As AI-led search becomes a real driver of discovery, an old assumption is back with new urgency. If AI systems infer quality from user experience, and Core Web Vitals (CWV) are Google’s most visible proxy for experience, then strong CWV performance should correlate with strong AI visibility.
The logic makes sense.
Faster page load times result in smoother page load times, increased user engagement, improved signals, and AI systems that reward the outcome (supposedly)
But logic is not evidence.
To test this properly, I analysed 107,352 webpages that appear prominently in Google AI Overviews and AI Mode, examining the distribution of Core Web Vitals at the page level and comparing them against patterns of performance in AI-driven search and answer systems.
The aim was not to confirm whether performance “matters”, but to understand how it matters, where it matters, and whether it meaningfully differentiates in an AI context.
What emerged was not a simple yes or no, but a more nuanced conclusion that challenges prevailing assumptions about how many teams currently prioritise technical optimisation in the AI era.
Why distributions matter more than scores
Most Core Web Vitals reporting is built around thresholds and averages. Pages pass or fail. Sites are summarized with mean scores. Dashboards reduce thousands of URLs into a single number.
The first step in this analysis was to step away from that framing entirely.
When Largest Contentful Paint was visualized as a distribution, the pattern was immediately clear. The dataset exhibited a heavy right skew.
Median LCP values clustered in a broadly acceptable range, while a long tail of extreme outliers extended far beyond it. A relatively small proportion of pages were horrendously slow, but they exerted a disproportionate influence on the average.
Cumulative Layout Shift showed a similar issue. The majority of pages recorded near-zero CLS, while a small minority exhibited severe instability.
Again, the mean suggested a site-wide problem that did not reflect the lived reality of most pages.
This matters because AI systems do not reason over averages, if they reason on user engagement metrics at all.
They evaluate individual documents, templates, and passages of content. A site-wide CWV score is an abstraction created for reporting convenience, not a signal consumed by an AI model.
Before correlation can even be discussed, one thing becomes clear. Core Web Vitals are not a single signal, they are a distribution of behaviors across a mixed population of pages.
Correlations
Because the data was uneven and not normally distributed, a standard Pearson correlation was not suitable. Instead, I used a Spearman rank correlation, which assesses whether higher-ranking pages on one measure also tend to rank higher or lower on another, without assuming a linear relationship.
This matters because, if Core Web Vitals were closely linked to AI performance, pages that perform better on CWV would also tend to perform better in AI visibility, even if the link was weak.
I found a small negative relationship. It was present, but limited. For Largest Contentful Paint, the correlation ranged from -0.12 to -0.18, depending on how AI visibility was measured. For Cumulative Layout Shift, it was weaker again, typically between -0.05 and -0.09.
These relationships are visible when you look at large volumes of data, but they are not strong in practical terms. Crucially, they do not suggest that faster or more stable pages are consistently more visible in AI systems. Instead, they point to a more subtle pattern.
The absence of upside, and the presence of downside
The data do not support the claim that improving Core Web Vitals beyond basic thresholds improves AI performance. Pages with good CWV scores did not reliably outperform their peers in AI inclusion, citation, or retrieval.
However, the negative correlation is instructive.
Pages sitting in the extreme tail of CWV performance, particularly for LCP, were far less likely to perform well in AI contexts.
These pages tended to exhibit lower engagement, higher abandonment, and weaker behavioral reinforcement signals. Those second-order effects are precisely the kinds of signals AI systems rely on, directly or indirectly, when learning what to trust.
This reveals the true shape of the relationship.
Core Web Vitals do not act as a growth lever for AI visibility. They act as a constraint.
Good performance does not create an advantage. Severe failure creates disadvantage.
This distinction is easy to miss if you examine only pass rates or averages. It becomes apparent when examining distributions and rank-based relationships.
Why ‘passing CWV’ is not a differentiator
One reason the positive correlation many expect does not appear is simple. Passing Core Web Vitals is no longer rare.
In this dataset, the majority of pages already met recommended thresholds, especially for CLS. When most of the population clears a bar, clearing it does not distinguish you. It merely keeps you in contention.
AI systems are not selecting between pages because one loads in 1.8 seconds and another in 2.3 seconds. They are selecting between pages because one explains a concept clearly, aligns with established sources, and satisfies the user’s intent, whereas the other does not.
Core Web Vitals ensure that the experience does not actively undermine those qualities. They do not substitute for them.
Reframing the role of Core Web Vitals in AI strategy
The implication is not that Core Web Vitals are unimportant. It is that their role has been misunderstood.
In an AI-led search environment, Core Web Vitals function as a risk-management tool, not acompetitive strategy. They prevent pages from falling out of contention due to poor experience signals.
This reframing has practical consequences for developing an AI visibility strategy.
Chasing incremental CWV gains across already acceptable pages is unlikely to deliver returns in AI visibility. It consumes engineering effort without changing the underlying selection logic AI systems apply.
Targeting the extreme tail, however, does matter. Pages with really bad performance generate negative behavioral signals that can suppress trust, reduce reuse, and weaken downstream learning signals.
The objective is not to make everything perfect. It is to ensure that the content you want AI systems to rely on is not compromised by avoidable technical failure.
Why this matters
As AI systems increasingly mediate discovery, brands are seeking controllable levers. Core Web Vitals feel attractive because they are measurable, familiar, and actionable.
The risk is mistaking measurability for impact.
This analysis suggests a more disciplined approach. Treat Core Web Vitals as table stakes. Eliminate extreme failures.
Protect your most important content from technical debt. Then shift focus back to the factors AI systems actually use to infer value, such as clarity, consistency, intent alignment, and behavioral validation.
Core Web Vitals: A gatekeeper, not a differentiator
Based on an analysis of 107,352 AI visible webpages, the relationship between Core Web Vitals and AI performance is real, but limited.
There is no strong positive correlation. Improving CWV beyond baseline thresholds does not reliably improve AI visibility.
However, a measurable negative relationship exists at the extremes. Severe performance failures are associated with poorer AI outcomes, mediated through user behavior and engagement.
Core Web Vitals are therefore best understood as a gate, not a signal of excellence.
