How are marketers adapting? What’s working?

Your buyers have already changed how they research decisions.

72% of them use AI tools every day. More than half use AI chat as a primary research tool. Clearly, the buyer journey has changed. So what are we doing about it?

Our recent survey of 1000+ content marketers has the answers. We held back this data from the report because it really deserves its own analysis.

This report looks at what marketers are doing, what AI search tools are checking for and what correlates with performance. Here it is…

71% of content marketers are adapting for AI search …sort of

Most marketers have taken at least one step to improve their AI visibility. That’s good. But the popularity of the tactics tells you nothing about whether those tactics work.

The data shows that the most popular tactics are the least effective and the least popular tactics are the most effective. We saw this inverse pattern throughout the survey. Marketers are more likely to do what’s easy, not what works.

The good news is that every tactic beat the performance benchmark (14% of respondents reported “strong results” in 2026). Good to see marketers adapting. Still, nearly a third of marketers aren’t doing anything about AI search at all, and they are the least likely to report strong results.

Only 36% have an AI search strategy. They’re the ones winning.

We asked marketers if they’re actively optimizing content to be cited or recommended by AI chatbots. Do they have a deliberate AI search strategy? Are they experimenting? Do they even know what this means?

Map this to performance and only the marketers with an actual strategy beat the benchmark. Marketers who do nothing about AI search underperform their peers.

The advice that marketers get from SEO blogs

What do SEO blogs recommend?

If marketers are widely adopting the least effect tactics, maybe it’s just because that’s what everyone recommends.

Earlier this year, we analyzed 150 articles about AI search marketing to see what they recommend. The advice on these blogs is nearly unanimous. 93% recommend FAQs and answer-focused content. 89% recommend schema. Consensus.

But when you map it to the performance of the marketers who do these things, it’s obvious that the most recommended tactics are not the most effective tactics.

We checked the tools too: 100 free “AI Visibility Audits”

What do the tools check for?

AI visibility audits are everywhere now. Almost every day I get a cold DM on LinkedIn with an offer to do a scan of my site. These come from SEO software companies, AI startups and search marketing agencies.

What’s in these things?

We reviewed 100 free AI visibility audits (the “enter your URL” kind) to see what they check. No surprise, like the marketers, they do the quick and easy things. Makes sense for a free tool.

Audits check things that are easy to detect: markup, files and crawler settings. But these free graders skip the things that are hard to measure. 40% of tools never ask an AI tool a single question but still give you an “AI visibility” score.

Ironically, the best-performing strategy is publishing original data (24% of the marketers who do it report strong results). But only 26% of marketers do it. It’s the fifth most common recommendation in SEO articles. And just 3% of AI visibility audits check for it.

Everyone is overlooking the same thing.

Marketers who are adapting to AI search never stopped doing SEO

Strangely, fewer marketers are researching keywords, that foundational search strategy of aligning pages with phrases.

Maybe these marketers read an “SEO is dead” article and no longer think that search algorithms are a thing. Maybe they don’t know that AI searches for users millions of times every day.

But keyword research still correlates with marketing performance.

Marketers who understand how machines see the web are better at digital. They think about information retrieval. They know that deterministic algorithms are still important in a probabilistic world.

The marketers who were already doing SEO are adapting faster to AI search. Meanwhile, the marketers who don’t understand SEO aren’t doing anything about AI search either.

Are marketers tracking AI search traffic?

In our conversion research across 97 GA4 accounts, AI search traffic was just 0.5% of total visits. But it is also the traffic that is most likely to convert. It’s the best traffic those websites get. AI visitors converted into leads at roughly 3x the rate of organic search visitors.

But 34% of content marketers aren’t tracking AI traffic at all.

Maybe it’s because AI traffic is notoriously hard to track. Sometimes, it’s impossible. Visitors who get a recommendation from AI may leave the chat and go directly to a website. Or they may tap on a link in their AI app. These visitors usually show up in direct traffic. This may explain why direct traffic recently surpassed organic search traffic in our data set of 56M sessions across 86 websites.

Same job. New name.

Many content marketers are already doing the things that power AI search optimization, even if they don’t think of it as such: original research, earned mentions, answering real questions, measuring results. They aren’t new at all.

Maybe AI search optimization isn’t a brand new playbook. Maybe it’s the old playbook with a few new considerations. Here are the five big ones:

1. Answer the questions your buyers actually ask …not just basic FAQs
2. Support every claim with evidence …not just simple E-E-A-T
3. Publish original data worth citing …not just typical content marketing
4. Earn mentions in the places AI learns about your specific niche …not just standard PR
5. Measure AI referrals and leads, watch direct traffic …not just standard GA4 reports)

That list was working before AI search existed, but deeper in every case. Probably, right now, someone is asking an AI for recommendations in your vertical. Winning that recommendation depends on those five marketing activities.

---

Karine Abbou, Content strategist & AI search educator “Yes, AI search is new. Yes, the LLM sitting between us and our buyers is new. Yes, there’s no AI Search playbook (yet). None of that is a reason to leave ‘build our AI Search strategy’ off next year’s marketing plan. Simply making it a top priority puts you ahead of everyone still waiting for certainty. You don’t need to understand it all to start. So let’s borrow from Nike: just do it.”

---

About this research

This article pulls from five Orbit studies conducted in 2026. Each one looked at the same shift from a different angle:

1. Buyer behavior: 55% of people now use AI chat as a primary or frequent research tool (AI vs. Google survey)
2. Bot activity: AI crawlers read almost everything, but nearly half the pages they crawl never send a visitor (AI crawl report)
3. AI traffic: AI visitors are just 0.5% of traffic, but they convert at roughly 3x the rate of search visitors (AI conversion research)
4. SEO advice: 150 articles of AI search advice mostly recommend the same five tactics (What SEOs get wrong about AI search)
5. Marketer actions: 1,042 content marketers on what they’re doing and what’s working (13th annual blogger survey)

Survey results are self-reported, and findings are associations, not causation. The AI visibility audit review is new for this article. It covers 100 free tools, based on what each tool says it checks. We did not run the audits. Stay tuned for a full report on this soon. Direct traffic data is from 86 websites in GA4, January 2023 to July 2026.

It’s one of the most important questions in marketing: how to generate leads. Some companies make it look easy, while others never quite figure it out. And when it isn’t working, everyone has a different theory about why.

That’s because lead generation isn’t one thing. It’s 35 things. Fail at just one or two and you’ll feel it somewhere downstream.

Today we’ll take a big step back and look at how all of those 35 things fit together on one big lead generation process diagram. Here’s everything required to build a lead generation machine.

A quick note: Our focus here is lead generation through organic channels. That means no outreach, no ads, no paid channels of any kind. Those may work, but here our focus is on inbound leads, content marketing and organic lead generation through earned and owned content.

It’s a planning tool and checklist. Print it. Hang it on the fridge.

Strategy and Branding

It starts with clarity and alignment: why we’re in business, who we sell to, why they value it and who is in our competitive set. Your brand is your prospects’ perception of your position in the market.

1. Mission, vision and values

Why does the company exist? What do you actually stand for? These aren’t just questions for a leadership retreat. They affect everything that comes later: what you write, who you hire, which clients you turn down. Skip this and you’re building on sand.

2. Research: our buyer and our competition

Know your niche: who are your potential customers, what do they need, and who else is trying to reach them? Content strategy is the bait we’ll use, but audience strategy is the pond we fish in.

The B2B lead gen marketer doesn’t need demographic data. You need personas. Today that often means AI-generated personas, validated with the sales team and call transcripts. They include the buyer journey, the buying committee, the top questions, fears and hopes.  Accurate, documented personas make everything more targeted forever after.

3. Positioning

Now we get specific. What’s your unique point of difference? Why do they care? What wins the sales call?

Positioning is the answer to a simple question: why you and not them? It’s the exact intersection of what your buyer needs, what you do best and what your competitors don’t do quite as well.

Weak positioning hurts the brand and also lead quality. If you don’t stand apart, you attract sales leads who are shopping on price instead of prospects who really need what you’re really good at.

Your positioning also shapes what AI says about you. When a buyer asks for a recommendation, the model looks for the brand that best matches their needs …and vague, watered-down brands don’t get matched.

4. Messaging

How do you describe the value you deliver? What evidence backs up your claims? Align the message with the position and the values, then find a voice that’s clear and concise.

This is also where AI visibility and recommendations begin. The LLMs learn what your business does and who you sell to from the words you use to describe it.

5. Visual standards

Colors, type, imagery, tone. Carry them through the website, the decks, the social media posts and every piece of content. This visual consistency is a trust signal and trust is a conversion factor.

