Starting in March 2026, Google Merchant Center will enforce a new system for multi-channel products — items sold both online and in physical stores — requiring advertisers to use separate product IDs when those products differ by channel.
What’s changing. Under the new approach, online product attributes will become the default. If a product’s in-store details differ, advertisers will need to create a second version with a distinct product ID and manage it independently in their feeds.
What advertisers should do. Google has started emailing affected accounts, flagging products that need updates ahead of the March deadline. Retailers should review their product data feeds now to ensure online and in-store items are properly segmented — especially if they rely on Local Inventory Ads or sell across multiple Google surfaces.
Why we care. Many retailers currently manage online and in-store versions of the same product under a single ID. Google’s update changes that assumption, pushing advertisers to explicitly separate products when attributes like price, availability, or condition aren’t identical.
The big picture. This update gives Google cleaner, more consistent product data across channels, but shifts more feed management responsibility onto advertisers — particularly large retailers with complex inventories.
First seen. The update and news of Google’s comms was first mentioned by PPC News Feed founder Hana Kobzová.
Bottom line. If your online and in-store products aren’t truly identical, Google will soon require you to treat them as separate items, or risk issues with visibility and eligibility.
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The new year is here, which means it’s time to deliver your end-of-year (EOY) PPC report.
But an EOY report isn’t just a longer version of your monthly performance check-in.
It’s typically read by a different audience – often leadership teams who don’t see your regular reporting – and it needs to tell a different story.
Done well, your year-end report sets the tone for 2026, earns buy-in for your strategy, and positions you as a strategic partner rather than just a campaign manager.
Done poorly, it creates confusion and undermines confidence in your work.
Here’s how to build an EOY PPC report that speaks to leadership and sets your work up for success in the year ahead.
1. Identify your audience and their priorities
You wouldn’t launch a new campaign without clearly defined goals and audiences.
Don’t do it with your EOY report, either. Different stakeholders evaluate performance through very different lenses.
For example, among the clients I’m preparing reports for this year are:
A leadership team I’ve never met (despite working with this client for eight years) that wants a maximum five-page report at a very high level.
A data-driven CEO who wants a clear narrative connecting PPC spend, decisions, and outcomes.
A new director who wants context on the competitive landscape, performance, and specific opportunities for next year.
If I were to use a carbon-copy EOY report template for all of them, I’d have at best one happy leadership team and two confused or frustrated teams.
I don’t care for those odds. Instead, I’m customizing each report to match the readers and their specific needs.
If you don’t know the recipients (and if you’re at an agency, there’s a good chance you don’t), ask your primary contact questions like:
Who will be receiving the report?
What do they care about most?
What’s top of mind for them heading into next year?
What decisions will they be making based on this report?
The answers should directly inform the report’s structure, depth, metrics, and length.
When your audience is clearly defined upfront, the final report is far more likely to drive clarity, alignment, and confidence.
2. Create an easy-to-read executive summary
Your executive summary has one job – help leadership quickly understand how PPC performed across key metrics.
Think of it as the “at a glance” page that sets the context for everything that follows.
If you studied communications formally, you probably learned to write executive summaries last, even though they appear first.
Since you’re pulling data rather than crafting prose, flip that approach.
Build this section first to guide the flow of what comes next.
Lead with the KPIs that matter most
Start with the metrics your audience actually cares about – the ones that were established as priorities at the beginning of the engagement or year.
This will usually include revenue, leads, and conversions, but your mileage may vary.
If your leadership team obsesses over market share or engagement, lead with those instead.
Include meaningful benchmarks
Unless your leadership team is dialed into PPC goals and performance, you need to give them benchmarks so they have a comparison tool.
Use at least one of these three key benchmarks:
Year-over-year performance: How did this year stack up against last year?
Performance against target: Did we hit the goals we set out to achieve?
Industry benchmarks: How do we compare to competitors or industry averages?
In the example below, I’m showing revenue, ROAS, and cost for the year, with both percentage changes and raw numbers from the previous year.
This format does the heavy lifting for busy executives.
At a quick glance, they know what happened and whether it’s good news.
More importantly, it sets the stage for invisible CTAs and the deeper analysis that follows.
3. Break down performance details
In the following section, you’ll move from “what happened” to “why it happened and what we learned.”
The executive summary told your reader whether the year was successful. Now you need to show them the engine under the hood.
The level of detail will depend on the format. A five-page executive report may only have room for a few pivotal moments, while a more comprehensive report can get into the weeds.
In either case, selectivity is critical. You can’t — and shouldn’t — document every metric, test, or optimization from the year.
Instead, focus on insights that either explain the results in your executive summary or point directly to opportunities for the year ahead.
Here are some categories to get you started.
What performed best
Show them the winners: your best-converting creative, highest-revenue products, or most efficient channels.
Leadership loves to see what’s working, and it can point to where to double down.
How resources were allocated
Break down spend distribution across campaign types, the split between brand and non-brand, or platform-specific investments like Google versus Bing.
Leadership wants to know if you’re putting money where it matters most, and this section answers that question.
If you made tracking or conversion definition changes during the year, call them out here.
Leadership needs to understand if a metric shift reflects actual performance or a measurement change.
Keep this section platform-specific and substantive. Each insight should clearly tie back to the executive summary.
Use visuals (charts, trend lines, and comparison tables) to make complex data easier to interpret. And resist the temptation to include everything you track.
If a metric doesn’t explain results, answer a question from leadership, or inform future strategy, leave it out.
You’ve already explained what happened in the account and why performance moved the way it did.
Now you need to zoom out and show leadership what else was happening. What external forces shaped your results, for better or worse?
This is where you separate execution from environment.
Without this context, strong strategic work can look mediocre in a difficult year, or weak decisions can hide behind tailwinds.
Leadership needs to understand what you controlled versus what you were responding to.
Think of it this way: performance details add context to your KPIs. External factors add context to your performance.
Digital marketing factors
What influenced performance that was external to paid search execution, but internal to the broader marketing ecosystem?
Non-PPC marketing initiatives: Product launches, pricing changes, promotions, website redesigns, or messaging shifts can all impact conversion rates and search behavior – positively or negatively.
Non-PPC channel performance: Performance in organic search, email, social, affiliates, or offline channels can meaningfully influence paid search results. It also provides a clearer picture of market factors beyond paid channels.
Platform and policy changes:Google Ads feature rollouts, automation shifts, policy enforcement, or reporting changes often affect performance in ways that aren’t immediately visible in metrics alone.
Competitive dynamics: New entrants, aggressive bidding, creative shifts, or changes in competitor behavior can alter auction pressure and efficiency over time.
Macro-economic factors
What forces outside the marketing organization shaped demand, behavior, and constraints?
A useful way to structure this analysis is with a lightweight PESTEL lens, adapted for a marketing context.
Political: Gov actions and policy shifts (e.g., tariffs, shutdowns).
Environmental: Weather and seasonality (e.g., storms, climate shifts).
Legal: Regulations and compliance (e.g., privacy laws, labor rules).
You don’t need to address every category. The goal is to highlight the factors that materially influenced performance and decisions during the year.
In a volatile year like this one, it can even make sense to highlight big events thatdidn’t have an impact on performance, just to assuage any worries.
Doing this helps stakeholders understand what factors contributed to performance. And just as important, it positions you as someone who sees beyond the interface to meaningful business implications.
5. Answer the ‘what’s next?’ question
Leadership wants to know what to expect for next year.
They’re not necessarily expecting a crystal ball, but they do want confidence that there’s a plan, even if the path changes.
The reality is that most paid search strategy isn’t mapped a year in advance.
