PPC advice in 2025 leaned hard on AI and shiny new tools.
Much of it sounded credible. Much of it cost advertisers money.
Teams followed platform narratives instead of business constraints. Budgets grew. Efficiency did not.
As 2026 begins, carrying those beliefs forward guarantees more of the same.
This article breaks down three PPC myths that looked smart in theory, spread quickly in 2025, and often drove poor decisions in practice.
The goal is simple: reset priorities before repeating expensive mistakes.
Myth 1: Forget about manual targeting, AI does it better
We have seen this claim everywhere:
AI outperforms humans at targeting, and manual structures belong to the past.
Consolidate campaigns as much as possible.
Let AI run the show.
There is truth in that – but only under specific conditions.
AI performance depends entirely on inputs. No volume means no learning. No learning means no results.
A more dangerous version of the same problem is poor signal quality. No business-level conversion signal means no meaningful optimization.
For ecommerce brands that feed purchase data back into Google Ads and consistently generate at least 50 conversions per bid strategy each month, trusting AI with targeting can make sense.
In those cases, volume and signal quality are usually sufficient. Put simply, AI favors scale and clear outcomes.
That logic breaks down quickly for low-volume campaigns, especially those optimizing to leads as the primary conversion.
Without enough high-quality conversions, AI cannot learn effectively. The result is not better performance, but automation without improvement.
How to fix this
Before handing targeting decisions entirely to AI, you should be able to answer “yes” to all three of the questions below:
Are campaigns optimized against a business-level KPI, such as CAC or a ROAS threshold?
Are enough of those conversions being sent back to the ad platforms?
Are those conversions reported quickly, with minimal latency?
If the answer to any of these is no, 2026 should be about reassessing PPC fundamentals.
Do not be afraid to go old school when the situation calls for it.
In 2025, I doubled a client’s margin by implementing a match-type mirroring structure and pausing broad match keywords.
It ran counter to prevailing best practices, but it worked.
The decision was grounded in historical performance data, shown below:
Match type
Cost per lead
Customer acquisition cost
Search impression share
Exact
€35
€450
24%
Phrase
€34
€1,485
17%
Broad
€33
€2,116
18%
This is a classic case of Google Ads optimizing to leads and delivering exactly what it was asked to do: drive the lowest possible cost per lead across all audiences.
The algorithm is literal. It does not account for downstream outcomes, such as business-level KPIs.
By taking back control, you can direct spend toward top-performing audiences that are not yet saturated. In this case, that meant exact match keywords.
If you are not comfortable with older structures like match-type mirroring – or even SKAGs – learning advanced semantic techniques is a viable alternative.
Those approaches can provide a more controlled starting point without relying entirely on automation.
Myth 2: Meta’s Andromeda means more ads, better results
This myth is particularly frustrating because it sounds logical and spreads quickly.
The claim is simple: more creative means more learning, which leads to better auction performance.
In practice, it far more reliably increases creative production costs than it improves results – and often benefits agencies more than advertisers.
Creative volume only helps when ad platforms receive enough high-quality conversion signals.
Without those signals, more ads simply mean more assets to rotate. The AI has nothing meaningful to learn from.
Andromeda generated significant attention in 2025, and it gave marketers a new term to rally around.
In reality, Andromeda is one component of Meta’s ad retrieval system:
“This stage [Andromeda] is tasked with selecting ads from tens of millions of ad candidates into a few thousand relevant ad candidates.”
That positioning coincided with Meta’s broader pivot from the metaverse narrative to AI. It worked.
But it also led some teams to conclude that aggressive creative diversification was now required – more hooks, more formats, more variations, increasingly produced with generative AI.
Similar to Google Ads’ push around automated bidding, broad match, and responsive search ads, Andromeda has become a convenient justification for adopting Advantage+ targeting and Advantage+ creative.
Creative diversification helps platforms match messages to people and contexts. That value is real. It is also not new. The same fundamentals still apply:
Creative testing requires a strategy. Testing without intent wastes resources.
Measurement must be planned in advance. Otherwise you’re setting yourself up for failure.
Business-level KPIs need to exist in sufficient volume to matter.
This myth breaks down most clearly when resources are limited – budget, skills, or time. In those cases, platforms often rotate ads with little signal-driven direction.
Review tracking. More tracked conversions improve performance.
Improve the customer journey to increase conversion rates and signal volume.
Map higher-margin products to support more efficient spend.
Test new channels or networks using budget saved from excessive creative production.
The pattern is consistent. Creative scale follows signal scale, not the other way around.
Myth 3: GA4 and attribution are flawed, but marketing mix modeling will provide clarity
Can you think of 10 marketers who believe GA4 is a good tool? Probably not.
That alone speaks to how poorly Google handled the rollout.
As a result, more clients now say the same thing: GA4 does not align with ad platform data, neither feels trustworthy, and a more “serious” solution must be needed.
More often than not, that path leads to higher costs and average results.
Most brands simply do not have the spend, scale, or complexity required for MMM to produce meaningful insight.
Instead of adding another layer of abstraction, they would be better served by learning to use the tools they already have.
For most brands, the setup looks familiar:
Media spend is concentrated across two or three channels at most – typically Google and Meta, with YouTube, LinkedIn, or TikTok as secondary options.
The business depends on a recurring but narrow customer base, which creates long-term fragility.
Outside that core audience, marketing is barely incremental, if incremental at all.
In those conditions, MMM does not add clarity. It adds abstraction.
With such a limited channel mix, the focus should remain on fundamentals.
The challenge is not modeling complexity, but identifying what is actually impactful.
How to fix this
The priorities below deliver more value than MMM in these scenarios:
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/01/myths-vs-facts-800x450-yQ9Ix6.jpg?fit=800%2C450&ssl=1450800http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-01-12 15:00:002026-01-12 15:00:003 PPC myths you can’t afford to carry into 2026
It gutted an entire economy built on informational arbitrage – niche blogs, affiliate sites, ad-funded publishers, and content-led SEO businesses that had learned how to monetize curiosity at scale.
Now, large language models are finishing the job. Informational queries are answered directly in search. The click is increasingly optional. Traffic is evaporating.
So yes, on the surface, it sounds mad to say this:
Copywriting is once again becoming the most important skill in digital marketing.
But only if you confuse copywriting with the thing that just died.
AI didn’t kill copywriting
What AI destroyed was not persuasion.
It destroyed low-grade informational publishing – content that existed to intercept search demand, not to change decisions.
“How to” posts.
“Best tools for” roundups.
Explainers written for algorithms, not people.
LLMs are exceptionally good at this kind of work because it never required judgment. It required:
Synthesis.
Summarization.
Pattern matching.
Compression.
That’s exactly what LLMs do best.
This content was designed to intercept purchase decisions by giving users something else to click before buying, often with the hope that a cookie would track the stop in the journey and reward the page for “influencing” the buyer journey.
That influence was rewarded either through analytics for the SEO team or through an affiliate’s bank account.
But persuasion – real persuasion – has never worked like that.
Persuasion requires:
A defined audience.
A clearly articulated problem.
A credible solution.
A deliberate attempt to influence choice.
Most SEO copy never attempted any of this. It aimed to rank, not to convert.
So when people say “AI killed copywriting,” what they really mean is this: AI exposed how little real copywriting was being done in the first place.