---

The Website

The website is two things at once: the platform for publishing and the machine for capturing leads. It has to attract qualified prospects, build trust and give them a low-friction path toward conversion. These are two of the 10 critical web design requirements.

6. Keyword research and fanout queries

People are searching for you right now, as you read this sentence. They’re querying search engines and prompting AI chatbots. We need to know the phrases they search for (traditional search engine optimization) and the questions they ask (AI search optimization), then align pages with those phrases and questions.

When AI searches before recommending, it doesn’t just use a single query. It breaks the question into several related searches then summarizes the results. These are called fan-out queries.

Do this before the sitemap.

7. Sitemap

Your sitemap shapes two separate things:

• The UX: how visitors navigate
• The URLs: what gets surfaced in search results and AI responses

A well planned website has many possible entry points, so the homepage is one of many landing pages. Aligning with keyphrases and fan-out queries means making a page for every service, location, topic and team member.

Once they land, they see the navigation labels and menu structure. So build the sitemap for both audiences: humans and bots are reading the same labels.

8. Wireframes: UX, conversion paths, flexibility

Wireframes are the black-and-white layouts for each page template. Here’s where you plan the user experience and conversion paths on various devices. They also affect SEO and AI search (elements for text) and determine how easy it is to update pages later (page block library).

9. Moodboards

Wireframes are structure, moodboards are style. Here you decide typography, colors, backgrounds, textures, image treatments, button styles. And everything aligns with current brand standards.

10. Design

Next the wireframes and moodboards combine into the storyboards. Add in copy and images (more in a moment) and the pages come to life. The style, structure and assets come together. The visual prominence of each element is balanced in a single layout that informs visitors and guides them toward action.

A webpage is a visual hierarchy that guides the eyes through a series of messages that emulate a great sales call.

11. Development: URL structure, integrations

Front-end design fidelity, back-end CRM and the custom features in between. But it’s not just programming. Decisions with big marketing implications happen here.

Bad choices about URL structure can wreck search performance. Careless coding may slow down load times. A little decision in development can cause big problems in what gets measured in Analytics.

Then there are the integrations. Connect the site with the CRM, marketing automation tools, cookie consent and tracking codes. From APIs to Google Tag Manager, the developer wires everything together. These are the lead generation tools that turn a form fill into a tracked opportunity for pipeline revenue.

---

Copywriting

We’ve numbered this list, but really, copywriting happens during the design process. As soon as the sitemap is final, the scope of writing is set. Start early. It’s the most important factor in lead generation and the most common reason web projects stall.

12. Conversion Strategy

What questions does your buyer have? What are their hopes and dreams? Their fears and doubts? What proof would persuade them? What emotions would trigger action? Do they need to get buy-in from a committee? If so, what do those people need to see?

Continuously map the writing to the persona. Marketing is all about empathy.

13. Write

It’s about what they need to hear, not just what we want to say. It’s totally focused on the visitor and yet doesn’t miss any keyword opportunities.

• Conversion copy. It’s not overly branded or vague. Specificity correlates with search rankings, AI recommendations and conversion rates. Taglines don’t train the AI.
• Calls to action. They’re low-commitment and clear. Watch your verbs.
• Story and history. One page is special. The about page is really focused on the brand. It’s where you tell your unique story.
• Persona alignment. Check everything again with your persona.

Edit as you go. But don’t obsess. Web content is free and fast to change. Please don’t let a paragraph delay the launch for two weeks. Digital ink is never dry.

14. Evidence

Testimonials, data, case studies, awards and more. There are at least 14 ways to add evidence to a webpage, so there’s no need to make unsupported claims.

Evidence reduces anxiety and overcomes objections. So it persuades visitors, but it also tells the AI systems that you’re legit.

15. Content entry

Everything goes into the CMS: copy, images, video, metadata, page titles. It’s done through programming for some sections (migrations and integrations) and through painstaking work done carefully by hand (key conversion pages). Check everything on every screen size: the formatting, headers, bullets and links.

16. LAUNCH

The big day. Everything is tested before launch and again after launch. Features work on every screen size, lead capture forms drop prospects right into the CRM and conversions are tracked in Analytics.

For many marketers, it comes just a few times in their career. Most businesses redesign once every two to four years. The brand has a new home.

---

Create Content

A website without content is an online brochure. Nothing to share in streams or with prospects. Nothing to send as newsletters. No reason to visit twice.

But the brand that publishes is part of the conversation before the buyer is even in the market. They have early access to the customer journey. They have a platform for sharing ideas and research. They grow awareness and trust that eventually supports lead generation.

17. Content marketing strategy

First, define the audience, the topics and what’s in it for them. That’s your content marketing mission statement and the foundation of your strategy. Then define your formats and frequency. Next list the influencers and PR targets. The plan includes the people, time and resources.

18. Create: articles, images, video, original research

Write the articles, shoot the video, schedule the webinars, record the podcast and conduct the research.

Original research is an all-powerful topic in content marketing. New data and statistics are catnip for editors and are cited by journalists. According to our own annual survey, content programs that publish original research are 50% more likely to report strong results.

19. Curate: customer stories and influencer collaboration

Repurpose from other sources, but add your own perspective. Interviews, roundups, event recaps.

But customer stories are the format that directly support lead generation. Your past projects and happy clients are the evidence your prospects are looking for. They’re one of the few formats for content that go directly onto your service pages.

20. PUBLISH

Make it live. For articles, use a URL that includes the target phrase. For videos, post to YouTube then embed the video within articles.

---

Priscilla McKinney, Little Bird Marketing “Companies love to write about themselves. But that’s the fastest way to kill a content program. Your content isn’t about you; it’s about your client’s most pressing problem. Start there and #alwaysbehelping becomes your north star. People remember who helped them. That’s who gets the call when they’re ready to buy.”

---

Promote

The best content doesn’t win. The best promoted content wins. Next, the steps that make the content visible and discoverable. Time to market your marketing.

Search

Ranking in search can provide a consistent source of traffic, in traditional search results and AI responses. This takes research, a bit of editing and a credible domain.

21. Keywords and fanout queries

As with the URLs of your service pages, every article is a potential keyword opportunity. Select based on the three criteria of keyword research.

1. Search volume (anyone looking for this?)
2. Competition (do you have a chance of ranking for this based on your domain authority?)
3. Relevance (does your buyer care about this?)

Then go one level deeper. What related questions would an AI assistant ask to answer this one? These are called fanout queries.

22. Guest posting

One of the few shortcuts in digital marketing, writing for other sites puts your content directly in front of your target audience. A nice, bylined article on a relevant site earns you a link, reaches a new audience, and puts your name in the retrieval paths of every major model. It’s PR, link building and AI model training.

23. Search-savvy PR

If you can create a bit of buzz, you can earn attention from editors and the links and mentions that go with it. Being mentioned on relevant websites can shape what AI systems know about your brand. Do something newsworthy. Then let editors know about it.

Social Media

Social is both a promotion channel and a networking tool. Both outcomes feed lead generation.

24. Research trends and influencers

You’re looking for specific people worth connecting with: influencers, podcasters, editors, journalists and even prospects. Build lists. These are your future collaborators.

25. Social sharing

It’s the easiest and fastest promotion activity. Post the content in the networks where your audience spends time. Write these posts with loving care. Add a personal angle or perspective. Tag the collaborators. You should believe in what you wrote enough to share it more than once, across days and weeks.

26. Conversation

Don’t just fill your feeds with content promotion. Talk to people. Ask questions. Engage on their content. Be part of conversations relevant to the next article before it goes live.

Move the conversation to something more direct. Comments become direct messages. Direct messages become a call. Social media is about networking. If you’re not making friends, you’re doing it wrong.

Email Marketing

Search engines and social networks are both big tech. You are marketing through an algorithm. But email is a direct line of communication to your audience. You own your list. An engaged list of subscribers is still the most reliable asset in digital marketing.

27. Copy and creative

The email template should be light, mobile-friendly and easy to manage. The subject line should be descriptive but leave room for curiosity. Add an unexpected word near the front. The sender name, subject line and preheader text determine your open rates. The body copy and the call to action determine your clickthrough rate.

28. Timing and cadence

When is your audience most likely to open? Should you try weekends? How often should you send? If your sales process is long, bi-weekly may be plenty. But as always, quality beats quantity.

29. SEND

Use a pro email service with strong deliverability and easy-to-understand reports. Keep the list clean by removing bounces and dead addresses. Email service providers charge more for big lists, so list hygiene can save you money.

---

AI: Visibility and Recommendations

This wasn’t on this chart when we first published it way back in 2013.

Today, buyers often use an AI assistant before they click anything. They describe their problem, AI searches then summarizes options. They ask a few more questions and eventually, AI makes a recommendation.

