chunk of market share in a craft that’s older than the automobile.
That’s impressive.
This ad shows exactly how they do it.
Instead of positioning crochet as “learn a new skill,” they highlight what makes them different, then continue stacking their differentiators in a way that makes the purchase feel almost inevitable:
Cute, modern projects people actually want to make.
Designed for true beginners.
Thicker yarn and a chunky hook.
Step-by-step video tutorials.
That’s really the point of a strong USP ad. It’s not just “we’re unique.” It’s “here’s why this is easier, better, and faster.”
Testimonial/UGC → Minimal brand talk → Trust does the selling
Not every ad needs to look and sound like an ad. In fact, some of the best-performing Facebook ads in 2026 are the ones that take you a second to realize they’re sponsored.
This is the “let the customer do the talking” template, and it’s everywhere on Instagram and TikTok because it works.
Think creator-style, user-generated content (UGC), testimonials, and review-driven ads that feel real, slightly imperfect, and way less polished than traditional brand messaging.
Oddly enough, the lack of polish is part of the appeal. It reads as “honest,” not “salesy.”
Example: Allbirds
Allbirds runs a simple, product-focused ad for the Tree Dasher 2, pairing a customer quote with a simple image of the shoe.
“Wore these @allbirds for 13 hours and could’ve gone another 13. I never want to take them off.”
That line pretty much does all the work for the ad.
It implies:
Comfort that lasts all day.
No break-in period.
Real-world wearability.
The creative itself is even straightforward: product image, a few lifestyle shots, and a clean layout. It’s not trying to be flashy, it’s trying to be believable.
Plug-and-play copy starter
“I didn’t think anything would help, but this actually worked.”
[Show the proof]
If you’re dealing with [problem], try [product] → [CTA]
Sometimes people don’t want a story. They want clarity.
This template works especially well on Facebook because it’s built for how people actually scroll: fast, distracted, and looking for something that solves a problem right now.
Instead of writing paragraphs, you give them a handful of “yes, I want that” benefits they can absorb in two seconds.
The “quick win” Checklist format:
Reduces decision fatigue.
Makes value instantly scannable.
Highlights benefits without over-explaining.
Works beautifully for cold audiences who don’t know your brand yet.
Example: Little Sleepies
Little Sleepies uses a simple visual and benefit callouts to answer the parent question underneath the question:
“Is this actually going to make my life easier?”
Instead of trying to be clever, the ad clearly lists the practical wins:
Double zippers for easier diaper changes.
Ultra-soft bamboo for comfort.
Fits longer (up to 3x) for better value.
This is a great reminder that in 2026, the ads that win aren’t always the funniest or most creative; they’re often the ones that make the buying decision feel effortless.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/01/Facebook-Ads-ClickUp-K50Pp0.png?fit=393%2C649&ssl=1649393http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-01-27 14:00:002026-01-27 14:00:004 Facebook ad templates that still work in 2026 (with real examples)
If you’ve spent any time in PPC communities, Reddit threads, Slack groups, or conference Q&As, you’ve probably noticed a recurring frustration: “Google Ads isn’t scaling. It’s not working, and we’re stuck.”
On the surface, everything looks fine. The campaigns are running, impression share is high, shopping feeds are clean, and budgets are flowing. But growth isn’t materializing.
This isn’t usually about “broken campaigns” – it’s about the limits of demand.
In niche markets or categories shaped by seasonality, growth is naturally capped.
Yes, running broad match or AI Max can expand your reach to adjacent queries, so impression share might not literally be 95%.
But these campaigns are still only capturing demand that already exists. Once you’ve covered the pool of relevant searches, you can’t spend your way into more.
That’s the uncomfortable truth: Google Ads doesn’t create demand. It captures it.
If fewer people are searching this month, or if your category naturally has a small audience, your results will reflect that.
You can dominate what’s there, but you can’t conjure demand out of thin air.
So when growth stalls, the real question isn’t “What’s wrong with Google Ads?” but “What are we doing to create demand that fuels future searches?”
Search and shopping = Demand capture, not creation
Let’s call Search and Shopping what they are: demand capture channels.
They’re excellent for getting in front of people when they’re ready to buy, or at least actively researching. But they are reactive by design.
Ads only appear once someone types a query. No query, no ad.
That’s why impression share (IS) can be deceptive.
A 90% IS looks like you’re winning (and you are). But if there are only 500 relevant searches in your market this month, you’ll never scale to 5,000 clicks just by raising bids.
Broad match and campaigns like AI Max can stretch coverage by surfacing adjacent queries.
But these still rely on intent. If nobody is searching for related terms, there’s nothing to match against.
Contrast this with platforms like Meta or TikTok, where more budget literally means more reach.
Search doesn’t work that way. It’s not a demand generator – it’s a closer.
Where demand really comes from
So if Search and Shopping can’t create demand, what does?
Marketers have long grouped channels into three buckets: owned, earned, and paid.
It’s old-school terminology, but it’s still the most practical way to break down where demand actually originates.
If Search and Shopping are just there to capture demand at the end, you need to understand which levers create it upstream.
Owned
These are the channels you control: your website, email, content, and CRM. They don’t usually create brand-new demand, but they’re critical for nurturing it.
Think of a D2C brand running a simple “VIP early access” sign-up before Black Friday. That list fuels branded searches once the sale goes live.
Or a SaaS company publishing an FAQ blog that shows up for early research queries, nudging prospects who later Google the brand directly.
Owned channels ensure that once curiosity is sparked, it’s effectively nurtured toward a search.
Earned
These are the channels you don’t directly pay for: PR mentions, SEO visibility, reviews, organic social, and word of mouth.
A product that lands in a holiday gift guide? Branded searches spike the next week.
A TikTok that goes viral organically? Google Trends charts it days later.
Positive Trustpilot reviews? They push people back to Google to check your site or compare pricing.
Earned channels matter because they carry credibility. They don’t just spark curiosity; they make people trust you enough to type your name into the search bar.
Paid
Paid media includes both demand-capture channels (Search and Shopping) and demand-creation channels.
Search and Shopping capture existing intent, but platforms like Meta, TikTok, YouTube, Pinterest, and Display create it.
These channels don’t wait for someone to type a query, they put your brand in front of people who weren’t already looking.
A TikTok showing your product in action.
A YouTube pre-roll highlighting your brand story.
A Pinterest ad that lands on someone’s gift board weeks before purchase.
These sparks generate curiosity, which later turn into branded searches.
