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7 Marketing AI Adoption Challenges (And How to Fix Them)

January 13, 2026/0 Comments/in Web Design News/by

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.

Further reading: What Is Generative AI and How Does It Work?

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:

Cut blog production time from 8 hours to 5 hours
Reduce email draft revisions from 3 rounds to 1
Create 50 social media posts weekly instead of 20

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.

Further reading: 8 Awesome AI SEO Tools We Love Using

3. Your Team Lacks the Training to Use AI Confidently

Most marketing teams roll out AI tools without training team members how to use them.

In fact, only 39% of people who use AI at work have received any training from their company.

61% of workers who use AI at work received no training from their company

And when training does exist, it might focus on generic AI concepts rather than specific job applications.

The answer is better training that connects to the work your team does.

The Fix: Role-Specific Training

Generic training explains how AI works. Role-specific training shows people how to use AI in their actual jobs.

Here’s the difference:

Role	Generic Training (Lower Priority)	Role-Specific Training (Start Here)
Social Media Manager	AI concepts and how large language models work	How to automate content calendars and schedule posts faster
SEO Specialist	Understanding neural networks and machine learning	AI-powered keyword research and competitor analysis
Email Marketer	Machine learning algorithms and data processing	Using AI for personalization and subject line testing
Content Writer	How AI models generate text and natural language processing	Using AI to research topics, create outlines, and edit drafts
Paid Ads Manager	Deep learning fundamentals and algorithmic optimization	AI tools for ad copy testing, audience targeting, and bid management

When training connects directly to someone’s daily tasks, they actually use what they learn.

For example, Mastercard applies this approach with three types of training:

Foundational knowledge for everyone
Job-specific applications for different roles
Reskilling programs where needed.

Companies like KPMG, Accenture, and IKEA have also developed dedicated AI training programs for their teams.

This is likely because they learned that generic training creates enterprise AI adoption challenges at scale.

Employees complete courses but never apply what they learned to their actual work.

Ikea – AI training programs for their teams

But you don’t need enterprise-scale resources to make this work.

Start by mapping what each role actually does with AI.

For example:

Your content team uses AI for research, strategy, outlines, and drafts
Your ABM team uses it for account research and personalized outreach
Your social team uses it for video creation and caption variations
Your marketing ops team uses it for workflow automation and data integration

Once you know what each role needs, pick your training approach.

Platforms like Coursera and LinkedIn Learning offer specific AI training programs that work well for flexible, self-paced learning.

Training may also be available from your existing tools.

Check whether your current marketing platforms offer AI training resources, such as courses or documentation.

For example, Semrush Academy offers various training programs that also cover its AI capabilities.

For teams with highly specific workflows, external trainers can be useful.

This costs more. But it delivers the most relevant results because the trainer focuses only on what your team actually needs to learn.

For example, companies like Section offer AI adoption programs for enterprises, including coaching and custom workshops.

But keep in mind that training alone won’t sustain marketing AI adoption.

AI tools evolve constantly, and your team needs continuous support to adapt.

Create these support systems:

Set up a dedicated Slack channel for AI questions where your team can share wins and troubleshoot problems
Run weekly Q&A sessions where people discuss specific challenges
Update training materials as new features and use cases emerge

Pro tip: How to Build an AI-Ready SEO Team: A Complete Guide

4. Team Members Fear AI Will Replace Their Roles

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.

AI Article Generator creates SEO-friendly drafts from keywords.

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.

Semrush – Social Content AI Kit – Ideas by topic

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.

In fact, 57% of enterprise employees input confidential data into AI tools.

Types of Sensitive Data Employees Input Into AI Tools

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 Never share: Customer names, email addresses, revenue data, internal campaign plans, pricing strategies, unannounced product details”
Review Requirements	Document who reviews what type of content before publication	“Social posts: Peer review Blog posts: Content lead approval Legal/compliance content: Legal team review”
Approval Workflows (optional)	Clarify who approves AI content at each stage	“Internal drafts: Content team Customer-facing materials: Marketing director Compliance-related content: Legal sign-off”

Beyond documenting the rules, establish who team members should contact when they encounter situations the policy doesn’t address.