In an AI-led search landscape, this clarity matters.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/01/distribution-largest-contentful-paint-uke2z8.png?fit=1980%2C1180&ssl=111801980http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-01-13 16:39:392026-01-13 16:39:39What 107,000 pages reveal about Core Web Vitals and AI search
You’ve likely invested in AI tools for your marketing team, or at least encouraged people to experiment.
Some use the tools daily. Others avoid them. A few test them quietly on the side.
This inconsistency creates a problem.
An MIT study found that 95% of AI pilots fail to show measurable ROI.
Scattered marketing AI adoption doesn’t translate to proven time savings, higher output, or revenue growth.
AI usage ≠ AI adoption ≠ effective AI adoption.
To get real results, your whole team needs to use AI systematically with clear guidelines and documented outcomes.
But getting there requires removing common roadblocks.
In this guide, I’ll explain seven marketing AI adoption challenges and how to overcome them. By the end, you’ll know how to successfully roll out AI across your team.
Free roadmap: I created a companion AI adoption roadmap with step-by-step tasks and timeframes to help you execute your pilot. Download it now.
First up: One of the biggest barriers to AI adoption — lack of clarity on when and how to use it.
1. No Clear AI Use Cases to Guide Your Team
Companies often mandate AI usage but provide limited guidance on which tasks it should handle.
In my experience, this is one of the most common AI adoption challenges teams face. Regardless of industry or company size.
Vague directives like “use AI more” leave people guessing.
The solution is to connect tasks to tools so everyone knows exactly how AI fits into their workflow.
The Fix: Map Team Member Tasks to Your Tech Stack
Start by gathering your marketing team for a working session.
Ask everyone to write down the tasks they perform daily or weekly. (Not job descriptions, but actual tasks they repeat regularly.)
Then look for patterns.
Which tasks are repetitive and time-consuming?
Maybe your content team realizes they spend four hours each week manually tracking competitor content to identify gaps and opportunities. That’s a clear AI use case.
Or your analytics lead notices they are wasting half a day consolidating campaign performance data from multiple regions into a single report.
AI tools can automatically pull and format that data.
Once your team has identified use cases, match each task to the appropriate tool.
After your workshop, create assignments for each person based on what they identified in the session.
For example: “Automate competitor tracking with [specific tool].”
When your team knows exactly what to do, adoption becomes easier.
2. No Structured Plan to Roll Out AI Across the Organization
If you give AI tools to everyone at once, don’t be surprised if you get low adoption in return.
The issue isn’t your team or the technology. It’s launching without testing first.
The Fix: Start with a Pilot Program
A pilot program is a small-scale test where one team uses AI tools. You learn what works, fix problems, and prove value — before rolling it out to everyone else.
A company-wide launch doesn’t give you this learning period.
Everyone struggles with the same issues at once. And nobody knows if the problem is the tool, their approach, or both.
Which means you end up wasting months (and money) before realizing what went wrong.
Plan to run your pilot for 8-12 weeks.
Note: Your pilot timeline will vary by team.
Small teams can move fast and test in 4-8 weeks. Larger teams might need 3-4 months to gather enough feedback.
Start with three months as your baseline. Then adjust based on how quickly your team adapts.
Content, email, or social teams work best because they produce repetitive outputs that show AI’s immediate value.
Select 3-30 participants from this department, depending on your team size.
(Smaller teams might pilot with 3-5 people. Larger organizations can test with 20-30.)
Then, set measurable goals with clear targets you can track. Like:
Schedule weekly meetings to gather feedback throughout the pilot.
The pilot will produce department-specific workflows. But you’ll also discover what transfers: which training methods work, where people struggle, and what governance rules you need.
When you expand to other departments, they’ll adapt these frameworks to their own AI tasks.
After three months, you’ll have proven results and trained users who can teach the next group.
At that point, expand the pilot to your second department (or next batch of the same team).
They’ll learn from the first group’s mistakes and scale faster because you’ve already solved common problems.
Pro tip: Keep refining throughout the pilot.
Update prompts when they produce poor results
Add new tools when you find workflow gaps
Remove friction points the moment they appear
Your third batch will move even quicker.
Within a year, you’ll have organization-wide marketing AI adoption with measurable results.
Employees may resist AI marketing adoption because they fear losing their jobs to automation.
Headlines about AI replacing workers don’t help.
Your goal is to address these fears directly rather than dismissing them.
The Fix: Have Honest Conversations About Job Security
Meet with each team member and walk through how AI affects their workflow.
Point out which repetitive tasks AI will automate. Then explain what they’ll work on with that freed-up time.
Be careful about the language you use. Be empathetic and reassuring.
For example, don’t say “AI makes you more strategic.”
Say: “AI will pull performance reports automatically. You’ll analyze the insights, identify opportunities, and make strategic decisions on budget allocation.”
One is vague. The other shows them exactly how their role evolves.
Don’t just spring changes on your team. Give them a clear timeline.
Explain when AI tools will roll out, when training starts, and when you expect them to start using the new workflows.
For example: “We’re implementing AI for competitor tracking in Q2. Training happens in March. By April, this becomes part of your weekly process.”
When people know what’s coming and when, they have time to prepare instead of panicking.
Pro tip: Let people choose which AI features align with their interests and work style.
Some team members might gravitate toward AI for content creation. Others prefer using it for data analysis or reporting.
When people have autonomy over which features they adopt first, resistance decreases. They’re exploring tools that genuinely interest them rather than following mandates.
5. Your Team Resists AI-Driven Workflow Changes
People resist AI when it disrupts their established workflows.
Your team has spent years perfecting their processes. AI represents change, even when the benefits are obvious.
Resistance gets stronger when organizations mandate AI usage without considering how people actually work.
New platforms can be especially intimidating.
It means new logins, new interfaces, and completely new workflows to learn.
Rather than forcing everyone to change their workflows at once, let a few team members test the new approach first using familiar tools.
The Fix: Start with AI Features in Existing Tools
Your team likely already uses HubSpot, Google Ads, Adobe, or similar platforms daily.
When you use AI within existing tools, your team learns new capabilities without learning an entirely new system.
If you’re running a pilot program, designate 2-3 participants as AI champions.
Their role goes beyond testing — they actively share what they’re learning with the broader team.
The AI champions should be naturally curious about new tools and respected by their colleagues (not just the most senior people).