While broad match and Performance Max might unearth “new” queries, they’re still intent-driven.
The real creation happens upstream, through paid channels designed to spark awareness.
You’ve likely heard this before, but it’s worth being specific about where Search and Shopping actually fit.
They’re strongest at conversion, but they also show up during the consideration phase of the buyer’s journey, when people are still comparing options.
Here’s how the funnel really works.
Awareness
This is the stage where people first notice you exist.
For example, a skincare brand could run TikTok ads showing its serum in action, or a B2B SaaS company might run YouTube pre-roll explaining a popular platform feature.
In retail, a promoted Pinterest pin could land on someone’s gift board long before purchase.
Tip: This is where Meta video campaigns, TikTok ads, YouTube pre-roll, Pinterest-promoted pins, PR placements, and influencer content live. These channels don’t wait for intent – they spark it.
Consideration
During this stage, people compare, research, and explore.
For example, that skincare shopper might read reviews, sign up for “early access,” and later search “best vitamin C serum.”
In B2B, a prospect could download a case study and then Google “top CRM tools for small businesses.”
Tip: This is where generic search campaigns (e.g., “best [product]” or “affordable [category]”), shopping ads with comparison queries, CRM nurture flows, SEO content, and retargeting via Meta/display/YouTube come in. This stage is about reassurance, education, and visibility while the prospect weighs their choices.
Conversion
The stage where people buy. For example, two weeks after first becoming aware of the brand, the skincare shopper searches “Brand X serum” and buys via Shopping.
After much comparison, the B2B prospect searches “[Vendor name] pricing” and completes a demo-request form.
Tip: This is where branded search, high-intent shopping queries, retargeting to cart abandoners, and PMax remarketing close the deal.
That’s why the funnel matters. If you only play at conversion, you miss those critical mid-funnel searches where people decide between you and your competitors.
Skip awareness and consideration, and your funnel isn’t a funnel at all – it’s a drinking straw.
When growth stalls, the solution isn’t “spend more on search.” It’s fuelling demand earlier.
Here’s how to do exactly that, broken up by budget level.
If you’re working with smaller budgets, focus on high-leverage plays:
Grow your CRM list: Run simple lead-gen ads, like “sign up for early access” or “exclusive drops.” Even $300-$500 on Meta can build a list that costs nothing to email later.
Run warm-up campaigns: Low-cost video or carousel ads on Meta or TikTok build remarketing pools you can retarget with cheaper Google Display or YouTube Ads.
Optimize your site: Gift guides, FAQs, delivery cut-offs. A poor landing page wastes every click you’ve fought for.
Keep remarketing switched on: Display, YouTube, or PMax remarketing switched on is often cheaper than chasing new clicks in search.
If you’ve got bigger budgets, play full-funnel:
Run always-on awareness: Meta, YouTube, TikTok, Pinterest. Sequence your creative by teasing early, revealing mid-season, and then pushing offers when intent peaks.
Segment your CRM properly: VIPs deserve exclusives. Lapsed buyers need reactivation. Gift shoppers want bundles. Tailor the journeys.
Invest in influencers and PR: Gift guides, unboxings, trend-driven content. These placements fuel branded search demand faster than any keyword tweak.
Personalize your site: Recommendation engines and dynamic content keep people on the path to purchase.
Things everyone should check:
Check impression share: If you’re at 90%+, you’re near the ceiling. Broad match and AI Max might stretch coverage, but they won’t invent intent.
Track branded search: If branded queries aren’t rising, awareness is flat.
Keep remarketing on: It’s the lowest-hanging fruit.
Assets you need in place
Fix the basics before you pour money into awareness. Demand creation is wasted if your funnel leaks.
At a minimum, you need proper creative assets. Don’t just think about “a video” or “a few images.”
Different platforms require different formats and sizes, and if you don’t prepare variations, you’ll either be stuck with auto-cropping or miss placements altogether.
Meta: Vertical (Reels/Stories), square (Feed), and landscape (In-stream).
TikTok: Full-screen vertical, with captions/subtitles baked in as sound-off viewing is common.
YouTube: Horizontal 16:9 ratio for standard placements, but also vertical Shorts for mobile audiences.
Pinterest: Vertical lifestyle imagery tends to outperform product-only shots.
Display: Responsive formats mean you should plan both text + multiple image ratios so the algorithm has variety to test.
For small brands, this doesn’t mean expensive shoots. Scrappy user-generated content can be repurposed across platforms if you plan with aspect ratios in mind.
For bigger brands, building a creative matrix – every concept mapped across different formats and funnel stages – ensures consistency and saves on reshoots.
Landing pages
Don’t send awareness traffic to a generic homepage. Build pages that:
Answer FAQs
Highlight delivery cut-offs (critical in Q4)
Showcase bundles or gift guides for seasonal shoppers
For B2B: Tailor landing pages to industries or personas
CRM setup
Even a simple nurture flow is better than nothing. Capture the email at the awareness/consideration stage and follow up.
Larger brands should run segmentation and automated journeys:
VIPs: Exclusives.
Lapsed buyers: Reactivation flows.
Prospects: Educational sequences.
These assets make sure that when demand is created, it actually converts instead of leaking out of the funnel.
AI: Helpful, but not a shortcut
AI is everywhere right now. Tools like Performance Max, AI Max, and creative generators are powerful.
Or it can automate repetitive tasks, such as analyzing search term reports or adjusting bids, freeing you up to focus on strategy.
However, AI doesn’t change the rules of demand. It still relies on intent already being there. And if you let it run unchecked, you risk losing what makes your brand stand out.
Search Engine Land has repeatedly warned about this: over-reliance on AI can result in generic creative that lacks voice and originality, blending your ads into the crowd.
Think of AI as an accelerator: It can speed up execution, but it can’t define your brand, audience, or strategy. That still requires a human marketer.
Making it real for stakeholders, measuring demand creation
If you’re explaining this to a board or client, keep it simple:
Lead with this: Search responds to demand; it doesn’t generate it.
Show them impression share: If you’re already at 90%+, the problem isn’t coverage – it’s demand.
Point to branded search trends: Flat branded queries mean flat awareness.
Highlight competitor activity: Show where rivals are fuelling demand – Meta, TikTok, PR, or Pinterest. That’s why their branded search traffic is rising.
Don’t just show performance data. Show where the demand gap is.
Branded search is the clearest signal, but it isn’t the only one. Look at:
Direct traffic: More people typing your URL into their browser means brand awareness is working.