Both the awareness and consideration stages happen in the model. So by the time they arrive, they’re already pretty sure that you’re a good option. And they’re a more qualified prospect. Our research shows visitors from AI convert at 3x the rate of other visitors.

This is why the AI arrow on the chart reaches deeper into the funnel than any other. These are your highest-intent visitors.

30. Answers (clarity), evidence (trust)

You can’t see the conversation they had with AI. But you can influence what it finds and what it knows about your brand. Your content (both your webpages and your content marketing) trains the AI to recommend your brand for two reasons:

• Clarity. Answer the actual questions your buyers ask, in complete sentences, right there on the pages where the model expects to find them. Often, it’s the homepage. AI visits homepages 15x more than other types of pages. So don’t bury those answers in a PDF. Don’t know what questions to answer? Ask the sales team or your persona.
• Evidence. Support every claim with some type of evidence: testimonials, case studies, performance data, awards, certifications and anything else. Models are more likely to recommend a brand if the claims are supported.

31. Off-site signal support

Your site isn’t the only place where AI learns about your brand. You do the research: running ‘synthetic prompts’ and checking the sources that informed the responses.

You also ask for reviews, manage directory listings, network with editors who write roundups, participate in community threads and win third-party mentions. When a model is picking a brand to recommend, it weighs what others say about you, not just what you say about yourself.

Notice where this connects on the chart. It’s fed by Delivery and Loyalty, by reviews from satisfied customers and referrals from existing customers. This means the most effective AI visibility strategy available to most companies is doing excellent work and asking happy customers to say so publicly.

---

Measure

Analytics is a decision support tool. Use it for real analysis, not just routine reporting. Form hypotheses, run experiments and check the results. That’s what a data-driven marketer does.

32. Visitors

Topline traffic is interesting, but not all that useful. Consider looking at these slices of data instead:

• Traffic per landing page
• Traffic per landing page per traffic source (use a secondary dimension)
• Traffic per campaign, sorted by average time on page
• Traffic per traffic source, compared to previous period
• Traffic to subsequent pages after the landing page (with this path exploration)

Here’s a little guide to conducting your own content marketing audit. It has nine steps.

33. Sources: direct, search, social, AI, campaign

Where are visitors coming from? Which marketing channels are working well?

AI traffic is on the list. But Google Analytics doesn’t report it very well. Lots of AI referral traffic gets categorized as Direct traffic. If your direct traffic is mysteriously up, some of that is AI. Build a report to see traffic from AI separately, but don’t expect to ever find them all.

There’s no way to optimize the lead generation efforts of marketing campaigns if you don’t tag, report and analyze.

34. Content

Which articles connected? Which topics get traction? Which headlines got clicked? Which content drives traffic that actually converts?

35. Conversions: list growth and leads

The bottom line is total new leads generated. Traffic x conversion rate = leads. But those mid-funnel conversions are key to the performance of any content program. subscribers, downloads and registrants.

Watch lead quality, not just lead volume. Not all marketing qualified leads are sales qualified leads. Some channels may sends fewer, better-qualified leads into the sales funnel. Some directories may send leads, but none ever turn into deals.

Lead scoring, account based marketing and signal-based research are coming to you soon if you’re not doing them yet.

The Funnel: From visitor to lead to advocate

In the chart, the 1-35 numbers are things that you do. The A-F letters describe the customer journey.

Visitors enter the top of the sales funnel (A), then subscribe if the content is relevant (B), then may download something later on landing pages (C). When the time is right, they remember your content. Or they searched and clicked. Or they asked an AI, and it recommended you. They landed, converted, a sales conversation started and a lead was born (D).

After the lead comes delivery (E) and loyalty (F). Paying clients may leave reviews and send referrals, which feeds the rest of the system, directly or indirectly. That’s why the post-sale belongs on this lead generation chart.

---

Is that all?

Thirty-five steps and six stages. Yes, it’s a lot. But at least we can see how the pieces fit together.

This is how lead generation works. It’s a system of elements and actions that all work together. Lead generation strategies fail because they skipped steps in the middle.

Great brand, bad copywriting. Great content, bad promotion. Great copywriting, bad website structure.

Success is a flywheel. Results compound. Any fix can make a difference: more traffic, better conversion rates, higher lead quality and eventually revenue growth.

The AI section of this chart is new and still widely misunderstood. AI isn’t really a separate channel. It’s a layer on top of the internet. All kinds of marketing mistakes hurt you in AI and marketing successes get rewarded in AI.

But it’s driven by the same things that have always powered lead generation: awareness, clarity and trust through aligned, comprehensive marketing. It’s the same thing that successful lead generation has always needed.

Anything you missed? 

Every year we ask 1000+ content marketers 24 questions. The goal is to discover what marketers create, how often, how long it takes, how they promote it, and most recently, how they use AI.

The survey includes one final, simple question: Does your content deliver results?

For 12 years, the answer to that question mostly held steady. Year after year, between 20% and 30% of marketers told us their blog delivers “strong marketing results.” The number bounced around a little but always stayed within that range.

This year it dropped.

14% report “strong results.” That’s six points below the previous twelve-year low. That’s a huge dip.

At the same time, 18% report “I don’t know if the blog delivers results.” That’s a big jump. For a decade, that number always fell between 9% and 14%. Today, nearly one in five content marketers can no longer tell you whether the work they do is working.

Clearly, something happened.

But first, the 2026 numbers at a glance…

• 1,312 words – The average blog post, down from a peak of 1,427 in 2023
• 3 hours, 20 minutes – The average time to write one, down from a peak of 4:10 in 2022
• 13.9% say their blog delivers strong results, an all-time low
• 92.4% now use AI for blogging …and it correlates with nothing

How to cite this research… You are welcome to use any of these stats and charts in your content with attribution to Orbit Media. Just link back to this page. Questions? Need a custom cut of the data? Just drop us a line.

The four eras of content marketing

The data goes back far enough to give us a unique view into the evolution of content marketing. We can break it down across four specific eras. When we step back and look at it from a distance, we can see an entire industry change its mind several times.

2014–2019: The arms race

In this era, everything went up together.

Time spent per post went up, climbing from two-and-a-half hours to almost four hours. Articles got much longer, rising from 800 words to more than 1,200 words. The use of email marketing nearly doubled. SEO promotion climbed eighteen percent.

The idea was very simple: content marketing is a production contest, and whoever invests the most wins.

And that playbook actually worked. “Strong results” ran 25–30% through this whole stretch. Nearly every measure of effort correlated with better outcomes. The winners were those who published longer articles, more frequent articles and spent more time on each.

2020–2022: Peak effort

Then the arms race topped out.

Time spent writing per article peaked back in 2022. Length peaked in 2023. Updating older articles hit a high of 73%. Content marketers were maxing out on effort and one in four reported strong results.

Publishing frequency changed. Marketers publishing 2–6 times per week fell from 28% in 2014 all the way down to 12% by 2022. Monthly publishing doubled. The focus was on quality, not quantity.

The industry converged on a common strategy: fewer, bigger, better. And it worked. Our own correlation charts from past surveys kept showing that spending 6+ hours writing 2,000-word posts roughly doubled the odds of strong results.

But the return on investment for greater effort was flattening out. Performance stopped rising even as effort peaked. It’s just that the change was so small that none of us saw the trend.

2023–2025: The AI shock

Along comes AI and marketers jumped in with both feet. The use of AI went from virtually zero to 93% in just three years. That’s the fastest adoption curve in the history of digital marketing. But concerns remain.

With AI came speed. As we saw already, time per post has fallen four years in a row. But the big open question remains:

“Does AI make content programs better? Or just faster and cheaper?”

The answer is now clear.

AI is table stakes. It doesn’t give marketers an edge.

Using AI does not improve content performance.

It certainly makes content production faster, as we just saw. We did the math and found that the average content marketer now spends 50 fewer hours writing than in 2022 (57 posts per year, 50 minutes faster per post). That’s a week of work saved. But for what? In the same period, “strong results” fell from 26% to 14%.

None of the use cases for AI in the survey correlate with performance. Marketers just got faster at making the same things.

Maybe it was an experimentation phase. Many content pros are already pulling back from the “AI for everything” era. Almost every use case for AI dropped in 2026.

Today, 92% of marketers use AI. 8% of marketers do not. Both of those groups are equally likely to report “strong results.” No difference. But non-AI users are much more likely to report “disappointing results.” These two groups have the same odds of succeeding, but very different odds of failing.

Using AI doesn’t make you more likely to outperform. But not using AI appears to make you more likely to underperform.

So the use of AI doesn’t explain the collapse in performance. It must be something else. To find out what, we need to look at what marketers quit doing.

Why did content performance drop?

