Google is rolling out new loyalty integrations across Google Ads and Merchant Center, giving retailers tools to highlight member-only pricing and shipping benefits to their most valuable customers.
How it works:
Personalized annotations display member-only discounts or shipping benefits in both free and paid listings.
A new loyalty goal in Google Ads helps retailers optimize budgets toward high-value shoppers, adjusting bids to prioritize lifetime value.
Sephora US saw a 20% lift in CTR by surfacing loyalty-tier discounts in personalized ads.
Why we care. With 61% of U.S. adults saying tailored loyalty programs are the most compelling part of a personalized shopping experience (according to Google), retailers face pressure to prove value beyond discounts.
By surfacing member-only perks directly in search and shopping results, retailers can boost engagement from their most valuable customers and optimize spend toward higher lifetime value, not just single conversions. It’s a way to tie loyalty programs directly to ad performance — and win more share of wallet from existing shoppers.
The big picture. Loyalty features are Google’s latest move to keep retail advertisers invested in its ecosystem — positioning search and shopping as not just discovery channels, but retention engines. Expect more details at Google’s Think Retail event on Sept. 10.
Over the last 30 years, we’ve seen nonstop shifts and transformations in platforms and tactics.
Search, social, and mobile have each gone through their own waves of evolution.
But AI represents something bigger – not just another tactic, but a fundamental shift in how people research, evaluate, and buy products and services.
Estimates vary, but Gartner projects that AI-driven search could account for 25% of search volume by the end of 2026.
I suspect the true share will be much higher as Google weaves AI deeper into its results.
For digital marketers, it can feel like we need a crystal ball to predict what’s next.
While we don’t have magical foresight, we do have the next best thing: lessons from the past.
This article looks back at the early days of search, how user behavior evolved alongside technology, and what those patterns can teach us as we navigate the AI era.
The early days: Wild and wonderful queries
If you remember the early web – AltaVista, Lycos, Yahoo, Hotbot – search was a free-for-all.
People typed in long, rambling queries, sometimes entire sentences, other times just a few random words that “felt” right.
There were no search suggestions, no “people also ask,” and no autocorrect.
It was a simpler time, often summed up as “10 blue links.”
Searchers had to experiment, refine, and iterate on their own, and the variance in query wording was huge.
For marketers, that meant opportunity.
You could capture traffic in all sorts of unexpected ways simply by having relevant pages indexed.
Back then, SEO was, in large part, about one thing: existing in the index.
Or are the results as good as ever, but the underlying sites have declined in quality?
It’s tricky to call.
What is certain is that as traffic declined, many sites got more aggressive – adding more ads, more pop-ups, and sneakier lead gen CTAs to squeeze more value from fewer clicks.
The search results themselves have also become a bewildering mix of ads, organic listings, and SERP features.
To deliver better results from shorter queries, search engines have had to guess at intent while still sending enough clicks to advertisers and publishers to keep the ecosystem running.
And as traffic-starved publishers got more desperate, user experience took a nosedive.
Anyone who has had to scroll through a food blogger’s life story – while dodging pop-ups and auto-playing ads – just to get to a recipe knows how painful this can be.
It’s this chaotic landscape that, in part, has driven the move to answer engines like ChatGPT and other large language models (LLMs).
People are simply tired of panning for gold in the search results.
The AI era: From compression back to conversation
Up to this point, the pattern has been clear: the average query length kept getting shorter.
But AI is changing the game again, and the query-length pendulum is now swinging sharply in the opposite direction.
Tools like ChatGPT, Claude, Perplexity, and Google’s own AI Mode are making it normal to type or speak longer, more detailed questions again.
We can now:
Ask questions instead of searching for keywords.
Refine queries conversationally.
Ask follow-ups without starting over.
And as users, we can finally skip the over-optimized lead gen traps that have made the web a worse place overall.
Here’s the key point: we’ve gone from mid-length, varied queries in the early days, to short, refined queries over the last 12 years or so, and now to full, detailed questions in the AI era.
The way we seek information has changed once more.
We’re no longer just searching for sources of information. We’re asking detailed questions to get clear, direct answers.
And as AI becomes more tightly integrated into Google over the coming months and years, this shift will continue to reshape how we search – or, more accurately, how we question – Google.
Now, we can ask more detailed, multi-part questions and get thorough answers – without battling through the lead gen traps that clutter so many websites.
The reality is simple: this is a better system.
This is progress.
Want to know the best way to boil an egg – and whether the process changes for eggs stored in the fridge versus at room temperature? Just ask.
Google will often decide if an AI Overview is helpful and generate it on the fly, considering both parts of your question.
What is the best way to boil an egg?
Does it differ if they are from the fridge?
The AI Overview answers the question directly.
And if you want to keep going, you can click the bold “Dive deeper in AI Mode” button to continue the conversation.
Inside AI Mode, you get streamlined, conversational answers to questions that traditional search could answer – just without the manual trawling or the painfully over-optimized, pop-up-heavy recipe sites.
From shorter queries to shorter journeys
Stepping back, we can see how behavior is shifting – and how it ties to human nature’s tendency to seek the path of least resistance.
The “easy” option used to be entering short queries and wading through an increasingly complex mix of results to find what you needed.
Now, the path of least resistance is to put in a bit more effort upfront – asking a longer, more refined question – and let the AI do the heavy lifting.
A search for the best steak restaurant nearby once meant seven separate queries and reviewing over 100 sites. That’s a lot of donkey work you can now skip.
It’s a subtle shift: slightly more work up front, but a far smoother journey in return.
This change also aligns with a classic computing principle: GIGO – garbage in, garbage out.
A more refined, context-rich question gives the system better input, which produces a more useful, accurate output.
Historic recurrence: The pattern revealed
Looking back, it’s clear there’s a repeating cycle in how technology shapes search behavior.
The early web (1990s)
Behavior: Long, experimental, often clumsy queries.
Why: No guidance, poor relevance, and lots of trial-and-error.
Marketing lesson: Simply having relevant content was often enough to capture traffic.
Google + Autocomplete (2000s)
Behavior: Queries got shorter and more standardized.
Why: Google Suggest and smarter algorithms nudged users toward the most common phrases.
Marketing lesson: Keyword targeting became more focused, with heavier competition around fewer, high-volume terms.
Mobile and voice era (2010s–early 2020s)
Behavior: Even shorter, highly predictable queries.
Why: Tiny keyboards, voice assistants, and SERP features that answered questions directly.
Marketing lesson: The long tail collapsed into clusters. Zero-click searches rose. Winning visibility meant optimizing for snippets and structured data.
AI conversation era (2023–present)
Behavior: Longer, natural-language queries return – now in back-and-forth conversations.
Why: Generative AI tools like ChatGPT, Gemini, and Perplexity encourage refinement, context, and multi-step questions.
Marketing lesson: It’s no longer about just showing up. It’s about being the best answer – authoritative, helpful, and easy for AI to surface.
Technology drives change
The key takeaway is that technology drives changes in how people ask questions.
And tactically, we’ve come full circle – closer to the early days of search than we’ve been in years.
Despite all the doom and gloom around SEO, there’s real opportunity in the AI era for those who adapt.
What this means for SEO, AEO, LLMO, GEO – and beyond
The environment is changing.
Technology is reshaping how we seek information – and how we expect answers to be delivered.
Traditional search engine results are still important. Don’t abandon conventional SEO.
But now, we also need to optimize for answer engines like ChatGPT, Perplexity, and Google’s AI Mode.
That means developing deeper insight into your customer segments and fully understanding the journey from awareness to interest to conversion.
Talk to your customers.
Run surveys.
Reach out to those who didn’t convert and ask why.
Then weave those insights into genuinely helpful content that can be found, indexed, and surfaced by the large language models powering these new platforms.
It’s a brave new world – but an incredibly exciting one to be part of.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/08/Google-Search-10-blue-links-sIsPKU.webp?fit=583%2C601&ssl=1601583http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-08-26 13:00:002025-08-26 13:00:00Historic recurrence in search: Why AI feels familiar and what’s next
Retail media networks are projected to be worth $179.5 billion by 2025, but capturing share and achieving long-term success won’t hinge solely on growing their customer base. With over 200 retail media networks now competing for advertiser attention, the landscape has become increasingly complex and crowded. The RMNs that stand out will be those taking a differentiated approach to meeting the evolving needs of advertisers.