Organic search traffic (non-branded): If this grows, your content is pulling people in who may later convert via paid.
Social engagement and reach: Demand creation platforms build traction, even if the final conversion happens in Google.
Ultimately, owned, earned, and upper-funnel paid activity all create demand; Search and Shopping are there to capture it.
The ceiling isn’t Google Ads – it’s demand
The truth is, this is the direction PPC is heading.
Query growth is flattening, AI search is reshaping how results appear, and brand demand is becoming the real performance lever.
The next time someone says, “Google Ads isn’t driving traffic,” flip the question on them: Was there any demand to capture in the first place?
Because if you’re only running Search and Shopping, you can’t grow beyond the demand that already exists.
The brands that win aren’t the ones squeezing bids and obsessing over CPC swings.
They’re the ones consistently fuelling demand upstream: awareness, SEO, content, influencers, CRM, video, and social – all working together to prime the market.
So when growth stalls, the real question isn’t, “What’s wrong with Google Ads?” It’s “What are we doing to create demand that fuels future searches?”
The European Commission has formally opened new proceedings to spell out how Google must share key Android features and Google Search data with rivals under the Digital Markets Act.
The Commission on Tuesday opened two formal “specification proceedings” to guide how Google must comply with key DMA obligations, effectively turning regulatory dialogue into a structured process with defined outcomes.
Why we care. The European Commission is escalating its oversight of Google under the Digital Markets Act, with moves that could reshape competition in mobile AI and search — and limit how much advantage Google can extract from its own platforms. If Google is required to share search data and Android AI capabilities more broadly, it could accelerate competition from alternative search engines and AI assistants, potentially fragmenting reach and measurement.
Over time, that may affect where advertisers spend, how much inventory is available, and how dependent campaigns are on Google-owned platforms.
First focus — Android and AI interoperability. Regulators are examining how Google must give third-party developers free and effective access to Android hardware and software features used by Google’s own AI services, including Gemini.
The goal is to ensure rival AI providers can integrate just as deeply into Android devices as Google’s first-party tools.
Second focus — search data sharing. The Commission is also moving to define how Google should share anonymised search ranking, query, click and view data with competing search engines on fair, reasonable and non-discriminatory terms.
That includes clarifying what data is shared, how it’s anonymised, who qualifies for access, and whether AI chatbot providers can tap into the dataset.
Between the lines. This isn’t just about compliance checklists. The Commission is signaling that AI services are now squarely in scope of DMA enforcement, especially where platform control over data and device features could tilt fast-growing markets before competitors have a chance to scale.
What’s next: Within three months, the Commission will send Google its preliminary findings and proposed measures. The full proceedings are set to conclude within six months, with non-confidential summaries published so third parties can weigh in.
The backdrop. Google has been required to comply with DMA obligations since March 2024, after being designated a gatekeeper across services including Search, Android, Chrome, YouTube, Maps, Shopping and online ads.
Bottom line. The EU is moving from theory to execution on the DMA — and Google’s handling of AI features and search data is becoming an early test of how aggressively regulators will shape competition in the next phase of the digital economy.
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OpenAI is pitching premium-priced ads in ChatGPT — with far less data than advertisers are used to getting.
What’s happening. According to a report, OpenAI is pricing ChatGPT ads at roughly $60 per 1,000 impressions — about three times higher than typical Meta ads. Despite the cost, advertisers will receive only high-level reporting, such as total impressions or clicks, with no insight into downstream actions like purchases.
Why we care. ChatGPT is emerging as a brand-new, high-attention ad environment — but one that comes with trade-offs. The high CPMs and limited reporting mean early tests will be more about brand exposure and learning than performance efficiency.
For marketers willing to experiment, this offers a first-mover chance to understand how ads perform inside AI conversations before the format scales or measurement improves.
The tradeoff. OpenAI has left the door open to expanding measurement in the future, but it has publicly committed to never selling user data to advertisers and keeping conversations private. That stance limits the kind of targeting and attribution advertisers expect from platforms like Google or Meta.
Who will see ads. The first ads will roll out in the coming weeks to users on ChatGPT’s free and lower-cost Go tiers, excluding users under 18 and conversations involving sensitive topics such as mental health or politics.
Between the lines. OpenAI is positioning ChatGPT ads as a premium, trust-first product — betting that context, attention, and brand safety can justify higher prices even without granular performance data.
Bottom line. ChatGPT ads may appeal to brands willing to pay more for visibility in a new AI-driven environment, but the lack of measurement will make performance-focused advertisers think twice.
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Google is working toward a future where it understands what you want before you ever type a search.
Now Google is pushing that thinking onto the device itself, using small AI models that perform nearly as well as much larger ones.
What’s happening. In a research paper presented at EMNLP 2025, Google researchers show that a simple shift makes this possible: break “intent understanding” into smaller steps. When they do, small multimodal LLMs (MLLMs) become powerful enough to match systems like Gemini 1.5 Pro — while running faster, costing less, and keeping data on the device.
The future is intent extraction. Large AI models can already infer intent from user behavior, but they usually run in the cloud. That creates three problems. They’re slower. They’re more expensive. And they raise privacy concerns, because user actions can be sensitive.
Google’s solution is to split the task into two simple steps that small, on-device models can handle well.
Step one: Each screen interaction is summarized separately. The system records what was on the screen, what the user did, and a tentative guess about why they did it.
Step two: Another small model reviews only the factual parts of those summaries. It ignores the guesses and produces one short statement that explains the user’s overall goal for the session.
By keeping each step focused, the system avoids a common failure mode of small models: breaking down when asked to reason over long, messy histories all at once.
How the researchers measure success. Instead of asking whether an intent summary “looks similar” to the right answer, they use a method called Bi-Fact. Using its main quality metric, an F1 score, small models with the step-by-step approach consistently outperform other small-model methods:
Gemini 1.5 Flash, an 8B model, matches the performance of Gemini 1.5 Pro on mobile behavior data.
Hallucinations drop because speculative guesses are stripped out before the final intent is written.
Even with extra steps, the system runs faster and cheaper than cloud-based large models.
How it works. Intent is broken into small pieces of information, or facts. Then they measure which facts are missing and which ones were invented. This:
Shows how intent understanding fails, not just that it fails.
Reveals where systems tend to hallucinate meaning versus where they drop important details.