Platforms change, competitors react, budgets shift, and new constraints appear with little warning.
What matters isn’t having every answer upfront, it’s having a clear way to decide what to do next when conditions change.
This section of your EOY report is your opportunity to show that decision-making framework, and get your audience excited to work with you on what’s to come.
Next steps and recommendations
These are the initiatives you’re committed to pursuing; the strategic moves grounded in last year’s data:
Applied learnings: How insights from the past year are shaping priorities, structure, and decision-making going forward.
Identified opportunities: Areas where data consistently pointed to upside: channels, audiences, products, or tactics that warrant attention.
Known risks: Challenges leadership should expect, along with how you’re monitoring or mitigating them.
Resource clarity: What additional budget, tools, or support would enable — and what remains constrained without them. Be concrete: “With X additional budget, we can test Y based on Z insight from last year.”
These recommendations should feel inevitable; the logical next steps given what happened last year.
Testing pipeline
Then there’s the other category: things you’re watching, interested in, or ready to jump on if circumstances align.
These scratch leadership’s itch for innovation and cutting-edge solutions without overcommitting:
New platform features you’ll test when they’re released.
Emerging platforms or initiatives worth monitoring.
Competitive tactics you’ve identified but need more validation.
Opportunistic tests if budget or priorities shift.
Frame these as “if/then” scenarios or “things we’re monitoring” rather than firm commitments.
Leadership gets to feel like you’re on top of industry trends without expecting guarantees.
A final pass through a leadership lens
You’ve covered a lot of ground.
This final pass is about tightening credibility and making sure this work pays dividends in the coming years, not just this one.
Give your report a final pass
Before sending, review the report the way leadership will:
Source your data clearly: Label where each chart’s data came from and when it was pulled. This prevents follow-up questions and builds trust.
Address negatives head-on: Leadership expects challenges. What erodes confidence isn’t bad news, it’s unexplained bad news. Show what didn’t work, why, and what you did about it.
Pressure-test against the brief: Review your stakeholders’ original requests. Did you actually answer their questions? Ask a colleague (or AI) for a second set of eyes.
Make next year easier
Now that you’ve done the heavy lifting, leverage this work going forward:
Turn your EOY report into a client-specific template: A single format won’t work across all clients, but once you find a structure that resonates with a given audience, reuse it year over year. Incorporate feedback and refresh the data, but keep the core framework consistent.
Track big issues as they happen: Document key events as you progress through the year. Keep a running list, outside of emails and reports. Even the biggest issues today will be hard to accurately remember in 12 months without this.
Year-end reports take real effort. Make sure yours actually resonates.
Follow these steps to strengthen stakeholder relationships and position yourself as a strategic partner for 2026 and beyond.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/01/sel-reports-25-G1ZyHX.webp?fit=1544%2C486&ssl=14861544http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-01-06 14:00:002026-01-06 14:00:00A 5-step framework for year-end PPC reports that resonate with leadership
When approached thoughtfully, it allows you to apply professional understanding to intent‑driven inventory without breaking the bank.
The key is knowing how the targeting methods work together across the various campaign types.
What follows is a practical guide to using LinkedIn data inside Microsoft Advertising, including:
LinkedIn in Search campaigns (includes Multimedia ads).
Using LinkedIn insights to inform broader audience strategy.
Performance Max targeting signals.
Directional insights into audience reach and composition through Audience Planner.
Disclosure: I am a Microsoft employee. I attempted to keep this article as objective as possible, focusing on how LinkedIn targeting works as well as action items for targeting, reporting, and creative message mapping.
LinkedIn profile targeting in search
LinkedIn profile targeting is fully available in Microsoft Advertising search campaigns and lets you layer professional attributes on top of keyword targeting.
The supported attributes are:
Company.
Industry.
Job function.
These audiences apply across Microsoft‑owned environments such as Bing Search, Microsoft Edge, Microsoft Start, and other eligible search surfaces, as long as users are signed in.
In search, LinkedIn targeting works best as a contextual guide, not a standalone target.
The keywords still do the heavy lifting. LinkedIn data helps you respond differently when professional relevance is present.
How to approach it
Start with keywords that already convert: LinkedIn targeting can help amplify existing intent on proven keywords. Apply bid adjustments to campaigns/ad groups where search terms already demonstrate business value. This might mean a 10%-15% increase if you’re bidding aggressively, or a more aggressive bid adjustment if your impression share lost to rank is high.
Choose one professional dimension first: Begin with either company, industry, or job function – not all three. If you’re targeting someone who works for a company in an industry you’re also targeting, it’s very easy to bid on them twice.
Use bid‑only mode to establish a baseline: Observation gives you performance clarity before you make delivery decisions. Treat this as audience research on who is engaging with you in a profitable way.
LinkedIn Professional Demographics in Audience ads
Audience ads support LinkedIn Professional Demographics as both a targeting and observation layer – bringing professional context into native, display, and video formats designed for scalable reach.
While Audience ads are not driven by keyword intent, Professional Demographics provide a way to anchor delivery and insights in a real‑world business context, bridging the gap between broad reach and professional relevance.
Audience ads allow you to apply company, industry, and job function as professional audience layers.
These can be used either to observe performance trends or to influence delivery, depending on campaign objectives.
Unlike search, where intent is explicit, Audience ads rely more heavily on audience signals and creative relevance.
LinkedIn Professional Demographics help ensure that reach is oriented toward users who are more likely to be operating in a business mindset, even when browsing content.
How to approach it
Start in observation to understand natural performance: Use Professional Demographics in observation mode to learn which industries, job functions, or company types naturally engage with your Audience ads before applying delivery constraints.
Let LinkedIn data inform creative, not just delivery: Because Audience ads appear in feed‑based and content‑rich environments, creative matters more than targeting alone. Use insights from high‑performing professional segments to inform tone, examples, and value framing in messaging.
Align format choice with professional mindset: Different formats serve different roles:
Native and display perform well for awareness and education within professional segments
Video supports storytelling and category framing, particularly when aligned with industry‑specific narratives
Professional Demographic insights help guide which formats are most appropriate for different business audiences.
LinkedIn data in Performance Max: Guiding automation with purpose
Within Performance Max, these signals help the system understand which professional profiles have a high probability for profit to your business and help influence how budget is allocated across inventory.
Professional signals are most effective in Performance Max when they are representative and directional, not exhaustive.
They work best when they give the system a strong starting point rather than a narrow definition of success.
How to approach it
Select signals that reflect your best customers, not every customer: Use LinkedIn attributes that describe your most valuable segments, not the full universe of potential buyers. This is especially important if the different personas represent different ROAS/CPA goals, as all asset groups in a PMax campaign will share the same ROAS/CPA bidding.
Pair LinkedIn signals with strong conversion definitions: Automation performs better when professional context is reinforced by clear success metrics. It’s critical to ensure there are at least 30 conversions in a 30-day period for any autobidding.
Allow time for learning: Audience signals need sufficient volume to influence delivery. Avoid frequent changes in the first learning period (two weeks). Once you clear this, budget changes of up to 15% can be made without triggering learning period fluctuation.
Aggregated reporting for LinkedIn audiences is broken down by company, industry, and job function, allowing you to see how different professional segments contribute to performance across campaigns.
LinkedIn reporting can be found in Reporting > Professional demographics, and includes any LinkedIn targeting or audiences applied through predictive targeting.
How to approach it
Look for consistency across time, not single spikes: Patterns that repeat across weeks or months are more actionable than short‑term anomalies. Give “observation” audiences the time to prove themselves out. If you don’t have time for that, lean on Audience Planner to help you make informed decisions at scale.