The industry’s concentration creates interesting dynamics. While some platforms have achieved significant scale, nearly 70% of RMN buyers cite “complexity in the buying process” as their biggest obstacle. That tension, between explosive growth and operational complexity, is forcing the industry to evolve beyond traditional approaches.
As the landscape matures, which strategies will define the next wave of growth: global expansion, hyperlocal targeting, or both?
The evolution of retail media platforms
To understand where the industry is heading, it’s worth examining how successful platforms are addressing advertisers’ core challenges. Lack of measurement standards across platforms continues to frustrate advertisers who want to compare performance across networks. Manual processes dominate smaller networks, making campaign management inefficient and time-consuming.
At the same time, most retailers lack the digital footprint necessary for standalone success. This has created opportunities for platforms that can solve multiple problems simultaneously: standardization, automation, and scale.
DoorDash represents an interesting case study in this evolution. The platform has built its advertising capabilities around reaching consumers at their moment of local need across multiple categories. With more than 42 million monthly active consumers as of December 2024, DoorDash provides scale and access to high-intent shoppers across various categories spanning restaurants, groceries and retail.
The company’s approach demonstrates how platforms can address advertiser pain points through technology. DoorDash’s recent platform announcement showcases this evolution: the company now serves advertisers with new AI-powered tools and expanded capabilities. Through its acquisition of ad tech platform Symbiosys, a next-generation retail media platform, brands can expand their reach into digital channels, such as search, social, and display, and retailers can extend the breadth of their retail media networks.
The challenge lies in building platforms that work seamlessly across countries while maintaining local relevance. International expansion requires handling different currencies, regulations, and cultural contexts—capabilities that many networks struggle to develop.
DoorDash’s acquisition of Wolt illustrates how platforms can achieve global scale while maintaining local connections. The integration enables brands to manage campaigns across Europe and the U.S. through a single interface—exactly the kind of operational efficiency that overwhelmed advertisers seek.
The combined entity now operates across more than 30 countries, with DoorDash and Wolt Ads crossing an annualized advertising revenue run rate of more than $1 billion in 2024. What makes this expansion compelling isn’t just the scale—it’s how the integration maintains neighborhood-level precision across diverse geographies.
The hyperlocal advantage: context beats demographics
Here’s what’s really changing the game: the shift from demographic targeting to contextual precision. Privacy regulations favor contextual targeting over behavioral tracking, but that’s not the only reason smart networks are going hyperlocal.
Location-based intent signals provide dramatically higher conversion probability than traditional demographics. Real-time contextual data—weather patterns, local events, proximity to fulfillment—influences purchase decisions in immediate, actionable ways that broad demographic targeting simply can’t match.
DoorDash built its entire advertising model around this insight, reaching consumers at the exact moment of local need across multiple categories. The platform provides scale and access to high-intent shoppers with contextual precision. A recent innovation that exemplifies this approach is Dayparting for CPG brands, which enables advertisers to target users in their local time zones—a level of time-based precision that distinguishes hyperlocal platforms from broader retail media networks.
In one example, Unilever applied Dayparting to focus on late-night and weekend windows for its ice cream campaigns, aligning ad delivery with peak demand periods. Over a two-week period, 77% of attributed sales were new-to-brand, demonstrating the power of contextual timing in driving incremental reach.
Major brands, including Unilever, Coca-Cola, and Heineken, utilize both DoorDash and Wolt platforms for hyperlocal targeting, proving the model is effective for both endemic and non-endemic advertisers seeking neighborhood-level precision.
Technology evolution: measurement and automation
The technical requirements for next-generation retail media networks extend far beyond basic advertising capabilities. Self-serve functionality has become standard for international geographies—not because it’s trendy, but because manual campaign management doesn’t scale across dozens of countries with different currencies, regulations, and cultural contexts.
Cross-country campaign management requires unified dashboards that manage complexity while maintaining simplicity for advertisers. Automation isn’t optional anymore; it’s necessary to compete with established players who’ve built machine learning into their core operations.
But here’s what’s really transforming measurement: new attribution methodologies that go beyond traditional ROAS. When platforms can integrate fulfillment data with advertising exposure, they enable real-time performance tracking that connects ad spend to actual business outcomes rather than just clicks and impressions.
Progress on standardization continues through IAB guidelines addressing measurement consistency, alongside industry pushes for technical integration standards. The challenge lies in balancing standardization with differentiation—networks need to offer easy integration and consistent measurement while maintaining unique value propositions.
In a move toward addressing advertisers’ need for measurement consistency, DoorDash recognized that restaurant brands valued both click and impression-based attribution for their sponsored listing ads, and recently introduced impression-based attribution and reporting in Ads Manager. This has enabled restaurant brands to gain a deeper understanding of performance and results driven on DoorDash.
Global technology challenges add another layer of complexity: multi-currency transactions, local payment methods, regulatory compliance across countries, and cultural adaptation while maintaining platform consistency. These aren’t afterthoughts for international platforms, they’re core competencies that determine success or failure.
Industry outlook: consolidation and opportunity
Retail media is heading toward consolidation, but not in the way most people expect. Hyperlocal networks are positioned to capture share from undifferentiated RMNs that compete solely on inventory volume. Geographic specialization is becoming a viable alternative to traditional scale-focused approaches.
Simultaneously, community impact measurement is gaining importance for brand strategy. Marketers are discovering that advertising dollars spent on local commerce platforms create multiplier effects—supporting neighborhood businesses and strengthening local economies in ways that traditional e-commerce advertising doesn’t achieve.
The networks that understand this dynamic, that can offer global platform capabilities with genuine local industry expertise, are the ones positioned to define retail media’s next chapter. Success requires technology integration that enables contextual and location-based targeting, plus measurement solutions that prove incrementality beyond traditional metrics.
The path forward
As retail media networks mature, success lies not in choosing between global scale and local relevance, but in achieving both simultaneously. The DoorDash-Wolt combination provides a compelling blueprint, demonstrating how technology platforms can enable international expansion while deepening neighborhood-level connections.
For marketers navigating this evolution, the fundamental question shifts from “where should we advertise?” to “how can we reach consumers at their moment of need?” Networks that answer this effectively—through global reach, hyperlocal precision, or ideally both, will write retail media’s next chapter.Interested to learn more about DoorDash Ads? Get started today.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/08/Doordash-20250822-1920x1020-from-client-oCOpjR.jpg?fit=1920%2C1080&ssl=110801920http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-08-26 11:00:002025-08-26 11:00:00Global expansion and hyperlocal focus redefine the next chapter of retail media networks by DoorDash
Trusted contributors get invited back and land bigger features faster.
-Send thank-yous
-Offer value beyond the pitch
-Track warm journalist relationships in a simple CRM
For instance, let’s say you’re a B2B SaaS marketer trying to rank a key feature page.
Look for HARO or Qwoted queries where the topic aligns with the problem your product solves.
If you can offer a helpful, relevant perspective — one that happens to mention your company or approach — that’s a win. Even if the link doesn’t show up right away.
The bottom line?
When you know what wins you’re aiming for, you’re far more likely to hit them.
Greg Heilers, co-founder of Jolly SEO, puts it simply:
“Depending on your criteria, writing skill, and site/figurehead optimization, you can achieve a win as frequently as 1 in every 3 pitches.”
Step 2: Establish Realistic Expectations (This Will Save Your Sanity)
Most people give up on journalist outreach too soon.
Not because the tactic doesn’t work — but because their expectations are wildly off.
They expect quick wins, a high response rate, and instant SEO impact.
The reality is slower, less glamorous, and a lot more sustainable if you approach it with the right mindset.
Typical Success Rates (and What to Expect Over Time)
Even top-tier media outreach experts don’t land every pitch.
For beginners, a 3–5% response rate is normal. As you gain experience, that can climb to 8–12%, and with refined systems and strong positioning, 15–20% is achievable.
That means you might need to send 10–30 pitches just to earn one mention.
This isn’t failure — it’s the math behind consistent results.
So, what does that actually look like over time?
Month 1: You’re learning the workflow. Scanning opportunities, testing your messaging, and getting familiar with the process. Landing even one or two mentions is a meaningful start.