Designate a department lead, governance contact, or weekly office hours as the escalation point for:

Scenarios not covered in your guidelines
Technical site issues with approved AI tools
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.

If you want a step-by-step implementation plan, download our Marketing AI Adoption Roadmap.

Need help choosing which AI tools to pilot? Our AI Marketing Tools guide breaks down the best options by use case.

The post 7 Marketing AI Adoption Challenges (And How to Fix Them) appeared first on Backlinko.

Read more at Read More

How to choose a link building agency in the AI SEO era by uSERP

January 13, 2026/0 Comments/in Search Engine Land, Web Design News/by

Remember when a handful of links from sites in your niche could drive steady organic traffic? That era is over.

Today, Google’s AI Overviews and the rise of answer engines like ChatGPT raise the bar. You have to do more to stay visible. Hiring an experienced link building agency is one efficient way to meet that challenge.

It’s also one of the most important investments you’ll make. The right partner doesn’t just build links. They position your brand as a trusted, cited source in the AI era.

So how do you choose the right agency for your company?

While the interface has changed, the core ranking signals remain largely the same. What’s changed is their priority.

LLMs need credible sources to ground their answers. That makes authoritative link building more important than ever.

This article shows you how to vet and choose a link building agency that understands these new priorities and can help your brand win trust in the AI-driven SEO landscape.

How link building and SEO are changing

Gartner predicted search engine volume to drop by 25% as AI takes over more answers. That makes working with an agency that understands AI SEO essential.

But how do you know which agencies actually do?

The real indicators are holistic authority and AI visibility. Only one in five links cited in Google’s AI Overviews matched a top-10 organic result, according to an Authoritas study. Even more telling, 62.1% of cited links or domains didn’t rank in the top 10 at all.

The takeaway is simple. AI systems and search engines don’t evaluate websites the same way. We’re no longer building links just for Google’s crawler.

Link equity alone isn’t enough. Sites need topical authority, brand mentions, and real market presence. The goal is to build a footprint that AI models recognize and can’t ignore.

The new criteria: Evaluating a link building agency for AI SEO

Choosing the right link building agency comes down to how well they prioritize the factors that matter now.

This section shows you what to look for.

Prioritizing quality, relevance, and traffic

I see this mistake all the time. A marketing director evaluates link quality based only on Domain Rating (DR).

High DR matters, but at uSERP, we know it’s not the finish line. You should also look for:

Relevance: A link from a DR 60, niche-specific site in your industry often beats a DR 80 general news site that covers everything from crypto to keto.
Minimum traffic standards: If a site doesn’t rank for keywords or attract real traffic, its links won’t help you rank. That’s why strict traffic minimums matter.

When vetting an agency, ask for contractual site-traffic guarantees.

A confident agency won’t hesitate to sign a Statement of Work that guarantees every link comes from a site with a minimum traffic threshold, such as 5,000+ monthly organic visitors.

If they won’t put traffic minimums in writing, they’re likely planning to place links on “ghost town” sites. These domains appear strong, but they lack a real audience, which protects their margins rather than supporting your growth.

Look for a content-driven approach and digital PR

Links don’t exist in a vacuum. The strongest ones come from being part of a real conversation.

The best agencies no longer operate like traditional link builders. They act more like content marketing and digital PR teams.

Instead of asking for links, the best agencies create linkable assets — data studies, expert commentary, and in-depth guides that journalists and publishers want to cite – because they understand:

Google’s algorithms and AI models are continually getting better at identifying paid placements. A content-led approach keeps links natural, editorial, and valuable to readers.
Guest posting in the AI SEO era isn’t about a disposable 500-word article. It’s about thought leadership that positions your CEO as a credible expert.

At uSERP, for example, we created — and continuously update — our State of Backlinks for SEO report.

Red flags: Recognizing outdated or dangerous tactics

Choosing the wrong partner doesn’t just waste your budget. It puts your brand reputation — and potentially your company’s future — at risk.