And that matters, because the environment we’re moving into makes persuasion more important, not less.
Traditional search engines forced users to translate their problems into keywords.
Someone didn’t search for “I’m an 18-year-old who’s just passed my test and needs insurance without being ripped off.” They typed [cheap car insurance] and hoped Google would serve the best results.
This created a monopoly in SEO. Those who could spend the most on links usually won once a semi-decent landing page was written.
It also created a sea of sameness, with most ranking websites saying exactly the same thing.
LLMs reverse this process. They:
Start with the problem.
Understand context, constraints, and intent.
Decide which suppliers are most relevant.
That distinction is everything.
LLMs are not ranking pages. Instead, they seek and select the best solutions to solve users’ problems.
And selection depends on one thing above all else – positioning.
Not “position on Google,” but strategic positioning.
Who are you for?
What problem do you solve?
Why are you a better or different choice than the alternatives?
If an LLM cannot clearly answer those questions from your website and third-party information, you will not be recommended, no matter how many backlinks you have or how “authoritative” your content once looked.
This is why copywriting suddenly sits at the center of SEO’s future.
Availability means increasing the likelihood that your business will be surfaced in a buying situation.
That depends on whether your relevance is legible.
Most businesses still describe themselves in static, categorical terms:
“We’re an SEO agency in Manchester.”
“We’re solicitors in London.”
“We’re an insurance provider.”
These descriptions tell you what the business is.
They do not tell you what problem it solves or for whom it solves that problem. They are catchall descriptors for a world where humans use search engines.
This is where most companies miss the opportunity in front of them.
The vast majority of “it’s just SEO” advice centers on entities and semantics.
The tactics suggested for AI SEO are largely the same as traditional SEO:
Create a topical map.
Publish topical content at scale.
Build links.
This is why many SEOs have defaulted to the “it’s just SEO” position.
If your lens is meaning, topics, context, and relationships, everything looks like SEO.
In contrast, the world in which copywriters and PRs operate looks very different.
Copywriters and PRs think in terms of problems, solutions, and sales.
All of this stems from brand positioning.
Positioning is not a fixed asset
A strategic position is a viable combination of:
Who you target.
What you offer.
How your product or service delivers it
Change any one of those, and you have a new position.
Most firms treat their current position as fixed.
They accept the rules of the category and pour their effort into incremental improvement, competing with the same rivals, for the same customers, in the same way.
LLMs quietly remove that constraint.
If you genuinely solve problems – and most established businesses do – there is no reason to limit yourself to a single inherited position simply because that’s how the category has historically been defined.
No position remains unique forever. Competitors copy attractive positions relentlessly.
The only sustainable advantage is the ability to continually identify and colonize new ones.
This doesn’t mean becoming everything to everyone. Overextension dilutes brands.
It means being honest and explicit about the problems you already solve well.
This is something copywriters understand well.
A good business or marketing strategist can help uncover new positions in the market, and a good copywriter can help articulate them on landing pages.
From SEOs’ ‘what we are’ to GEOs’ ‘what problem we solve’
Take insurance as a simple example.
A large insurer may technically offer “car insurance.” But the problems faced by:
An 18-year-old new driver.
A parent insuring a second family car.
A courier using a vehicle for work.
Are completely different.
Historically, these distinctions were collapsed into broad keywords because that’s how search worked.
LLMs don’t behave like that. They start with the user problem to be solved.
If you are well placed to solve a specific use case, it makes strategic sense to articulate that explicitly, even if no one ever typed that exact phrase into Google.
A helpful way to think about this is as a padlock.
Your business can be unlocked by many different combinations.
Each combination represents a different problem, for a different person, solved in a particular way.
If you advertise only one combination, you artificially restrict your AI availability.
Have you ever had a customer say, “We didn’t know you offered that?”
Now you have the chance to serve more people as individuals.
Essentially, this makes one business suitable for more problems.
You aren’t just a solicitor in Manchester.
You’re a solicitor who solves X by Y.
You’re a solicitor for X with a Y problem.
The list could be endless.
Why copywriting becomes infrastructure again
This is where copywriting returns to its original job.
Good copywriting has always been about creating a direct relationship with a prospect, framing the problem correctly, intensifying it, and making the case that you are the best place to solve it.
That logic hasn’t changed.
What has changed is that the audience has expanded.
You now have to persuade:
A human decision-maker.
A LLM acting as a recommender.
Both require the same thing: clarity.
You must be explicit about:
The problem you solve.
Who you solve it for.
How you solve it.
Why your solution works.
You must also support those claims with evidence.
This is not new thinking. It comes straight out of classic direct marketing.
Drayton Bird defined direct marketing as the creation and exploitation of a direct relationship between you and an individual prospect.
Eugene Schwartz spent his career explaining that persuasion is not accidental – benefits must be clear, claims must be demonstrated, and relevance must be immediate.
The web environment made it possible to forget these fundamentals for a while.
Informational traffic is being stripped out of the system.
Traffic only became a problem when it stopped being a measure and became a target.
Once that happened, it ceased to be useful. Volume replaced outcomes. Movement replaced progress.
In an AI-mediated world, fewer clicks does not mean less opportunity.
It means less irrelevant traffic.
When GEO and positioning-led copy work, you see:
Traffic landing on revenue-generating pages.
Brand-page visits from pre-qualified prospects.
Fewer exploratory visits and more decisive ones
No one can buy from you if they never reach your site. Traffic still matters, but only traffic with intent.
In this environment, traffic stops being a vanity metric and becomes meaningful again.
Every click has a purpose.
What measurement looks like now
The North Star is no longer sessions. It is commercial interaction.
The questions that matter are:
How many clicks did we get to revenue-driving pages this month versus last?
How many of those visits turned into real conversations?
Is branded demand increasing as our positioning becomes clearer?
Are lead quality and close rates improving, even as traffic falls?
Share of search still has relevance – particularly brand share – but it must be interpreted differently when the interface doesn’t always click through.
AI attribution is messy and imperfect. Anyone claiming otherwise is lying. But signals already exist:
Prospects saying, “ChatGPT recommended you.”
Sales calls referencing AI tools.
Brand searches rising without content expansion.
Direct traffic increasing alongside reduced informational content
These are directional indicators. And they are enough.
The real shift SEO needs to make
For a decade, SEO rewarded people who were good at publishing.
The next decade will reward people who are good at positioning.
That means:
Fewer pages, but sharper ones.
Less information, more persuasion.
Fewer visitors, higher intent.
It means treating your website not as a library, but as a set of sales letters, each one earning its place by clearly solving a problem for a defined audience.
This is not the death of SEO.
SEO is growing up.
The reality nobody wants, but everyone needs
Copywriting didn’t die.
Those spending a fortune on Facebook ads embraced copywriting. Those selling SEO went down the route of traffic chasing.
The two worlds had different values.
The ad crowd embraced copy.
The SEO crowd disowned it.
One valued conversion. The other valued traffic.
We are entering a world with less traffic, fewer clicks, and an intelligent intermediary between you and the buyer.
That makes clarity a weapon. That makes good copy a weapon.
In 2026, the brands that win will not be the ones with the most content.
They will be the brands that return to the basics of good copy and PR.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/01/Why-copywriting-is-the-new-superpower-in-2026-gpK2Qc.jpg?fit=1920%2C1080&ssl=110801920http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-01-12 13:00:002026-01-12 13:00:00Why copywriting is the new superpower in 2026
Marketing mix modeling (MMM) has shifted from an enterprise luxury to an essential measurement tool.