The paper also shows that messy training data hurts large, end-to-end models more than it hurts this step-by-step approach. When labels are noisy — which is common with real user behavior — the decomposed system holds up better.
Why we care. If Google wants agents that suggest actions or answers before people search, it needs to understand intent from user behavior (how people move through apps, browsers, and screens). This research moves this idea closer to reality. Keywords will still matter, but the query will be just one signal. In this future, you’ll have to optimize for clear, logical user journeys — not just the words typed at the end.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/01/google-intent-extraction-v7vgZo.webp?fit=1920%2C1080&ssl=110801920http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-01-26 16:32:272026-01-26 16:32:27Google research points to a post-query future for search intent
AI Overviews, which place generated answers directly at the top of search results, are improving the search experience for users.
For businesses that rely on content to drive traffic from search engines, the impact is far less positive.
Google has been moving toward more “helpful” results for years, and zero-click searches are nothing new.
AI Overviews accelerate that shift, absorbing much of the traffic opportunity that search has historically provided.
How AI changes the work of search
For years, search followed a familiar pattern:
A user entered a short query, such as “team building companies.”
Google returned a page of paid and organic results.
The user did the work of reviewing and refining.
Most of the effort happened at the end of the process.
Google organized results based on intent and behavioral signals, but users still had to click through listings, conduct follow-up searches, and piece together an answer.
AI reverses that flow:
The user asks a more detailed question.
AI runs multiple searches and processes the results.
AI delivers a summarized response.
Traditional search allows for refinement, but each new query effectively resets the experience.
AI, by contrast, is conversational. Each interaction builds on the last, narrowing in on what the user actually wants.
The result is a faster, cleaner path to an answer – with far less effort required from the user.
The path of least resistance
This shift matters because it aligns with a basic human tendency.
People generally choose the easiest available option. If something is easier and produces a better result, adoption follows quickly.
Seeking the path of least resistance is an evolutionary trait that likely served humans well in earlier eras.
Today, however, it often shapes behavior in less intentional ways, including how people interact with ads and information.
AI is not perfect, but it is typically faster, easier, and more effective than digging through traditional search results.
That advantage makes widespread adoption inevitable, especially as AI continues to be integrated into the websites, apps, and devices people already use.
Generative answers are shifting where users enter the funnel, with engagement increasingly starting mid-funnel around content that demonstrates experience and expertise.
This is the type of content users historically would only engage with on a company’s website, or through other owned channels such as YouTube.
This does not mean top-of-the-funnel content is no longer important. Blogs, guides, and videos still matter, videos in particular. However, it may be worth reconsidering how that content is distributed rather than relying solely on traditional organic search.
With the rise of AI tools such as Gemini and ChatGPT, users can now handle much of this comparison work through AI, saving significant time.
For example, the shift looks like this:
From “Mid market ERP platforms.” Where the user must sift through results, compare options, build spreadsheets, and conduct extensive manual review.
To “Which mid-market ERP platforms work best for manufacturing firms, integrate with our existing stack of X, Y, and Z, and won’t collapse during implementation?”
This changes where the user must exert effort.
A more detailed question or input produces a far stronger response or output.
You could argue that traditional search had degraded into a form of garbage in, garbage out (GIGO), where short, generic queries produced ad-heavy, blended results that were time-consuming to mine for real answers.
The result is user fatigue. Endless clicking, avoiding ads, and sorting through widely varying content has become a chore.
AI offers a cleaner, faster, and less cluttered experience, delivering summarized pros, cons, and supporting evidence at each stage of the decision-making process.
All of this can happen inside an AI tool, without the user ever needing to visit the site where the content originated.
AI is increasingly becoming the default interface for information. These are still early days, and the experience will continue to improve, becoming faster, smoother, and more effective over time.
If you want AI to recommend your brand or include it in increasingly nuanced research, your most important content must be visible and accessible so it can be retrieved and used to generate AI answers through retrieval-augmented generation, or RAG.
Frameworks such as “They Ask, You Answer” (TAYA) by Marcus Sheridan are particularly effective here.
The premise is simple: If customers ask the question, you should answer it.
The framework focuses on five core areas, identified through extensive research, that address customer needs, drive engagement, and provide AI with the detailed information it needs to map to real user questions.
This approach works because it makes sense. It benefits users, improves visibility, drives leads, and supports sales. It is not an abstract AI strategy. It is good marketing.
These are the five key areas that TAYA focuses on:
Pricing and cost: If users search for pricing and cannot find it, they do not assume they should call for details. They often assume the product is too expensive or that information is being withheld, and they move on, or ask AI for a competitor’s pricing. Even when pricing is custom, you should explain the factors that influence cost.
Problems: Address the obvious issues. This includes problems with your product, your industry, and the drawbacks of specific solutions. Being transparent about limitations builds trust more effectively than excessive positivity.
Versus and comparisons: Buyers are choosing between alternatives. If you do not create comparison content, someone else will. Be objective. If a competitor is better for a specific use case, say so and focus on your ideal customer profile.
Reviews and ratings: People look for the best options and trust peer opinions more than brand claims. Create honest reviews of products and services in your space, including competitors. This process is informative for both users and brands.
Best in class: Users frequently search for “best” solutions. Lists such as “Top AI marketing agencies in [city]” are effective, even when they include competitors. Including alternatives demonstrates that customer fit matters more than self-promotion.
From an AI and SEO perspective in 2026, these five topics represent some of the highest-value data points for RAG systems.
Tools such as the Value Proposition Canvas and SCAMPER can support ideation and content variation, helping AI better understand your offerings.
Checklist: RAG-friendly formatting tips
Do not break content into meaningless fragments. Instead, use formatting that helps RAG systems navigate comprehensive resources:
Use question-based headers: Mirror real user questions in H2s and H3s, such as “How much does X cost?”
Lead with the answer: Apply the inverted pyramid. Start with the direct response, then add context.
Use bulleted lists for attributes: Bullets help RAG systems extract structured information.
Define key terms: Provide clear, one-sentence definitions for industry jargon.
Link to evidence: Cite sources for statistics and results to support credibility.
Treat blog posts as a knowledge base for AI. The clearer and more specific the information, the more retrievable your brand becomes.
Write for humans, not for bots
It bears repeating: Content should not be simplified solely for AI.
The environment is shifting, and new tools are changing how people find information and make decisions. Yet many fundamentals remain.
SEO tactics still apply, but AI now acts as a superconsumer and summarizer of the information that influences choice.