The answer is obvious in the data. Performance dropped because content marketers are using the most effective strategies less often. Also, marketers are using the least effective strategies more often. Consider:

• The strategies that correlate with strong performance are ALL on the decline: influencer collaboration, paid content promotion, faithful use of analytics, keyword research, publishing original research, using a formal editing process…
• The strategies that correlate with weak performance are ALL on the rise: publish less visual content, never research keywords, publish monthly or less, replacing human editors with AI…

It’s not true that content marketing is less effective. It is true that the best approaches to content marketing have been abandoned.

---

Ann Handley, Marketing Profs “AI makes it stupid-easy to remove effort. We all know that. But this data is a reminder that we need to be much (MUCH!) more discerning about which effort we remove, and which we keep. Things like publishing original research, human editing, collaboration… These aren’t inefficiencies to optimize away with our robot friends. They might look like extra steps. But they’re often where the greatest value enters the work. It’s a cautionary tale about the value of the long road in a shortcut world.”

---

In 2026, it seems marketers are looking for shortcuts. Most marketers are doing less. It’s as if the entire industry has pulled back. There’s broad disinvestment in content despite the fact that the fundamentals still work just as well as they ever have.

This chart shows both the correlation of each approach with performance (blue text) and the decline in that approach over the last year (red bar). The reason for declining performance is clear.

Meanwhile, content marketing has gotten steadily harder over time. Over the last eight years, we’ve asked which channels are getting harder. Nothing has gotten easier since 2019.

The problem isn’t that content marketing is less effective. The problem is that content marketers stopped doing that most effective content marketing. This is clear in the data.

What works now in content marketing?

Here is the correlation between content strategies and marketing performance. There’s a lot of practical insight in this chart. It shows what’s working now and why a minority of content programs are still driving business outcomes.

If your content program involves consistent influencer collaboration, long-form keyword-focused articles, original research reports, and webinars, you are very likely still seeing strong performance from your content program.

Let’s look at nine of the difference-making aspects of content strategies more closely…

1. Elbow grease still makes a difference

Marketers who put more effort into each piece are still more likely to drive marketing results, although outcomes are less certain than before.

---

Mark Schaefer, Keynote Speaker, Best Selling Author “The research presents two truths and a mystery. Truths: The most effort produces the best results. The least effort produces the worst results. Now the mystery: In the middle range of effort, more work is not necessarily a predictor of business results. Perhaps a new class of blogger is emerging who puts in the “right” type of human work. They create unique insights and original research, but they also know how to use AI as an honest and efficient co-worker for mundane research and editing. Honestly, I believe I would be in this new category.”

---

2. Collaboration drives performance

The most performant content marketers don’t go it alone. The most effective marketers work with experts and influencers. They pitch articles to editors, putting their content directly in front of new audiences. And they earn mentions by publishing citable research.

We are in a post single point-of-view era for content marketing.

Red bars show how many marketers do it. Blue bars show how many of those marketers report strong results.

Influencer collaboration is the single best predictor of success in the dataset. Those who do it beat the benchmark by 2.6x. Sadly, it’s also the most abandoned: down from 25% of marketers in 2017 to 7% today.

Yikes. What happened to working with friendly experts?

3. Visual content outperforms

The more visual the content, the more likely it is to drive performance. Yet most content marketers add very few visuals to a typical piece.

Put those visuals and collaboration together and you have a piece that is filled with images and multiple points of view. What else checks those boxes? Social streams, which as we know are highly engaging. The more your content looks and feels like a social stream, the more likely it is to perform well.

4. Content marketers who consider keywords outperform

SEO is alive and well. The big change is that AIs are doing searches on behalf of buyers. But the search is happening, keywords are used, and content that is easy for machines to retrieve performs well.

Not every URL is a keyword opportunity. But the content marketers who understand search, know how to measure topical demand and align pages with phrases are much more likely to report success. SEOs make good content marketers.

Despite the fact that keyword research still correlates with performance, content marketers are less likely than ever to consider keywords in their content. This is another effective strategy that content marketers are skipping.

5. Some content formats perform much better than others

Blogging isn’t enough. Content can be produced in many formats and some formats perform much better than others. Unfortunately, the most effective formats are also the least common. The least effective formats are the most common.

Here again, we see the correlation between performance and the more collaborative, more visual formats. Again, red bars show how many marketers do it. Blue bars show how many of those marketers report strong results.

6. Original research still outperforms

Some content programs publish new research, creating new data and statistics that didn’t exist before, making the brand the primary source for new information. This is something AI cannot do, so it’s a highly differentiated format in the era of AI generated content.

Yet content marketers have also pulled back from this strategy. After rising in popularity for nearly a decade, fewer marketers now publish new research studies, despite the fact that it improves the likelihood of performance by 50%.

---

Cyrus Shepard, Zyppy SEO “Wow. The idea that bloggers are retreating from original research is completely counterintuitive, as Google and AI engines have shifted to preferring exactly this type of content. That, and AI tools actually make original research easier to produce. While “strong results” for publishers are down across the board, happy to see research publishers fare better.”

---

7. Human editors drive content performance

Some marketers edit their own work. Some tell AI to do it. But the marketers who have a more formal approach with human oversight are much more likely to drive performance. They have built-in defense against AI slop.

---

Eddie Shleyner, Very Good Copy “Not surprising at all that the AI group reports worse results than the benchmark while the human-editor group beats it by nearly 2X. Context is everything. Nothing works the same way twice. It all depends on the current circumstances. Figuring that out is what thinking, feeling humans are good at.”

---

8. Some promotion channels work much harder than others

Here’s yet another perfect inverse correlation between popularity and performance. The easiest and cheapest promotion channels are the least likely to drive results. The more difficult, more expensive promotion channels are more likely to drive results.

9. Not all marketers measure

Our last popularity-vs-success correlation is about measurement. We’ve seen this every year since the inception of this research in 2014. Data-driven marketers are more likely to report success. Those who check performance are more likely to see performance.

But what are marketers tracking? And how does tracking that metric correlate with results?

We asked respondents which metrics matter most, then grouped them into three categories. Here we see both the popularity of these types of metrics and the likelihood that those marketers report “strong results.”

Traffic is still one of the most common ways to measure performance. But it’s become less meaningful in the era of AI, where buyers ask AI for a recommendation.

• If AI recommended the brand, the buyer then clicks a citation or searches for the brand or types in the domain directly. They’re ready to act. The source of this visitor is hard to see, but the conversion rates are high.
• If AI didn’t recommend the brand, nothing happens at all. There’s nothing to measure.

So in the AI era, traffic goes down and conversion rates go up.

Traffic was always an imperfect metric. It always suffered from Goodhart’s Law:

“When a measure becomes a target, it ceases to be a good measure.”

Of course, the best marketing metrics were always in your CRM: qualified leads, deals and revenue.

Most content programs are just blogs

We just listed eight content strategies that correlate with performance. We’ll set aside measurement because it isn’t a strategy. It’s how you know whether the others are working. So we’ll stick with eight.

How many do you do?

The more of those strategies you use in your content program, the more likely you are to win. Use six or more, you are nearly three times as likely to report strong results.

How many respondents do all eight? Zero. None of the 1,042 respondents do them all.

Note about Orbit’s own content program: We publish 6-8 research studies per year. All articles include many visuals and quotes from people smarter than us. Amanda edits everything before it gets published. We run monthly events: a webinar and a LinkedIn Live. We host an annual conference, Content Jam. Our program took years to build but it works like a champ.

Finally, our own reflections on content strategies after 19 years of publishing and 13 years of conducting this survey…

Too many content programs are just blogs. The strategy is simply to put out an endless series of text heavy, how-to articles, promoted through email and social media. They rarely use video and the content doesn’t come from a specific person.

They may write keyphrase focused blog posts, but search traffic to articles that rank for information-intent phrases has been completely torched. It will never come back to previous levels. These marketers measure traffic and they’re sad.

There’s very little creative consideration of how topics, formats and influencers combine and which combinations connect best with their specific audience.

But a small minority of content programs do things very differently. Influential experts are built into the program. Guest posting is built into the program. Annual research studies are built into the program. Events such as webinars and in-person meetups are built into the program. (By the way, events are one of the few ways to get a “deadline” into your content.)

AI makes you faster, but speed isn’t the point. If the strategy is weak, you’ll fail with or without AI. Put time spent per post, the number of strategies, and the odds of “strong results” on a 3×3 grid, you’ll see that faster isn’t better. It’s clear why AI doesn’t correlate with performance.

AI can be used for better performance or it can be used to create spam. And some content out there has so little value, it really is just spam.

Content at scale is a crazy idea, at least in B2B, because it misses the point of publishing entirely, which is to foster relationships (networking), show unique expertise (trust), and create early awareness (brand). And yes, eventually, leads and revenue.