Tech giants like Google, Meta, and Uber have released powerful open-source MMM frameworks that anyone can use for free.
The challenge is understanding which tool actually solves your problem and which require a PhD in statistics to implement.
Open-source MMM tools are often grouped together but solve different problems
The landscape can be confusing because these tools serve fundamentally different purposes despite being mentioned together.
Google’s Meridian and Meta’s Robyn are complete, production-ready MMM frameworks that take your marketing data and deliver actionable budget recommendations.
They include everything needed:
Data transformations that model advertising decay.
Saturation curves that capture diminishing returns.
Visualization dashboards and budget optimizers that recommend spend allocation.
Uber’s Orbit and Facebook’s Prophet occupy different niches.
Orbit is a time-series forecasting library that can be adapted for MMM, but it requires months of custom development to build MMM-specific features.
Prophet is a forecasting component used within other frameworks, not a standalone MMM solution.
Think of it like transportation:
Meridian and Robyn are complete cars you can drive today.
Orbit is a high-performance engine that requires you to build the transmission, body, and wheels.
Prophet is the GPS system that goes inside the car.
Meta built Robyn specifically to democratize MMM through automation and accessibility.
The framework uses machine learning to handle model building that traditionally required weeks of expert tuning.
Upload your data, specify channels, and Robyn’s evolutionary algorithms explore thousands of configurations automatically.
What makes Robyn distinctive is its approach to model selection.
Rather than claiming one “correct” model, it produces multiple high-quality solutions that show trade-offs between them.
Some fit historical data better but recommend dramatic budget changes.
Others have slightly lower accuracy but suggest more conservative shifts.
Robyn presents this range, allowing decisions based on business context and risk tolerance.
The framework also excels at incorporating real-world experimental results.
If you have run geo-holdout tests or lift studies, you can calibrate Robyn using those results.
This grounds statistical analysis in experiments rather than pure correlation, improving accuracy and giving skeptical executives evidence to trust the outputs.
However, Robyn assumes marketing performance remains constant throughout the analysis period.
In practice, algorithm updates, competitive changes, and optimization efforts mean channel effectiveness often varies over time.
Unlike Robyn’s pragmatic optimization, Meridian models the mechanisms behind advertising effects, including decay, saturation, and confounding variables.
This theoretical rigor allows Meridian to better answer, “What would happen if we changed budget allocation?” rather than simply, “What patterns existed in the past?”
Its standout capability is hierarchical, geo-level modeling.
While most MMMs operate at a national level, Meridian can model more than 50 geographic locations simultaneously using hierarchical structures that share information across regions.
Advertising may perform well in urban coastal markets but struggle in rural areas.
National models average these differences away.
Meridian’s geo-level approach identifies regional variation and delivers market-specific recommendations that national models can’t.
Another distinguishing feature is its paid search methodology, which addresses a fundamental challenge: when users search for your brand, is that demand driven by advertising or independent of it?
Meridian uses Google query volume data as a confounding variable to separate organic brand interest from paid search effects.
If brand searches spike because of viral news or word-of-mouth, Meridian isolates that activity from the impact of search ads.
The technical complexity, however, is significant.
Meridian requires deep knowledge of Bayesian statistics, comfort with Python, and access to GPU infrastructure.
The documentation assumes a level of statistical literacy most marketing teams lack.
Concepts such as MCMC sampling, convergence diagnostics, and posterior predictive checks typically require graduate-level training.
It’s a time-series forecasting library from Uber with a notable feature: Bayesian time-varying coefficients, or BTVC, which address a fundamental MMM challenge.
Imagine presenting MMM results to your CEO, who asks, “This assumes Facebook ads had the same ROI in January and December? But iOS 14 hit in April, and we spent months recovering. How can one number represent the whole year?”
That is the credibility-breaking moment practitioners fear because it exposes a simplifying assumption executives correctly recognize as unrealistic.
Traditional MMM frameworks assign one coefficient per channel for the entire analysis period, producing a single ROI or effectiveness estimate.
For stable channels like TV, this can work.
For dynamic digital channels, where teams constantly optimize, respond to algorithm changes, and face shifting competition, assuming static performance is clearly flawed.
Orbit’s BTVC allows channel effectiveness to change week by week.
Facebook ROI in January can differ from December, while the model keeps estimates stable unless the data shows clear evidence of real change.
The reality, however, is that while time-varying coefficients are powerful, Orbit lacks the other components required for a complete MMM solution.
Orbit makes sense only for data science teams building proprietary frameworks that require advanced capabilities and have the resources for significant custom development.
For most organizations, the cost-benefit tradeoff does not justify that investment.
Teams are better served using Robyn or Meridian while acknowledging their limitations, or working with commercial MMM vendors that have already built time-varying capabilities into production-ready systems.
Facebook Prophet: The misunderstood component
Prophet is Meta’s time-series forecasting tool.
It’s highly effective at its intended purpose but is often misrepresented as an MMM solution, which it is not.
Prophet decomposes time-series data into trend, seasonality, and holiday effects.
It answers questions, such as:
“What will our revenue be next quarter?”
“How do Black Friday spikes affect baseline performance?”
This is forecasting, or predicting future values based on historical patterns, which is fundamentally different from attribution.
Prophet can’t identify which marketing channels drove results or provide guidance on budget optimization.
It detects patterns but has no concept of marketing cause and effect.
Prophet’s primary role is as a preprocessing component within larger systems.
Robyn uses Prophet to remove seasonal patterns and holiday effects before applying regression to isolate media impact.
Revenue often rises in December because of holiday shopping rather than advertising.
Prophet identifies and removes that seasonal effect, making it easier for regression models to detect true media impact.
This preprocessing is valuable, but Prophet addresses only one part of the overall attribution problem.
Marketing teams should use Prophet for standalone KPI forecasting or as a component within custom MMM frameworks, not as a complete attribution or budget optimization solution.
Choosing between these tools requires an honest assessment of your organization’s capabilities, resources, and needs.
Do you have data scientists comfortable with Bayesian statistics and complex Python?
Or marketing analysts whose statistical training ended with basic regression?
The answer determines which tools are viable options and which are aspirational.
For about 80% of organizations, Meta’s Robyn is the right choice.
This includes:
Teams without deep data science resources but still need rigorous MMM insights.
Digital-heavy advertisers seeking attribution without lengthy implementations.
Organizations that require insights in weeks rather than quarters.
The learning curve is manageable, implementation takes weeks rather than months, and outputs are presentation-ready.
A large, active user community also shares solutions when challenges arise.
Google’s Meridian suits:
Small and midsize businesses and enterprise organizations with dedicated data science teams comfortable working in Bayesian frameworks.
Multi-regional operations where geo-level insights would meaningfully influence budget decisions.
Complex paid search programs requiring more precise attribution.
Stakeholders who prioritize causal inference over pragmatic correlations can justify Meridian’s added complexity.
Uber Orbit is appropriate only for data science teams building proprietary frameworks with requirements that Robyn and Meridian can’t meet.
The opportunity cost of spending months on custom infrastructure rather than using existing tools is substantial unless proprietary measurement itself provides a competitive advantage.