Here are the biggest red flags to avoid when hiring an agency:

Guaranteed rankings

No one can guarantee a number-one ranking on Google. Any agency that promises specific keyword positions on a fixed timeline is likely doing one of two things:

Using risky, short-term tactics to force a temporary spike.
Selling you snake oil.

These agencies often rely on private blog networks (PBNs) or aggressive anchor text manipulation to manufacture fast results.

You might see an early jump, but the crash that follows—and the risk of a penalty when Google’s spam systems catch up—is never worth it.

Lack of transparency

If an agency won’t explain how they earn links or where placements will come from before you pay, walk away.

Reputable agencies are transparent. They’ll show real examples of past placements and share relevant case studies from your industry.

Agencies that hide their inventory usually do it for a reason. Those sites are often part of a low-quality network or link farm.

Self-serve link portfolios

If you’re a marketer or SEO on LinkedIn, chances are you’ve received a message like this:

This is a common tactic among low-quality link builders: reselling backlinks from a shared inventory. I understand the appeal.

Strategic link acquisition is hard. Buying and flipping links is easy.

The problem — for you — is the footprint. If an agency can secure a link by filling out a form, anyone can. That includes casino affiliates, gambling sites, adult content, and outright scammers.

That’s not a natural link profile. Google has almost certainly already identified and burned those domains.

In the best case, you pay for a link that passes zero authority. In the worst case, Google flags your site as part of a link scheme.

Dirt-cheap packages

SEO and link building deliver incredible ROI, but they aren’t cheap.

You can’t buy a high-quality article with a real, earned link from an authoritative site for $50. Speaking as someone who runs an AI SEO agency, the true cost of quality content, editing, outreach, and relationship building is at least an order of magnitude higher.

That’s why cheap packages that promise multiple high-authority links are a major red flag. They almost always rely on:

Fully AI-generated, barely edited content.
Low-value link farms or resold inventory.
Toxic backlinks.

None of those will help you show up on AI search engines or Google.

Partnering with a link building agency for a sustainable market presence

Link building in the AI era is a long-term investment. It’s about building a durable market presence, not chasing quick wins.

The right partner sees themselves as an extension of your team. They care about:

Your backlink gap compared to competitors.
Your brand mentions across LLMs.
Your overall search and AI visibility.

They help you navigate content syndication, backlink audits, content marketing, and modern link building strategies with a unified approach.

If you’re ready to move past vanity metrics and start building authority that drives revenue and AI citations, it’s time to be selective about who you trust with your domain.

The right link building agency is out there. You just need to know how to spot them.

Read more at Read More

News publishers expect search traffic to drop 43% by 2029: Report

January 12, 2026/0 Comments/in Search Engine Land, Web Design News/by

News executives expect search referrals to drop by more than 40% over the next three years, as search engines continue evolving into AI-driven answer engines, according to a new Reuters Institute report. That shift is squeezing publisher traffic and accelerating a move away from classic SEO toward AEO and GEO.

Why we care. Google’s AI Overviews and chatbot-style search are changing how people get information, often without clicking through. SEO visibility, attribution, and ROI models built on old playbooks are breaking fast.

What’s happening. Publishers expect search traffic to nearly halve. Survey respondents forecast search engine traffic down 43% within three years, with a fifth of respondents expecting losses above 75%.

Google referrals are already falling. Chartbeat data cited in the report show organic Google search traffic down 33% globally from November 2024 to November 2025, and down 38% in the U.S. over the same period.
AI Overviews are a major factor. Google’s AI Overviews appear at the top of roughly 10% of U.S. search results, with studies showing higher zero-click behavior when they appear, according to the report.
The impact is uneven. Lifestyle and utility content (e.g., weather, TV guides, horoscopes) appear to be the most exposed, while hard news queries have been more insulated so far.

SEO to AEO and GEO. The Reuters Institute expects rapid growth in answer engine optimization (AEO) and generative engine optimization (GEO) as publishers and agencies adapt to AI-led interfaces.