Have them share what they discover in a team Slack channel or during standups:
Specific tasks that are now faster or easier
What surprised them (good or bad)
Tips or advice on how others can use the tool effectively
When others see real examples, such as “I used Social Content AI to create 10 LinkedIn posts in 20 minutes instead of 2 hours,” it carries more weight than reassurance from leadership.
For example, if your team already uses a tool like Semrush, your champions can demonstrate how its AI features improve their workflows.
Keyword Magic Tool’s AI-powered Personal Keyword Difficulty (PKD%) score shows which keywords your site can realistically rank for — without requiring any manual research or analysis.
Your content writers can input a topic, set their brand voice, and get a structured first draft in minutes. This reduces the time spent staring at a blank page.
Social Content AI handles the repetitive parts of social media planning. It generates post ideas, copy variations, and images.
Your social team can quickly build out a week’s content calendar instead of creating each post from scratch.
Don’t have a Semrush subscription? Sign up now and get a 14-day free trial + get a special 17% discount on annual plan.
6. No Governance or Guardrails to Keep AI Usage Safe
Without clear guidelines, your team may either avoid AI entirely or use it in ways that create risk.
They paste customer data into ChatGPT without realizing it violates data policies.
Or publish AI-generated content without approval because the review process was never explained.
Your team needs clear guidelines on what’s allowed, what’s not, and who approves what.
Free AI policy template: Need help creating your company’s AI policy? Download our free AI Marketing Usage Policy template. Customize it with your team’s tools and workflows, and you’re ready to go.
The Fix: Create a One-Page AI Usage Policy
When creating your policy, keep it simple and accessible. Don’t create a 20-page document nobody will read.
Aim for 1-2 pages that are straightforward and easy to follow.
Include four key areas to keep AI usage both safe and productive.
Policy Area
What to Include
Example
Approved Tools
List which AI tools your team can use — both standalone tools and AI features in platforms you already use
“Approved: ChatGPT, Claude, Semrush’s AI Article Generator, Adobe Firefly”
Data Sharing Rules
Define specifically what data can and can’t be shared with AI tools
“Safe to share: Product descriptions, blog topics, competitor URLs
Concerns about whether AI-generated content is accurate or appropriate
Questions about data sharing
The goal is to give them a clear path to get help, rather than guessing or avoiding AI altogether.
Then, post the policy where your team will see it.
This might be your Slack workspace, project management tool, or a pinned document in your shared drive.
And treat it as a living document.
When the same question comes up multiple times, add the answer to your policy.
For example, if three people ask, “Can I use AI to write email subject lines?” update your policy to explicitly say yes (and clarify who reviews them before sending).
7. No Reliable Way to Measure AI’s Impact or ROI
Without clear proof that AI improves their results, team members may assume it’s just extra work and return to old methods.
And if leadership can’t see a measurable impact, they might question the investment.
This puts your entire AI program at risk.
Avoid this by establishing the right metrics before implementing AI.
The Fix: Track Business Metrics (Not Just Efficiency)
Here’s how to measure AI’s business impact properly.
Pick 2-3 metrics your leadership already reviews in reports or meetings.
These are typically:
Leads generated
Conversion rate
Revenue growth
Customer acquisition
Customer retention
These numbers demonstrate to your team and leadership that AI is helping your business.
Then, establish your baseline by recording your current numbers. (Do this before implementing AI tools.)
For example, if you’re tracking leads and conversion rate, write down:
Current monthly leads: 200
Current conversion rate: 3%
This baseline lets you show your team (and leadership) exactly what changed after implementing AI.
Pro tip: Avoid making multiple changes simultaneously during your pilot or initial rollout.
If you implement AI while also switching platforms or restructuring your team, you won’t know which change drove results.
Keep other variables stable so you can clearly attribute improvements to AI.
Once AI is in use, check your metrics monthly to see if they’re improving. Use the same tools you used to record your baseline.
Write down your current numbers next to your baseline numbers.
For example:
Baseline leads (before AI): 200 per month
Current leads (3 months into AI): 280 per month
But don’t just check if numbers went up or down.
Look for patterns:
Did one specific campaign or content type perform better after using AI?
Are certain team members getting better results than others?
Track individual output alongside team metrics.
For example, compare how many blog posts each writer completes per week, or email open rates by the person who drafted them.
If someone’s consistently performing better, ask them to share their AI workflow with the team.
This shows you what’s working, and helps the rest of your team improve.
Share results with both your team and leadership regularly.
When reporting, connect AI’s impact to the metrics you’ve been tracking.
For example:
Say: “AI cut email creation time from 4 hours to 2.5 hours. We used that time to run 30% more campaigns, which increased quarterly revenue from email by $5,000.”
Not: “We saved 90 hours with AI email tools.”
The first shows business impact — what you accomplished with the time saved. The second only shows time saved.
Other examples of how to frame your reporting include:
Build Your Marketing AI Adoption Strategy
When AI usage is optional, undefined, or unsupported, it stays fragmented.
Effective marketing AI adoption looks different.
It’s built on:
Role-specific training people actually use
Guardrails that reduce uncertainty and risk
Metrics that drive business outcomes
When those pieces are in place, AI becomes part of how work gets done.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-01-13 15:57:132026-01-13 15:57:137 Marketing AI Adoption Challenges (And How to Fix Them)
If there’s one takeaway as we look toward SEO in 2026, it’s that visibility is no longer just about ranking pages, but about being understood by increasingly selective AI-driven systems. In 2025, SEO proved it was not disappearing, but evolving, as search engines leaned more heavily on structure, authority, and trust to interpret content beyond the click. In this article, we share SEO predictions for 2026 from Yoast SEO experts, Alex Moss and Carolyn Shelby, highlighting the shifts that will shape how brands earn visibility across search and AI-powered discovery experiences.
Key takeaways
In 2026, SEO focuses on visibility defined by clarity, authority, and trust rather than just page rankings
Structured data becomes essential for eligibility in AI-driven search and shopping experiences
Editorial quality must meet machine readability standards, as AI evaluates content based on structure and clarity
Rankings remain important as indicators of authority, but visibility now also includes citations and brand sentiment
Brands should align their SEO strategies with social presence and aim for consistency across all platforms to enhance visibility
A brief recap of SEO in 2025: what actually changed?