Facebook Prophet should be used for KPI forecasting or as a preprocessing component within larger systems, never as a complete attribution solution.
Matching MMM tools to real-world team capabilities
The most advanced tool delivers little value if it can’t be implemented effectively.
A well-executed Robyn implementation running consistently provides more value than an abandoned Meridian project that never progressed beyond a pilot.
Tools should be chosen based on what teams can realistically use and maintain, not on the most impressive feature set.
For most marketing teams, Robyn and Meridian represent pragmatic choices that balance performance with accessibility.
Automation handles much of the statistical work, allowing analysts to focus on insights rather than debugging code.
Strong community support and documentation reduce friction, and teams can move from zero to actionable insights in weeks instead of months, which matters when executives want answers quickly.
For enterprises with substantial technical resources and multi-regional operations, Google Meridian can deliver returns through more reliable causal estimates and geo-level granularity that materially improve budget allocation.
The investment in infrastructure, expertise, and implementation time is significant, but at a sufficient scale, better decision-making can justify the cost.
Uber Orbit offers advanced capabilities for organizations that truly need time-varying performance measurement and have the resources to build complete MMM systems around it.
For most teams, commercial vendors that have already incorporated time-varying capabilities into production-ready platforms are more cost-effective than extended custom development.
These open-source frameworks have made marketing measurement accessible beyond Fortune 500 companies.
The priority is choosing the tool that fits current capabilities, implementing it well to earn stakeholder trust, and using insights to make better decisions.
Competitive advantage comes from allocating budgets more effectively and faster than competitors, not from maintaining a technically impressive system that is too complex to sustain.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/01/Budget-allocation-with-Robyn-856wR5.png?fit=1371%2C1600&ssl=116001371http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-01-12 11:00:002026-01-12 11:00:00Not all MMM tools are equal: Meridian, Robyn, Orbit, and Prophet explained
With AI tools at everyone’s fingertips, what does “great” content writing mean in 2026?
Content writing is about using words and psychology to deliver value, earn trust, and move readers toward action.
It includes blog posts, social media content, newsletters, and white papers. Or it can be scripts for video, podcasts, and presentations.
Content Type
Purpose
Key Characteristics
Blog posts
Educate; build brand awareness and authority
In-depth, structured, research-backed
Social media posts
Engage, entertain, build community
Conversational, visual, platform-specific
Email newsletters
Nurture relationships; drive action
Personal tone, value-driven, scannable
Video/podcast scripts
Entertain; educate through audio/visual
Conversational, paced for speech, engaging hooks
Presentations/webinars
Educate and engage viewers for awareness
Educational, crisp content presented visually
Unlike copywriting, which persuades the audience to take an action, content writing builds trust through teaching.
Thanks to AI tools, filling pages is easier and faster than ever.
And as content becomes easier to produce, attention becomes harder to earn — whether readers are scrolling social feeds, skimming search results, or asking AI tools for quick answers.
The best content writers bring a full toolkit: deep research, sharp critical thinking, strategic judgment, and the ability to apply those strengths in ways AI can’t replicate.
In this guide, you’ll learn eight content writing skills that set top performers apart, shaped by my work with leading brands and insights from my colleagues at Backlinko.
Important: Research and editing are learnable skills. But the instinct for what makes content memorable — what makes someone stop scrolling, what creates emotional resonance — that’s the human layer AI can’t recreate.
1. Build and Hone Your Research Skills
Strong research is what separates fluff from content people trust.
Here’s how to build a hands-on research process.
Start with Your Audience
Audience research is the easiest way to understand your readers: their pain points, goals, and hesitations.
Start your research in a few simple but effective ways:
Mine social media platforms to find emotional drivers behind buying decisions
Skim product reviews to learn what excites or frustrates your audience
Talk directly to your audience through polls, surveys, or 1:1 interviews
Browse community forums to see real conversations around your subject
For example, if you’re writing about the “best SaaS tools,” don’t rely on generic feature lists to inspire your content.
Rosanna Campbell, a senior writer for Backlinko, shares what she looks for when researching an audience:
At a minimum, I like to spend time learning the jargon, current issues, etc., affecting my target reader — usually by lurking on platforms like Reddit, Quora, industry forums, LinkedIn threads, etc. I’ll also find one or two leading voices and read some of their recent content.
But you don’t have to do all the heavy lifting yourself.
AI can speed up much of this process.
Note: AI won’t write great content for you, but it can streamline your research and editing process. Throughout this guide, I’ve included prompts to help you work smarter and faster — not let AI do the thinking for you.
For instance, Michael Ofei, our managing editor, uses a strategic prompt to aggregate audience insights from multiple channels.
Copy/paste this prompt into any AI tool to jumpstart your research (just update your topic description first).
You are a content strategist researching audience pain points for: [TOPIC DESCRIPTION]
RESEARCH SOURCES: Analyze discussions from Reddit, Quora, YouTube comments, LinkedIn posts, and People Also Ask sections from the last 12 months.
PAIN POINT CRITERIA:
Written as first-person “I” statements
Specific and actionable (not vague)
Include emotional context where relevant
Reflect different sophistication levels (beginner to advanced)
OUTPUT FORMAT: First, suggest 3-5 pain point categories for this topic’s user journey.
Then create a table with:
Category (from your suggested categories)
Pain Point Statement (first person)
User Level (Beginner/Intermediate/Advanced – use one for each pain point)
Emotional Intensity (Low/Medium/High)
Semantic Queries (related searches)
Aim for 8-12 total pain points that help content rank for both traditional search and LLM responses. Provide only the essential table output, minimize explanatory text.
After using this prompt for the topic “journalist outreach,” Michael received a helpful list of pain points mapped to user level and emotional intensity.
Perform a Search Analysis
Next, it’s time to review organic search results to assess what content already exists and where you can add value.
Chris Shirlow, our senior editor, stresses the importance of looking closely at who’s ranking and how when studying search results:
Analyzing search results gives me a quick pulse on the topic: how people are talking about it, what questions they’re asking, and even what pain points are showing up. From there, I can identify gaps, spot patterns in language and structure, and figure out how to create something that adds value, rather than just echoing what’s already out there.
Pay attention to:
Content depth: Is the content shallow (short posts) or comprehensive (long guides)?
Authority: Who’s ranking — big brands, niche experts, or smaller sites?
Visuals: What kind of visuals can make your content stand out?
Gaps and missing angles: What’s missing that you could add?
Then, repeat the same process with large language models (LLMs) like ChatGPT, Claude, and Perplexity.
AI has changed how people discover and consume information.
This means it’s no longer enough to rank on Google; your content also needs to surface in AI-generated answers.
Notice the type of insights coming up in AI-generated responses, and find gaps in the results.
Pay attention to the frequently cited brands and content formats to understand what AI considers “trusted.”
Study those articles closely to see how they’re earning citations and mentions.
Map Out Key Topics with Content Tools
Tools like Semrush’s Topic Research also help you learn more about the topics your audience is interested in.
Enter a topic like “lifecycle email marketing” and you’ll get a visual map of related themes like “loyalty program” and “segmenting your audience.”
This gives you insight into the subtopics to cover, questions to answer, and angles that resonate with your audience.
2. Find Fresh Angles to Create Standout Content
Don’t fall into the trap of rehashing what’s already ranking.
Find new angles and content ideas to break through the crowd.