Month 3: You start to see patterns. Which types of queries are worth your time. Which angles tend to get picked up. You might even get quoted by the same journalist twice.
Month 6: You have momentum. Pitches get easier. You might even start getting inbound requests from writers who’ve seen your previous contributions.
The payoff builds slowly — but it compounds.
Beyond the byline: Search is evolving fast. Journalist quotes are now surfacing in tools like ChatGPT, Perplexity, and Claude. If you’re featured in a top-tier article, there’s a real chance your name, company, or insight will show up in AI-generated answers.
Why Most Pitches Fail (And It’s Not Your Fault)
The biggest myth in journalist outreach is that great writing is enough.
Spoiler: It’s not.
Journalists get dozens — sometimes hundreds — of pitches for a single request.
Many already have sources in mind.
Others are on tight deadlines and go with the first relevant response they see. If your pitch arrives an hour late, it might not get opened at all.
This doesn’t mean your pitch was bad. It means timing and fit beat polish more often than not.
Step 3: Set Up Your Inbox and Tracking
The fastest way to burn out in journalist outreach?
Drowning in irrelevant pitches, deadlines you’ll never meet, and inbox chaos.
Here’s the good news: A few quick workflows can save you hours a week and help you stay consistent over time.
Make Your Emails Credible at a Glance
Journalists scan dozens of emails a day, and first impressions matter.
A polished inbox setup instantly signals trust and professionalism.
Include:
Branded email address: Avoid generic Gmail accounts when possible. Use a domain-linked email to show legitimacy.
Uploaded headshot: Many platforms now require it
Branded signature: Add your name, title, company, LinkedIn, and a link to your website. Make it easy for journalists to verify who you are.
You don’t need a huge platform. You just need to look like someone worth quoting.
Build a Simple Filtering System
Start by organizing your inbox to reduce the cognitive load.
Create folders or labels by platform (e.g., “HARO Outreach”), and add filters to automatically route incoming queries.
Then, block off 15-minute review windows, no more than three times a day.
You don’t need to monitor your inbox all day — just be consistent.
Use the “5-second scan” rule: if it’s not clearly relevant within a few seconds, archive and move on.
Use Fast, Practical Qualification Criteria
Not every opportunity is worth your time — and trying to pitch everything will tank your efficiency.
For each request, ask:
Is this in my area of expertise? If not, don’t force a fit. Weak relevance leads to ignored pitches.
Do I meet the specific requirements? Many queries ask for certain job titles or credentials. Skip it if you’re not eligible.
Is the deadline realistic? If you can’t hit the cutoff, don’t let it clog your pipeline.
Is the publication worth your time? Not every outlet will align with your goals. Use your win criteria (from Step 1) to filter.
For instance, if you’re a freelance content strategist, a request asking for insights from “Fortune 500 CEOs” is a clear pass.
Save your effort for a request that matches your actual experience.
Step 4: Choose Your Platforms Strategically
Not all journalist outreach platforms are created equal.
Some are great for quick wins. Others shine when you’re targeting high-authority publications or niche audiences.
The key isn’t choosing the platform with the most opportunities — it’s choosing the one that aligns with your actual goals.
That means considering more than just volume.
You’ll want to look at average link quality, pitch-to-publication turnaround, cost, and whether the requests match your expertise.
Note: We’re gathering updated data on additional platforms like Qwoted and will expand this comparison in future updates.
Platform
Best For
Avg DR
Cost
Turnaround
Featured
Easy wins, building confidence
70
Free/Paid
23 days
Help a B2B Writer
B2B content, SaaS brands
73
Free
44 days
ProfNet
Premium publications
79
Paid
39 days
HARO
Broad topics
76
Free/Paid
37 days
Source of Sources
Niche expertise
81
Free
35 days
Don’t feel like you need to master every platform out of the gate.
Start with one or two that align with your goals, get really good at using them, and expand once your workflow is dialed in.
How to Choose the Right Platform (Fast)
Not sure where to start? Think of this as your cheat sheet for getting started.
Just getting your reps in? Start with Featured — it’s simple, fast, and great for building confidence early.
Need high-authority links that actually move rankings? Go with Qwoted — it consistently surfaces high domain rating (DR) opportunities from recognizable media outlets.
Want placements in premium, name-brand publications? Choose ProfNet — fewer opportunities, but often higher caliber if you have the budget.
Targeting marketers, founders, or SaaS buyers?Help a B2B Writer delivers curated, niche-relevant requests in your exact lane.
Need a high volume of relevant opportunities to work with?Source of Sources gives you a steady stream of niche pitches — just be ready to filter.
Looking for general-topic visibility at scale?HARO still delivers breadth and quantity — just expect to dig for quality.
Looking for country-specific platforms?
Many regions have their own journalist request tools worth exploring. For example, SourceBottle is widely used in Australia, and ResponseSource is popular among PR pros and journalists in the U.K.
Just try a quick Google search like “journalist request platform [your country].”
You’ll usually uncover a few local options — no massive directory needed.
Step 5: Write Pitches That Win (Without Taking Forever)
The best pitches don’t win because they’re long or clever.
They win because they’re skimmable, useful, and immediately quotable.
Your job isn’t to impress the journalist — it’s to make their job easier.
Establish Credibility in 8 Words or Less
Start with a strong subject line. Greg emphasizes combining relevance with instant credibility.
Use this structure:
Subject line formula: [Your credentials] + [specific value] + [topic]
Examples:
SaaS CEO’s Take on Fixing Churn
SEO Consultant’s Local Link Playbook
Copywriter’s Formula for High-Converting Headlines
Then, build your pitch. It should look something like this:
Hi [first name],
I’m [name], [title] at [company]. [One-line credibility builder].
Pro tip: AI tools can help you brainstorm angles — but the final quotes should sound human, specific, and ready to publish. Use AI for speed, not substitution.
Make Your Quotes Instantly Usable
Journalists aren’t grading your writing.
They’re looking for clean, usable quotes they can drop straight into a draft.
As Greg puts it:
“Journalists want quotes they can immediately copy and paste into their articles, no changes needed.”
Here’s how to make that happen:
Pro tip: For the full checklist — and why each step matters — use the Pitch Checklist tab in our journalist outreach toolkit.
Not all pitches are created equal.
Here’s what gets picked up — and what gets ignored.
❌ Bloated, vague, and completely unusable:
✅Clear, specific, and quote-ready:
Pitch Faster Without Losing Quality
Greg recommends prewriting as much as possible so you’re never starting your press outreach from scratch.
Have 3–4 versions of your bio ready to go, tailored for different beats (e.g., SaaS, marketing, AI).
Build a few quote templates for your most common talking points. And give yourself a hard limit: Aim to finish each pitch in 10 minutes or less.
The more reps you get, the easier this becomes.
Don’t forget your first line does heavy lifting. It shows up in inbox previews and often determines whether your pitch even gets opened. Make it count.
Caveat: Structure helps, but sameness kills. AI tools and mass pitching have flooded inboxes with lookalike answers. Don’t just fill in a template — say something only you would say. That’s what gets quoted.
Yes, You’re Qualified. Here’s Why.
One of the biggest blockers in journalist outreach? Thinking you’re not “qualified” to respond.
But here’s the truth: You don’t need a blue checkmark or a book deal to be helpful.
If you can help readers understand something better or offer a useful perspective, you’re already ahead.
Credibility doesn’t mean status. It means relevance.
That could be your job title, your years of experience, a client result, or just a smart way of framing the problem.
When in doubt, try this five-part framework to surface story ideas from your own work:
The situation: What were you working on?
The challenge: What made it tricky?
Your approach: What did you try or test?
The result: What changed? What worked?
The insight: What do you wish you’d known earlier?
The bottom line?
If you’ve solved what they’re writing about, you belong in their inbox.
Step 6: Master the Follow-Up (Without Being Annoying)
It’s tempting to send a pitch and move on.
But following up is one of the easiest ways to multiply the value of your efforts.
It’s low-effort, high-return, and totally underused.
The key is to keep it respectful, useful, and brief. Here’s how to do it without sounding pushy.
Turn Mentions Into Links
Let’s say you’ve been quoted but not linked.
Here’s a simple, polite ask that turns visibility into real SEO value:
Hi [Name],
Thanks so much for including me in the [article title]!