2025 marked a clear shift in how SEO works. Visibility stopped being defined purely by pages and rankings and began to be shaped by how well search engines and AI systems could interpret content, brands, and intent across multiple surfaces. AI-generated summaries, richer SERP features, and alternative discovery experiences made it harder to rely solely on traditional metrics, while signals such as authority, trust, and structure played a larger role in determining what was surfaced and reused.
As we outlined in our SEO in 2025 wrap-up, the brands that performed best were those with strong foundations: clear content, credible signals, and structured information that search systems could confidently understand. That shift set the direction for what was to come next.
By the end of 2025, it was clear that SEO had entered a new phase, one shaped by interpretation rather than isolated optimizations. The SEO predictions for 2026 from Yoast experts build directly on this evolution.
2026 SEO predictions by Yoast experts
The SEO predictions for 2026 shared here come from our very own Principal SEOs at Yoast, Alex Moss and Carolyn Shelby. Built on the lessons SEO revealed in 2025, these predictions focus less on reacting to individual updates and more on how search and AI systems are evolving at a foundational level, and what that means for sustainable visibility going forward.
TL;DR
SEO in 2026 is about understanding how signals such as structure, authority, clarity, and trust are now interpreted across search engines, AI-powered experiences, and discovery platforms. Each prediction below explains what is changing, why it matters, and how brands can practically adapt in the coming year.
Prediction 1: Structured data shifts from ranking enhancer to retrieval qualifier
In 2026, structured data will no longer be a competitive advantage; it will become a baseline requirement. Search engines and AI systems increasingly rely on structured data as a layer of eligibility to determine whether content, products, and entities can be confidently retrieved, compared, or surfaced in AI-powered experiences.
For ecommerce brands, this shift is especially significant. Product information such as pricing, availability, shipping details, and merchant data is now critical for visibility in AI-driven shopping agents and comparison interfaces. At the enterprise level, the move toward canonical identifiers reflects a growing need to avoid misattribution and data decay across systems that reuse information at scale.
What this means in practice:
Brands without clean, comprehensive entity and product data will not rank lower. They will simply not appear in AI-driven shopping and comparison flows at all.
Treat structured data as part of your SEO foundation, not an enhancement. Tools like Yoast SEO help standardize the implementation of structured data. The plugin’s structured data features make it easier to generate rich, meaningful schema markup, helping search engines better understand your site and take control of how your content is described.
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Prediction 2: Agentic commerce becomes a visibility battleground, not a checkout feature
Agentic commerce marks a shift in how users discover and choose brands. Instead of browsing, comparing, and transacting manually, users increasingly rely on AI-driven agents to recommend, reorder, or select products and services on their behalf. In this environment, visibility is established before a checkout ever happens, often without a traditional search query.
This shift is becoming more concrete as search and commerce platforms move toward standardised ways for agents to understand and transact with merchants. Recent developments around agentic commerce protocols and Universal Commerce Protocol (UCP) highlight how AI systems are being designed to access product, pricing, availability, and merchant information more directly. As a result, platforms such as Shopify, Stripe, and WooCommerce are no longer just infrastructure. They increasingly act as distribution layers, where agent compatibility influences which brands are surfaced, recommended, or selected.
What this means in practice:
In 2026, SEO teams will be accountable for agent readiness in much the same way they were once accountable for mobile-first readiness. If agents cannot consistently interpret your brand, product data, or availability, they are more likely to default to competitors that they can understand with greater confidence.
How to act on this:
Focus on making your brand legible to automated decision systems. Ensure product information, pricing, availability, and supporting metadata are clear, structured, and consistent across your site and feeds. This is not about optimising for a single platform or protocol, but about reducing ambiguity so AI agents can accurately interpret and act on your information across emerging agent-driven discovery and commerce experiences.
Prediction 3: Editorial quality becomes a machine readability requirement
In 2026, editorial quality is no longer judged only by human readers. AI systems increasingly evaluate content based on how efficiently it can be parsed, summarized, cited, and reused. Verbosity, fluff, and circular explanations do not fail editorially. They fail functionally.
Content that is concise, clearly structured, and well-attributed has higher chances of performing well. Headings, lists, definitions, and tables directly influence how information is chunked and reused across AI-generated summaries and search experiences.
“Helpful content” is being held to higher editorial standards. Content that cannot be summarized cleanly without losing meaning becomes less useful to AI systems, even if it remains readable to human audiences.
How to act on this:
Make editorial quality measurable and machine actionable. Utilize tools that assist you in aligning content with modern discoverability requirements. Yoast SEO Premium’s AI features, AI Generate, AI Optimize, and AI Summarize, help you assess and improve how content is structured and optimized, supporting both search engines and AI systems in understanding your intent.
Prediction 4: Rankings still matter, but as training signals, not endpoints
Despite ongoing speculation, rankings do not disappear in 2026. Instead, their role changes. AI agents and search systems continue to rely on top-ranked, trusted pages to understand authority, relevance, and consensus within a topic.
While rankings are no longer the final KPI, abandoning them entirely creates blind spots in understanding why certain brands are included or ignored in AI-driven experiences.
What this means in practice:
Teams that stop tracking rankings altogether risk losing insight into how authority is established and reinforced across search and AI systems.
How to act on this:
Continue to use rankings as diagnostic signals, but don’t treat them as the sole indicator of success in 2026. Alongside traditional performance metrics for SEO in 2026, look at how often your brand is mentioned, cited, or summarized in AI-generated answers and recommendations.
Tools like Yoast AI Brand Insights, available as part of Yoast SEO AI+, help surface these broader visibility signals by showing how your brand appears across AI platforms, including sentiment, citation patterns, and competitive context.
See how visible your brand is in AI search
Track mentions, sentiment, and AI visibility. With AI Brand Insights and Yoast SEO AI+, you can start monitoring and improving your performance.
Prediction 5: Brand sentiment becomes a core visibility signal
Brand sentiment increasingly influences how search engines and AI systems assess credibility and trust. Mentions, whether linked or unlinked, contribute to a broader understanding of how a brand is perceived across the web. AI systems synthesize signals from reviews, forums, social platforms, media coverage, and knowledge bases to form a composite view of legitimacy and expertise.
What makes this shift more impactful is amplification. Inconsistent messaging or negative sentiment is not smoothed out over time. Instead, it becomes more apparent when systems attempt to summarize, compare, or recommend brands across search and AI-driven experiences.