Angles come from tension. This can be a surprising insight, a common mistake, a high-stakes story, or a view that challenges the norm.
Without tension, you’re just adding to the noise. Here’s how to find them.
Find Gaps in Existing Content
Study the top-ranking and frequently cited articles for your topic, and see what’s missing.
It could be:
Shallow sections that need a deeper analysis
Topics explained without visuals, examples, or case studies
Predictable “safe takes” that ignore alternative perspectives and bold advice
Use this framework to document these gaps.
Content Gap
What to Assess
Depth
Is the content surface-level? Are key topics rushed, repetitive, or missing nuance?
Evidence
Are claims backed by credible proof like examples, case studies, data, or visuals?
Perspective
Does it repeat what everyone else is saying, or bring a fresh angle?
Format
Is the information structured logically and easy to scan?
Consider Opportunities for Information Gain
Information gain adds unique value to your content compared to the existing content on the same topic.
Think original data, free templates, and new strategies.
Basically, it helps your content stand out from the crowd. And creates an “aha” moment for your readers.
Use these tips to add information gain to your articles:
Find concrete proof: Support your claims with original research, case studies, quotes, or real examples from your own experience or industry experts
Expand on throwaway insights: Take loosely discussed ideas and cover them in detail with additional context, data, and actionable takeaways
Counter predictable advice: Stand out with contrarian perspectives, exceptions, or overlooked approaches
Address unanswered questions: Find what confuses readers and fill those gaps with your content
At Backlinko, our writers and editors consider information gain early in outlining to uncover gaps and add value from the start.
Here’s how our senior editor, Shannon Willoby, approaches it:
I try not to default to common industry sources when gathering research. Everyone pulls from these, which is why you’ll often see industry blogs all quoting the same people, statistics, and insights. Instead, I look for lesser-known sources for information gain, like podcasts with industry experts, webinar transcripts, niche newsletters, and conference presentations. AI tools can also help with this task, but you’ll have to thoroughly vet the recommendations.
In my own article on ecommerce SEO audits, I proposed a simplified, goal-based structure for the outline, with an actionable checklist — something missing from existing content.
This approach gave readers a clearer roadmap instead of just another generic audit guide.
Use AI as a Creativity Multiplier
AI content tools make great sparring partners that enhance your thinking.
For instance, Shannon shares her process for using AI to refine her research.
Once I’ve drafted my main points, I’ll ask ChatGPT or Claude a question like, ‘What’s the next question a reader might have after this?’ This helps me spot gaps and add supporting details that make the article more valuable to the audience.
The following prompts can help you find deeper angles and improve your audience alignment:
How to use AI to improve content
Prompts
Find blind spots
Here’s my research for an article on [topic]. What questions or objections would readers still have after going through this? List gaps I should address to make it feel more complete.
Challenge assumptions
I’m arguing that [insert your point]. Play devil’s advocate: what would be the strongest counterarguments against this view, and what evidence could support them?
Explore alternative perspectives
Rewrite this idea as if you were speaking to: (a) a total beginner, (b) a mid-level practitioner, and (c) a skeptic. Show me how each group would interpret or question it differently.
3. Back Up Your Points with Evidence
Evidence-backed content gives weight to your arguments and makes abstract ideas easier to digest.
It also helps your content stick in readers’ minds long after they’ve clicked away.
This includes firsthand examples, data, case studies, and expert insights.
The key is using reputable, industry-leading sources in your content writing. And backing up claims with verifiable proof.
Pro tip: LLMs favor evidence-backed content when generating responses — boosting both your authority as a writer and your clients’ visibility.
Here’s how different types of evidence can strengthen your content:
Recent research data: Backs up trends and industry shifts with hard numbers
Case studies: Proves outcomes are achievable with real-world results
Expert quotes: Adds credibility when challenging assumptions or introducing new ideas
Examples: Makes abstract concepts concrete and relatable
4. Structure Your Ideas in a Detailed Outline
An outline organizes your ideas and insights into a clear structure before you start writing.
It maps out the key sections you’ll cover, supporting evidence, and the order in which you’ll present your points.
I included a working headline, H2s, and main points. I also added my plans for information gain.
This shows clients or employers how you’ll deliver unique value — and keeps you focused on differentiating your content from the start.
To get started with your outline, think of your core argument: what’s the most important takeaway you want readers to leave with?
From there, use the inverted pyramid to create an intuitive structure.
Include the most important details at the start of every section, then layer additional context as you go.
Pro tip: Save time with Semrush’s SEO Brief Generator. Add your topic and keywords, and it generates a solid outline instantly. From there, you can refine it with your own research and insights.
5. Develop Your Unique Writing Voice
Two people can write about the same topic.
But the one with a distinct voice is the one people quote, bookmark, and remember.
Assess Your Writing Personality
To define your writing personality, start by analyzing how you naturally communicate.
Look at your emails, Slack messages, and social posts.
Notice patterns in tone, humor, pacing, analogies, pop-culture references, or how often you use data and stats.
Then, distill these insights into a few adjectives that describe how you want to sound.
Like professional, insightful, and authoritative.
Use these to guide your writing voice.
For example, let’s say your adjectives are conversational, humorous, and authentic.
Here’s how that might look in practice:
Conversational: Short sentences with casual, relatable language. “Let’s be real — writing your first draft is 90% staring at a blinking cursor.”
Humorous: Use wit or funny references to engage readers. Instead of “Most introductions are too long,” you might say, “Most intros drag on longer than a Marvel end-credit scene.”
Authentic: Add stories from your lived experiences to make people feel seen. “When I first launched my blog, my mom was my only reader for six months.”
Get Inspired by Your Favorite Writers
To keep sharpening your voice, study writers you admire.
Pay attention to their rhythm, tone, and structure.
What terms do they use? How do they hold your attention — whether in a long-form blog post or a quick LinkedIn update?
Borrow what works, then put your own spin on it so it still sounds like you.
Adapt to Your Clients’ Voices
As a content writer, clients and employers will often expect you to adapt your writing to their brand voice.
This might mean adjusting your tone, pacing, or word choice to match their brand’s personality.
Study a few of their blog posts or emails to understand their style.
Note patterns in rhythm and vocabulary, and mirror those in your draft — without losing what makes your writing yours.
AI tools can help you check how well your draft matches your client’s voice.
Upload both the brand’s voice guidelines and your draft to an LLM and use this prompt:
I’ve added the brand voice guidelines and my draft for this brand.
Compare my draft against the guidelines and tell me:
Where my tone, word choice, or style drifts away from the brand voice
Specific sentences I should rewrite to better match the guidelines
Suggestions for how to make the overall flow feel more consistent with the brand voice
6. Add Rich Media to Improve Scannability
Even the best ideas lose impact when hidden behind walls of text.
Plus, research shows that most people skim web pages. Their eyes dart to headlines, opening lines, and anything that stands out visually.
That’s why adding visual breaks, such as images, screenshots, and tables, is so important.
Visual content works well when you want to illustrate a point.
It also simplifies or amplifies ideas that are hard to convey with text alone.
As Chris Hanna, our senior editor, puts it:
Often, words alone just won’t make full sense in the reader’s mind, or they won’t have the desired impact on their own. Anytime you’d personally prefer to see a visual explanation, it’s worth thinking about how you can convey it through visuals. If you can imagine watching a video on the topic you’re writing about, use that as your guide for how you could illustrate it with graphics.