When first I saw the response data, I reached out to Ann. I said, “I’m sad to say that the data is a bit concerning. The performance of content is down.” She replied, “I agree that the data is concerning… but also I kind of hope it’s a wake-up call, TBH.”

There is no better takeaway.

I love content marketing, partly because I dislike ads (the other kind of marketing). I believe that good content marketers make the internet better. We create value for real people. In the process, we drive business growth, personal growth and friendships.

ProTip: Give this report to AI and have a serious conversation about your content strategy. Make it a PDF so it can see the charts. Yes, PDFs require more resources for AI to process (energy, carbon emissions, tokens and money) but it’s worth it if it guides you toward a more effective and efficient content program.

At the start of summer (~3 months ago), I built my own LLM visibility tracker using Claude Code.

The idea was simple: Track the AI-generated answers and citations across the four major LLMs (ChatGPT, Claude, Gemini & Perplexity) for 12-40 prompts per client, run weekly.

I also tracked the Google SERP results with similar or identical keywords to compare traditional Google results to AI chatbot answers. I wanted to understand what each engine was citing, how the answers and citations varied across LLMs, and how they changed over time to answer a few simple questions:

1. Do the different LLMs agree on who to cite? Do they agree on who to recommend?
2. How do citations and recommendations change over time, from one run to the next?
3. Do the Google SERPs line up with AI-generated answers?

The purpose was three-fold; to play around with building my own tools with AI, to track AI visibility for my SEO clients, and to gather some data on what’s working right now in AI search.

I’ve recorded 13,184 citations and 1,765 answers so far. This article aims to break down what I’ve learned.

Methodology: How I collected this data

The setup is simple enough to replicate. I asked Claude Code to help me build an LLM citation tracker that scrapes the text answer and logs every URL each model cites, plus whether each brand gets mentioned in the answer text, and runs every week. Then I did some testing and tweaking till I liked the result.

I tracked 72 prompts across three B2B brands:

1. My own website (billwidmer.com)
2. My friend’s Amazon agency (evolveadagency.com)
3. My client, Semrush (semrush.com)

I included questions like “best all-in-one SEO platform for marketing teams” and “is it worth paying an agency to manage Amazon?” Every week, a script runs each question through four models: ChatGPT (with web search forced on), Claude, Perplexity, and Gemini.

The citations and answers went into a spreadsheet:

This is part of the AI visibility audit I run for all of my clients. That article walks through the full audit, and you can grab my exact tracker setup there.

Now, there are some limitations to this data I’m aware of:

• 12 to 40 questions per brand is a directional sample, not statistical proof.
• My Claude data comes from an agent-with-search setup rather than the raw API, so its citation style may differ slightly from claude.ai. The other three are run with API.
• Gemini sometimes only reveals the domain it cited, not the exact page.

Data through August 23, 2026.

A note on mentions vs. citations

For clarity’s sake: A “mention” is when the AI names or recommends your brand in its answer. A “citation” is when it links your page as a source.

In my data, citations are almost a strict subset of mentions: out of 1,052 query runs, only 17 cited a brand’s page without also mentioning the brand. Mentions without citations, though, are everywhere.

Across the whole dataset:

• Claude: brands mentioned in 70% of answers, cited in 40% (a 30-point gap)
• Gemini: 72% mentioned, 41% cited (31 points)
• Perplexity: 67% mentioned, 44% cited (23 points)
• ChatGPT: 56% mentioned, 47% cited (only 8 points)

ChatGPT is the outlier. When it names a brand, it usually links the brand, too. Claude and Gemini talk about brands freely without sourcing them.

Finding #1: The models almost never cite the same sources

Across 1,792 query-and-domain combinations, all four models only agreed on citing the same domain for the same question 30 times. That’s 1.7%.

Even the two biggest names barely overlap. On one brand’s question set, ChatGPT and Perplexity shared 7.6% of their cited domains. On another, 5.4%.

Each model appears to have a consistent sourcing personality:

• Perplexity cites heavily: 19.2 sources per answer on average. Its all-time favorites in my data are LinkedIn (374 citations), YouTube (248), and Reddit (232).
• Gemini averages 8.1 sources per answer and leans on Reddit and listicle roundups.
• ChatGPT averages 4.5 and skews towards primary sources like Google’s own documentation, arXiv papers, and vendors’ pricing and help pages.
• Claude averages 3.6 and loves review sites and curated listicles. The review platform Clutch is its single most-cited third-party domain in my data.

The most interesting finding, in my opinion, is that across hundreds of answers, Claude never cited Reddit once.

Andy analyzed 150 SEO articles about AI search earlier this year and found that 79% of them recommend building a presence on Reddit and communities. His conclusion was that sources are category-specific and prompt-specific, and that for B2B brands “it’s probably not Reddit.”

My data backs him up. In last week’s run across 72 B2B buyer questions, Reddit was 0% of Claude’s citations, 0% of ChatGPT’s, 3% of Perplexity’s, and 1% of Gemini’s.

This also tracks with ChatGPT’s recent update that slashed Reddit citations to near-zero.

What got cited instead? Niche listicles, review platforms, and comparison posts.

Finding #2: The models don’t agree with Google, either

Ranking on page one of Google is neither necessary nor sufficient for getting cited by AI. For the questions in my tracker where I could map the conversational prompt to a keyword search, I pulled Google’s top 10 organic results the same week and measured overlap with what each model cited.

• At the domain level, only 10% to 30% of the domains the models cited also ranked in Google’s top 10 for the matching query, depending on the model and question set.
• At the exact-URL level, it collapses: roughly 1% to 5% for Gemini and ChatGPT. Perplexity tracks Google closest, and even it only overlaps 13% to 23% at the URL level.

Growth consultant Lara Stiris told me a story that backs this up: Researching accounting software for her own business, she asked AI for recommendations and the biggest incumbent in the category never came up.

“All that came up was startups… companies that were newer, that had more social interactions around them,” she said. A brand with enormous Domain Authority and a mountain of ranking content, absent from the conversation. “That to me is entirely different from SEO.”

Before you conclude SEO is dead (it isn’t), here’s the balancing data point: Fruzsi Peti, a startup marketing consultant I interviewed, told me her clients earned AI visibility without any dedicated AEO budget.

“We have been focusing on PR activities… and what we noticed is that that kind of activity already put us forward in agentic search.”

Strong fundamentals still feed the machine. The assets overlap even when the URLs don’t.

Finding #3: The models don’t even agree with themselves

I ran the same 72 questions through the same models twice, two days apart. Claude kept only 30% of the URLs it had cited two days earlier. Gemini kept 38% of its domains. Perplexity was the most stable at 65%.

In Andy’s off-site optimization piece, Britney Muller cautioned that AI outputs are non-deterministic and that even ten runs of a prompt give you “a very crude directional signal.”

The takeaway? A single AI answer is weather. A tracked rate over weeks is climate. Never make a long-term strategic decision (or panic) based on the weather.

Bonus: The more famous you are, the less your citations tell you

One interesting thing I noted is that Semrush, which has a huge online footprint, had a 50-point gap between mentions and citations. Compare that to my website and Evolve Ad Agency, which had a 5- and 4-point gap (as of last week’s run).

Semrush (an established SEO software company) gets mentioned in 83% to 100% of relevant answers on every model. Its citation rate on those same answers ranges from 83% all the way down to 0%. In last week’s run, half of its query runs were mention-without-citation.

Here’s my theory: Famous brands live in the models’ training data, so the AI recommends them from memory while sourcing the answer from third-party pages. Unknown brands only get mentioned when the model actually retrieves and reads their site.

Granted, Semrush has far more content than either of the other two sites, so this could also just be that my brand and Evolve’s brand were only mentioned when their name was in the prompt. But this could also back up the theory that AI doesn’t pull citations when it has sufficient training data.

Which leads to a rule I haven’t seen anywhere else: citation dashboards understate visibility for big brands and track it almost one-to-one for small ones. 

A 0% citation week means something completely different for a household name than for a startup. Know which end of that spectrum you’re on before you panic, and before you let a vendor scare you with it.

Conclusion: You don’t need four different strategies. You need one comprehensive one.

After all this divergence data, you’d expect me to prescribe a per-engine playbook. Reddit for Gemini, LinkedIn for Perplexity, etc.

Nope! I actually think the answer is creating a single, comprehensive marketing strategy. My belief is that AEO/GEO is not just SEO. It’s plain ol’ good marketing.