What this means in practice:
SEO, brand, PR, and social teams increasingly influence the same visibility signals. When these efforts are misaligned, credibility weakens. When they reinforce one another, trust becomes easier for systems to establish and maintain.
How to act on this:
Focus on consistency across owned, earned, and shared channels. Pay attention not only to where your brand ranks, but also to how it is discussed, described, and contextualized across various platforms. As discovery expands beyond traditional search results, reputation and narrative coherence become essential inputs into how brands are surfaced and understood.
Prediction 6: Multimodal optimization becomes baseline, not optional
Search behavior is no longer text-first. Images, video, audio, and transcripts now function as retrievable knowledge objects that feed both traditional search and AI-powered experiences. In particular, video platforms continue to influence how expertise and authority are understood at scale.
Platforms like YouTube function not only as discovery engines, but also as training corpora for AI systems learning how to interpret topics, brands, and creators.
What this means in practice:
Brands with strong written content but weak visual or video assets may appear incomplete or “thin” to AI systems, even if their articles are well-optimized.
How to act on this:
Treat multimodal content as part of your SEO foundation. Support written content with relevant visuals, video, and transcripts. Clear structure and readability remain essential, and tools like Yoast SEO help ensure your core content remains accessible and well-organized as it is reused across formats.
Prediction 7: Social platforms become secondary search indexes
Discovery will increasingly happen outside traditional search engines. Platforms such as TikTok, LinkedIn, Reddit, and niche communities now act as secondary search indexes where users validate expertise and intent.
AI systems reference these platforms to verify whether a brand’s claims, expertise, and messaging are substantiated in public discourse.
What this means in practice:
Presence alone is not enough. Inconsistent or unclear messaging across platforms weakens trust signals, while focused, repeatable narratives reinforce authority.
How to act on this:
Align your SEO strategy with social and community visibility to enhance your online presence. Ensure that your expertise, terminology, and positioning remain consistent across all discussions about your brand.
Prediction 8: Email reasserts itself as the most controllable growth channel
As discovery fragments and platforms increasingly gate access to audiences, email regains importance as a high-signal, low-distortion channel. Unlike search or social platforms, email offers direct access to users without algorithmic mediation.
In 2026, email plays a supporting role in reinforcing authority, engagement, and intent signals, especially as AI systems evaluate how audiences interact with trusted sources over time.
What this means in practice:
Brands that underinvest in email become overly dependent on platforms they do not control, which increases volatility and reduces long-term resilience.
How to act on this:
Focus on relevance over volume. Segment audiences, align content with intent, and use email to reinforce expertise and trust, not just drive clicks.
Prediction 9: Authority outweighs freshness for most non-news queries
For non-news content, AI systems increasingly prioritize credible, historically consistent sources over frequent updates or constant publishing. Freshness still matters, but only when it meaningfully improves accuracy or relevance.
Long-standing domains with coherent narratives and well-maintained content benefit, provided their foundations remain clean and trustworthy.
What this means in practice:
Scaled/programmatic content strategies lose effectiveness. Publishing frequently without maintaining quality or consistency introduces noise rather than value.
How to act on this:
Invest in maintaining and improving existing content. Update thoughtfully, reinforce expertise, and ensure that your most important pages remain accurate, structured, and authoritative.
Prediction 10: SEO teams evolve into visibility and narrative stewards
In 2026, SEO will extend far beyond search engines. SEO teams are increasingly influencing how brands are perceived by both humans and machines across search, AI-generated answers, and discovery platforms.
Success is measured not only by traffic alone, but also by inclusion, citation, and trust. SEO becomes a strategic function that shapes how a brand is represented and understood.
What this means in practice:
SEO teams that focus solely on production or technical fixes risk losing influence as visibility becomes a cross-channel concern.
How to act on this:
Shift focus toward clarity, consistency, and long-term trust. The most effective teams help define how a brand is understood, not just how it ranks.
What SEO is no longer about in 2026 (misconceptions to discard)
As SEO evolves in 2026, many long-standing assumptions no longer reflect how search engines and AI-driven systems actually determine visibility. The table below contrasts common SEO myths with the realities shaped by recent changes and expert insights from Yoast.
Diminishing relevance
What actually matters in 2026
SEO is mainly about ranking pages
Rankings still matter, but they serve as signals for authority and relevance, rather than the final measure of visibility
Structured data is optional or a ranking boost
Structured data is now a baseline requirement for eligibility in AI-driven search, shopping, and comparison experiences
Publishing more content leads to better performance
Authority, clarity, and maintenance of fewer strong assets outperform high-volume publishing
Editorial quality is subjective
Content quality is increasingly evaluated by machines based on structure, clarity, and reusability
Brand reputation is a PR concern, not an SEO one
Brand sentiment directly influences how AI systems interpret, trust, and recommend brands
Search is still primarily text-based
Images, video, audio, and transcripts are now core retrievable knowledge objects
SEO can be measured only through traffic
Visibility spans AI answers, social platforms, agents, and citations, requiring broader performance signals
Looking ahead: what will shape SEO in 2026
The focus is no longer on isolated tactics or short-term wins, but on building visibility systems that search engines and AI platforms can reliably understand, trust, and reuse.
Clarity and interpretability matter more than clever optimization. Content, products, and brand narratives need to be easy for machines to interpret without ambiguity. Structured data has become foundational, not optional, determining whether brands are eligible to appear in AI-powered shopping, comparison, and answer-driven experiences.
Authority is built over time, not manufactured at scale. Search and AI systems increasingly favor sources with consistent, well-maintained narratives over those chasing volume. Visibility also extends beyond the SERP, spanning AI-generated answers, citations, recommendations, and cross-platform mentions, making it essential to look beyond traffic as the sole measure of success.
Finally, SEO in 2026 demands alignment. Brand, content, product, and platform signals all contribute to how systems interpret trust and relevance.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-01-13 13:11:412026-01-13 13:11:41SEO in 2026: Key predictions from Yoast experts
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:
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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.
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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 new year is here, which means it’s time to deliver your end-of-year (EOY) PPC report.
But an EOY report isn’t just a longer version of your monthly performance check-in.
It’s typically read by a different audience – often leadership teams who don’t see your regular reporting – and it needs to tell a different story.