Use reporting to inform creative and bids together: When a professional segment outperforms, examine both messaging and bidding before making changes. It’s possible that the audience really resonated with the creative, but you also want to confirm you didn’t overbid on a particular group.
Avoid over‑segmentation early: Too many audience cuts can dilute signal strength (especially if you’re running up against conversion scarcity).
Bidding with LinkedIn audiences
In Microsoft Advertising, you can use bid adjustments alongside automated bidding strategies, giving flexibility in how LinkedIn audiences influence auctions.
Because users can belong to multiple professional dimensions, bid adjustments may compound when audiences overlap within auctions, making overlap awareness an important part of bid strategy.
Bidding adjustments are most effective when they are incremental and reversible. The goal is calibration, not acceleration.
How to approach it
Keep initial bid adjustments small: Single‑digit percentage changes preserve learning while still allowing differentiation.
Audit audience overlap before increasing bids: Review how company, industry, and job function audiences intersect within campaigns.
Apply bid changes gradually and sequentially: Adjust one audience dimension at a time to understand its individual impact.
Reassess after enough volume accumulates: Make decisions only after performance reaches statistical relevance.
Creative strategy: Professional relevance without narrow assumptions
LinkedIn targeting shapes who is more likely to see your ads. Creative determines whether those impressions turn into engagement.
Professional cohorts include a wide range of experiences, identities, and perspectives. Effective creative respects that diversity while remaining relevant to the shared context.
Creative works best when it reflects professional empathy – acknowledging challenges, goals, and constraints without relying on shortcuts or stereotypes.
How to approach it
Anchor creative in shared problems, not titles: Focus on challenges that span roles and seniority levels within a LinkedIn targeting segment.
Keep language inclusive and adaptable: Avoid assumptions about background, experience, or decision‑making authority.
Use AI tools to localize, not homogenize: Adapt tone or examples by region or industry while preserving message intent.
Test creative alongside audience layers: Evaluate messaging performance within LinkedIn segments to refine both together.
Extending LinkedIn insights across B2B campaigns
LinkedIn targeting in Microsoft Advertising presents an opportunity to combine professional expertise with intent-driven media in a way that is scalable, privacy-conscious, and economically sustainable.
For teams already running LinkedIn Ads, it also provides a practical way to extend learnings into additional inventory through automation, supporting reach and efficiency beyond search.
The value doesn’t come from complexity. It comes from alignment – between data, mechanics, and human behavior.
Key takeaways:
LinkedIn profile targeting is fully available across Search and Performance Max on Microsoft‑owned surfaces.
Professional attributes function as targeting layers in search and as optimization signals in Performance Max.
Observation‑first approaches build understanding before commitment.
Aggregated reporting supports informed optimization without exposing individual data.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/01/LinkedIn-profile-targeting-in-search-s5Y61z.png?fit=1498%2C555&ssl=15551498http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-01-06 13:00:002026-01-06 13:00:00How to use LinkedIn targeting in Microsoft Advertising
Google introduced Creator Search, which allows advertisers to discover YouTube creators using keywords or channel handles, then narrow results by subscriber count, average views, location, and contact availability. The update significantly reduces the manual work involved in creator research and outreach.
Alongside search, Google added a new Management section that centralizes creator communications. Advertisers can now see creator names, inquiry status, subjects, latest updates, and respond-by dates in one place, with direct email access built in.
Why we care. As creator-led campaigns become more central to media strategies, advertisers need better tools to find the right creators and keep partnerships organized. Google Ads’ latest update to Creator Partnerships (beta) aims to solve both problems.
First seen. This update was first spotted by Google Ads Specialist Thomas Eccel when he shared it on Linkedin.
The big picture. These changes move Creator Partnerships closer to a full-fledged workflow tool, helping teams manage creator collaborations with the same structure and accountability as other paid media efforts.
Bottom line. By improving both discovery and organization, Google Ads is making it easier for advertisers to run creator partnerships at scale.
December made one thing clear: AI is no longer a feature layered on top of marketing. It is the system deciding what gets seen, what gets skipped, and what earns trust.
Search pushed deeper into zero-click behavior. Paid ads lost prime real estate. Influencer content matured into a full‑funnel channel. Platforms added tools while quietly tightening control. At the same time, security and data ownership became real business risks, not abstract concerns.
This roundup breaks down what actually mattered in December and how to adjust before these shifts harden in 2026.
Key Takeaways
Google accelerated AI-first search with Gemini 3, AI Mode, and AI-powered Search Console reporting.
AI Overviews and AI Mode are pushing both organic and paid clicks down, reshaping SERP strategy.
Influencer marketing expanded beyond Gen Z, pulling older, high-value audiences into creator ecosystems.
LinkedIn doubled down on video and events, reinforcing its position as the B2B growth platform.
Security threats like Google Ads MCC hijacks escalated, making account governance a priority.
Search & AI
AI is now deciding what gets seen before a click ever happens. December’s updates show Google tightening its grip on discovery while pushing brands to earn visibility inside AI systems.
Search Console Gets AI-Driven Reporting
Google rolled out AI-powered configuration in Search Console, allowing users to request custom reports using natural language. Instead of manually stitching filters together, teams can now ask questions the way they think about performance.
Our POV: This changes who gets access to insight. Reporting no longer bottlenecks around technical SEO or analytics specialists. Strategy conversations can happen faster, and closer to the business question that triggered them.
What this unlocks: Faster pattern recognition across large sites, quicker validation of hypotheses, and fewer reporting cycles spent just getting the data into shape.
What to do next: Standardize a small set of executive-level prompts tied to growth questions (discovery, decline, opportunity). Use this to shorten the distance between signal and decision.
Gemini 3 Lands Directly in Google Search
Google deployed Gemini 3 straight into Search across 120 countries, delivering richer answers, visuals, and interactive elements without requiring users to leave the results page.
Our POV: This is Google asserting itself as the destination, not the doorway. Content that once earned traffic by being explanatory or comparative now competes with Google’s own synthesized answers.
Strategic impact: Informational content becomes less about volume and more about authority. If your content is interchangeable, it becomes invisible.
What to do next: Identify where your content overlaps with Gemini-style answers. Invest more heavily in insight, proprietary data, and perspective that AI cannot compress without losing value.
Google Embeds AI Mode Into the Search Flow
When users tap “show more” under an AI Overview, Google now routes them into a full AI chat experience rather than expanding citations.
Our POV: This confirms that Google is intentionally reducing outbound traffic in favor of guided, AI-mediated discovery.
Strategic impact: Attribution gets murkier. Influence matters more than visits. Brands that only measure success by clicks will underinvest in visibility where decisions actually form.
What to do next: Start treating AI inclusion as a visibility channel. Track brand mentions, citations, and presence inside AI responses alongside traditional KPIs.
AI Overviews Push Ads Below the Fold
Research shows that roughly a quarter of search results now place paid ads beneath AI Overviews, with mobile SERPs most affected.
Our POV: Paid search is losing guaranteed prominence. Bidding harder no longer guarantees being seen.
Strategic impact: Paid media performance becomes dependent on how well it aligns with AI-generated context, not just auction dynamics.
What to do next: Re-evaluate high-value keywords where ads routinely fall below AI content. Coordinate paid and organic teams so messaging reinforces what users see first.
Branded Query Filtering and Chart Notes Arrive in GSC
Search Console now separates branded and non‑branded queries automatically and allows chart-level annotations.
Our POV: This finally closes long-standing reporting gaps that distorted SEO performance narratives.