If it’s possible to link my company name to [URL], that would be amazing—but I totally understand either way. Appreciate your great work on this piece.
Best, [Name]
Turn Replies Into Relationships
The journalists who quote you today could become recurring collaborators — if you give them a reason to remember you.
Hi [Name],
Loved your recent piece on [topic]. Your point about [specific insight] really resonated.
I’m always happy to contribute insights on [your expertise areas] if you’re working on related stories.
Best, [Name]
Done right, a follow-up turns one good pitch into long-term visibility, stronger links, and a journalist who might actually remember your name.
Step 7: Find Hidden Wins (Most People Miss These)
You might already be getting results — and not even know it.
“People message me and say, ‘I’ve sent dozens of pitches, but I can’t get any wins. What am I doing wrong?’
My first question is always: ‘Have you tried looking for them yet?’”
The good news?
You don’t need expensive tools or a manual content audit. A few smart searches and a weekly routine are all it takes.
Use Google Search Operators
Advanced search syntax lets you find live mentions with precision. Run these searches weekly to uncover wins:
“Your Name” + “Your Brand Name”
“CEO of [Brand]” site:targetpublication.com
“[Your unique quote]” site:[domain]
Use quotes to force exact matches and ”site:” to limit the search to specific outlets.
Set Up Google Alerts
Track new mentions passively by creating alerts for:
Your name + company
Your job title (e.g., “CMO of Backlinko”)
Distinctive quotes or phrasing you tend to use
This won’t catch everything, but it will help surface a steady stream of new wins.
Manual Checking Schedule
Most people stop after they hit “send.”
But Greg estimates you’ll never be told about 90% of your wins.
So if you don’t go looking, you’ll never even know they happened.
Build a simple check-in routine:
Weekly: Run your branded Google searches
Monthly: Review recent articles from journalists you’ve pitched
Quarterly: Use SEO tools (like Ahrefs or Semrush) to spot backlinks or citations
Pro tip: Use the Win Finder (in the toolkit) to uncover hidden mentions.
Step 8: Build Your Journalist Network
Every pitch is more than a one-time shot at a link — it’s the start of a potential relationship.
If a journalist quotes you once, there’s a good chance they’ll want insights from you again.
But only if you make it easy, relevant, and respectful to stay in touch.
Track Relationships Like You Track Links
Use a simple CRM (even a spreadsheet works) to track journalist contacts the same way you’d track sales prospects:
Name + outlet
Contact info + beat
History (quoted, linked, mentioned)
Relationship stage (cold, warm, repeat, advocate)
Last contact date + next follow-up
If you’ve contributed to multiple stories or gotten links from the same writer, mark them as high-priority for future outreach. These are your warmest leads.
Build Trust Without Pitching
You don’t need a quote request to stay visible.
In fact, the best relationship-building moments often happen when you’re not asking for anything.
Promote their articles on social with a thoughtful comment — not just a tag. If you come across a story angle or source that fits their beat, send it their way.
If they mentioned a topic they’re covering next month, follow up. Even better: introduce them to another trusted source in your network.
These small, useful gestures build familiarity over time.
That’s how you become more than a random inbox name. You move from pitching to being pitched.
Pro tip: Use our Outreach CRM Tracker (in the toolkit) to start tracking pitches and wins instantly.
Step 9: Measure and Prove ROI
If you’re investing time, you need to show what it’s worth — to your team, your stakeholders, or your clients.
That means going beyond raw link counts and telling the full story of impact.
Track What Matters
Link counts are a starting point, but they’re not the whole picture.
Look at which platforms consistently deliver wins, how many hours go into each link, and which journalists become repeat collaborators.
Track your mentions, even when there’s no link.
Watch for traffic spikes after a story goes live, and pay attention to whether rankings improve on pages earning coverage.
For example, if a single article mention leads to a 12% lift in branded search and earns a backlink to your pricing page, that’s clear momentum.
When you combine reach, effort, and outcome, you start to see the full return.
Use a Simple ROI Framework
When you need to quantify results for stakeholders, use this basic formula to translate time and effort into value:
Link Value = (Average link cost in your industry) × (number of links)
Time Investment = (Hours spent) × (Your hourly rate)
ROI = (Total link value – Time investment) / Time investment × 100
For example:
You earned five links in a month — all from DR 70+ publications.
Let’s say the average market cost for that caliber of link is $800, and you assign a DR adjustment factor of 1 (used to reflect link quality; 1.0 = solid, relevant fit):
Link value: $800 (avg. link cost) x 5 links = $4,000
Time investment: 12 hours × $100/hr = $1,200
ROI: ($4,000 – $1,200) / $1,200 × 100 = 233%
Now compare that to sponsored content, digital PR retainers, or even PPC — and suddenly, this starts looking like a serious channel.
Build a Stakeholder-Ready Report
The final piece is packaging your results in a way that stakeholders understand and care about.
Keep it simple, visual, and focused on outcomes:
A summary of links earned by domain authority range
Growth in brand mentions across media and social
Traffic lift or ranking movement tied to earned placements
Estimated link value compared to paid alternatives
A standout example or case study from that month
When stakeholders can see the momentum — not just the metrics — they’re far more likely to stay bought in.
Start Earning Links That Actually Matter
You’ve got everything you need to get started. Now, it’s time to make your move.
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Google announced it will shut down the Content API for Shopping on Aug. 18, 2026, officially making the Merchant API the new standard for managing Merchant Center accounts.
Why we care. For over a decade, advertisers and retailers have relied on the Content API to push product data into Google Shopping. The new Merchant API promises a simpler, more powerful way to control how products appear across both organic and ad surfaces – but it means developers and PPC teams need to start planning migrations now.
Details:
The Merchant API has been available in beta since May 2024, but is now generally available.
Google describes it as a “simplified interface” for scaling product feeds and gaining programmatic access to data, insights, and unique capabilities.
It will serve as the primary tool for product data management, spanning both paid and organic listings.
What’s next. The Content API remains available until August 2026, but Google urges advertisers to migrate sooner.
Help docs are live to guide developers through the transition.
Expect growing forum chatter as advertisers share migration challenges and best practices.
Bottom line. If your ecommerce business relies on the Content API, the clock is ticking. Moving to the Merchant API isn’t optional, and early adopters may gain a smoother path to scaling feeds and campaigns.
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Is the number of clicks on your top-ranking content starting to slip? It’s time to find out where your traffic has gone and how to get it back.
AI Overviews are reshaping SERPs as we know them. Google now answers user queries directly in the SERP, and traditional blue links are getting pushed further down the page. You might still be ranking, but your visibility is shrinking.
This doesn’t mean SEO is dead. It just means the playbook has changed. To stay competitive, you need to understand how AI Overview optimization works and start building content designed to earn those coveted AI citations.
Key Takeaways
AI Overviews are rerouting traffic, not killing it. Your rankings may hold, but clicks drop because Google satisfies user intent directly in the SERP.
Answer-first content wins. Structuring pages with concise answers, logical headings, and clear formatting increases your chances of being cited in AI Overviews.
Authority signals matter more than backlinks. Brand mentions, topical trust, and consistent visibility across multiple platforms influence AI citations.
Owning your audience is your safety net. Diversifying channels and building first-party data ensures long-term visibility, even as search behavior evolves.
Why You’re Ranking But Still Losing Traffic
If your content still ranks in the top 10 SERP positions but traffic is slipping, there’s a good chance AI overviews are the culprit. Google’s AI-generated summaries dominate the top of the page, pushing organic listings below the fold. Users get the answer they want without ever clicking. In fact, almost 60% of Google searches end without users even making a single click.
You can see from this screenshot that when your search results load, there’s no organic results in site. In fact, in this instance, AI overviews even push sponsored results below the fold. This is the new reality of zero-click searches. Impressions might look steady, but clicks drop because users can satisfy their intent without leaving Google.
The solution is to stop thinking only in terms of traffic volume. Start focusing on visible influence: appearing in AI Overviews and being recognized as an authority, even when users don’t click.
AI Overviews And How They Are Turning The Funnel Upside-Down
Traditional search funnels start with discovery, move to consideration, and end in conversion. AI Overviews flip that script.