Done well, your year-end report sets the tone for 2026, earns buy-in for your strategy, and positions you as a strategic partner rather than just a campaign manager.
Done poorly, it creates confusion and undermines confidence in your work.
Here’s how to build an EOY PPC report that speaks to leadership and sets your work up for success in the year ahead.
1. Identify your audience and their priorities
You wouldn’t launch a new campaign without clearly defined goals and audiences.
Don’t do it with your EOY report, either. Different stakeholders evaluate performance through very different lenses.
For example, among the clients I’m preparing reports for this year are:
A leadership team I’ve never met (despite working with this client for eight years) that wants a maximum five-page report at a very high level.
A data-driven CEO who wants a clear narrative connecting PPC spend, decisions, and outcomes.
A new director who wants context on the competitive landscape, performance, and specific opportunities for next year.
If I were to use a carbon-copy EOY report template for all of them, I’d have at best one happy leadership team and two confused or frustrated teams.
I don’t care for those odds. Instead, I’m customizing each report to match the readers and their specific needs.
If you don’t know the recipients (and if you’re at an agency, there’s a good chance you don’t), ask your primary contact questions like:
Who will be receiving the report?
What do they care about most?
What’s top of mind for them heading into next year?
What decisions will they be making based on this report?
The answers should directly inform the report’s structure, depth, metrics, and length.
When your audience is clearly defined upfront, the final report is far more likely to drive clarity, alignment, and confidence.
2. Create an easy-to-read executive summary
Your executive summary has one job – help leadership quickly understand how PPC performed across key metrics.
Think of it as the “at a glance” page that sets the context for everything that follows.
If you studied communications formally, you probably learned to write executive summaries last, even though they appear first.
Since you’re pulling data rather than crafting prose, flip that approach.
Build this section first to guide the flow of what comes next.
Lead with the KPIs that matter most
Start with the metrics your audience actually cares about – the ones that were established as priorities at the beginning of the engagement or year.
This will usually include revenue, leads, and conversions, but your mileage may vary.
If your leadership team obsesses over market share or engagement, lead with those instead.
Include meaningful benchmarks
Unless your leadership team is dialed into PPC goals and performance, you need to give them benchmarks so they have a comparison tool.
Use at least one of these three key benchmarks:
Year-over-year performance: How did this year stack up against last year?
Performance against target: Did we hit the goals we set out to achieve?
Industry benchmarks: How do we compare to competitors or industry averages?
In the example below, I’m showing revenue, ROAS, and cost for the year, with both percentage changes and raw numbers from the previous year.
This format does the heavy lifting for busy executives.
At a quick glance, they know what happened and whether it’s good news.
More importantly, it sets the stage for invisible CTAs and the deeper analysis that follows.
3. Break down performance details
In the following section, you’ll move from “what happened” to “why it happened and what we learned.”
The executive summary told your reader whether the year was successful. Now you need to show them the engine under the hood.
The level of detail will depend on the format. A five-page executive report may only have room for a few pivotal moments, while a more comprehensive report can get into the weeds.
In either case, selectivity is critical. You can’t — and shouldn’t — document every metric, test, or optimization from the year.
Instead, focus on insights that either explain the results in your executive summary or point directly to opportunities for the year ahead.
Here are some categories to get you started.
What performed best
Show them the winners: your best-converting creative, highest-revenue products, or most efficient channels.
Leadership loves to see what’s working, and it can point to where to double down.
How resources were allocated
Break down spend distribution across campaign types, the split between brand and non-brand, or platform-specific investments like Google versus Bing.
Leadership wants to know if you’re putting money where it matters most, and this section answers that question.
If you made tracking or conversion definition changes during the year, call them out here.
Leadership needs to understand if a metric shift reflects actual performance or a measurement change.
Keep this section platform-specific and substantive. Each insight should clearly tie back to the executive summary.
Use visuals (charts, trend lines, and comparison tables) to make complex data easier to interpret. And resist the temptation to include everything you track.
If a metric doesn’t explain results, answer a question from leadership, or inform future strategy, leave it out.
You’ve already explained what happened in the account and why performance moved the way it did.
Now you need to zoom out and show leadership what else was happening. What external forces shaped your results, for better or worse?
This is where you separate execution from environment.
Without this context, strong strategic work can look mediocre in a difficult year, or weak decisions can hide behind tailwinds.
Leadership needs to understand what you controlled versus what you were responding to.
Think of it this way: performance details add context to your KPIs. External factors add context to your performance.
Digital marketing factors
What influenced performance that was external to paid search execution, but internal to the broader marketing ecosystem?
Non-PPC marketing initiatives: Product launches, pricing changes, promotions, website redesigns, or messaging shifts can all impact conversion rates and search behavior – positively or negatively.
Non-PPC channel performance: Performance in organic search, email, social, affiliates, or offline channels can meaningfully influence paid search results. It also provides a clearer picture of market factors beyond paid channels.
Platform and policy changes:Google Ads feature rollouts, automation shifts, policy enforcement, or reporting changes often affect performance in ways that aren’t immediately visible in metrics alone.
Competitive dynamics: New entrants, aggressive bidding, creative shifts, or changes in competitor behavior can alter auction pressure and efficiency over time.
Macro-economic factors
What forces outside the marketing organization shaped demand, behavior, and constraints?
A useful way to structure this analysis is with a lightweight PESTEL lens, adapted for a marketing context.
Political: Gov actions and policy shifts (e.g., tariffs, shutdowns).
Environmental: Weather and seasonality (e.g., storms, climate shifts).
Legal: Regulations and compliance (e.g., privacy laws, labor rules).
You don’t need to address every category. The goal is to highlight the factors that materially influenced performance and decisions during the year.
In a volatile year like this one, it can even make sense to highlight big events thatdidn’t have an impact on performance, just to assuage any worries.
Doing this helps stakeholders understand what factors contributed to performance. And just as important, it positions you as someone who sees beyond the interface to meaningful business implications.
5. Answer the ‘what’s next?’ question
Leadership wants to know what to expect for next year.
They’re not necessarily expecting a crystal ball, but they do want confidence that there’s a plan, even if the path changes.
The reality is that most paid search strategy isn’t mapped a year in advance.
Platforms change, competitors react, budgets shift, and new constraints appear with little warning.
What matters isn’t having every answer upfront, it’s having a clear way to decide what to do next when conditions change.