AEO and GEO services are set to surge. Agencies are repurposing SEO playbooks for chatbots and overview boxes, with new demands on how content is written, structured, and surfaced.
Publishers are dialing back traditional SEO. Many survey respondents plan to reduce investment in classic Google SEO and focus more on distribution through AI platforms like ChatGPT, Gemini, and Perplexity.

Between the lines. This is about more than rankings. It’s about distribution inside platforms that publishers do not control.

Chat referrals are growing, but remain small. Traffic from ChatGPT is rising quickly, but the report calls it a rounding error compared with Google.
Attribution is getting murkier. If AI agents summarize content and complete tasks for users, it becomes unclear what counts as a visit and how monetization works.
Licensing is becoming a parallel strategy. As referral risk grows, publishers are turning to AI licensing, revenue-sharing deals, and negotiated citation or prominence as another path to value.

What to watch. A new KPI stack is emerging. Metrics like share of answer, citation visibility, and brand recall may matter as much as clicks.

Utility content faces the biggest squeeze. Categories built for fast answers are easiest for AI systems to commoditize.
A measurement arms race is coming. Expect new tools to separate human visits from agent consumption and to measure value beyond raw traffic.

Bottom line. Publishers are bracing for a world where search still matters, but clicks matter less. The report’s message is clear: when AI answers become the interface, AEO, GEO, and attribution strategy are no longer optional. They are a core modern search strategy.

The report. Journalism, media, and technology trends and predictions 2026

Read more at Read More

Google opens Olympic live sports inventory to biddable CTV buys

January 12, 2026/0 Comments/in Search Engine Land, Web Design News/by

Live sports advertising is getting more programmatic — and more measurable.

Driving the news. Google is expanding biddable live sports in Display & Video 360, giving advertisers programmatic access to NBCUniversal’s Olympic Winter Games inventory ahead of a crowded 2026 global sports calendar.

Why we care. Live sports remain one of the few media environments that consistently deliver massive, attentive audiences. By moving premium sports inventory into biddable CTV, Google gives advertisers more control, stronger measurement, and simpler activation — without sacrificing reach.

What’s new. Advertisers can now pair Google audience signals with NBCUniversal’s live sports CTV inventory to reach fans on the big screen and re-engage them across YouTube and other Google surfaces.

New household-level frequency management reduces overexposure, while Google’s AI-powered cross-device conversion tracking connects CTV impressions to downstream purchases at no additional cost.
Google is also streamlining access to live sports with a redesigned Marketplace.
You can activate curated sports packages in just a few clicks instead of managing fragmented media buys.

The big picture. As fans move fluidly between connected TV, YouTube, Search and social feeds, advertisers are under pressure to follow attention across screens. Google is positioning Display & Video 360 as the hub that connects those moments, from the living room to mobile.

Bottom line: By unlocking Olympic and live sports inventory inside Display & Video 360, Google is making premium sports advertising easier to buy, easier to measure, and far more accountable.

Read more at Read More

Google expands Shopping promotion rules ahead of 2026

January 12, 2026/0 Comments/in Search Engine Land, Web Design News/by

Inside Google Ads’ AI-powered Shopping ecosystem: Performance Max, AI Max and more

Google is broadening what counts as an eligible promotion in Shopping, giving merchants more flexibility heading into next year.

Driving the news. Google is update its Shopping promotion policies to support additional promotion types, including subscription discounts, common promo abbreviations, and — in Brazil — payment-method-based offers.

Why we care. Promotions are a key lever for visibility and conversion in Shopping results. These changes unlock more promotion formats that reflect how consumers actually buy today, especially subscriptions and cashback offers. Greater flexibility in promotion types and language reduces disapprovals and makes Shopping ads more competitive at key decision moments.

For retailers relying on subscriptions or local payment incentives, this update creates new ways to drive visibility and conversion on Google Shopping.

What’s changing. Google will now allow promotions tied to subscription fees, including free trials and percent- or amount-off discounts. Merchants can set these up by selecting “Subscribe and save” in Merchant Center or by using the subscribe_and_save redemption restriction in promotion feeds. Examples include a free first month on a premium subscription or a steep discount for the first few billing cycles.