Users can start—and sometimes finish—their journey right on Google. With features like AI-generated summaries and featured snippets, the need to click through is lower than ever. Voice search and even short-form video integrations accelerate this shift, creating an environment where Google does the explaining for you.
For marketers, this means clicks are no longer the whole story. Your content has to deliver more than just clicks. It needs to capture attention inside the SERP and give users a reason to engage when they do click through. Strong on-page structure, engaging CTAs, and retention strategies like scroll-depth optimization now matter just as much as ranking. This is the essence of Search Everywhere optimization, which focuses on meeting users wherever they’re consuming content, not just on your site.
How To Optimize For AI Overviews
If you want your content featured in AI Overviews, you need to create pages that are easy for Google to summarize and trust. Here’s how to give Google what it wants:
Lead with an answer-first layout: Open your page with a concise, 2–3 sentence answer to the core query. This immediately gives AI a clear takeaway, increasing the odds of being cited in an overview. Expand into supporting details afterward with a logical flow.
Use structured formatting: Break your content into clean H2s and short paragraphs so Google can scan and interpret it quickly. Bulleted and numbered lists help AI extract step-by-step processes or summaries.
Add schema and FAQs: Implement FAQ and How-To schema to highlight your key answers for AI. Include a short FAQ section at the end of your article to increase your odds of citation for question-based queries.
Target long-tail, conversational keywords: AI Overviews thrive on natural, question-based searches. Integrate these phrases into headings and early sentences to align with how users talk to search engines and voice assistants.
Publish fresh, authoritative content: Share unique insights, proprietary data, or first-hand expertise to meet E-E-A-T signals—experience, expertise, authority, and trustworthiness. AI favors credible, original content over generic summaries.
Support with media: Embed YouTube videos, charts, or screenshots to improve engagement and reinforce authority. Use descriptive alt text so search engines can understand and reference your visuals.
Combining structure, authority, and clarity makes it easy for AI to pull your content and keep your brand visible in the new SERP landscape.
YouTube and Video: Your Shortcut to Visibility
Video content—especially on YouTube—is one of the fastest ways to gain visibility in AI Overviews. Google and Gemini favor YouTube because it’s part of their ecosystem, and AI models naturally pull from sources they already trust.
Short, keyword-focused videos can surface in AI-generated results even if your text content isn’t cited. A 60–90 second explainer video that directly answers the search query gives AI a clean snippet to work with while also boosting your chances of appearing in video carousels.
The charts below show just how effective video is. They show the categories of YouTube videos that have shown up in AI overviews and how fast the trend of videos showing up in AI overviews has grown over time.
Create concise, educational videos tied to core keywords.
Embed them on relevant blog posts or landing pages to reinforce topical authority.
Add captions or transcripts so AI models can understand and summarize your video content.
Video can reclaim lost search visibility while building multi-surface authority across AI-driven and traditional search.
Off-Page Signals Matter More Than Backlinks
In the age of AI Overviews, Google and AI models are looking beyond traditional backlinks. They increasingly value off-page signals like brand mentions and expert quotes in reputable sources.
AI models evaluate whether your brand is recognized and trusted across the web. A mention in an industry publication, a quote in a news article, or a stat cited in a whitepaper can be as impactful as a link for AI visibility.
To strengthen your off-page signals:
Pursue public relations (PR) opportunities in industry-relevant media and blogs.
Share original data or research that journalists and peers want to reference.
Encourage brand discussions on platforms like LinkedIn, Reddit, and Quora, which AI crawlers frequently mine. Internally, we’ve seen tremendous growth for our client, TurboTax, by helping them launch a branded Reddit campaign—including discussions and engagement.
The goal is to create a trustworthy footprint online. When AI sees your brand cited in multiple credible sources, you’re far more likely to be included in its summaries, even without a traditional backlink.
Build Topical Trust Across the Web
AI Overviews reward brands that show consistent authority on a topic, not just one-off content. Google and AI models look for a pattern: Are you producing relevant, high-quality content across multiple platforms that reinforces your expertise?
To build topical trust:
Publish blog posts, guides, and FAQs that cover your key themes in depth.
Share insights across social media and YouTube, giving AI more signals that your brand is active and authoritative.
Leverage user-generated content (UGC), like community discussions, testimonials, and real-world examples, to demonstrate authenticity.
Ensure your content aligns with E-E-A-T across every channel.
Maintaining a consistent and credible presence wherever your audience searches makes it easy for AI to recognize your brand as a reliable source. That recognition is what creates a trustworthy brand footprint that AI can work with.
You Need to Diversify Your Channels Now
Relying solely on Google for traffic is riskier than ever. The shrinking SERP visibility caused by AI overviews and zero-click searches means that even top-ranking content might not deliver the same ROI it once did.
To protect your brand, you need to diversify your traffic sources:
Combine SEO and paid search to maintain visibility and retarget your most valuable branded keywords.
Invest in social media, email, and YouTube to capture attention outside of Google.
Build a strategy that prioritizes owning your audience instead of depending on any single platform.
Diversifying channels doesn’t just protect your current visibility. It’s a great way to grow your online brand. A strong multi-channel approach captures leads you might otherwise miss, making you less vulnerable to Google’s constant evolution. Ultimately, the brands that thrive in the AI era are the ones that meet their audience everywhere, not just in search results.
When AI Overviews dominate search, the brands that win are the ones creating proprietary insights that can’t be found anywhere else. AI models favor content that provides original data because it signals authority and adds value beyond generic summaries. Internal research is your secret weapon.
Instead of relying solely on public stats, collect your own:
Run audience surveys to uncover trends or opinions in your niche.
Conduct polls or quizzes to generate quick, shareable insights that can be repurposed into blogs and social posts.
Analyze internal data like customer behavior, conversion trends, or product usage to produce unique reports.
Turn these findings into case studies and data-driven articles. Proprietary insights make your brand more likely to appear in AI Overviews and attract backlinks and press coverage, compounding your authority across the web.
FAQs
How do I optimize for AI Overviews?
Start with an answer-first structure: give a concise response in the first 2–3 sentences, then expand with supporting details. Use a clear structure with H2s and bulleted lists so Google can easily scan and summarize your content. Implement FAQ or how-to schema, and include a dedicated FAQ section to match AI’s preferred Q&A format. Fresh, authoritative content supported by brand mentions and backlinks will boost your chances of being cited.
How are AI Overviews changing the SERPs?
AI Overviews now dominate the top of Google results, pushing organic listings further down the page. This creates more zero-click searches, where users get answers without visiting your site. Even if your rankings haven’t changed, your visibility and clicks may decline. Making AI-friendly formatting and multi-channel strategies more important than ever.
Conclusion
There’s no need to panic. AI Overviews aren’t erasing traffic, they’re simply rerouting it. Your pages may still rank, but when Google’s summaries dominate the top of the SERP, visibility doesn’t always translate into clicks. The old playbook of relying on impressions and top rankings isn’t enough anymore.
To win in this era of search, your SEO strategy has to include AI overview optimization. Content needs to be structured for AI-first discovery, with clear answers and logical formatting that gains LLMs’ trust. Now, success is about building influence. When your brand appears in AI Overviews and consistently reinforces topical expertise, you maintain visibility even when users don’t land on your site.
The final step is ownership. Diversifying channels and leveraging Search Everywhere optimization gives your brand resilience, while first-party data ensures you can nurture and convert your audience on your own terms. If done right, AI can be your biggest opportunity, not just a threat.
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When it comes to AI-powered search, visibility isn’t just about ranking – it’s about being included in the answer itself.
That’s why generative engine optimization (GEO) matters. The same technical SEO practices that help search engines crawl, index, evaluate, and rank your content also improve your chances of being pulled into AI-generated responses.
The good news? If your technical SEO is already strong, you’re halfway there. The rest comes down to knowing which optimizations do double duty: improving your rankings while boosting your visibility in generative results.
This article breaks down four technical pillars with the biggest impact on GEO success:
Schema markup.
Site speed and performance.
Content structure.
Technical infrastructure.
1. Schema markup: Speaking AI’s language
Schema has long been essential for SEO because it removes ambiguity. Search engines use it to understand content type, identify entities, and trigger rich results.
For GEO, schema clarity is even more important. LLMs favor structured data because it reduces ambiguity and speeds extraction. If your content is marked up clearly, it’s more likely to be selected and cited.