But don’t take my word for it. I interviewed Nick Lafferty, Founding Marketing Engineer at Profound, the AI visibility platform. When I asked whether they optimize differently for different engines, given the variation in their own data, he was blunt:

“Candidly, we’re not doing things differently for different engines. We’re kind of just optimizing for AI search broadly as a bucket.” Their priority is doing genuinely interesting things (original research, a podcast, even subway ads), knowing the AI visibility follows: “It’s like, let’s do this thing because it’s cool. We think our audience will like it. And then it’ll have knock-on benefits in AI search.”

Lara Stiris pushes the same direction from the measurement side. She warns that people treat AI visibility data “as the absolute truth versus directional, because they don’t always understand the methodology underneath it,” and that plenty of vendors are “overselling what they actually do.”

So here’s how I reconcile my divergence data with their engine-agnostic advice: the work itself (real expertise, consistent entity information, presence on the sources that keep showing up) is one strategy that feeds all four engines at different rates.

The 4-step playbook

Here are the four things I’m focused on based on my findings:

1. Build a list of real buyer questions and track it weekly. Pull questions from sales calls and support conversations, not keyword tools. Andy’s sales-call transcript method is the best version of this I’ve seen. Track both mentions and citations, per model.
2. Find your category’s gatekeepers in the citation data. List every third-party page cited instead of you, count repeats, and pitch the ones that keep appearing. One inclusion on a page that all four models trust beats four engine-specific tactics.
3. Judge trends on three-plus weeks of data. With 30% to 65% week-to-week churn, single snapshots will make you chase ghosts.
4. Keep doing the fundamentals. The engines disagree on URLs, but they all reward the same underlying things: real expertise, consistent entity information across the web, and third-party validation. That’s why one strategy can serve four disagreeing machines.

At the end of the day, this is a fun experiment I ran. If you notice any other flaws or gotcha’s, DM me on LinkedIn and I’ll try to address them!

I’m in a client meeting. We’re reviewing Analytics. They notice a lot of “Direct traffic” and they ask me what it includes. I start explaining …and they are surprised by the answers.

This isn’t unusual. I’ve found that a lot of marketers don’t fully understand what it includes or appreciate its value. Yet a huge amount of traffic in GA4 is “Direct.”

It’s probably at or near the top of your own Traffic acquisition report. It almost always is.

Direct traffic is the largest of all traffic channels. We looked at 145 Google Analytics accounts and “Direct” was the number one or number two channel for 92% of them. Across the 56 million sessions recorded, 36% of traffic is categorized as direct. That’s a lot.

For the biggest and best-known brands, Direct is by far the largest source of traffic. According to Similarweb’s benchmark report, for the highest-traffic sites, 65% of their traffic is Direct. The stronger your brand, the more direct traffic you have.

“Direct” is probably the least understood and most important traffic channel.

Most marketers treat direct traffic like an Analytics problem. But it’s actually the best brand metric in your analytics. And it’s also the only channel big tech can’t take away from you.

Today let’s take a few minutes to refresh ourselves on this underrated metric. This little guide will answer three questions:

1. What traffic gets categorized as “Direct?”
2. What does this metric really measure and why does it matter?
3. How can marketers increase direct traffic? ← I’ve been in a million meetings, but I’ve never once heard this question. 

Scan through and see if the answers surprise you.

What traffic gets categorized as “Direct?”

All kinds of traffic, really. Here’s a quick list showing what they did, then what it means.

1. Visitor typed in your URL (brand awareness or offline exposure)
2. Visitor clicked a bookmark or selected your URL from browser history (prior brand experience)
3. Visitor clicked a link in a file: PDF, PPT, DOCX, etc. (engaged with content beyond the website)
4. Visitor clicked a link in an app: email, Slack, SMS, AI, etc. (recommendation or word of mouth)
5. Visitor arrived through any link where attribution was lost (tracking issue)

Yes, it could be that your URL was typed into an address bar (we all knew that). But it could also be any untagged link clicked in anything that wasn’t a browser (like a QR code). Or it could be just a tracking issue (a bad UTM parameter or a redirect).

In the addendum at the end of this guide, we’ve added a flowchart that shows exactly how GA4 decides how to categorize traffic.

What does direct traffic really indicate?

Look back at the list. Here it is again but with each item flipped:

1. Brand awareness or offline exposure (Visitor typed in your URL)
2. Prior brand experience (Visitor clicked a bookmark or selected your URL from browser history)
3. Engaged with content beyond the website (Visitor clicked a link in a file: PDF, PPT, DOCX, etc.)
4. Recommendation or word of mouth (Visitor clicked a link in an app: email, Slack, Teams, SMS, AI, etc.)

Looks different, doesn’t it? Suddenly, “Direct” looks like an excellent indicator of awareness and reputation. Rather than an attribution problem, it’s a brand measurement metric.

Direct traffic is a “Brand Strength Indicator.”

This is a strange idea for performance marketers, but perfectly natural for brand marketers. (Yes, these are the two kinds of marketers). And because most GA4 users are performance marketers seeking accurate attribution, direct traffic is highly underrated. Here’s a lighthearted take on the differences in perspective…

---

Two marketers walk into a bar. Each orders a Direct Traffic Special.

The bartender says, “Here you go. It’s a mix of everything I couldn’t identify.”

The performance marketer asks, “Where’d this come from? I have no idea what’s in this.”

The brand marketer says, “Who cares? Everybody here knows its name. We need another round!”

---

If the goal of marketing is to grow a brand, then direct traffic is your best indicator of success.

Once you have an established brand, you have short-circuited all of the digital marketing channels. You are no longer beholden to big tech to send traffic. You are less reliant on advertising. The visitor doesn’t search or ask AI. They come straight to you. They visit directly.

---

Wil Reynolds, Seer Interactive “People are sharing links with other people. I can’t track their referring source, so that’s direct traffic growing. These trust channels are so much more efficient. The problem is, they’re really hard to track. We like the easy to track shit.”

---

The power of branding is right there in your GA4 data. You can see it in two ways: changes in direct traffic (Traffic acquisition with a YoY comparison) and the conversion rate from Direct (session key event rate for a high-intent action such as lead generation).

When we look at those across a large number of GA4 accounts, two things become obvious: consistency and quality. Let’s look at each.

Direct traffic is undisrupted

It’s a steady source of traffic over time, undisrupted by changes to search. In fact, as a share of all traffic, it’s on the rise. Here we see the percentage of traffic that is categorized as direct across 86 accounts with 140M visits over 3.5 years.

Direct traffic visitors drive demand

It’s a consistent source of form fills. Organic search and referral traffic convert at higher rates, but Direct is a huge contributor to total demand generated.

We looked across 143 properties and found Direct was the second-largest source of leads, producing 27% of all demand.

Note: Some “organic search” traffic is really people searching for the brand (navigational queries) which are really the same as direct traffic. So the actual power of the brands is stronger than any direct traffic analysis shows.

---

Goldie Chan, Founder at Warm Robots & Author of Personal Branding for Introverts “Strong branding means that you will constantly be driving direct and returning traffic. It’s consistent visibility from warm visitors. It also allows you to be less reliant on search engines for awareness.”

---

The quality of this traffic is excellent. The conversion data shows two things about direct traffic.

• Direct has high conversion rates for high-intent actions (contact us, request a demo, get started, etc.)
• Direct has low conversion rates for low-intent actions (subscribe, download resources, etc.)

As a brand becomes better known, it becomes less dependent on organic and paid channels for discovery and demand. Prospects stop looking for it and start going to it directly to get help. Awareness and trust are in place.

Traffic consistency and quality. Two things every marketer should love. Which brings us to our final question…

How can I increase direct traffic?

It’s a question no one is asking. But we’ll answer it anyway. Here are 10 ways to increase direct traffic. It’s basically a list of actions that build brands but drive unattributable traffic. We’ve ranked them by likelihood of business impact for B2B brands.