What to do next: Capture a baseline brand vs non‑brand split now. Add annotations for launches, migrations, PR wins, and algorithm shifts to preserve institutional knowledge.
Paid Media & Risk
Automation keeps increasing, but so does exposure. December highlighted how fragile performance can be without strong governance and clear safeguards.
OpenAI Pauses ChatGPT Ads
OpenAI halted its early test of native ads inside ChatGPT after users struggled to distinguish sponsored content from AI-generated answers.
Our POV: This pause is less about ads failing and more about timing. Conversational interfaces collapse the distance between advice and influence, which raises the bar for trust.
Strategic impact: Future AI advertising will not behave like traditional display or search ads. Brands will compete on usefulness, credibility, and contextual fit rather than interruption.
What to do next: Start pressure-testing what value-driven, answer-oriented advertising could look like for your category. Focus on scenarios where a brand genuinely helps a user decide, not just where it can appear.
Google Ads MCC Hijacks Surge
Phishing attacks targeting Google Ads manager accounts increased sharply, allowing attackers to drain budgets and lock out advertisers within hours.
Our POV: This is no longer an edge case. As accounts scale, risk compounds.
Strategic impact: Performance gains mean little if governance fails. Security lapses can erase months of optimization and undermine executive confidence in paid media.
What to do next: Treat access control as part of your growth strategy. Limit permissions aggressively, audit users regularly, and align security reviews with budget planning.
Product, Design & UX
Product and design updates are quietly shaping how fast teams can ship, test, and iterate. December brought one change that materially reduces friction between design and development.
Figma Introduces CSS Grid-Like Layout Controls
Figma rolled out a new grid system that more closely mirrors how CSS Grid and Flexbox behave in production. Designers can now edit rows and columns directly, reposition elements with keyboard controls, and build layouts that respond more like real front-end frameworks.
Our POV: This narrows the long-standing gap between design intent and shipped experience. Fewer handoff mismatches mean faster iteration and fewer compromises downstream.
Strategic impact: Design systems become more scalable when layouts behave predictably across breakpoints. Teams that rely on rapid experimentation benefit most.
What to do next: Revisit your design system and layout standards. Align designers and developers on grid conventions so prototypes map cleanly to production.
Social & Creator Economy
Creator content is no longer niche or youth-driven. Platforms are shaping social into a full-funnel, multi-generational influence engine.
LinkedIn Sees Another Video Surge
LinkedIn reported continued double-digit growth in video uploads and watch time, with short-form content driving disproportionate reach.
Our POV: LinkedIn has quietly become a daily content destination, not just a professional directory.
Strategic impact: B2B visibility increasingly depends on consistent, human-led storytelling. Brands that delay video adoption will find it harder to build authority as the feed fills up.
What to do next: Commit to a repeatable LinkedIn video cadence. Prioritize clarity and expertise over production polish, and measure engagement trends over time.
LinkedIn Upgrades Event Ads
New integrations with ON24 and Cvent allow LinkedIn Event Ads to capture and route leads directly into CRMs.
Our POV: Events are moving out of the brand bucket and into the revenue conversation.
Strategic impact: This blurs the line between awareness and pipeline, making events accountable in ways they historically avoided.
What to do next: Reframe events as performance channels. Align messaging, registration, and follow-up under a single measurement framework.
Influencer Content Expands Beyond Gen Z
New data shows that more than half of adults aged fifty-five to sixty-four now watch influencer content weekly, often via connected televisions.
Our POV: Influencer marketing has crossed into mainstream media behavior. This is no longer a youth or trend-driven channel.
Strategic impact: Influencers are shaping consideration and trust for higher-value purchases, not just discovery for impulse buys.
What to do next: Test creator partnerships that emphasize expertise and credibility. Treat influencer content as a mid-funnel and upper-funnel asset, not just awareness.
Meta Enhances the Creator Marketplace
Instagram expanded its Creator Marketplace with better discovery, AI recommendations, and stronger paid amplification tools.
Our POV: Meta is positioning creators as a scalable performance input, not just an organic reach lever.
Strategic impact: The line between influencer marketing and paid social continues to erode. Creative quality and creator trust now directly affect efficiency.
What to do next: Identify creators whose content already performs organically. Use paid support to scale what works instead of forcing performance from scratch.
PR, Media, and Trust
As AI pulls from third-party sources, brand credibility is being shaped outside your owned channels. Relationships and presence matter more than volume.
Journalists Push Back on AI Pitches
Surveys show most journalists still prefer human-led outreach, citing AI-written pitches as generic and misaligned with their coverage needs.
Our POV: Efficiency without judgment damages relationships.
Strategic impact: As AI-generated noise increases, thoughtful and relevant outreach becomes a stronger differentiator.
What to do next: Use AI for research and preparation, not substitution. Preserve human insight where trust and creativity matter most.
Discord Emerges as a Media Hub
PR teams are increasingly using Discord servers as live, on-demand press rooms.
Our POV: This flips traditional outreach from push to pull.
Strategic impact: Brands that make themselves accessible become resources journalists return to, not just sources they react to.
What to do next: Pilot a controlled Discord environment for media. Offer clear channels, real access, and timely updates without overwhelming participants.
Platform Playbooks
Smaller platform updates often hide the most practical gains. December delivered clear lessons on how context and native execution drive results.
Reddit Releases Dynamic Product Ad Guidance
Reddit published best practices showing that focused optimizations can lift Dynamic Product Ad performance meaningfully.
Our POV: Reddit rewards relevance over polish.
Strategic impact: Brands that adapt creative to platform norms outperform those that recycle ads from other networks.
What to do next: Speak directly to subreddit context, keep messaging tight, and test incrementally to isolate what actually moves performance.
Conclusion
December reinforced a hard truth: visibility is no longer owned. It is earned repeatedly across AI systems, platforms, and communities.
The brands that win in 2026 will build authority machines, not traffic hacks. They will secure their data, design for AI interpretation, and show up consistently wherever decisions are shaped.
If you want help translating these shifts into a durable growth strategy, the NP Digital team is already doing this work every day.
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The PPC landscape in 2025 shifted faster than ever, with updates arriving at a pace unmatched in the industry’s 20-year history. At SMX Next, a panel of industry experts broke down what’s working, what’s failing, and what advertisers should prepare for in 2026 and beyond.
The state of PPC
The panelists agreed that 2025 marked a major shift, especially in how quickly Google responded to advertiser feedback.
Ameet Khabra, founder of Hop Skip Media, called the year “interesting” and said he was genuinely surprised by Google’s willingness to listen to advertisers, especially on channel reporting for Performance Max.
“It was really cool to see the people who were in that room sit there and be like, this is exactly what we asked for,” she noted, referring to discussions at Google Marketing Live.
Chris Ridley, head of paid media at Evoluted, said 2025 wasn’t just about Google listening — it was the year AI and AI search truly took off.
“Everyone is now talking about the different platforms available, like Perplexity, ChatGPT, Gemini, and they just seem to be dominating. AI Overviews have kind of taken over as well.”
Reva Minkoff, founder and president of Digital4Startups, called 2025 “the year of the max,” pointing to Performance Max, AI Max, and the growing list of “max” campaign types. She said more features launched this year than at any other time in her 20-year search career.
“It’s just every day there’s a new thing, which is really exciting. But there’s definitely a lot happening now.”
What’s working in PPC
Back to basics: Structure and signals
All panelists stressed that success in 2025 came from returning to the fundamentals.
Minkoff stressed the importance of proper campaign structure and quality signals:
“It’s still important to have a good search campaign with keywords that you control and ads you create that goes to an audience that you think it should be going to.”