Here are a few places where infographics can supplement your writing:
Comparisons:
Tables or side-by-side visuals
Frameworks and models:
Diagrams or matrices
Workflows and processes:
Flowcharts or timelines
Abstract concepts:
Layered visuals (like Venn diagrams)
At Backlinko, we track visual break density (VBD) — the ratio of visuals to text.
Our goal is a visual break density of 12% or higher for every article.
That’s about 12 visuals (images, GIFs, callout boxes, or tables) per 1,000 words to keep content easy to scan and engaging.
Here’s how this looks in practice:
We do this to improve the readability, retention, and engagement of our articles, from start to finish.
7. Understand How to Sell Through Your Content
Every piece of content sells something — a product, a signup, a return visit.
But good content doesn’t read like a pitch.
It gently nudges people to take action by building trust and solving real problems.
Lead with Value
This is what Klaviyo, an email marketing platform, does through its blog content.
They include helpful examples, original data, and actionable tips in their content writing.
But they also weave in product mentions that feel helpful, not salesy.
There are case studies, screenshots, and examples that show how real clients used their platform to increase revenue.
This is smart for a few reasons.
It proves their expertise, reinforces how their product solves real problems, and delivers value — even if the reader never becomes a customer.
Focus on Outcomes, Not Features
People don’t care what a company offers — they care what it helps them achieve.
Features talk about what you offer. Outcomes show people how they can benefit.
Here’s what this looks like in practice:
Feature-driven writing
Outcome-driven writing
“Redesigned homepage using Figma and custom CSS”
“After my redesign, load time dropped to 2 seconds and conversions jumped 40%. Here’s how I planned it.”
“Our tool automates monthly reporting.”
“One agency cut reporting time from 5 hours to 1 and reinvested those 4 hours into client growth. Let’s break down this workflow to help you achieve similar results.”
Show people you understand their frustrations by baking their pain points into your content writing.
When readers sense you’ve been in their shoes, they’re more open to your advice.
Take this HubSpot CRM product page, for example.
It highlights real frustrations — setup hassles, messy migrations, lost data — the exact headaches their audience feels.
Then, it shifts to outcomes with copy like “unified data” and higher productivity from “day one.”
That’s outcome-driven content writing. It connects with the audience immediately and makes the benefits crystal clear.
Share Your Firsthand Struggles
Authority matters, but so does humility.
Be honest about your wins and failures. It makes your content feel real.
Here’s an example from one of my Backlinko articles where I shared my struggles with creating a social media calendar:
I relate to the audience with language like “too many tabs” and “overwhelming categorization.”
And provide a free calendar template so readers can apply what they learn.
Pro tip: Free resources, such as tools, frameworks, and templates, make your content more actionable. Even a simple checklist or worksheet can help readers take the next step, and make your work far more memorable.
8. Finalize Your Work
Here’s the truth: your first draft is never your best draft.
Editing is where your content truly comes alive.
Step Away from Your Draft
One of the simplest editing tricks in the book? Give your draft some breathing room.
Chris Shirlow, our senior content editor, explains why:
Spend too much time in an article and you lose all perspective. Take a walk, sleep on it, or do something totally unrelated. When you come back, you’ll see what’s working — and what’s not — much more clearly.
It may take a few rounds of editing and refining before you get everything just right:
Round 1 (quick wins): Go through the article. Does it flow logically? Is it easy to understand? Do your examples clearly illustrate the core ideas?
Round 2 (structure): Ask AI for editing feedback. What are you missing? Does the structure/writing flow naturally? Is there any room to add more value?
Round 3 (polish): Tighten sentences, transitions, audience alignment, and examples
Here’s a prompt you can use for Round 2:
You are an expert editor specializing in long-form content writing. Please analyze my draft on the topic [ADD TOPIC] for its structure, flow, and reader experience.
Specifically, give feedback and suggestions on:
Structure: Are the sections ordered logically? Does each section build on the previous one?
Depth and focus: Which parts feel under-explained or too detailed? How can I tighten or expand them to improve the flow?
Reader journey: Where might readers drop off or lose context?
Summarize your feedback into 3–5 actionable editing priorities.
Pro tip: AI suggestions feel generic? Train the tool on your style first. Both Claude and ChatGPT let you upload writing samples and guidelines so their suggestions align with your voice.
Prioritize Clarity Over Cleverness
If your audience has to re-read a sentence to understand it, you’ve lost them.
As Yongi Barnard, our senior content writer, says:
A clever turn of phrase is nice, but the goal is for readers to understand your point immediately. Edit out any language that makes them pause to figure out what you mean.
Take a quick litmus test: Is this sentence/phrase/word here because it helps my audience, or because I like how it sounds?
You’ll know a sentence/phrase needs to be cut if it…
Slows down the flow
Makes the point harder to understand
Is redundant
Common issues in content writing (and how to fix them) include:
Problem Areas
Weak Example
Strong Example
Wordiness
“At this point in time, in order to improve your rankings, you need to be focusing on the basics of SEO.”
“To improve rankings, focus on SEO basics.”
Jargon
“We need to leverage synergies across verticals.”
“We need different teams to work together.”
Abstract Claims
“Content quality is important for SEO success.”
“Sites that publish in-depth content (2,000+ words) rank higher than thin pages.”
Build Your Personal Editing Checklist
Every writer has blind spots: repeated grammar errors, overused words, or formatting mistakes.
That’s why Yongi suggests creating a personal editing checklist that includes common errors and recurring feedback from editors.
Chris Hanna suggests going through the checklist before submitting your draft:
Run a cmd+F (Mac) or CTRL+F (Windows) search in the doc each time. It’ll help you catch the most important but easy-to-fix errors.
Over time, you’ll naturally make fewer mistakes.
Here’s an editing checklist to get you started:
The Self-Editing Checklist
Big picture
Does the piece serve the reader (not me)?
Is the main takeaway crystal clear from the start?
Does the flow make sense, with each section leading naturally to the next?
Clarity and value
Is every section genuinely useful, not filler?
Did I back up claims with examples, data, or stories?
Did I explain the ideas simply enough that my target readers would get it?
Language and style
Am I prioritizing clarity over cleverness?
Are any sentences too long or clunky — could I cut or split them?
Did I cut filler words (actually, very, really, in order to, due to the fact that)?
Engagement
Did I vary sentence lengths?
Does the tone feel human — not robotic, not overly formal?
Is there at least a touch of personality (humor, storytelling, relatability)?
Polish
Are transitions smooth between sections?
Did I run a spell-check and grammar-check?
Did I read it out loud (or edit bottom-up) to catch awkward phrasing?
Did I run through my personal “repeat offender” list (words/phrases I overuse)?
Final Pass
Did I add relevant internal links?
Does the article end with a clear, valuable takeaway?
Did I include a natural next step (CTA, resource, or link) without sounding pushy?
Pro tip: Use a free tool like Hemingway Editor to tighten your writing. It gives you a readability grade and highlights long sentences, passive voice, and other clarity issues.
How to Become a Content Writer: A Quick Roadmap
If you’re starting from scratch, don’t worry — every great content writer began exactly where you are.
Here’s how to build momentum and get noticed.
Find a Niche You’re Passionate About
The fastest way to level up as a writer? Specialize.