The task is to identify, create, and structure that information so that when users ask a question, you have already answered it and are part of the conversation.
Search Central Live is coming back to South America! After many successful events in the region,
we’re continuing our mission to help you enhance your site’s performance in Google Search.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2021/12/web-design-creative-services.jpg?fit=1500%2C600&ssl=16001500http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-01-26 06:00:002026-01-26 06:00:00Search Central Live is coming back to South America
Everybody wants smoother workflows and fewer manual tasks. And thanks to AI models, automation is at the center of conversations in marketing departments across all industries.
But most rarely get the results they’re looking for.
According to Ascend2’s State of Marketing Automation Report, only 28% of marketers say their automation “very successfully” supports their objectives.
While 69% felt it was only somewhat successful.
While this specific stat is from 2024, I imagine the broad idea is still true. Especially since there are so many more automation options and tools. It can get overwhelming to decide a go-forward plan and implement effectively.
So if you feel stuck in the camp of “not bad, but not great” marketing automation, you’re not alone.
The good news?
Once you understand the core building blocks, you can turn messy, half-automated systems into workflows that actually move the needle.
A good marketing automation usually involves four basic steps:
A trigger: A catalyst event that starts the automation
An action: One or more steps that happen in sequence after the trigger
An output: The end result
A loop or exit point: A new trigger, or an event that stops the automation
In this article, we’re going to discuss how to use these steps to automate:
The mechanics of content creation (and no, we won’t just be telling you to “write it with AI”)
Beyond the basics of email nurtures
Your PR strategy
Social media engagement
Automate the Mechanics of Content Creation
Content marketers are creative people. We don’t want to automate away the creative work that drives results.
That said, we can automate marketing workflows that come before and after creating. (So we can spend more time on high-impact work.)
Here are some simple ways to get started.
1. Basic Brief Builder
Tools required:
Make (free for 1,000 credits per month, paid plans start at $9/month)
Your favorite keyword research tool (plans vary)
Project management platform (tools like Asana offer a free plan)
Google Sheets, Google Docs (free plan available)
Every week, content marketers around the world spend hours researching keywords, pulling search data, creating new briefs, and adding tasks to their project management systems.
What if you could do most of that with one automation?
Here are the basics of how this works:
Trigger: A new row is added to a Google Sheet (your new keyword)
Action: That keyword is run through your SEO tool, which pulls keyword difficulty, search volume, related terms, and top organic results
Output: A new Google Doc with the data inside, and a new task in your project management tool
In the end, the automation will look like this:
And if this seems scary, don’t worry: I’m going to walk you through each step to create this with Make. (Or, you can go ahead and copy this Scenario into your own Make account here.)
First, you’ll need a Google Sheet for your source.
Start with columns for your new keyword, status, brief URL, and task URL. To get started faster, copy this template here.
Next, add Google Sheets as the trigger step, and select “Watch New Rows.”
After that, select the Google Sheet you want to watch.
This runs the automation every time you add a new keyword to that sheet.
Now, it’s time to gather information from your SEO tool. For this example, we’re going to use Semrush. (You could also use an API like DataForSEO.)
Our first Semrush module will be “Get Keyword Overview.” (You might see different options depending on the specific tool you use.)
You can choose whether to see the keyword data in all regional databases, or just one region.
In this task, you’ll map the “Phrase” to the “Keyword” column from your Google Sheet. Then, choose what you want to get as an output. (In this case, I only want to see the search volume.)
Now, let’s create another Semrush model to “Get Related Keywords” to gather relevant keywords from Semrush.
Again, you’ll map the “Phrase” to the keyword column from our Google Sheet, and choose what data you want to export. (I chose the keyword and search volume.)
You can also decide:
How the results are sorted
Whether to add filters
How many results to retrieve
Now, you’ll need to add a text aggregator into your workflow. This tool compiles the results from Semrush so we can use them in a Google Doc later on.
Here, simply map the source (our Semrush module).
Then, in the “Text” field, map the data as you want it to appear.
Next, we’ll create a Semrush module that runs “Get Keyword Difficulty.”
Again, we’ll map the “Phrase” to our keyword from the Google Sheet, and choose to export the “Keyword Difficulty Index.”
Next, run the “Get Organic Results” module from Semrush to export the sites that are ranking for your new target keyword.
Select the “Export Columns,” or the data that you want to see, and limit the number of results you get (we chose 10).
Since we’re getting multiple results, this module will also need a text aggregator to transform those results into plain text for our Google Doc.
We’ll set it up exactly the same way, but this time map the “Get Organic Results” module.
In the “Text” field, I’ve added “Bundle order position” (where that result is ranking in the SERP), and the URL of the ranking page.
Now, for the fun part.
It’s time to build your basic content brief in a Google doc.
Before you add this into Make, you’ll need to create a Google Doc as a template. This template should have variables that can be mapped to the results you get in your automation.
To show up as variables, you’ll need to wrap them in curly brackets. So, your template will look something like this:
Now, you’ll create a new module in your Make scenario to “Create a Document from a Template.”
Once you connect the Google Doc template you created, you’ll see all of the variables you added in curly brackets as fields in the configuration page.
Now, all you have to do is map those variables to the results you’ve gotten from Semrush and your text aggregators.
Now it’s time to add this new brief into your project management tool. Make lets you connect several tools, including Asana, Trello, Monday, and Notion.
In this scenario, I already have an Asana project for content production.
So I choose the “Create a Task or a Subtask” module for Asana, and map that existing project.
I can also add project custom fields (like a link to the brief in Google Docs), choose the task name (like the keyword), and automatically assign it to someone on my team.
Lastly, I want to go back and update my original Google Sheet so that I can see which keywords have already been run, and where their briefs and tasks live.
So, I add Google Sheets again as the final step in the automation and connect the same spreadsheet that we had at the beginning. Under “Values,” I can map the brief URL from Google Docs and the new task URL from Asana to columns in my spreadsheet.
I also set this so the “Status” column is updated to “Done.”
Now, let’s run this scenario and see what happens.
First, I add a new keyword to my Google Sheet.
This triggers the automation to run.
The first thing that’s produced is a brand new Google Doc with all of the SEO data from Semrush. You’ll see this new doc appear in your Drive, and you’ll find the link in Asana.
Next, I’ll see a new task appear in my Asana project (with the brief link included).
And finally, the Google sheet will be updated to show us that the task has been completed.
Plus, it adds in the links to the new brief in Google Docs and the new task in Asana.