This section of your EOY report is your opportunity to show that decision-making framework, and get your audience excited to work with you on what’s to come.
Next steps and recommendations
These are the initiatives you’re committed to pursuing; the strategic moves grounded in last year’s data:
Applied learnings: How insights from the past year are shaping priorities, structure, and decision-making going forward.
Identified opportunities: Areas where data consistently pointed to upside: channels, audiences, products, or tactics that warrant attention.
Known risks: Challenges leadership should expect, along with how you’re monitoring or mitigating them.
Resource clarity: What additional budget, tools, or support would enable — and what remains constrained without them. Be concrete: “With X additional budget, we can test Y based on Z insight from last year.”
These recommendations should feel inevitable; the logical next steps given what happened last year.
Testing pipeline
Then there’s the other category: things you’re watching, interested in, or ready to jump on if circumstances align.
These scratch leadership’s itch for innovation and cutting-edge solutions without overcommitting:
New platform features you’ll test when they’re released.
Emerging platforms or initiatives worth monitoring.
Competitive tactics you’ve identified but need more validation.
Opportunistic tests if budget or priorities shift.
Frame these as “if/then” scenarios or “things we’re monitoring” rather than firm commitments.
Leadership gets to feel like you’re on top of industry trends without expecting guarantees.
A final pass through a leadership lens
You’ve covered a lot of ground.
This final pass is about tightening credibility and making sure this work pays dividends in the coming years, not just this one.
Give your report a final pass
Before sending, review the report the way leadership will:
Source your data clearly: Label where each chart’s data came from and when it was pulled. This prevents follow-up questions and builds trust.
Address negatives head-on: Leadership expects challenges. What erodes confidence isn’t bad news, it’s unexplained bad news. Show what didn’t work, why, and what you did about it.
Pressure-test against the brief: Review your stakeholders’ original requests. Did you actually answer their questions? Ask a colleague (or AI) for a second set of eyes.
Make next year easier
Now that you’ve done the heavy lifting, leverage this work going forward:
Turn your EOY report into a client-specific template: A single format won’t work across all clients, but once you find a structure that resonates with a given audience, reuse it year over year. Incorporate feedback and refresh the data, but keep the core framework consistent.
Track big issues as they happen: Document key events as you progress through the year. Keep a running list, outside of emails and reports. Even the biggest issues today will be hard to accurately remember in 12 months without this.
Year-end reports take real effort. Make sure yours actually resonates.
Follow these steps to strengthen stakeholder relationships and position yourself as a strategic partner for 2026 and beyond.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/01/sel-reports-25-G1ZyHX.webp?fit=1544%2C486&ssl=14861544http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-01-06 14:00:002026-01-06 14:00:00A 5-step framework for year-end PPC reports that resonate with leadership
December made one thing clear: AI is no longer a feature layered on top of marketing. It is the system deciding what gets seen, what gets skipped, and what earns trust.
Search pushed deeper into zero-click behavior. Paid ads lost prime real estate. Influencer content matured into a full‑funnel channel. Platforms added tools while quietly tightening control. At the same time, security and data ownership became real business risks, not abstract concerns.
This roundup breaks down what actually mattered in December and how to adjust before these shifts harden in 2026.
Key Takeaways
Google accelerated AI-first search with Gemini 3, AI Mode, and AI-powered Search Console reporting.
AI Overviews and AI Mode are pushing both organic and paid clicks down, reshaping SERP strategy.
Influencer marketing expanded beyond Gen Z, pulling older, high-value audiences into creator ecosystems.
LinkedIn doubled down on video and events, reinforcing its position as the B2B growth platform.
Security threats like Google Ads MCC hijacks escalated, making account governance a priority.
Search & AI
AI is now deciding what gets seen before a click ever happens. December’s updates show Google tightening its grip on discovery while pushing brands to earn visibility inside AI systems.
Search Console Gets AI-Driven Reporting
Google rolled out AI-powered configuration in Search Console, allowing users to request custom reports using natural language. Instead of manually stitching filters together, teams can now ask questions the way they think about performance.
Our POV: This changes who gets access to insight. Reporting no longer bottlenecks around technical SEO or analytics specialists. Strategy conversations can happen faster, and closer to the business question that triggered them.
What this unlocks: Faster pattern recognition across large sites, quicker validation of hypotheses, and fewer reporting cycles spent just getting the data into shape.
What to do next: Standardize a small set of executive-level prompts tied to growth questions (discovery, decline, opportunity). Use this to shorten the distance between signal and decision.
Gemini 3 Lands Directly in Google Search
Google deployed Gemini 3 straight into Search across 120 countries, delivering richer answers, visuals, and interactive elements without requiring users to leave the results page.
Our POV: This is Google asserting itself as the destination, not the doorway. Content that once earned traffic by being explanatory or comparative now competes with Google’s own synthesized answers.
Strategic impact: Informational content becomes less about volume and more about authority. If your content is interchangeable, it becomes invisible.
What to do next: Identify where your content overlaps with Gemini-style answers. Invest more heavily in insight, proprietary data, and perspective that AI cannot compress without losing value.
Google Embeds AI Mode Into the Search Flow
When users tap “show more” under an AI Overview, Google now routes them into a full AI chat experience rather than expanding citations.
Our POV: This confirms that Google is intentionally reducing outbound traffic in favor of guided, AI-mediated discovery.
Strategic impact: Attribution gets murkier. Influence matters more than visits. Brands that only measure success by clicks will underinvest in visibility where decisions actually form.
What to do next: Start treating AI inclusion as a visibility channel. Track brand mentions, citations, and presence inside AI responses alongside traditional KPIs.
AI Overviews Push Ads Below the Fold
Research shows that roughly a quarter of search results now place paid ads beneath AI Overviews, with mobile SERPs most affected.
Our POV: Paid search is losing guaranteed prominence. Bidding harder no longer guarantees being seen.
Strategic impact: Paid media performance becomes dependent on how well it aligns with AI-generated context, not just auction dynamics.
What to do next: Re-evaluate high-value keywords where ads routinely fall below AI content. Coordinate paid and organic teams so messaging reinforces what users see first.
Branded Query Filtering and Chart Notes Arrive in GSC
Search Console now separates branded and non‑branded queries automatically and allows chart-level annotations.