Minkoff noted that Performance Max is working well, but only when the signals are right — “if you’re not putting good stuff in, you won’t get good stuff out.”
She also pointed to strong results from Demand Gen (formerly Video Action campaigns), user-generated content, and influencer marketing:
“I think people want to hear from real people.”
Khabra stressed the importance of using scripts and automation oversight to catch issues before they turn into problems.
“We’ll have scripts in place that are like anomaly detectors, just so we know that tracking is off. The broken URL script is a lifesaver, honestly — how many times have we had a developer push a change and we didn’t even know it happened?”
The human touch in creative
Ridley underscored the need for authentic creative in an AI-driven landscape:
“Going back with our authentic user-generated content is getting really good results compared to this slick, polished stuff, especially with AI coming out now and people questioning whether it’s real or not. Having that human touch is really working for us.”
“Making sure that we understand what their business objectives are rather than just their ROAS and CPAs” has been essential for success.
What not working in PPC
Automatically created assets (ACAs)
The panel unanimously agreed that Automatically Created Assets are problematic, primarily from a brand safety perspective.
Khabra was particularly critical:
“We can’t put in guidelines. We’re not allowed to approve things beforehand. So we really have to sit there and kind of just figure out what the system has created for us.”
“AI is a pattern matcher, not a creator. It’s going to generate the most probable thing, not something that’s actually new or exciting, or even correct.”
Minkoff echoed these concerns:
“A lot of clients need to be able to control their brand story and what they’re talking about, and the words that they use. I just don’t trust the automatically generated anything to reflect those guidelines.”
Minkoff noted that automatically generated content often misses business nuances, such as which products deserve budget and which items shouldn’t be advertised at all.
User interface and UX issues
Ridley voiced frustration with ongoing platform user interface (UI) and user experience (UX) changes.
“Having to click campaign, campaign, campaign makes no sense. I’m finding everything a lot easier to do in Editor now or using tools like Optmyzr where it kind of skips that UI.”
He apologized to Google representatives on other panels but maintained that UI changes are “counterproductive in terms of making it quicker, easier, more natural for people to find what they need.”
The problem is compounded by gaps between the UI and Editor, forcing advertisers to jump back and forth between the two.
Learning periods and flexibility
Minkoff pointed to extended learning periods as a major challenge, especially for smaller campaigns or time-sensitive moments like Black Friday and Cyber Monday.
“How do you navigate a learning period on these platforms that feel no longer designed to let you do those pushes for one day that are honestly a real part of the business calendar?”
Measurement challenges
Khabra flagged measurement as a major pain point, especially for small business owners with limited budgets and data.
“Trying to figure out how to make that work with automation that needs a lot of it has been really, really interesting.”
Khabra warned that Google’s modeled conversions reflect a “best possible outcome” scenario that business owners may mistakenly treat as reality.
Biggest surprises of 2025
Google Marketing Live announcements
Ridley said Google Marketing Live was his biggest surprise, noting that Google “announced loads of new things for small and medium businesses, but also big things we’ve been asking for.” Key announcements included:
“I did not see that coming. I think it’s very exciting, although the next step is going to be being able to do something about it, which is kind of what I’m hoping for come soon.”
“That was definitely not on my bingo card. I would’ve never, ever in a million years thought the Waze pins would be a placement in PMax.”
The speed of AI/LLM rollout
Minkoff was struck by how quickly AI Overviews and LLMs became ubiquitous.
“It felt like overnight in a way. It was kind of coming out and then it was out and it’s there a good chunk of the time. The cat is out of the bag and it is very out of the bag and not coming back.”
The channel reporting debate
The Performance Max channel reporting discussion exposed tension between what advertisers want and what the platform was built to do.
The problem
Minkoff explained that many campaigns now see 95% or more of their budget flowing into a single placement, usually display:
“I just don’t think that was the point of PMax. The thing that I’ve always liked about PMax is that it can fill the whole funnel, that it can fill these different placements, that it wasn’t gonna be completely overrun by one.”
The fence-sitting position
Khabra acknowledged sitting on the fence:
“It was meant to be a black box this entire time. Although I’m really happy about the channel reporting, there was a little piece of you that was like, were we supposed to — should this have actually happened?”
She worried that everyone is now trying to manipulate the system in ways that defeat its purpose:
“We’re supposed to put in clean data, we’re supposed to put in good signals, and it’s supposed to do its job.”
Potential solutions
Ridley raised an intriguing idea: What if Google offered media mix controls that let advertisers suggest percentage splits — like 20% search and 30% display — as guidance rather than hard limits?
Minkoff suggested bid adjustments as a middle ground:
“Bid up, bid down. I want more of this, I want less of this. I’m not even necessarily asking for me to figure it out because if I was right, I would just run them in the other campaign. But more a matter of like, do a little more of this, do a little less of this.”
The consensus was clear: until better controls exist, advertisers should focus on sending the right signals so Google can make smarter decisions on the backend.
Biggest struggles right now
Controlling automated AI features
Ridley called the automatic rollout of AI recommendations and features the biggest challenge:
“Even sometimes after you turn it off and you go through the whole review, the campaign setup, you see it turned back on.”
He pointed to Matt Beswick’s recent experience, where forgetting to disable search partners led to most of the budget being spent on wasted traffic.
The challenge goes further with hidden toggles and hard-to-find settings, creating constant stress for advertisers trying to stay in control.
Finding hidden settings
Minkoff echoed this concern:
“A lot of the boxes are hidden, so it’s hard to even find where it was turned on or turned off, or the option that you can adjust it.”
Measurement for small businesses
Khabra’s biggest concern remains measurement challenges, especially with privacy concerns making tracking increasingly difficult:
“I think that’s just gonna continually become more of an issue.”
What we’ll be talking about in 2026
The unknown unknown
Minkoff offered a fascinating perspective: “My favorite thing about this question is that I honestly don’t know. And I feel like this is the first time I can say that—the first time where I felt like things were changing that quickly.”
She emphasized that the biggest thing we’ll discuss in a year probably hasn’t even been released yet:
“We have to make sure that we have budget, we have flexibility to factor that into our planning. I really think it’ll be something completely new, which is super exciting, but also kind of crazy.”
The antitrust trial
Khabra is watching the Google antitrust trial closely:
“They lost the first part of it. They’re appealing it. I’m really curious just to see what happens on that front and what the implications are.”
Ads within AI platforms
Ridley expects AI to remain the focus a year from now, but with ads running inside AI platforms.
“Ads within each of the AI platforms as well, and probably Google and other platforms integrating them as network partners as well.”
The only certainty in PPC is uncertainty
PPC changed at an unprecedented pace in 2025. Google finally listened to advertisers while pushing deeper into AI-driven automation. The advertisers who performed best embraced automation without giving up strategic control, prioritized quality signals over volume, and stayed agile enough to adapt to changes that seemed to come weekly, rather than quarterly.
As 2026 approaches, platforms are evolving faster than ever, and the biggest changes likely haven’t even been announced yet. Advertisers who build flexibility into their strategies, stick to strong fundamentals, and feed high-quality signals into automated systems will be best positioned to succeed — whatever 2026 brings.
Watch: 2026 PPC trends to get ahead of now + Live Q&A
Here is the full panel discussion from SMX Next 2025:
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Tired of wasting time digging through Google Ads change history, jumping between reports, campaigns, and ad groups? A new “Go to…” button removes those extra clicks. It’s a small user interface change that saves time during audits and troubleshooting.