And there you go: you now have a basic content brief builder automation.
Are these complete briefs? No. But the information provides a great start, gives the writer SERP context, and frees up more time to fill out other important content brief elements.
Resources for this automation: To get started faster, use these templates:
Tools required: Your favorite project management tool (paid or free options available)
Project management tools are great for organizing your content workflow.
But the more tasks you create over time, the harder it is to keep track of and manage those systems.
Many project management platforms give you built-in automation tools to help things run more smoothly. Let’s talk about automations that can help your content workflow specifically.
Triggers might include:
A new task is added to a project
A custom field changes
A new assignee is added
A subtask is completed
Due date is changed (or coming up soon)
A task is overdue
And actions could be:
Add to a new project
Auto-assign to a team member
Update a status
Move task to a new section
Create a subtask
Add a comment
For this example, we’re going to use the Rules system in Asana, but the same basic principles apply to almost any major project management tool.
To start, click the “Customize” button in the upper-right corner of your content management project, and create some custom fields.
Especially important here is the “Status” field. The options here should follow the steps in your content process, and will probably mirror the sections in your Project.
Once your “Sections” and “Fields” are set up, you can create some rules.
These can help dictate what happens when a new brief enters your content workflow and assign it to whoever is in charge of moving it forward in the process.
Use a Rule to auto-assign someone on your team (for example, your content manager or editor) to the task.
Now, let’s say a new article is now in progress with a writer.
Create a rule that moves the task to the corresponding section of your project when the status is set to “Writing.”
If your content tasks have subtasks (like “create outline,” “write article,” “edit,” or “design”), you can track completion and use that to move pieces forward.
In this case, you can set a rule that once all subtasks are complete, the task moves to the “Ready to Publish” section.
Once the task moves to that section, set a rule to auto-assign it to the team member who publishes posts.
Then, when the status is set to “Published,” the task could be moved into a separate project where completed tasks of published content are stored.
This allows you to clear the tasks from your main production workflow, but still keep them on hand in case the piece needs to be updated in the future.
What if a piece of content isn’t completed by its deadline?
Set up an automation that checks in with the team to see what the status is.
There are plenty of other automations you can run in Asana or other tools.
But these basic workflow automations will help your content production process have better handoffs and less friction.
We do this at Backlinko using Monday.com as our project management tool.
Email nurtures are relatively easy to put together in any basic email tool: for example, sending a welcome email to a new newsletter subscriber, or a transactional email to a new customer.
But let’s talk about some ways to take those automations even further.
A trigger: Such as someone signing up for an email list
An action: The new contact is added to a list or segment
An output: They new receive a series of pre-made emails
An exit condition: The sequence finishes once all the emails are sent, or once the contact takes a specific action, like buying a product
Exit conditions are especially important, because you don’t want people to receive another email from you after they’ve already completed an action. (Hello, promo email that arrives after I already made a purchase.)
Let’s walk through how to use marketing automation tools for email.
3. Behavior-Based Nurtures and Follow-Ups
Tools required: ActiveCampaign (paid plans start at $15/month, although other email platforms offer automation capabilities too)
When you trigger an email sequence based on real behavior, you’re catching people in the moment when they’re more likely to engage.
For example, if you want to help a new user get to know your platform, you can trigger onboarding emails based on the actions they’ve taken so far.
Or, if you want to reduce cart abandonment, you can send a special promotion for customers who have items in their cart.
This improved targeting can lead to better engagement from your email list.
All you have to do is match the right trigger to the right action. For example:
Trigger
Action
Someone downloads a resource
They receive a series of emails on that topic
A customer purchased a product a few months ago
They get a reminder to replenish their stock
A contact browses a product category, but doesn’t make a purchase
They get an email reminding them of what they looked at
A new user subscribes to your platform
They get a series of emails walking them through specific actions
Your exit condition could be when the person:
Completes their purchase
Books a call
Starts a free trial
Replies to your email
For example, let’s say you want to send a series of emails reminding someone that their subscription is reaching its end date. It could look something like this:
Trigger: End date is within 20 days from now
Action: Send series of three emails up to the last day of their subscription (we don’t want to send too many)
Exit condition: Customer responds to the email, or renews their subscription
Here’s a great example for home insurance renewal:
Or, let’s say a new lead just signed up for a free trial or freemium account.
You could create a workflow that pulls information from the onboarding survey in your tool, and builds a personalized, 1:1 email sequence.
Check out this example from HubSpot:
When I signed up for the account, I identified myself as a self-employed marketer. HubSpot pulled that information into this new trial campaign to make the email even more personalized.
So the question is: how do you get started?
Here’s a quick overview of how you could build a behavior-based email nurture automation in ActiveCampaign.
Let’s say you want to send an email sequence to a known contact who visited a certain page on your website. For example, imagine someone who subscribes to your email newsletter, but isn’t a customer, just visited your pricing page. (In other words, they may be close to signing up — they just aren’t quite convinced yet.)
Before you start this automation, you’ll need to enable Site Tracking on your account in ActiveCampaign. To do this, install the tracking code on your website so ActiveCampaign can see page views.
To start the automation, you’ll add new contacts who enter through any pipeline.
Now, when a known contact (someone who’s already in your database) visits a tracked page, ActiveCampaign associates that page view with the contact’s record, and can start an automation.
The real trigger is the next step: “Wait until conditions are met.”
In this case, the condition is that the contact has visited an exact URL on your website.
Pro tip: You can also adjust this so the email series only runs when the person visits a page multiple times, showing a higher level of interest.
Next, set a waiting period from the time the person sees the page to when the email is sent.
And finally, write your email and add it to the workflow.
After that, you could:
Wait a certain amount of time, then send another email
Set an exit condition if the contact replies or makes a purchase
All of this effort turns into an email like this one that I received from Brooks after visiting one of their product pages:
This makes me way more likely to revisit the shoes I was looking at than a generic reminder email (or no email at all).
4. Webinar Lifecycle Automation
Tools required:
Demio (plans start at $45/month)
HubSpot (limited free plan available)
Webinars are an entire customer journey, including promotion, confirmation, reminders, and post-event follow-ups.
The trigger is normally one event: Someone signed up for your webinar.
The actions include:
Confirmation email
Day before and day-of reminders
“Happening now” email
Post-event replay email
For example, here’s a great reminder email from Kiwi Wealth:
Immediately after the webinar is finished, you might send an email like this one from Beefree:
And you’ll also want to follow up later with a replay and some action items for people who attended, like this:
Note: We got these examples from Really Good Emails, which is a great resource for getting inspiration for your own campaigns.