1. Referrals and word of mouth
   In project close meetings, at quarterly check-ins, ask if they know anyone else you can help. Build reciprocal relationships with partners. A genuine recommendation converts better than anything else …and shows up as direct.
2. Publish original research on an annual cycle
   Recurring studies (“State of X 2026”) are the compounding assets. They create a reason to return on a schedule, year after year. Because they get bookmarked, cited and revisited …they show up as direct.
3. Run an email newsletter with real subscriber loyalty
   Consistent email programs drive genuine brand awareness and repeat visits. And if the links are untagged or their email program strips the referrer …they show up as direct.
4. Speak at events
   Give a presentation to a room of people in your target audience. If it’s practical, relevant and memorable, the attendee then types in your URL (or scans an untagged QR code in a slide) …they show up as direct.
5. Be a guest on (or sponsor) podcasts
   Listeners can’t click. This is why podcast performance attribution is a nightmare. But if the listener was impressed or the host-read sponsorship was relevant, they type your brand …they show up as direct.
6. Build a free tool
   Tools get revisited in ways articles don’t. For example, if someone bookmarks our AI Bot Access Checker and uses it every month …they show up as direct.
7. Get active in online communities
   Your future buyer may be active in private professional groups. If someone recommends you in Slack or WhatsApp …they show up as direct.
8. Sponsor something: industry and community
   Industry association sponsorships (events, emails, logo placement) grow visibility with the same qualified audience repeatedly. Community sponsorships (sports, schools, nonprofits, fundraising events) do the same locally, and support causes you care about. When these people eventually visit …they show up as direct.
9. Local broadcast: TV and radio
   Broadcast has been written off by most B2B marketers, which is why it’s uncontested. The geographic and demographic targeting may be excellent, but because they don’t give listeners or viewers anything to click …they show up as direct.
10. Invest in offline marketing: print, swag, signage and direct mail
    Yes, it may be the lowest measurable ROI with the longest lag. Like broadcast, it’s a category that performance marketers have abandoned. When offline impressions drive visitors …they show up as direct.

None of these produce clicks you can accurately trace, which is why they’ve been defunded by the performance marketers and optimizers. But the audience they attract converts on high-intent actions at roughly the rate a search visitor does.

One more channel belongs on that list…

What about AI mentions?

AI search optimization is similar to word-of-mouth marketing. It’s just the words of an artificial mouth. And like WOM, traffic is low but conversions are high. AI traffic is both part of a complex buyer journey and difficult for Google Analytics to track.

• Someone reads an AI answer, leaves the conversation, and types your URL later. That’s genuine brand awareness …and they show up as direct.
• Someone clicks a link in an AI answer and GA4 fails to classify the referral. That’s a measurement problem …and it inflates direct traffic numbers.

Both of these do the same thing: direct gets bigger. And yes, GA4 now has an AI Assistant channel, but it catches only some of this traffic. Yet again, the rest land in direct.

Direct traffic is evidence your marketing is working

Direct isn’t just an Analytics problem. And it really isn’t a channel you can optimize. It’s more important than that. It’s about delivering massive, consistent volume from people who already care about your brand.

It’s the outcome of building a brand.

Because of its value and challenges, I have two predictions in the years to come…

• I predict an increase in direct traffic, especially as a share of all website traffic. After all, it’s one of the few metrics that isn’t subject to changes in big tech.
• I predict an increase in the importance of this metric, especially as attribution gets harder because AI becomes a bigger part of the buyer journey.

Addendum: How traffic is categorized by GA4

Marketers work hard to fix tracking issues and tag links for more accurate tracking. That’s good. But in the end, “Direct” is still the “Google doesn’t know” bucket. It’s last in the decision flow that GA4 uses to categorize traffic.

Ever seen this? This is the flowchart that shows how mediums, sources and channels work in Google Analytics:

The technical definition of all 18 traffic sources is in our complete guide on traffic sources. That’s the one with the boat metaphor some people like. Direct traffic (and branding) is the current under the boat.

There’s so much talk about what AI can do. But what can’t it do?

Years ago, my friend Steve Rayson published a report that analyzed the performance of a million articles to see what gets links and shares. His conclusion changed my content strategy forever…

---

Steve Rayson, Founder of BuzzSumo “If you want to create content that achieves a high level of both shares and links, then you should concentrate on opinion-forming, authoritative content on current topics or well-researched and evidenced content.”

---

He’s telling us that two types of content consistently outperform: strong opinion and original research. This was seven years before the launch of ChatGPT, but it’s more relevant today than ever.

Why? Because AI can’t make this kind of content. Only humans can.

Let’s explore these types of content and reconsider our own content strategies. Let’s future-proof our content for the AI-writes-lots-of-everything era. And let’s consider a few AI prompts to help us make these kinds of articles. Because it can.

Why can’t AI do this?

Everything AI does is based on what it has already read. It summarizes, restructures, rephrases, and averages. We sometimes joke that AI should stand for “Average Information.” Yes, averaging the internet is genuinely useful. And yes, most content gets better when you use AI.

But two kinds of input are not in the AI training data:

1. The thoughts in your head: your experience, your perspective and the opinions you’ve formed over years of doing the work
2. The data you create through research: Both new research and the data you may already have in your business, which are numbers only your company can see

These are the two categories of input that come from outside the machine, out of reach from AI and physically impossible for it to generate.

Let’s look at each and (ironically?) look for ways to use AI to surface novel ideas for the content strategist.

Type 1: Your own opinions (thought leadership)

We humans have experiences that shape our perspectives and opinions. But AI has no experiences, no perspective and no opinions. AI models are trained to find the middle, where people agree. AI never takes a stand.

Ask AI for a contrarian take and you’ll get a confident sentence, but it’s probably something that no one would disagree with. But remember, if you can’t disagree with it, it isn’t thought leadership. Most content marketing doesn’t meet the definition of thought leadership.

Here’s the test that separates a real position from a fake one: could a smart, informed person in your industry sincerely argue the opposite? This, from legit marketing thought leader Seth Godin:

---

Seth Godin, Marketing Legend “Thought leadership always creates tension. It’s about making assertions. You have to be willing to be wrong. You can be certain that some people will disagree.”

---

But that doesn’t mean you have to be controversial. No need to touch the third rail. There are many relatively mundane topics that still qualify as thought leadership. Here are a few thoughts in my head:

• “Delete TikTok and Instagram. The more boring your phone, the more productive you’ll be.”
• “Marketers should never use themselves as examples in meetings. You’re a dataset of one.”
• “I would never wear clothing with logos, unless it’s my brand or a brand that I actually support.”

Did you feel something when you read those? Those statements aren’t really controversial, but they create a bit of tension, don’t they? And guess what? None of those sentences could have been written by AI.

Those assertions don’t really fit in my content strategy. I was just being provocative. To be a fit, the counter-narrative has to be safe enough to say on a podcast or in front of clients, but spicy enough that someone wants to reply.

Strong opinion content drives engagement in social media.
The opinion drives responses from other users; those responses trigger the algorithm; the algorithm shows it to many more people. That’s why opinion-based content is catnip for social streams.

You can also differentiate your content from AI just by adding a bit more of yourself in your content. Share a little story. Add some humanity. Be yourself. Just look back at the first four words of this article. AI can’t write that.

AI can’t write opinion-based content. But it can help trigger ideas. Here’s a prompt that lets you use AI to find the one thing AI can’t do. It’s a fun one, I think…

Thought Leadership (provocative, not controversial) Topics Prompt

My role / industry: [job title + industry]
Positions I actually hold (optional): [any contrarian beliefs, pet peeves, or things you think the industry gets wrong]

You are an expert content strategist who finds thought leadership angles that get quoted. Your job is to surface positions in the Sound-Bite Zone: contrarian enough to make someone stop scrolling and want to reply, but safe enough that a real executive would happily say them on a podcast and put their name to them. You are not looking for controversy, hot takes that could damage a reputation, or anything political, personal, or inflammatory. The target is “spicy to say, safe to own.”

Work through these five veins of provocative-but-safe opinion for this role:

1. Consensus is wrong — a widely accepted best practice, common piece of advice, or belief about a trending topic that this person could credibly argue is mistaken or even harmful.
2. The unspoken — a mundane or unglamorous topic people in this role hold surprisingly strong opinions about, or a question they’re quietly afraid to answer out loud.
3. The buried lede — an important topic that industry blogs and thought leaders consistently ignore or under-cover.
4. The outsider lesson — a seemingly unrelated field or discipline that holds a crucial, transferable lesson for this industry.
5. The reframe / forecast — a common fear this role thinks is overblown, a counterintuitive approach that quietly works, or a prediction most peers would call unlikely.

Step 1: Generate two candidate positions per vein (ten total).

Step 2: Commit. Score each on provocativeness 0–5 (does it stop you and make you want to argue?) and flag reputational risk as green (safe to say publicly), amber (needs careful framing), or red (genuinely controversial — cut or heavily caveat). Rank all candidates and drop anything below 3 on provocativeness or flagged red. Winners are high-provocativeness, green or amber.

Step 3: For the top [3–5] survivors, write the actual sound bite: one quotable line, 15 words or fewer, that a journalist or podcast host could pull verbatim. A topic isn’t finished until it has a line.

Present the results as a table: the position, which vein it came from, its sound bite, provocativeness score, risk flag, and a one-line note on why it lands without crossing into controversy. Lead with the strongest.

Type 2: Your internal data or stats you produce (original research)

New research is the other category of content that will be forever differentiated from AI content. There are two obvious reasons:

• AI doesn’t spontaneously generate new primary research. No matter how smart the AIs get, they’ll never do this.
• AI doesn’t train on the data that you have inside your business. No matter how smart the AIs get, they’ll never have this. They have no access.