Priority schema types for GEO
Focus on evergreen types that improve visibility:
FAQPage: Clearly labeled Q&A helps LLMs match user queries and surface your answers.
HowTo: Structured step-by-step processes are easy for AI to extract.
Product / Service: Defines pricing, availability, and specifications for accurate inclusion.
Article / NewsArticle with Author: Authorship adds a trust signal to your content.
Organization / LocalBusiness: Reinforces your identity, entity clarity, and local authority.
Review / AggregateRating: Provides social proof that AI engines use as quality signals.
VideoObject / ImageObject: Makes your multimedia easier for AI to find and feature.
BreadcrumbList: Improves context and page hierarchy mapping.
Implementation best practices
Use JSON-LD format (Google’s recommended approach).
Test rigorously with Google’s Rich Results Test and Schema Markup Validator.
Keep markup synced with your visible content – outdated schema erodes trust.
Don’t overdo it: mark up only what helps explain the content.
Bottom line: Schema improves your chances of being cited in AI answers, keeping competitors out of the box.
2. Site speed and performance: A (dis)qualifying factor
Generative engines pull from billions of pages. If yours is slow or unstable, they can skip it in favor of faster, more reliable sources.
Quick performance wins
Compress images; use WebP or AVIF; enable lazy loading.
Eliminate render-blocking CSS and JavaScript.
Target a server response time (TTFB) under 200ms.
Use a CDN to reduce latency.
Bottom line: Speed could be a tiebreaker between equally relevant sources. Faster pages have higher odds of inclusion in AI-generated answers – and they convert better once users click through.
3. Content structure: Making information machine-readable
LLMs rely on clarity. The easier it is for machines to parse and organize your content, the more likely it is to appear in AI-generated results.
JavaScript rendering: Don’t hide core content behind heavy client-side rendering. Use server-side rendering for anything essential.
Bottom line: If search or generative engines can’t crawl, verify freshness, or trust your site, your content won’t be considered – no matter how authoritative it is.
Building for search and AI success
The technical elements that drive GEO success aren’t new. They build on SEO fundamentals you already know:
Schema.
Performance.
Structure.
Infrastructure.
But in the AI era, these aren’t just best practices – they’re the deciding factors between being featured and being forgotten.
Getting this right will preserve your search visibility and put your content at the center of AI-driven answers.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/08/technical-seo-geo-EJueUs.webp?fit=1920%2C1080&ssl=110801920http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-08-19 12:00:002025-08-19 12:00:00A technical SEO blueprint for GEO: Optimize for AI-powered search
Google’s AI results are changing everything about how local businesses get discovered—and reviews are now at the center of it all. They shape visibility, build trust, and, when leveraged effectively, drive conversions.
In this live webinar, GatherUp VP of Marketing Mél Attia and renowned Local SEO expert Miriam Ellis will share never-before-seen research findings on how AI and consumer behavior are reshaping local SEO. You’ll discover:
How Google’s AI-powered results are prioritizing local businesses
What consumers really care about when evaluating businesses
Why reputation and reviews are the ranking lever most agencies underutilize
New consumer data, benchmarks, and tactical frameworks to boost your clients’ results
Whether you’re helping clients gain visibility, prove trustworthiness, or turn reviews into revenue, this session will equip your agency with actionable insights—and a narrative that makes review strategy impossible to ignore. You can save your seat here!
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Search is changing fast. This year, we’ve seen more instances of search engine results sharing space with AI-powered features that are changing how people find information.
Along with the changes to how search engines display information, we’re also seeing users explore new methods to search for information. Google AI Mode, Gemini, ChatGPT, Perplexity – there are many large language models (LLMs) capturing users’ attention, providing new ways for users online to discover and make decisions about your brand.
Customer sentiment, shown through reviews and ratings, is becoming a key part of both local and branded search.
For brands looking to stay ahead, focusing on sentiment, review ratings, and authority signals will be key. These are the items that not only affect rankings but also impact what shows up in search snippets and LLM responses.
LLMs like Google’s AI Mode are pulling together and highlighting customer sentiment within their responses when asked about specific brands or for geo-modified search queries, think “home repair near me”.
For businesses, paying attention to their review strategy and reputation will be key to standing apart in local results, overall organic visibility, and showing up favorably in AI responses. However, even with these changes, many of the tried-and-true best practices that have helped brands succeed in local search in the past still apply.
Searches with local intent: Google’s AI Mode
When it comes to local search, “near me” queries continue to be highly important. In traditional search, these typically trigger a Local Pack followed by organic blue links.
In Google’s AI Mode, the experience is similar. Users are shown a list of local businesses, often with short descriptions, star ratings, and review summaries.
The links cited are usually citation platforms like Yelp or TripAdvisor, business websites, or publications, and it’s common to find Google Business Profile place cards. Clicking these opens the familiar Google Business Profile interface, keeping users within the Google ecosystem.
What does this mean for businesses aiming to capture visibility in AI-driven local search results? Many of the foundations of local SEO still apply.
NAP consistency: Ensure your business name, address, and phone number (NAP) are accurate and consistent across all listings.
Citations: Maintain listings on trusted third-party sites like Yelp, TripAdvisor, and local directories to help reinforce credibility.
Google Business Profile optimization: Fully complete and regularly update your profile with accurate info, photos, business hours, and relevant categories.
Reviews: Generate and respond to reviews to build trust and signal relevance to both users and search engines.
Branded search results for local businesses
When searching for a local business using branded terms in AI Mode, it’s common to see many of the same elements and data sources as traditional search. These business overviews often include a description of the company, the products or services offered, and customer sentiment.
Often, the customer sentiment section summarizes review data pulled from multiple sources, such as TripAdvisor, Yelp, industry-specific sites such as Apartments.com, and Google Business Profile.
What’s unique about AI Mode is that it provides unbiased summaries of pros and cons about a business based directly on available customer reviews, which can come directly from Google Business Profile or be a mixed of review data from trusted online sources. These clear overviews include overall sentiment and often link to the business profiles.
AI Mode isn’t the first time Google has experimented with review summaries.
Some industries, like restaurants, already have “Review Summaries” in organic search results. These generative AI summaries highlight Google Business Profile review data, usually with a more positive tone, alongside the star rating and list of reviews.
The importance of reviews
Reviews shape how your brand appears online, whether they are displayed front and center on your Google Business Profile or surfaced as snippets in responses from LLMs. Google’s AI Mode, ChatGPT, and Perplexity all returned some information or mention of customer reviews when searching for local businesses, especially for branded queries.
These responses emphasize how both positive and negative offline experiences can influence what is said about your brand online and the importance of customer perception, especially when those experiences get highlighted for customers who may be discovering your brand for the first time.
Businesses need to pay attention to reviews, if not across all platforms, then at least on Google Business Profile. Review data is being pulled into AI-driven results and also plays a role in local search visibility.
“Prominence means how well-known a business is. Prominent places are more likely to show up in search results. This factor’s also based on info like how many websites link to your business and how many reviews you have. More reviews and positive ratings can help your business’s local ranking.”
How can businesses adapt?
By following the tactics local businesses should already be doing to succeed in local search:
Focus on generating new, recent reviews.
Respond to both positive and negative reviews.
Read reviews to understand the strengths and weaknesses of your business. Seeing a trend in negative reviews? That could indicate it’s time to make some changes and address those weaknesses.
Monitor brand mentions not just for backlinks but also to understand what people are saying about your business online, including community forums, social media platforms, and online publications.
In addition to traditional review sites, platforms like Reddit, TikTok, and Quora are showing up more frequently in branded and local search results. These conversations are also being picked up and summarized in tools like Perplexity and ChatGPT. That means the things people are saying about your business in comment threads or short-form videos can influence how your brand is being represented across both organic and AI-powered results.
What else can be done:
Look closely at how your business is perceived online and do the same for your competitors.
Compare your review count and average star rating to those of businesses showing up alongside you in the Local Pack. How does your business stack up?
Check how AI tools like LLMs or Google’s AI Mode describe your competitors during branded searches and identify where they source that information.
Try asking AI tools to compare your business and a competitor. The way these tools summarize differences can give insight into strengths, weaknesses, and areas where you may need to improve to stay competitive in the market.