So, how do you create this automation?
Most great webinar tools allow you to do this. Demio, for example, allows you to automate marketing emails when you create a new event:
If you want to get really fancy, you can segment your post-webinar follow-up emails by whether or not the contact attended the webinar:
Demio’s built-in email is somewhat limited beyond an actual event.
So, you can connect it to HubSpot to add a new layer of segmentation to your lists.
Once this connection is live, Demio will import webinar attendance data into HubSpot.
For example, you can import data like:
Contacts who registered for the webinar
People who registered, but missed the event
People who attended the event
How long a contact stayed in the webinar
People who watched the replay
You can even add new contacts to lists directly in Hubspot if they don’t exist there already.
This automation will help your pre- and post-webinar flows run more smoothly. And hopefully get you more valuable engagement with those webinars.
Grow Your PR Strategy
For small marketing teams, PR outreach can use up a lot of valuable time.
Here are some easy automations to keep doing inbound and outbound PR requests, without spending your entire week on it.
Resource: Get your free PR Plan Template to help you pick the right goals, discover journalists, and make pitches that get press coverage.
5. PR Radar
Tools required:
BrandMentions (paid plans start at $79/month)
Zapier (free for 100 tasks/month, paid plans start at $19.99/month)
Google Sheets (free option available)
Want to keep an eye on new articles that are related to your brand that you could potentially get featured in or a backlink from? Let’s build an automatic PR radar.
Note: Most monitoring tools send alerts, but those notifications disappear into your inbox. This workflow creates a shared, searchable log your whole team can access without extra logins—plus you’ll have a historical record for spotting PR trends over time.
This workflow looks like:
Trigger: A new article mentions your brand or related topics
Action: Pull all new mentions into one place to scan through them easily
Output: A simple, regularly-updated list of PR mentions
There are several tools that do this, but for this example, we’re going to use BrandMentions.
Once you set up your account and your project, head into settings to adjust which sources you’ll collect data from.
Remove social media, and just leave the web option. That way, you’ll get a clean list of articles and webpages that mention your brand or the keywords you added.
Once this is set up, you can connect your BrandMentions project to Zapier.
This will trigger the automation to start when any new mentions are added.
You can choose whatever output works best for you: whether that’s a Slack message, a new row in Airtable, or an addition to an ongoing Google Sheet.
For this example, I chose Google Sheets as my output. All I had to do was tie the data pulled from BrandMentions to the right columns in my spreadsheet.
Once that’s done, the automation adds new articles like this automatically into my spreadsheet:
Pro tip: Want to add a reminder? You can add another step that sends a daily Slack message summarizing all the newly added rows.
6. Media Request Matchmaker
Tools required:
RSS.app (free plan available)
Zapier (free for 100 tasks/month, paid plans start at $19.99/month)
Airtable (free plan available)
PR would be nothing without the relationships we build with journalists and writers.
But it’s hard to know who’s writing about a topic that’s related to your brand. Or where your company’s internal subject matter experts can add their thoughts to promote your brand.
So, let’s build an automation to match new requests to your internal experts.
This involves:
Trigger: A new media request that matches relevant topics
Action: Classify new requests and match them to the internal expert with the most relevant expertise
Output: New requests are automatically routed to the right person
One of the most frequently updated places to find PR requests is on X/Twitter.
Search the hashtag #journorequest, and you’ll see hundreds of writers asking for expert contributions.
To prepare this for your automation, start by setting up an RSS feed with the hashtag #journorequest or #prrequest along with a relevant keyword.
For the simplest version of this, you can connect RSS.app directly to Slack and send a new message every time a new request is added to the feed.
But let’s be real: that could get overwhelming pretty quickly.
So, we’ll use Zapier for a more in-depth automation.
Start by adding “RSS by Zapier” as the trigger, and paste your RSS feed link into the configuration.
Pro tip: If you want to track journo requests for multiple topics, change the trigger event to “New Items in Multiple Feeds.” Then, simply paste in all of the RSS feed links. That way, they’ll all run through the same automation.
Next use “Formatter by Zapier” to extract the necessary information from the tweets.
First, in Formatter, choose the Action event “Text.”
Then, in the Configure menu, select “Extract Email Address,” and map the input to the description from your RSS feed.
Next, with another Formatter step, select “Text,” and “Extract Pattern.”
The input is still the same description (the original tweet).
In the Pattern box, in parentheses, add the keywords you want to track separated by a vertical bar, like this:
(cybersecurity|fintech|pets|saas)
Make sure that IGNORECASE is set to “Yes” so that the search isn’t case sensitive.
Now, it’s time to add that to a system you can use to keep track of new requests and route them to SMEs.
For this example, I’ve chosen to use Airtable. If you want to use this exact database, you can copy it here and we’ll use it as we move forward.
This database has tabs to keep track of your SMEs, the topics they can respond to, and the new requests that come in.
So, let’s connect that Airtable base to Zapier.
Our first step will be to find the right SME for the topic of our journo request.
To start, set the Action as “Find Record,” and link your Airtable base. We’ll pull from the SMEs table, and for “Search by Field” we’ll choose “Topics,” where we’ve previously added our SME’s favorite topics into the Airtable base.
Lastly for this step, map the “Search Value” to the previous step’s result (the topic from the PR query on X/Twitter).
Now, we’re going to create a new row in our “Requests” table in Airtable.
Add Airtable as the next step in this Zap, and select “Create Record” as the action. Link the same Airtable base, but this time select “Requests” as the Table.
Then, map the columns in that base to the information you’ve gathered. In this case, that would include:
Source = X/Twitter
Raw Text = The “Description” from RSS feed
Contact name = The “Raw Creator” from RSS feed
Contact Email = The output from our first Formatter step, which pulled the email from the original post
URL = Link from RSS feed
Topics = The output from our second Formatter step, which pulled the topic from the original post
SMEs = The “Fields Name” from our Airtable search step
Status = New
In the end, it should look like this:
And a new record is added into Airtable, like this:
If you want to get fancy with this, you can dig down into:
Which publications are requesting expertise, and rank them by their credibility
Automate messages to your SMEs to let them know there’s a new request for them
Get the Most Out of Social Media
For busy marketers, social media can be an incredible time-suck.
Keeping track of trends. Trying to post consistently.
All without getting stuck in an infinite doomscroll.