Google is also loosening restrictions on language. Common promotional abbreviations like BOGO, B1G1, MRP and MSRP are now supported, making it easier for retailers to mirror real-world retail messaging without risking disapproval.

In Brazil only, Google will now support promotions that require a specific payment method, including cashback offers tied to digital wallets. Merchants must select “Forms of payment” in Merchant Center or use the forms_of_payment redemption restriction. Google says there are no immediate plans to expand this change to other markets.

Between the lines. These updates signal Google’s intent to better align Shopping promotions with modern retail models — especially subscriptions and localized payment behaviors — while reducing friction for merchants.

The bottom line. By expanding eligible promotion types, Google is giving advertisers more room to compete on value, not just price, when Shopping policies update in January 2026.

Read more at Read More

Apple is finally upgrading Siri, and Google Gemini will power it

January 12, 2026/0 Comments/in Search Engine Land, Web Design News/by

Apple is teaming up with Google to power its next generation of AI features, including a long-awaited Siri upgrade.

What’s happening: Apple will use Google’s Gemini AI models and cloud infrastructure to support future Apple Foundation Models. The multi-year partnership is expected to roll out later this year.

Why we care. With Gemini powering Siri, Apple’s assistant should become a true AI answer engine. That will likely change how millions of iOS users find information, ask questions, and interact with search.

Driving the news. Apple said it chose Google after a “careful evaluation,” calling Gemini the “most capable foundation” for its AI ambitions.

We learned in September that Apple was in talks to use a custom Gemini model to power a revamped Siri.
Apple delayed its Siri AI upgrade last year, despite marketing the feature. The delay intensified scrutiny of Apple’s AI strategy.

What they’re saying. Here’s a statement Google shared via X:

Apple and Google have entered into a multi-year collaboration under which the next generation of Apple Foundation Models will be based on Google’s Gemini models and cloud technology. These models will help power future Apple Intelligence features, including a more personalized Siri coming this year. After careful evaluation, Apple determined that Google’s Al technology provides the most capable foundation for Apple Foundation Models and is excited about the innovative new experiences it will unlock for Apple users. Apple Intelligence will continue to run on Apple devices and Private Cloud Compute, while maintaining Apple’s industry-leading privacy standards.

The bigger picture. Google briefly crossed a $4 trillion market cap last week, surpassing Apple for the first time since 2019.

Google’s Gemini 3 model launched late last year as part of its broader AI push.
Apple largely stayed out of the AI arms race that followed ChatGPT’s launch in late 2022 while rivals poured billions into models, chips, and cloud infrastructure.

Read more at Read More

3 PPC myths you can’t afford to carry into 2026

January 12, 2026/0 Comments/in Search Engine Land, Web Design News/by

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.

Those approaches can perform well in the right conditions. They are not universally reliable.

Get the newsletter search marketers rely on.

See terms.

How to fix this

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.

When resources are constrained, CRO is a better use of your resources:

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:

Differentiate clearly from competitors.
Increase margins, even basic budget planning can move the needle.
Build a solid data foundation, including tracking, CRO, and conversion pipelines.
Diversify channels or ad networks.
Lock creative execution to real customer pain points.
Fix marketing execution wherever it breaks.

MMM – like any advanced tool – becomes useful once complexity demands it. Not before.

Used too early, it replaces accountability with abstraction, not insight.

The reality behind the myths

The common thread across these three myths is not AI, creative, or analytics. It is misuse.

Platforms do exactly what they are asked to do. They optimize against the signals provided, within the constraints of budget and structure.

When business fundamentals break, AI cannot fix the problem.

2026 is not about chasing the next abstraction. It is about business and ops focus, paired with disciplined execution, to scale profitably.

Read more at Read More

Why copywriting is the new superpower in 2026

January 12, 2026/0 Comments/in Search Engine Land, Web Design News/by

For the last few years, copywriting has been quietly written off.

Not with outrage. Not with ceremony.

Just sidelined. Replaced. Automated.