LLM data sources
LLMs pull from a range of online sources to build summaries about businesses. For local and branded search queries, much of the information they use closely mirrors what shows up in traditional organic search results. This includes data from:
Google Business Profiles.
Third-party review sites.
Official business websites.
Wikipedia.
Online directories and aggregators.
News articles.
Public conversations on forums or social media.
LLMs don’t use the same ranking algorithm as Google Search, but they rely on much of the same publicly available information.
Why this matters:
The efforts businesses make to improve local SEO, such as maintaining accurate listings, collecting reviews, and building authority, also help shape how their brand is represented in AI-generated search results.
Reinforces the importance of managing your presence across multiple platforms and staying aware of where your brand is mentioned.
Highlights trusted third-party sites where your business may be listed but not actively managed. These listings still influence visibility and should not be overlooked.
Identifies which platforms are trusted within your specific industry, revealing opportunities to strengthen your presence on niche or vertical-specific sites.
Managing reputation at scale for multi-location businesses
For multi-location and microbrand businesses, managing sentiment at the local level adds another layer of complexity. It is not just about how the overall brand is perceived, but how each location appears in search results. This is especially important for industries like senior living, apartment communities, and healthcare, where customer experience and trust are crucial in decision-making.
A few negative reviews tied to a single location can shape perception across the board. That is why reputation strategies need to scale while still staying localized. Each location needs a clear plan to monitor feedback, respond to reviews, and build a strong presence in both traditional and AI-powered search results.
Core local SEO principles remain
Search is evolving fast, and we can expect more LLMs and AI-powered features to continue to shape how information is delivered to users.
Customer sentiment and brand perception are now more important in shaping how a business appears online, whether it’s in traditional organic search results or another platform.
Why?
Because perception matters, both online and in real life. Tools like Google’s AI Mode, Perplexity, Gemini, and ChatGPT are putting reviews, ratings, and sentiment summaries front and center, making customer feedback more visible than ever.
Now is the time for brands to take a close look at how they appear in LLMs, understand the feedback being surfaced, and identify areas to improve. Doing this not only helps with visibility in AI-driven search but also strengthens your local market presence.
As part of a broader brand reputation and visibility strategy, it’s essential to regularly monitor how your business is showing up in both traditional and AI-powered search results. That includes checking branded SERP features like AI Overviews, People Also Ask, video carousels, and social content pull-ins. These elements shift often, and staying aware of what’s being surfaced helps inform both SEO and reputation efforts.
You don’t need to reinvent the wheel. To keep up with the changing search landscape, you just need to focus your efforts in the right direction.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/08/running-store-near-me-google-ai-mode-FbvBhZ.png?fit=767%2C622&ssl=1622767http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-08-18 18:03:342025-08-18 18:03:34Want to win at local SEO? Focus on reviews and customer sentiment
More of your customers are using AI to research products before they buy. Are you prepared?
To put this into perspective:
Last year, you might’ve searched “best bed sheets” on Google and scrolled through a few links or a Shopping ad.
This year, you’re asking ChatGPT:
“I sleep hot and have sensitive skin. Can you recommend some breathable bed sheets that won’t irritate me?”
Totally different input. Totally different rules for showing up.
AI Search still cares about the fundamentals — content, crawlability, internal links, and high-quality backlinks. But now, your visibility is influenced by more than just your website.
AI models reflect the full picture:
What people say about your brand
Where you’re mentioned
How your product is reviewed
It’s not just keyword targeting — it’s relevance engineering.
Shoutout to Mike King @ iPullRank for coining this term.
That’s where AI Search Optimization comes in.
In this guide, you’ll learn how to:
Make your product pages visible and understandable to LLMs
Structure your data with schema and product feeds
Submit your catalog to AI search platforms
Shift from keyword targeting to prompts and personas
Build an AI-friendly brand presence across the web
Track your visibility in a probabilistic, answer-first world
The future of ecommerce search isn’t about rankings. It’s about being part of the answer. This guide will show you how.
Step 1: Make Your PDPs Crawlable and Renderable
Before you do anything, start here: can bots actually see your product content?
When people started taking AI tools and chatbots seriously in 2022/23, some site owners turned to blocking their crawlers from accessing their site.
But if you block the crawler, it won’t be able to serve your pages in its responses.
Don’t Block AI Crawlers in Your Robots.txt File
Unless you actively took the step to block them, you shouldn’t need to do anything here. But it’s still worth verifying there are no lines in your robots.txt file like:
User-agent: GPTBot
Disallow: /
Don’t Serve Important Content Using JavaScript
The other aspect of crawlability to consider is how you’re serving your content.
If it’s not in the raw HTML, LLMs like these can’t see it. And if they can’t see it, you won’t show up in AI-generated product recommendations.
To make sure you’re not causing crawling issues here, you first need to understand how your ecommerce platform handles JavaScript. Every platform is different:
Shopify: Generally fine, but watch out for third-party apps injecting schema or content via JS.
WooCommerce: Depends heavily on your theme. Many use plugins that load parts of the page with JS.
Custom stacks: If you’re using React, Vue, or similar frameworks, check whether product pages render server-side or after load.
Next, check your PDPs manually. You can do this by right-clicking and selecting “Inspect” in your browser.
Then press Command+Shift+P on Mac, or Control+Shift+P on Windows/Linux.
In the Command Menu, start typing “javascript” and then select “Disable JavaScript”:
Reload the page, and you’ll see how it looks without JavaScript enabled — in other words, how LLMs like ChatGPT see the page:
In the Nike example above, the LLM would still see key info like the product title, description, and price.
But in the example below…
…it would see nothing.
You can see on the right that there’s still page code loading. But nothing is actually displayed to the user with JavaScript disabled. Meaning AI tools wouldn’t be able to pull any info from this page.
If you are using apps or components that rely on JavaScript to display key content, talk to your dev team about server-side rendering (SSR) or prerendering. The goal is to ensure all critical product info is delivered in the first HTML response.
Once your product pages are crawlable, the next step is making them understandable.
Structured data — specifically Schema.org markup in JSON-LD format — helps systems like ChatGPT, Perplexity, and Google understand what your product is, how much it costs, whether it’s in stock, and more.
In the world of SEO, we’ve long used schema markup to improve how our pages appear in traditional search results.
Here’s an example of a traditional Google results enhanced with schema markup, appearing as a rich snippets:
But for LLM visibility, schema helps the AI tools understand key details about your products. Which makes it easier for them to pull in your products when they’re making recommendations for users.
How do we know this?
Because Microsoft has told us. The tech giant, a major investor in OpenAI (behind ChatGPT), said:
“[Structured data] makes it easier for search engines not only to index your content, but to surface it accurately and richly in search results, shopping experiences, and AI-driven assistants.”
(Interestingly, Microsoft/Bing recommends combining this with IndexNow — a service that automatically pings search engines when you update your content.)
Plus, using structured data just makes sense — it helps make it easier for complex machines to understand our content. Whether that’s a search engine or an LLM, providing more context is generally always going to be a good idea.
Here’s how to use structured data to improve your ecommerce store’s LLM visibility:
Focus on Product Pages First
While there’s value in marking up other templates (like category pages, blog posts, or FAQs), your product pages are where it counts most.
This is the data that LLMs and search engines will use to:
Associate your product with relevant categories and attributes
Match your offering to long-tail purchase prompts
Feed structured knowledge into their product and shopping systems
Here are the fields to include:
@type: Product
GTIN, SKU, MPN
Brand
Description
Offer block (price, currency, availability, URL)
Review/rating info if available
Use your schema to reflect reality, not just fill fields. But also add as much context as you can.
If your product is eco-friendly, US-made, sweatproof — encode it. The better your markup, the more context LLMs have to surface your product in nuanced prompts.
Make sure the schema is present in the raw HTML — not loaded with JavaScript.
Bonus: Extend to Reviews, FAQs, HowTo
Once your product markup is solid, consider adding:
Review and AggregateRating blocks
FAQPage markup for your PDPs or Help Center
HowTo schema for tutorial content or sharing post-purchase use cases
These all help build context around your product and can influence how LLMs present or recommend it.
Once you’ve marked up your product pages, the next step is scaling an effective structure across your entire catalog. That’s where a high-quality product feed comes in.