But a few simple automations can help you get back some of the time you spend on manually managing your socials.
7. Video Clip Automator
Tools required:
Zoom (free plan available)
Dropbox (free plan available)
OpusClip (plans start at $15/month)
Zapier (free for 100 tasks/month, paid plans start at $19.99/month)
Short-form video has been gradually gaining a bigger voice in marketing.
If you’re already creating long-form video (or even just doing recorded interviews with in-house experts), we have a handy automation to help you create video clips faster.
Here’s how it works:
Trigger: New Zoom cloud recording is ready
Action: Auto-create clips, burn captions, and create a new task in Asana
Output: You get social-ready video clips, and a new task to publish them
First, adjust your Zoom settings so your recordings upload automatically into a folder in Dropbox.
Next, head over to Zapier.
Your trigger step will be a new video uploaded to that folder in Dropbox.
Your next step will use OpusClip, an AI video editing tool. Select “Clip Your Video,” and map that new video file to the one uploaded in Dropbox.
OpusClip will then take your long-form video from Dropbox and use AI to clip key pieces. It also crops the video for vertical sharing and embeds captions.
You can also add your own brand template so that videos are edited with your brand’s colors and font.
Now that you have new video clips to share, it’s time to add a task to review and publish them.
So the final step in your Zap is “Create Task” in Asana (or your preferred project management tool).
You’ll tie this to a project you’ve already created in Asana, and link the project ID from OpusClip.
In the end, you’ll have a few video clips prepared and ready — all you have to do is download, review, and publish them to your social channels.
8. Comment & Community Nudge
Tools required:
Social media monitoring tool (like BrandMentions, paid plans start at $79/month)
Automation tool (like Zapier, free for 100 tasks/month, paid plans start at $19.99/month)
Are people talking about your brand online?
To keep positive sentiment high, you need to engage in those conversations. But finding the right conversations, and knowing how to reply, can take a lot of time.
Using a tool like BrandMentions, you can create a similar automation to what we built for the PR Radar earlier:
Trigger: A new mention of your brand appears on Reddit, Facebook, or LinkedIn
Action: Those new mentions are added to a Google Sheet, and you get a daily Slack message summarizing new mentions
To build this, all you’d need to do is swap out the Sources in your BrandMentions settings. Instead of Web, you’d include all of the social media channels you want to track.
If you want to get notifications for every new mention, you could connect the workflow to Slack. Then, a new message will be sent in the channel every time your brand is mentioned.
This basic automation could work for smaller brands.
But when you start getting hundreds of mentions per day, this will quickly become chaotic.
Here’s an example of how one company faced with this issue was able to automate this process in a deeper way:
Webflow was getting over 500 mentions per day. Their two-person team couldn’t keep up with monitoring and responding (alongside their regular workload).
So, they built an automation.
With Gumloop, they monitor, analyze, and flag only the posts that require a response.
They started with a Reddit scraper to pull relevant threads.
Then, they added an AI analyzer to gauge sentiment, rank priority, and assign a category.
After that, they added a step that would send all high-priority mentions to Slack for a team member to handle directly.
The result?
After testing and scaling this process, they were able to build an automation that processes 500+ mentions per day and escalates only the 10-15 that need immediate attention.
If you’ve ever thought, “How can I use AI to automate my marketing tasks?”
This is a great example of an AI automation that works for you without taking over your job.
Is Automation the Right Move? Ask Yourself These Questions First
Automation is the hottest trend.
But it’s hard to know what’s going to save you time and money, and what’s just another fad.
If you’ve ever spent more time trying to automate a task than it would’ve taken you to do the task manually, you’ll know what I mean.
To weigh up whether an automation is worth building, ask yourself these questions:
How much time does it take me to do this task manually every week?
Is the automation available with a tool I currently use, or would I have to pay for a new tool?
Is there a documented automation/integration I can follow?
Would this task still require human intervention (even with automation)?
Does this fit easily into our current workflow or process?
If the task:
Doesn’t take much time to do manually
Would still require human intervention even when automated
Isn’t easy to build an automation for
…it may not be worth your time.
On the other hand, if the task:
Is repetitive
Uses up hours of your workweek
Can be automated in tools you already have in your stack
…it’s probably time to give automation a try.
Build Your Automation Foundations, Then Keep Growing
The hype cycle of automation and AI can be overwhelming.
But don’t feel like you’re behind just because you haven’t automated away your entire marketing team yet.
Instead, focus on the automations that save you time and are sustainable.
We’ve just discussed eight different automations. Why not choose one or two that are most relevant to your business and team?
Start with the foundational automations that help smooth out your existing processes.
Then, you’ll have a better basis for building more complex automations.
To automate even more areas of your marketing workflows, check out our curated list of our favorite AI marketing tools right now.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-01-22 21:47:412026-01-22 21:47:41How to Automate Marketing With 8 Simple Workflows
Google Shopping API migration deadlines are approaching, and advertisers who don’t act risk disrupted Shopping and Performance Max campaigns.
What’s happening. Google is sunsetting older API versions and pushing all merchants toward the Merchant API as the single source of truth for Shopping Ads. Advertisers can confirm which API they’re using in Merchant Center Next by checking the “Source” column under Settings > Data sources, where any listing marked “Content API” requires action.
Why we care. Google is actively reminding advertisers to migrate to the new Merchant API, with beta users required to complete the switch by Feb. 28th, and Content API users by Aug. 18th. If feeds aren’t properly reconnected, campaigns that rely on product data — especially those using feed labels — may stop serving altogether.
The risk. Feed labels don’t automatically carry over during migration. If advertisers don’t update their campaign and feed configurations in Google Ads, Shopping and Performance Max setups that depend on those labels for structure or bidding logic can quietly break.
What to do now. Google recommends completing the migration well ahead of the deadline, reviewing feed labels, and validating campaign delivery after reconnecting feeds. The transition was first outlined in mid-2024, but enforcement is now imminent as Google moves closer to fully retiring legacy APIs.
Bottom line. This isn’t a cosmetic backend change — it’s a technical cutoff that can directly impact revenue if ignored.
First seen. This update was spotted by Google Shopping Specialist Emmanuel Flossie, who shared the warnings he received on LinkedIn.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/01/shopping-api-reminder-2-piZqPR.jpg?fit=1044%2C1220&ssl=112201044http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-01-20 18:56:342026-01-20 18:56:34Google Shopping API cutoff looms, putting ad delivery at risk