What’s new. Google added a “Go to…” dropdown in the Change history report. You can jump straight from a logged change to the relevant campaign or ad group. This is especially helpful when reviewing bulk edits, script-driven changes, or updates made in Google Ads Editor.
How it works:
Select one or more changes in the Change history report.
Use the “Go to…” dropdown to navigate straight to the affected entity.
No more manual searching through account structure to find what changed.
What they’re saying. The update was first flagged by PPC Specialist Arpan Banerjee on LinkedIn.
Hana Kobzová, founder of on PPC News Feed, said the feature “eliminates extra steps in troubleshooting and speeds up navigation, especially when reviewing bulk edits or changes made through scripts or the Google Ads Editor.”
Why we care. This update removes friction from one of the most time-consuming parts of account management: figuring out what changed and where. The new “Go to…” button lets you jump straight from the change log to the affected campaign or ad group. That saves time during audits, troubleshooting, and bulk-edit reviews. For teams managing large accounts or relying on scripts and Google Ads Editor, those saved clicks add up fast.
Bottom line. It’s not flashy, but for advertisers who live in Change history, this shortcut can save you real time.
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Remember when link building was all the rage in SEO?
While it never disappeared, its role evolved as Google introduced clearer guidelines and placed greater emphasis on quality, relevance, and intent.
Today, as AI search reshapes the organic landscape, link building has shifted into a closely related – and increasingly prioritized – initiative: brand mentions.
You might think of brand mentions as “citations,” but in the context of AI search, citations describe how brands are referenced by LLMs.
Brand mentions are the input that leads to those citations. To avoid confusion, this article uses brand mentions to describe the tactic itself.
To build durable organic visibility for your brand or clients, brand mentions should be a priority in 2026.
Let’s break down what that looks like in practice.
How and why to prioritize brand mentions
Brand mentions have moved from a nice-to-have tactic to core infrastructure in an AI search environment.
LLMs look beyond links, so this is not a return to the backlink strategies that once dominated SEO.
Instead, they evaluate mentions, context, and repeated co-occurrence between your brand and the topics you want to rank for.
Brand mentions are part of the ranking moat.
They compound over time, and they matter even more when competitors are not investing in the same signals.
From a prioritization standpoint, brand mentions should come:
Right after technical and content fundamentals are in place, including crawlability, structured data, and on-page clarity.
Before heavy long-form expansion or content produced for its own sake. You can publish 200 articles, but without a citation footprint, LLMs have little reason to surface them.
Passive brand mentions occur when something you produce fills a gap in the broader information ecosystem.
The goal is to make your brand the easiest source to reference.
They are earned by creating referenceable assets, not just content. Examples include:
Original data or insights: Think mini research drops, annual reports, or proprietary trends. These stand out from the generic web, and LLMs are effective at finding and citing them even when overall citation volume is limited.
Highly scannable definition or explainer pages: When a brand becomes the canonical definition of a concept, it is cited disproportionately. The objective is to become the primary source, as I’ve been saying for a while now.
Useful tools, templates, or calculators: These encourage habitual linking from blogs, forums, and communities, helping brands surface broadly for relevant queries.
Active participation on visible platforms, including Reddit and industry forums, approached as a knowledgeable contributor rather than a brand billboard. These discussions are scraped and can surface in LLM training data.
The most effective outreach for earning brand mentions is relationship-driven and anchored in information value.
Key guidelines include:
Lead with the asset, not the ask: For example: “We published new proprietary data on [X] and thought it might support your upcoming coverage.”
Use narrative relevance, not conditional relevance: Pitch journalists and creators who have recently covered the topic, not those who might someday.
Deliver a clear angle: Providing a ready-made hook, such as a specific data comparison, significantly increases the likelihood of brand inclusion.
Blend outreach with thought leadership: Podcasts, community AMAs, expert panels, and webinars increase surface area for discovery and research.
Follow up with new value, not reminders: If there is nothing new to add, wait until there is.
The long-term objective is to build an outreach engine by developing relationships with writers and personalities who are more likely to reference your brand in future work.
In some cases, there is added value when those relationships extend into content collaboration.
When to bring on a PR resource
Beyond budget considerations, PR support is most effective for building brand mention momentum when:
A strong story or data engine exists, but distribution is limited.
Brand mentions need to scale quickly, such as for fundraising, major launches, or highly competitive categories.
Internal teams are not structured for ongoing media relationship management.
Credibility from tier-one sources, such as The Wall Street Journal or TechCrunch, is needed to strengthen perceived authority in LLM evaluations.
The category is reputation-driven, where trust and authority directly affect rankings. Health, finance, legal, property management, and AI fall into this group.
If technical SEO fundamentals are still unresolved or reference-worthy assets are not yet in place, PR is premature.
When a brand is ready to function as a source, PR accelerates the signal flywheel.
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In fact, 74% of shoppers give up because there’s too much choice, according to research by Business of Fashion and McKinsey.
Now?
A shopper submits a query. AI gives one clear answer — often with direct links to products, reviews, and retailers. They can even click straight to purchase.
So, how do you make sure AI recommends your fashion brand?
We analyzed how fashion brands appear in AI search. And why some brands dominate while others disappear.
In this article, you’ll learn how large language models (LLMs) interpret fashion, what drives visibility, and the levers you can pull to get your brand visible in AI searches (plus a free fashion trend calendar to help you plan).
There are three ways people will see your brand in AI search: brand mentions, citations, and recommendations.
Brand mentions are references to your brand within an answer.
Ask AI about the latest fashion trends, and the answer includes a couple of relevant brands.
Citations are the proof that backs up AI answers. Your brand properties get linked as a source. This could be product pages, sizing guides, or care instructions.
Citations also include other sites that talk about your brand, like Wikipedia, Amazon, or review sites.
Product recommendations are the most powerful form of AI visibility. Your brand isn’t just mentioned; it’s actively suggested when someone is ready to buy.
For example, I asked ChatGPT for recommendations of aviator sunglasses:
Ray-Ban doesn’t just show up as a mention — they’re a recommended option with clickable shopping cards.
How AI Models Choose Which Fashion Brands to Surface
If you’ve ever wondered how AI chooses which fashion brands to surface, here are the two basic factors:
By evaluating what other people say about you online
By checking how consistently factual and trustworthy your own information is
Let’s talk about consensus and consistency. Plus, we’ll discuss real fashion brands that are winning at both.
Consensus
If you ask all your friends for their favorite ice cream shop, they’ll probably give different answers.
But if almost everyone coincided in the same answer, you trust that’s probably the best place to go.
AI does something similar.
First, it checks different sources of information online. This includes:
Editorial websites, like articles in Vogue, Who What Wear, InStyle, and others
Community and creator content, including TikTok try-ons, Reddit threads, and YouTube product roundups
Retailer corroboration, like ratings and reviews on Amazon, Nordstrom, Zalando, and more
Sustainability verification from third parties like B Corp, OEKO-TEX, or Good On You
After analyzing this information, it gives you recommendations for what it perceives to be the best option.
Here’s an example of what that consensus looks like for a real brand:
Carhartt is mentioned all over the web. They appear in retail listings, editorial pieces, and in community discussions.
The result?
They get consistent LLM mentions.
Consistency
AI also judges your brand based on the consistency of your product information.
This includes:
Naming & colorways: Identical names/color codes across your own site, retailers, and mentions
Fit & size data: Standardized size charts, fit guides, and model measurements
Materials & care: The same composition and instructions across all channels
Imagery/video parity: The same SKU visuals (like hero, 360, try-on) on your site and retailer sites
Price & availability sync: Real-time updates during drops or restocks to avoid stale or conflicting data
For example, Lululemon does a great job of keeping product availability updated on their website.