Niching down builds authority — and makes clients trust you faster.
Passion: You care enough to keep learning and writing when it gets tough
Potential: There’s growing demand for this information
Profitability: Businesses invest in content on this topic
Pro tip: Validate before you commit. Check job boards, freelance platforms, and brand blogs to see who’s hiring and publishing in that niche. If both interest and demand line up, you’ve found a winner.
Build Expertise and Authority in Your Niche
Once you pick a niche, become a trusted voice.
This gives you multiple advantages:
Traditional and AI search engines see your content as authoritative
Readers are more likely to trust what you say
Your content is more likely to be shared and quoted
Start with what you know. Draw from your own experiences to add depth and credibility.
For example, the travel writer India Amos built her authority by writing firsthand reviews.
Her Business Insider piece about a ferry ride is grounded in real experience, making the content trustworthy and relatable.
But don’t limit yourself to content writing for clients. Get your name out there.
Perplexity, ChatGPT, Gemini: AI search insight and prompt-based content discovery
Pro tip: Consider pursuing niche-specific certifications to stand out. This is especially helpful in “Your Money or Your Life” (YMYL) fields like finance, health, or law, where expertise and trust matter most.
Show Proof of Work with a Portfolio
A portfolio showcases what you bring to the table and provides proof of your accomplishments as a writer.
But you don’t have to spend weeks (or months) building one.
What matters most is what’s inside your portfolio, such as:
A short intro about who you are and what you offer
Writing samples that showcase your expertise
Testimonials or references
Contact information
Tools like Notion, Contra, Authory, and Bento let you design a portfolio in minutes.
For instance, here’s my Authory portfolio:
I like this platform because it automatically adds all articles credited to my name.
You can also invest in a website for more control and search visibility.
I did both — having a portfolio and website helps me improve my online visibility:
LinkedIn can also double as your portfolio.
Add details about each client and link to your articles in the “Experience” section of your profile.
Share your on-the-job insights, feature testimonials, and engage in relevant conversations.
And don’t forget to post your favorite work, from blog posts to copywriting.
Unlike a static site, LinkedIn keeps you visible in real time.
Future-Proof Your Content Writing Skills
Use what you’ve learned here to create content that builds your reputation and lands clients.
Because great content writing doesn’t just fill pages. It opens doors.
And as AI continues to reshape the content world, the best writers don’t resist it — they evolve with it.
So, don’t fear artificial intelligence as a writer. Use it to your advantage.
Read our guide:How to Use AI to Create Exceptional Content. It’s packed with practical workflows, expert insights, and handy prompts that will help you work smarter and stay ahead.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-01-07 17:40:352026-01-07 17:40:35Content Writing 101: 8 Skills That Set Top Writers Apart
This first release of 2026 brings Site Kit by Google insights into your Yoast SEO Dashboard. After introducing the integration in phases throughout 2025, we are pleased to share that the rollout is now complete and available to all Yoast customers using WordPress.
What you can see in your Yoast SEO Dashboard
You can now view key performance data from Google Search Console and Google Analytics via Site Kit in your Yoast SEO Dashboard, without changing tools or tabs. These insights include search impressions, clicks, average click through rate, average position, and organic sessions, which are combined with your Yoast SEO and readability scores so you can better understand how content quality relates to real search performance.
Find opportunities faster
The integration also surfaces your top performing content and search queries, helping you quickly spot which pages and topics are driving results and where improvements may have the most impact. Connecting Site Kit by Google is straightforward. Once connected, insights become available immediately, giving you faster access to the data you need to guide your SEO work.
If you are interested in the technical background of this integration and our collaboration with Google, we share the full story on our developer blog.
Get started
Update to Yoast SEO 26.7 to start using Site Kit by Google insights in your Dashboard and streamline your workflow with key performance data in one place. For step by step guidance on enabling the integration, see our help center guide.
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The way people discover brands has changed faster than most teams realize. Visibility does not start on your website anymore. It begins in the places where people trade unfiltered opinions such as Reddit threads, TikTok videos, YouTube reviews, niche forums, expert interviews, creator breakdowns, and news articles.
These are the signals AI tools and search engines now rely on. They mirror the conversations people trust most. If your brand is not present in those conversations, you are handing visibility to someone else.
Digital PR sits in the middle of this shift. Not as a press release machine, but as the strategy that fuels the narrative across the platforms and communities that feed Google, TikTok, Instagram, YouTube, and every major language model.
This article breaks down how to use digital PR, social media content, and community engagement to increase discoverability everywhere people search today.
Key Takeaways
People move across 11 or more platforms while researching, comparing, and validating decisions. Your brand needs to meet them across those touchpoints.
Forums like Reddit and niche communities carry firsthand experience, which is why Google and language models are pulling them directly into search results.
Short-form video has become a high-impact discovery surface both inside social platforms and on Google page one.
Digital PR fuels AI visibility by supplying the fresh, authoritative, third-party information that language models prefer.
SEO becomes more powerful when PR, social content, and community insights work together.
The New Discovery Journey and Why Visibility Starts Beyond Your Website
A few years ago, someone looking for a new espresso machine would have gone straight to Google. They would have clicked a few product pages and made a decision. That journey looks nothing like what consumers do today.
Now they might ask ChatGPT for recommendations, check TikTok for short-form reviews, watch long breakdowns on YouTube, scan Reddit for real-world pros and cons, and then head to Google for price comparisons or final research.
By the time someone reaches your website, they already formed opinions based on content across half a dozen platforms.
This is the messy middle. It is where brands win or lose visibility.
People are searching more often and in more places. Google reflects this change. Page one now includes short-form videos, Reddit threads, social carousels, media articles, and AI Overviews. This is not a reinvention of search. It is Google responding to real user behavior.
To stay visible, your brand needs to show up where people learn, evaluate, and talk, not just where they click.
Why Forums and Community Conversations Matter More Than Ever
Reddit and niche forums are not fringe communities anymore. Reddit alone is projected to pass 1.5 billion monthly active users in 2026. The scale matters, but the reason it impacts search runs deeper.
Forums contain the firsthand experiences that AI and search engines trust.
Let’s talk a bit more about Reddit. Reddit content appears in at least four locations.
Reddit search.
Subreddit communities.
Reddit Answers, which is the platform’s AI search tool.
Google’s search results, especially in the Discussions and Forums section.
This means a well-written Reddit thread can live for years and continue influencing decisions long after the original post.
There are other reasons why forum and community content is so important today.
Forums Shape Brand Perception Faster Than Brands Realize
These conversations happen with or without you. People share frustrations, recommendations, and detailed use cases that no brand site ever captures.
Many brands worry Reddit is hostile. In reality, it performs well when brands participate genuinely and respectfully. NP Digital activated Reddit profiles for two major brands. One saw one hundred percent positive or neutral sentiment on every comment. The other reached ninety-eight point seven percent positive. Yet general Reddit conversation about the same brand sat around forty-one percent positive.
The difference was authentic participation that added value to the community.
Reddit Drives Traffic and Influences Search Behavior
Some brands have seen organic declines this year because users spend more time researching on social platforms and forums. Reddit helps fill that gap by driving referral traffic. If people are searching for “best espresso machines Reddit”, you want your brand involved in those discussions or at least contributing useful insights.