Step 3: Build a High-Quality Product Feed
Structured feeds have been essential for Google Shopping, Meta Advantage+, and TikTok Shop for a while.
And now, they’re becoming equally important for AI-powered discovery. Especially as platforms like Perplexity and OpenAI build out product recommendation systems.
Think of your feed as the dataset LLMs will eventually pull from when answering questions like this:
Perplexity has launched a Merchant Program accepting feed uploads, called the Perplexity Merchant Program. This lets ecommerce sellers have even more control over how their products can appear in AI responses.
Plus, OpenAI is quietly testing ways to let store owners upload feeds to improve their AI responses for product recommendations.
These feeds will likely drive future AI shopping experiences across chat, search, and even voice interfaces.
So how do you set your product feeds up in an LLM-friendly way?
What to Include
To optimize your product feeds for AI, start with the essentials:
Product title
Description
Price
Availability
Product URL
GTIN or MPN + Brand
Image URL
Note: Tools like ChatGPT may still generate their own versions of some of these (like titles). But it’ll still typically use information from places like your product feeds to inform its responses.
After you’ve added the basics, layer in high-value fields like:
Category or taxonomy
Color, material, and size variants
Shipping cost and speed
Review count and star rating
Custom labels for campaigns or segmentation
Use the same language your customers use.
This means writing product information the way your customers actually talk and search, not how your internal teams or suppliers describe things. For example:
Instead of:
“Athletic footwear with moisture-wicking synthetic upper”
Write:
“Running shoes that keep your feet dry”
How do you find out how they talk?
Look at your customer reviews, support tickets, and search queries that already drive traffic to your store.
For example, they might search for “cozy sweater” not “knitted pullover.” This can inform your title and description choices.
How to Submit Product Feeds to LLMs
Here’s how to submit your product feeds for three of the biggest AI interfaces.
Perplexity:
In 2024, Perplexity launched their Merchant Program. This fuels the platform’s shopping experience for Pro users. Your products may appear in carousel-style answers and shopping-focused prompts, and shoppers can buy without leaving Perplexity.
You can find out more about the program and sign up here.
OpenAI (ChatGPT):
OpenAI is piloting product discovery via ChatGPT’s “Search + Product Discovery” initiative. They’re exploring using uploaded feeds to power future buying experiences inside ChatGP.
Google’s Merchant Center feeds power Shopping Ads, organic Shopping listings, and likely influence how Google’s AI systems interpret and surface your products in AI Mode and AI Overviews.
Step 4: Monitor LLM Crawlers
Once you’ve put all the steps in place to make your ecommerce store crawlable by LLMs, the next step is to make sure they’re actually accessing your content and product pages.
Here’s how to do that:
Set Up Bot Monitoring
Use server logs or your CDN (like Cloudflare, Fastly, or Akamai) to track requests from:
GPTBot: This user agent is used by OpenAI to crawl web content that may be used in training their generative AI foundation models.
OAI-SearchBot: Used by OpenAI to link to and surface websites in search results in ChatGPT’s search features.
PerplexityBot: Identifies Perplexity’s AI search crawler when it accesses websites.
Google uses various Googlebot user agents to crawl the web, depending on the type of content being crawled (e.g., desktop, mobile, images). You can find a detailed list of common Googlebot user agent strings and their purposes in resources from Google for Developers.
For each of these bots, track:
Which pages they’re crawling (PDPs, collection pages, sitemap, feed)
How often they come back
How crawl patterns evolve over time
This helps confirm they’re discovering your content and gives you a baseline to measure progress.
Step 5: Shift from Keyword Lists to Prompts and Personas
Keyword research is still important. But you also need to think about how your customers are likely to prompt AI tools when looking for products like yours.
LLMs answer questions, interpret context, and make recommendations based on how people naturally speak.
That means you need to rethink how you optimize for product discovery. Not by keywords alone, but by personas, use cases, and prompt formats.
Start With What You Know
Your best-performing SEO and paid search keywords are still the foundation. They tell you:
Which products and categories convert
How people describe their intent in short-form searches
Use these to anchor your prompt strategy — but expand outward.
Think in Prompts, Not Just Queries
As people become more savvy with how AI tools work, more and more shoppers are going beyond just typing in “best bed sheets.” They’re asking:
Medium-length prompts:
“Best cooling sheets for hot sleepers”
“Softest bed sheets under $100”
“What kind of sheets stay on the bed all night?”
Longer, context-rich prompts:
“I’m a side sleeper who gets hot at night. What bed sheets will stay cool and not cling to my skin?”
“Looking for breathable, hypoallergenic sheets that work well in humid climates”
“I have sensitive skin and eczema. What’s a good sheet material that won’t irritate me?”
Your goal is to build context around your products that lines up with this kind of language and framing.
Note: You can’t predict exactly what your customers will ask, and there are infinite ways they can do it. But thinking about prompts — not just keywords — will put you in a good place to be able to optimize your ecommerce pages for LLMs.
Map Your Catalog to Prompt-Based Use Cases
Think in layers:
By need: cooling, breathable, wrinkle-resistant, organic
By persona: hot sleeper, allergy sufferer, luxury buyer, college student
By situation: new apartment, guest bedroom, summer refresh, wedding registry
By problem: sheets come loose, feel scratchy, trap heat, shrink in the wash
This is how you start to think of your items like answers and solutions, not just products.
Use These Prompts to Guide Content and Merchandising
Let this prompt structure inform your:
Product page copy and comparison points
Blog posts and videos
Social media posts
FAQs and Help Center content
Category names and filters
Product feed descriptions and attributes
LLMs can pull from all of it — so make sure you’re using the kind of language your real customers use everywhere.
Step 6: Seed Your Brand Across the Web
Even if your site is crawlable, your schema is perfect, and your feed is super optimized — LLMs still learn about your brand based on what people are saying about you elsewhere.
They’re trained on massive web-scale datasets, so third-party content — like reviews, Reddit mentions, YouTube transcripts, forums, blog posts — can carry as much (or more) weight than your owned channels.
If you want to show up in AI answers, your brand needs to already exist in the wider conversation.
Where You Want to Show Up
AI tools like ChatGPT, Perplexity, and Claude all lean on third-party review sites and forums in their answers to brand and product-related queries.
These are the places you’ll want to show up in order to be included in those answers:
Review sites: Trustpilot, Amazon, Google Reviews, BBB, niche review sites
Reddit, Quora, & niche forums: Participate in threads and subtly seed your product category (without being spammy)
YouTube: Appear in titles, transcripts, and product comparisons — even if you’re not the creator (consider partnering with creators to do this)
Affiliate content: Get included in roundups, listicles, and side-by-side comparisons
Showing up in these places is half the battle. The other component is how you show up.
Ideally, you’ll want to be mentioned alongside competitors (“like Brooklinen but…”). And in the right, relevant context (“these are some of the best cooling sheets for eczema”).
A lot of this is going to be completely out of your control (especially on platforms like Reddit). But good marketing practices can make it more likely that people will naturally talk about your brand in the way you want them to.
This Is Just Good Marketing
Gaining LLM visibility is a byproduct of an effective multichannel marketing strategy.
If you’re running a strong content program, building brand awareness, and actively participating in your category — you’re already seeding relevance.
What’s new is the urgency: LLMs are already using these signals to decide which brands deserve to be recommended.
Related: See our LLM Seeding Playbook for tactics, templates, and outreach strategies.
Step 7: Track Your AI Search Visibility
In traditional SEO, visibility was deterministic: rank #1 for a keyword, get X% of clicks.
That model is breaking.
AI-powered discovery works differently. Your brand might appear in one version of a response, but not the next.
Whether your ecommerce store is included depends on how the user phrases their prompt, how much brand recognition you have, and how often you’re referenced across the web.
So, your measurement strategy needs to adapt.
What to Track
Start by building a prompt library — real questions your customers might ask:
Organize prompts by topic (e.g., cooling sheets, organic materials, luxury bedding)
Group them by persona (e.g., hot sleepers, allergy sufferers, budget-conscious buyers)
Then choose a tool to test visibility: like Semrush AI SEO Toolkit, Peec.AI, or Profound
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-08-18 14:02:292025-08-18 14:02:29How to Optimize Your Ecommerce Store for AI Search (7 Steps)