If you ask AI where to find a specific product type, it directs you back to the Lululemon website.
This happens because Lululemon’s site provides accurate, up-to-date information.
Plus, it’s consistent across retailer pages.
The Types of Content That Dominate Fashion AI Search
Mentions get you into the conversation. Recommendations make you the answer. Citations build the credibility that supports both.
The brands winning in AI search have all three — here’s how to diagnose where you stand.
Let’s talk about the fashion brands that are consistently showing up in AI search results, and the kind of content that helps them gain AI visibility.
Editorial Shopping Guides and Roundups
Editorial content has a huge impact on results.
Sites like Vogue, Who What Wear, and InStyle are regularly cited by LLMs.
These editorial pieces are key for AI search, since they frame products in context — showing comparison, specific occasions, or trends.
There are two ways to play into this.
First, you can develop relationships with editorial websites relevant to your brand.
Start by researching your top three competitors. Using Google (or a quick AI search), find out which publications have featured those competitors recently.
Then, reach out to the editor or writers at those publications.
If they’re individual creators, you might send sample products for them to review.
Looking for mentions from bigger publications?
You might consider working with a PR team to get your products listed in articles.
To build consistency in that content, provide data sheets with information about material, fit, or care.
Second, you can build your own editorial content.
That’s exactly what Huckberry does:
They regularly produce editorial-style content that answers questions.
Many of these posts include a video as well, giving them more opportunity for discovery in LLMs:
Retailer Product Pages and Brand Stores
Think of your product detail page (PDP) as the source of truth for AI.
If you don’t have all the information there, AI will take its answers from other sources — whether or not they’re accurate.
Product pages (your own website or a retailer’s) need to reflect consistent, accurate information. Then, AI can understand and translate into answers.
Some examples might include:
Structured sizing information
Consistent naming and colorways
Up-to-date prices and availability
Ratings (with pictures)
Fit guides (like sizing guides and images with model measurements and sizing)
Materials and care pages
Transparent sustainability modules
For example,Everlane provides the typical sizing chart on each of its products. But they take it a step further and include a guide to show how a piece is meant to fit on your body.
You can even see instructions to measure yourself and find the right size.
That’s why, when I ask AI to help me pick the right size for a pair of pants, it gives me a clear answer.
And the citations come straight from Everlane’s website.
Everlane’s product pages also include model measurements and sizing.
So when I ask ChatGPT for pictures to help me pick the right size, I get this response:
However you choose to present this information on your product pages, just remember: It needs to be identical on all retailer pages as well.
Otherwise, your brand could confuse the LLMs.
User Generated Video Content
What you say about your own brand is one thing.
But what other people say about you online can have a huge influence on your AI mentions.
Of course, you don’t have full control over what consumers post about you online.
So, proactively build connections with creators. Or, try to join the conversation online when appropriate.
This can help you build a positive sentiment toward your brand, which AI will pick up on.
Not sure which creators to work with?
Try searching for your competitors on channels like TikTok or Instagram. See which creators are mentioning their products, and getting engagement.
Search by social channels, and filter by things like follower count, location, and pricing.
Here’s an example: Aritzia has grown a lot on TikTok. They show up in creator videos, fit checks, and unboxing-style videos.
In fact, the hashtag #aritziahaul has a total of 32k posts, racking up 561 million views overall.
Other fashion brands, like Quince, include a reviewing system on their PDPs.
This allows consumers to rate the fit and add pictures of themselves wearing the product.
LLMs also use this information to answer questions.
Creator try-ons, styling videos, and similar content can help increase brand mentions in “best for [body type]” or “best for [occasion]” prompts.
Pro tip: Zero-click shopping is coming. Perplexity’s “Buy with Pro” and ChatGPT’s “Instant Checkout” hint at a future where AI answers lead straight to one-click purchases. The effects are still emerging, but as with social shopping, visibility wins. So, make sure your brand shows up in the chats that drive buying decisions.
Reddit and Community Threads
Reddit is a major source of information for fashion AI queries.
This includes information about real-world fit, durability, comfort, return experiences, and comparisons.
For example, Uniqlo shows up regularly in Reddit threads and questions about style.
You can also find real reviews of durability about the products.
As a result, the brand is getting thousands of mentions in LLMs based on Reddit citations.
Plus, this leads to a ton of organic traffic back to the Uniqlo website.
Obviously, it’s impossible to completely control the conversation around your brand. So for this to work, there’s one key thing you can’t miss:
Your products need to be truly excellent.
A mediocre product that has a lot of negative sentiment online won’t show up in AI search results.
And no amount of marketing tactics can fool the LLMs.
Further reading: Learn how to join the conversation online with our Reddit Marketing guide.
Lab Tests and Fabric Explainers
This kind of content shows the quality of your products.
It gives LLMs a measurable benchmark to quote on things like pilling or color fastness.
This content could include:
“6-month wear” style videos
Pages that explain the fabrics and materials used
Third party tests
Clear care instructions
For example, Quince has an entire page on their website talking about cashmere.
And in Semrush’s AI Visibility dashboard, you can see this page is one of the top cited sources from Quince’s website.
Another option is to create content that shows tests of your products.
Here’s a great example from a brand that makes running soles, Vibram.
They sponsored pro trail runner Robyn Lesh, and teamed up with Huckberry to lab test some of their shoes.
This kind of content is helping Vibram maintain solid AI visibility.
And for smaller brands who don’t have Vibram’s sponsorship budget?
Try doing product testing content with your own team.
For example, have a team member wear a specific product every day for a month, and report back on durability.
Or, bury a piece of clothing underground and watch how long it takes to decompose, like Woolmark did:
Get creative, and you’ll have some fun creating content that can also help your brand be more visible.
Start by checking your AI visibility score. You’ll see how this measures up against the industry benchmarks.
You can prioritize next steps based on the Topic Opportunities tab.
There, you’ll see topics where your competitors are being mentioned, but your brand is missed.
Then, jump to the Brand Perception tab to learn more about your Share of Voice and Sentiment in AI search results.
You’ll also get some clear insights on improvements you can make.
Comparisons and Alternatives Content
AI loves a good comparison post (and honestly, who doesn’t?). So, creating content that compares your products to other brands is a great way to get more mentions.
It helps you get brand exposure without depending on organic traffic dependence. Plus, it helps level the playing field with bigger competitors.
For instance, Quince is often cited online as a cheaper alternative to luxury clothing.
I asked ChatGPT for affordable cashmere options, and Quince was the first recommendation.
So, why is this brand showing up consistently?
One reason is their comparison content.
In each PDP, you’ll see the “Beyond Compare” box, showing specific points of comparison with major competitors.
The right comparisons are handled honestly and tastefully.
Focus on real points of difference (like Quince does with price). Or, show which products are best for certain occasions.
For example: “Our sweaters are great for hiking in the snow. Our competitors’ sweaters are better for indoor activities.”
Comparisons give AI a reason to recommend your fashion brand when someone asks for an alternative.
What This Shift Means for Your Fashion Brand
AI search has changed the way people discover products, and even their path to purchase.
Before, this involved multiple searches, clicking on different websites, or scrolling through forums. Now, you can do this in one simple interface.
So, how is AI changing fashion, and how can your brand adapt?
Editorial, Retailer, and PDP Split
AI search doesn’t treat every source of information equally.
And depending on which model your audience uses, the “default” source of truth can look very different.
ChatGPT leans heavily on editorial and community signals.
It rewards cultural traction — what people are talking about, buying, and loving.