Our POV: This finally closes long-standing reporting gaps that distorted SEO performance narratives.
What to do next: Capture a baseline brand vs non‑brand split now. Add annotations for launches, migrations, PR wins, and algorithm shifts to preserve institutional knowledge.
Paid Media & Risk
Automation keeps increasing, but so does exposure. December highlighted how fragile performance can be without strong governance and clear safeguards.
OpenAI Pauses ChatGPT Ads
OpenAI halted its early test of native ads inside ChatGPT after users struggled to distinguish sponsored content from AI-generated answers.
Our POV: This pause is less about ads failing and more about timing. Conversational interfaces collapse the distance between advice and influence, which raises the bar for trust.
Strategic impact: Future AI advertising will not behave like traditional display or search ads. Brands will compete on usefulness, credibility, and contextual fit rather than interruption.
What to do next: Start pressure-testing what value-driven, answer-oriented advertising could look like for your category. Focus on scenarios where a brand genuinely helps a user decide, not just where it can appear.
Google Ads MCC Hijacks Surge
Phishing attacks targeting Google Ads manager accounts increased sharply, allowing attackers to drain budgets and lock out advertisers within hours.
Our POV: This is no longer an edge case. As accounts scale, risk compounds.
Strategic impact: Performance gains mean little if governance fails. Security lapses can erase months of optimization and undermine executive confidence in paid media.
What to do next: Treat access control as part of your growth strategy. Limit permissions aggressively, audit users regularly, and align security reviews with budget planning.
Product, Design & UX
Product and design updates are quietly shaping how fast teams can ship, test, and iterate. December brought one change that materially reduces friction between design and development.
Figma Introduces CSS Grid-Like Layout Controls
Figma rolled out a new grid system that more closely mirrors how CSS Grid and Flexbox behave in production. Designers can now edit rows and columns directly, reposition elements with keyboard controls, and build layouts that respond more like real front-end frameworks.
Our POV: This narrows the long-standing gap between design intent and shipped experience. Fewer handoff mismatches mean faster iteration and fewer compromises downstream.
Strategic impact: Design systems become more scalable when layouts behave predictably across breakpoints. Teams that rely on rapid experimentation benefit most.
What to do next: Revisit your design system and layout standards. Align designers and developers on grid conventions so prototypes map cleanly to production.
Social & Creator Economy
Creator content is no longer niche or youth-driven. Platforms are shaping social into a full-funnel, multi-generational influence engine.
LinkedIn Sees Another Video Surge
LinkedIn reported continued double-digit growth in video uploads and watch time, with short-form content driving disproportionate reach.
Our POV: LinkedIn has quietly become a daily content destination, not just a professional directory.
Strategic impact: B2B visibility increasingly depends on consistent, human-led storytelling. Brands that delay video adoption will find it harder to build authority as the feed fills up.
What to do next: Commit to a repeatable LinkedIn video cadence. Prioritize clarity and expertise over production polish, and measure engagement trends over time.
LinkedIn Upgrades Event Ads
New integrations with ON24 and Cvent allow LinkedIn Event Ads to capture and route leads directly into CRMs.
Our POV: Events are moving out of the brand bucket and into the revenue conversation.
Strategic impact: This blurs the line between awareness and pipeline, making events accountable in ways they historically avoided.
What to do next: Reframe events as performance channels. Align messaging, registration, and follow-up under a single measurement framework.
Influencer Content Expands Beyond Gen Z
New data shows that more than half of adults aged fifty-five to sixty-four now watch influencer content weekly, often via connected televisions.
Our POV: Influencer marketing has crossed into mainstream media behavior. This is no longer a youth or trend-driven channel.
Strategic impact: Influencers are shaping consideration and trust for higher-value purchases, not just discovery for impulse buys.
What to do next: Test creator partnerships that emphasize expertise and credibility. Treat influencer content as a mid-funnel and upper-funnel asset, not just awareness.
Meta Enhances the Creator Marketplace
Instagram expanded its Creator Marketplace with better discovery, AI recommendations, and stronger paid amplification tools.
Our POV: Meta is positioning creators as a scalable performance input, not just an organic reach lever.
Strategic impact: The line between influencer marketing and paid social continues to erode. Creative quality and creator trust now directly affect efficiency.
What to do next: Identify creators whose content already performs organically. Use paid support to scale what works instead of forcing performance from scratch.
PR, Media, and Trust
As AI pulls from third-party sources, brand credibility is being shaped outside your owned channels. Relationships and presence matter more than volume.
Journalists Push Back on AI Pitches
Surveys show most journalists still prefer human-led outreach, citing AI-written pitches as generic and misaligned with their coverage needs.
Our POV: Efficiency without judgment damages relationships.
Strategic impact: As AI-generated noise increases, thoughtful and relevant outreach becomes a stronger differentiator.
What to do next: Use AI for research and preparation, not substitution. Preserve human insight where trust and creativity matter most.
Discord Emerges as a Media Hub
PR teams are increasingly using Discord servers as live, on-demand press rooms.
Our POV: This flips traditional outreach from push to pull.
Strategic impact: Brands that make themselves accessible become resources journalists return to, not just sources they react to.
What to do next: Pilot a controlled Discord environment for media. Offer clear channels, real access, and timely updates without overwhelming participants.
Platform Playbooks
Smaller platform updates often hide the most practical gains. December delivered clear lessons on how context and native execution drive results.
Reddit Releases Dynamic Product Ad Guidance
Reddit published best practices showing that focused optimizations can lift Dynamic Product Ad performance meaningfully.
Our POV: Reddit rewards relevance over polish.
Strategic impact: Brands that adapt creative to platform norms outperform those that recycle ads from other networks.
What to do next: Speak directly to subreddit context, keep messaging tight, and test incrementally to isolate what actually moves performance.
Conclusion
December reinforced a hard truth: visibility is no longer owned. It is earned repeatedly across AI systems, platforms, and communities.
The brands that win in 2026 will build authority machines, not traffic hacks. They will secure their data, design for AI interpretation, and show up consistently wherever decisions are shaped.
If you want help translating these shifts into a durable growth strategy, the NP Digital team is already doing this work every day.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-01-05 20:00:002026-01-05 20:00:00December 2025 Digital Marketing Roundup: What Changed and What You Should Do About It