With these notes in mind, you don’t want to rush into a Reddit strategy. Follow a progression that respects the community. At NP Digital, we recommend sticking to a crawl-walk-run strategy.
Answer questions honestly. Participate in low-risk threads. Add context or correct misinformation. Avoid promotion entirely.
Run
Launch a brand subreddit if needed. Build content pillars. Create new threads that contribute information. Scale moderation and community responses.
Once your brand understands the culture and adds value, Reddit becomes a powerful discovery engine and insight tool.
Social Search as a Visibility Engine
Social is no longer just a place to publish helpful content. It has become a core part of the search journey for both consumers and business decision-makers. Sixty-seven percent of social users rely on social search at some point in their purchase process. That shift alone explains why Google has started indexing more social posts in page one results, including TikToks, LinkedIn posts, YouTube Shorts, and Instagram videos.
For example, if you search for a term like VPS hosting, you will often see a carousel labeled “What People Are Saying” that blends Reddit threads, TikToks, LinkedIn posts, and YouTube content in one feed.
Google is pulling from the places people already trust. It is a direct signal that social engagement and social authority now influence how visible a brand becomes across multiple search surfaces.
Social search depends on two things. You need keywords people are actively searching for, and you need content that earns engagement. Keyword research happens inside each platform. TikTok’s Creator Search Insights, Instagram’s autocomplete, YouTube’s search can all reveal the questions and topics users care about. Once you know those keywords, place them where platforms can detect them. Captions, spoken audio, on-screen text, subtitles, and alt text are all signals that help social platforms and search engines understand your content.
Engagement plays the second role. Social performs well when content feels timely, helpful, or relatable. It does not require studio production. You need clear audio, a strong hook, and information that teaches or entertains. Short-form video remains one of the most effective ways to earn reach, both inside social platforms and across search results. It is visible, digestible, and easy for people to interact with quickly.
How Platforms Understand Keywords
Platforms are using audio transcription, text recognition, caption scanning, and behavioral signals to understand what your content is about. Hashtags still help in some cases, but they are not the main factor anymore. If you say the keyword in your video, write it on screen, and include it in your caption, platforms know the topic and can connect your content to the right search behavior.
Why UGC and Employee-Generated Content Matter
User-generated content has been an effective marketing tool for years because it feels relatable and trustworthy. Now it plays a role in discoverability as well. Employee-generated videos carry even more authority because they combine authenticity with expertise. They help social content rise faster and make your presence stronger across search and AI surfaces.
Social search works best when keyword strategy, content quality, and audience signals all point in the same direction. When those elements align, your videos and posts can appear across multiple platforms, gain reach quickly, and support the rest of your visibility strategy.
How Digital PR Fuels AI Overviews, LLM Citations, and Brand Visibility
AI search tools and language models work by gathering information from sources they consider reliable. They scan news articles, expert commentary, public forums, brand websites, and structured datasets. The goal is simple. They want to provide information that feels trustworthy, current, and grounded in real experience.
Digital PR supports this optimization ecosystem by producing brand information that is easy for AI tools to interpret and cite. Data studies, surveys, annual roundups, expert insight, and product comparisons all fall into content types that language models treat as credible. When this information exists across reputable publications, media outlets, and authoritative websites, AI systems have more material to work with. That increases the chances of being referenced when users search for answers.
Recency also plays a strong role. In one example, news coverage tied to a press release led to AI Overview citations within a couple of days. This shows how quickly language models can incorporate new information when it comes from a trusted source. Fresh material signals relevance.
Where coverage appears also influences visibility. Some publishers partner with AI companies or contribute more frequently to the datasets models learn from. Securing placement with these outlets increases the likelihood that the information will be integrated into AI responses. Add community discussions from platforms like Reddit or structured content from first-party research, and brands create a multi-layered presence that AI tools can draw from.
This approach has measurable impact. Several brands that we’ve worked with at NP Digital saw substantial growth in referrals from AI tools. The increases ranged from nearly one thousand percent to more than sixteen hundred percent within a year.
Digital PR is a key part of all this success, helping supply the authoritative signals, data, and context that help AI tools understand a brand’s expertise. As search expands across platforms and models, PR becomes part of the information layer that shapes how brands are represented wherever users look for answers.
Bringing It All Together: How to Build a Unified Search Everywhere Strategy
With this in mind, let’s talk about using a strategy that leans on all these different levers to ensure an article on say, the most secure web browser, earns the most value by appearing where the ideal audience would be, versus forcing the fit.
Here is what a unified workflow would look like in those circumstances:
Create a first-party study that tests browser security.
Turn the findings into multiple assets. YouTube videos, Shorts, TikToks, Reels, media pitches, bylines, and Reddit threads.
Join relevant subreddit conversations that mention browser security and contribute insights or data.
Pitch journalists covering the topic.
Reach out to writers of existing articles to provide updated data that improves the piece.
Repurpose your content across newsletters, blog posts, paid ads, and social channels.
This is not just traditional SEO. This is visibility architecture. You are building a presence across every surface where people search, compare, and validate decisions. Search engines and AI tools follow those signals.
FAQs
How quickly can a brand appear in AI Overviews?
When a brand distributes fresh, authoritative information and earns credible media coverage, inclusion can happen in a matter of days.
Should every brand activate on Reddit?
Every brand should at least listen on Reddit. Activation makes sense once you understand the community and can contribute meaningfully. Listening alone offers valuable insights into customer needs, sentiment, and content opportunities.
Does social content influence search visibility?
Yes. Google increasingly pulls social posts and short-form videos into search results. High engagement on social platforms often correlates with stronger visibility across search surfaces.
What makes AI cite one brand more often than another?
Language models cite sources that appear reliable, current, widely referenced, and easy to interpret. Digital PR accelerates this by producing data, expert insight, and media mentions that models treat as credible.
Conclusion
People search everywhere now. They ask questions on social platforms, browse forums, follow creators, read news, and use AI tools before they ever visit a brand’s website. The brands winning visibility are shaping conversations in those places. They are publishing authoritative content, creating engaging social experiences, and participating in the communities that influence decisions.
Digital PR, social content, and community engagement support all of this. When these channels work together, your brand becomes easier to find across every surface where people search.
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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Google is updating its advertising policies to allow ads for Prediction Markets in the U.S. starting January 21st — but only for federally regulated entities.
Who qualifies. Eligibility is limited to entities authorized by the Commodity Futures Trading Commission (CFTC) as Designated Contract Markets (DCMs) whose primary business is listing exchange-listed event contracts, or brokerages registered with the National Futures Association (NFA) that offer access to products listed by qualifying DCMs. Advertisers must also apply for Google certification to run ads in the U.S.
Why we care. Prediction markets have long been restricted on Google Ads. This change opens a new advertising channel while keeping tight controls around compliance and regulation. The narrow eligibility and certification requirements mean only compliant, federally regulated players can participate, potentially reducing competition. For qualifying advertisers, this offers earlier access to a high-intent audience within a tightly controlled ad environment.
The fine print. All ads, products, and landing pages must comply with applicable local laws, financial regulations, industry standards, and Google Ads policies. The new policy will appear in the Advertising Policies Help Center, with references in the Financial Services and Gambling and Games sections, and is available now for preview.
The big picture. Google is cautiously expanding access for prediction markets by recognizing them as regulated financial products — while continuing to block unregulated platforms.
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.
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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.
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