For example, articles like this one from Vogue are a prime source for ChatGPT answers:
Meanwhile, Google’s AI Mode and Perplexity skew toward retailer PDPs.
They look for structured data like price, availability, or fit guides. In other words, they trust whoever has the cleanest, richest product data.
The most visible brands win in both arenas: cultural conversation and PDP completeness.
Here’s What You Can Do
To show up in all major LLMs, you need two parallel pipelines.
Cultural traction: Like press mentions, creator partnerships, and community visibility
Citation-ready proof: For example, complete and accurate PDPs across retailer channels
Here’s an Example: Carhartt
Carhartt is a great example of a brand that’s winning on both sides.
First, they get consistent cultural visibility.
For instance, Vogue reported that the Carhartt WIP Detroit jacket made Lyst’s “hottest product” list. That led to searches for their brand increasing by 410%.
This makes it more likely for LLMs to recommend their products in answers:
This is the kind of loop that works wonders for a fashion brand.
At the same time, Carhartt is also stocked across a huge range of retailers. You can find them in REI, Nordstrom, Amazon, and Dick’s, plus their own direct-to-consumer website.
So, Google AI Mode has an abundance of PDPs, videos, reviews, and Q&A to cite.
This makes Carhartt extremely “citation-friendly” in both models.
No wonder it has such a strong AI visibility score.
Trend Shocks and Seasonal Volatility
Trend cycles aren’t a new challenge in the fashion industry. But it becomes a bigger challenge to maintain visibility when those trends affect which brands appear in AI search.
Micro-trends pop up all the time, triggering quick shifts in how AI answers fashion queries.
When the trend heats up, LLMs pull in brands that appear online in listicles or TikTok roundups.
And when the trend cools? Those same brands disappear just as quickly.
Here’s What You Can Do
To stay present during each trend swing, you need a content and operations pipeline that speaks in real time to the language models are echoing.
Build a proactive trend calendar: Map your content to seasonal moments, like spring tailoring, fall layers, holiday capsules, back-to-school basics, and so on
Refresh imagery and copy to mirror trend language: Update PDPs, on-site copy, and retailer description to match the phrasing used in cultural content
Create rapid-fire listicles and lookbooks: Listicle-style content, creator videos, and other trend-related mentions can help boost visibility. This includes building your own content and working with creators and publications to feature your product in their content.
Anyone who was around for Y2K may have been shocked to see UGG boots come around again.
But the brand was ready to jump onto the trend and make the most of their moment.
Vogue reported that UGG made Lyst’s “hottest products” list in 2024.
Since then, they’ve been regularly featured in seasonal “winter wardrobe essentials” style roundups.
One analyst found that there had been a 280% increase in popularity for the shoes. Funny enough, that trend seems to be a regular occurrence every year once “UGG season” rolls around.
In fact, on TikTok, the hashtag #uggseason has almost 70k videos.
UGG stays visible even as seasons trends shift. That’s because the brand is always present in the content streams that LLMs treat as cultural indicators. By partnering with influencers, UGG amplified its presence so effectively that the boots themselves became a moment — something people wanted to photograph, share, and join in on without being asked.
The result?
They have one of the highest AI Visibility scores I saw while researching this article.
(As a marketer, I find this encouraging. As a Millennial, I find it deeply disturbing.)
Pro tip: Want to measure the results? Track how often your brand or SKUs appear in new listicles per month, plus how they rank in those roundups. Then use Semrush’s AI Visibility Toolkit to track your brand’s visibility using trend-related prompts.
Sustainability and Proof (Not Claims)
Sustainability has become one of the strongest differentiators for fashion brands in AI search.
But only when brands back it up with verifiable proof.
LLMs don’t reward vague eco-friendly language. Instead, they surface brands with certifications, documentation, and third-party validation.
Models also pull heavily from Wikipedia and third-party certification databases. These pages often act as trust anchors for AI search results.
Here’s What You Can Do
You need to build a clear, credible footprint that models can cite.
Centralize pages on materials, care, and impact: Make them brief, structured, and verifiable. Include materials, sourcing, certifications, and repair/resale info.
Maintain third-party profiles: Keep your certifications up-to-date. This includes things like Fair Trade, Bluesign, B-Corp, GOTs, etc.
Standardize sustainability claims across all retailers: If your DTC site says “Fair Trade Certified” but your Nordstrom PDP doesn’t? Models treat that as unreliable.
Here’s an Example: Patagonia
Patagonia is the ruler of AI visibility with a 21.96% share of voice.
In part, this is because of their incredible dedication to sustainability. They basically own this niche category within fashion.
Patagonia’s sustainability claims are backed up by third-party certifications.
And they’re displayed proudly on each PDP.
They’re also transparent about their efforts to help the environment.
They keep pages like this updated regularly.
These sustainable efforts aren’t just big talk.
Review sites and actual consumers speak positively online about these efforts.
They’ve made their claim as a sustainable fashion brand.
So, Patagonia shows up first, almost always, in LLMs when talking about sustainable fashion:
That’s the power of building a sustainable brand.
Make AI Work for Your Fashion Brand
You’ve seen how the top fashion brands earn AI visibility.
The path forward is simple: Consensus + Consistency.
Build consensus by getting people talking: Create shareable content, encourage customer posts, or work with creators and publications.
Build consistency by keeping your product info aligned across your site and retail partners.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-01-02 15:14:362026-01-02 15:14:36Fashion AI SEO: How to Improve Your Brand’s LLM Visibility
PPC didn’t stand still in 2025. It adjusted. These articles resonated because they answered the real questions advertisers are asking: how to stay competitive, cut wasted spend, work with automation instead of against it, and prepare for what’s next.
Below are links to the 10 most-read Search Engine Land PPC columns of 2025, written by our exceptional subject matter experts.
With the right strategy, even the smallest business can stand out, win customers, and make a lasting impact. Here’s how. (By Sophie Logan. Published Sept. 16.)
Shift your optimization mindset in 2025 with fresh strategies for keywords, Performance Max, and audience targeting. (By Pauline Jakober. Published Feb. 6.)
CPCs are rising – but how fast? Compare ad cost inflation to consumer price index and see what it means for your ad strategy. (By Mark Meyerson. Published April 16.)
AI-driven search is blurring the line between organic and paid. Learn how uniting SEO and PPC boosts visibility, intent, and brand authority. (By Jen Cornwell. Published Oct. 6.)
PPC scripts hit limits. Vibe coding removes the roadblocks. Turn complex seasonal patterns into simple, data-driven planning tools. (By Frederick Vallaeys. Published Aug. 21.)
Speed up your ad creation process without losing your message. Use generative AI to craft relevant, personalized copy that connects. (By Jason Tabeling. Published Aug. 1.)
These filtering tactics help refine your targeting, reduce spend on low-quality clicks, and uncover new keyword opportunities. (By Menachem Ani. Published July 22.)
Streamline campaign management with Google Ads scripts. Get insights, use cases, and practical tips for using automation to boost performance. (By Frederick Vallaeys. Published Jan. 9.)
Fewer clicks mean higher stakes. Win visibility with precise targeting, value-based bidding, and authority across paid and organic search. (By Sarah Stemen. Published Oct. 7.)
Some PPC practices no longer fit today’s automated Google Ads environment. Here’s what to phase out – and what to prioritize next year. (By Sarah Vlietstra. Published Nov. 4.)
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/12/ppc-columns-2025-search-engine-land-FOWDMV.jpg?fit=1920%2C1080&ssl=110801920http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-12-30 14:00:002025-12-30 14:00:00Top 10 PPC expert columns of 2025 on Search Engine Land