Words – the core material of SEO, landing pages, ads, and persuasion – were demoted during the traffic rush and later the AI gold rush.

Blog posts were generated. Product descriptions were bulked out. Landing pages were templated.

Content teams shrank. Freelancers disappeared. And a convenient narrative emerged to justify it all:

“AI can write now, so writing doesn’t matter anymore.”

Then Google made it worse.

The helpful content update, followed by AI Overviews and conversational search, didn’t just hurt SEO. It hurt the broader web.

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.

Dig deeper: SEO copywriting: 5 pillars for ranking and relevance

GEO isn’t about rankings

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.

Dig deeper: The new SEO imperative: Building your brand

From SEO to GEO: Availability beats visibility

Search engine optimization was about visibility.

Generative engine optimization is about AI availability.

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.

This is a key shift from semantic SEO to GEO.

You want LLMs to recommend your business to solve those problems.

Get the newsletter search marketers rely on.

See terms.

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.

AI brings them back.

Dig deeper: Why ‘it’s just SEO’ misses the mark in the era of AI SEO

Less traffic doesn’t mean less performance

Traffic is going to fall.

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.

The information era of SEO is over.

It’s time to get back to marketing.

Read more at Read More

Not all MMM tools are equal: Meridian, Robyn, Orbit, and Prophet explained

January 12, 2026/0 Comments/in Search Engine Land, Web Design News/by

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.

Dig deeper: Marketing attribution models: The pros and cons

Robyn: The accessible powerhouse

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.

Meridian: The statistical heavyweight

Meridian represents Google’s Bayesian causal inference approach to MMM.

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.

Meridian insights on channel contribution

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.

Dig deeper: How Bayesian testing lets Google measure incrementality with $5,000

Get the newsletter search marketers rely on.

See terms.

Uber Orbit: The time-varying specialist

Orbit is not technically an MMM tool.

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.

Dig deeper: MTA vs. MMM: Which marketing attribution model is right for you?

Making the right choice for your team

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.

Dig deeper: How to avoid marketing mix modeling mistakes that derail results

Read more at Read More

Content Writing 101: 8 Skills That Set Top Writers Apart

January 7, 2026/0 Comments/in Web Design News/by

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.

Go where real SaaS buyers are sharing feedback — places like G2 reviews and user-generated forums like Reddit.

These insights help you create genuinely helpful content that connects with readers.

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?

Google SERP – Lifecycle marketing strategy

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.”

Topic Research – Lifecycle email marketing – Mind Map

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.

Backlinko – AI Search Strategy – Article

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.

Backlinko – AI Optimization – Semrush research

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.

For example, here’s the outline I created for my Backlinko article on subdomains vs. subdirectories:

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.

Add expert quotes to emphasize each point

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.

The Inverted Pyramid Approach for Outlining Content

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?

LinkedIn – Get inspired by your favorite writers

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.

Backlinko – Increase Website Traffic – Article

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)

Use infographics to supplement your writing

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.

Klaviyo – Case studies, screenshots & examples

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:

Backlinko – Social Media Calendar – Shared struggles

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.

Backlinko – Social Media Calendar – Free template

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.

So, pick a niche (or two) and become an expert.

Rosanna Campbell – B2B SaaS Content Marketing

A good niche checks three boxes:

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.

Contribute guest posts and expert quotes to reputable sites
Speak at conferences or webinars
Share insights on social media

The more you’re cited as an expert, the stronger your credibility.

Learn SEO Fundamentals

SEO basics remain essential to content writing: keyword research, competitive analysis, and on-page optimization.

But you’ll also want to know how to write and structure LLM-friendly content.

YouTube videos, blogs, and courses can help you understand these topics quickly.

It’s also helpful to familiarize yourself with popular SEO tools.

Clients often expect you to know platforms like:

Clearscope, Surfer SEO, MarketMuse: Content optimization and readability scoring
Semrush, Ahrefs, Moz: Keyword research and competitive analysis
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

Essential Elements for a Writing Portfolio

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.

LinkedIn – Shreelekha Singh – Experience

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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