The benefits are big, but different from the benefits of thought leadership. Original research drives search rankings. Research attracts links. Links drive Domain Authority, which is the ranking potential of every URL on your website.

Research is also more likely to get clicks from citations in AI responses, maybe because people want to check AIs sources. If you’ve ever published research, you can confirm this yourself by using GA4 to check which of your pages gets traffic from AI models.

If you need an example of research in action, look at how I opened this article: I cited Steve’s research. Why? Because it supports my thesis. His study has also been linked to from 1,400 other websites. I checked.

Here are some other recent examples of research from the Orbit Media blog:

• AI crawls homepages 15x more than any other type of page
• Traffic from AI sources convert at 3x the rate of traffic from other sources
• X (FKA Twitter) is now the spammiest social network

And of course, research can become charts, and these visuals make for very compelling content. Here’s one from our annual blogger survey. Here you can see how repeating your research annually makes it more valuable over time.

AI can make charts, but it can’t produce the data behind the charts. AI doesn’t conduct new research. It just summarizes and cites data it finds.

So original research works. But what kind of research? You’re looking for a “missing stat” which is a data point that doesn’t yet exist on the internet. It fills a data gap for a frequently-asserted-but-rarely-supported assertion people are already making.

The internet is not finished yet.

The best research studies produce data that is useful and that is proprietary to your brand. It makes your website the primary source for new information.

Add your own hot take to the report (or fill it with quotes from subject matter experts and influencers) and you’ve added a splash of thought leadership to your research. Once complete, stand back and look at it. It will be obvious that AI never could have made any of it.

But amazing data isn’t enough to drive performance. The best content doesn’t win. The best promoted content wins. So before you publish a new report, scan through our Original Research Promotion Guide. It may give you a few ideas for better reach.

Trouble thinking of research topics? Here’s a prompt that may trigger a few ideas. It starts by asking you about the data you may already have…

Original Research Topic Prompt (Proprietary Data Edition)

My company: [name + industry]
Data I know we have (optional): [product usage, sales/CRM, support tickets, transactions, survey list, etc.]

You help me find original research topics that earn citations from journalists, editors, and bloggers. The best topics sit where two things overlap: a claim my industry repeats constantly but has never measured, and proprietary data we already hold — or could cheaply collect — that could settle it. No one else can publish our data, which is exactly why it gets cited.

Step 1: Before generating anything, ask me 3–4 questions to surface data assets I may have overlooked: what we capture through the product, what prospects keep asking that I could answer with numbers, a stat I cite internally but have never published, or what a quick survey to our list could reveal. Then wait for my answers.

Step 2: After I answer: list the claims my industry asserts but rarely supports with data, then filter to the ones our data could credibly prove or disprove.

Step 3: Give me the [3–5] strongest topics as a ranked table. For each, include: the industry claim it settles; the proprietary data that settles it; research approach (survey / analysis / experiment); why we’re uniquely credible to publish it; the headline a journalist might write; a note if a comparable study already exists; and a 0–5 score for newsworthiness and 0–5 for feasibility, each with a one-line rationale.

Rank by newsworthiness × feasibility, and lead with the one I could realistically ship soonest that still makes news.

Flag any topic relying on sensitive data (customer info, CRM, support content) so I can plan for aggregation or anonymization. Treat this exchange as confidential.

10 content formats that AI can’t make

Beyond the two categories of content, there are some content formats that AI can’t produce. I can think of at least ten. Do content in any of these formats, and you’ve done something AI can’t do.

1. Videos. Yes, AI makes video, but not as a named, human personality being themselves.
2. Conference presentations. A human from your brand, live on stage
3. Live webinars. Sure, the slides could be generated by AI, but a human handles the talking and Q&A.
4. Panels and roundtables. Humans having a real conversation. Perfect showcase for those spicy topics.
5. Podcast guest appearances. A human host asks questions. You, the human, answer in real time.
6. Workshops and training. A room full of humans, with someone from your brand leading the discussion.
7. Live teardowns and audits. Humans critiquing things in real time (like our monthly LinkedIn Live sessions).
8. Original photos and video of your team. Humans on camera in a physical office, keeping it real.
9. Contributor quotes and expert roundups. Actual humans, saying something they’d defend on the record.
10. Community spaces. Slack groups, forums, happy hours where humans gather to be with other humans.

The difference is obvious. Some of these aren’t even online. Some are older than the internet itself. These are the most human formats for content marketing and they aren’t going anywhere. They are immune to tech disruption.

Every content strategy should have at least a few of these in the mix. At Orbit, we do all ten of these on a regular basis. But if you could do just one? Our friend Jay Acunzo has a strong opinion on this…

---

Jay Acunzo – Speaker, Advisor, Coach “I believe the most defensible, irreplaceable, yet rare skill is your ability to speak well and deliver exceptional presentations, online and in-person.”

---

It’s easy to differentiate your content from AI

In fact, it’s natural. Most of the sentences we speak in conversations every day could never be written by AI. They are our own personal points of view. Yet most content programs rarely publish strong opinion content.

And few content programs ever publish new original data. Most websites are not the primary source for any data whatsoever.

Instead, marketers everywhere use AI to write and then use AI again to hide the fact that AI wrote it.

Sadly, one of the most upvoted “skills” in the AI skills directories is the Stop Slop Skill, which just tries to make writing sound more human (“Remove em dashes. Break formulaic structures. Avoid binary contrasts, dramatic fragmentation, rhetorical setups. No inanimate objects performing human actions. Vary rhythm…”)

This is absurd.

Let’s close with my favorite quote about AI from one of my favorite thought leaders.

---

Jay Baer – Author, Speaker, Advisor ““The problem is not that AI is doing what marketers can do. The problem is that marketers keep doing what can be done by an AI.”

---

I saw something strange in GA4 recently.

When I looked at the conversion rates across various traffic sources, I noticed that visitors who come from AI are much more likely to become a lead than visitors from other sources, especially when they land on the homepage.

You may see this in your own Analytics. But one GA4 account is an anecdote. Dozens of GA4 accounts would be better data. With enough accounts, we can answer the question:

Are visitors from AI sources more really likely to convert into leads?

To find out, we looked at 97 GA4 accounts across a range of B2B lead generation websites with a total of 29 million visits over one year, through June 2026.

Checking the conversion rates was tricky because these accounts have many different conversions (key events), named in many different ways. We manually categorized every key event. Leaving out “apply for job” along with many of the strange things people set up as key events (CTA clicks, contact page visits, scrolling, video views) that aren’t actually conversions.

We found the signal. Visitors who come from AI are 3x more likely to convert into leads than other organic traffic sources.

This pattern holds up on around two-thirds of sites individually, not just the aggregate. And when we looked at the per-site median (rather than all of the sessions and conversions pooled together), the conversion rates of visitors from AI sources are 7x higher.

Before we analyze the reasons this is true, let’s see what else the data holds.

High conversion rates, but very low traffic

Across 29 million visits, just 140,000 were from AI sources. Just 0.5% of all traffic was from AI sources. Wait, just 1 in 200 visitors? That is a tiny slice.

Organic search is still a huge traffic driver. Google is the entry point for the internet for millions of people. It may be just muscle-memory (we’ve all been using Google for decades) or the popularity of Chrome (with the Google homescreen).

When you put the traffic and conversion rates on a single chart, it’s striking how little traffic comes from AI sources.

Actual AI traffic is likely higher because traffic from Google’s AI Mode and AI Overviews are categorized as “Organic Search” in GA4, even though they are really AI sources. It’s not possible to separate those clicks from clicks on traditional search results. Also, traffic from AI apps is categorized as “Direct” because GA4 doesn’t know the source because the user isn’t coming from another page within a browser.

But we can look at the AI models separately. Here’s the breakdown of AI models across the 140,000 sessions from AI sources:

ChatGPT is by far the one AI that is most likely to send traffic to websites. It’s an all-purpose research tool, including research for buying decisions. Perplexity may position itself as a Google replacement, it isn’t sending much traffic to B2B websites. And the visitors who come from Perplexity aren’t as likely to convert.

The data also answers this question: What are ChatGPT vs Google Search conversion rates? ChatGPT converts 2.1% of visitors into leads and Google Search converts 0.5% of visitors into leads. But remember, Google is driving 100x more traffic. Organic search is still a more powerful source for lead generation.

Lead Generation vs. Subscribe to newsletter: Conversion rates from AI sources

Some conversions are higher in the funnel. The value of “download guide” and “schedule a call” are not the same. Good analysis means separating the high-intent and low-intent conversions. So all the key events in the dataset were carefully and manually categorized into two groups:

• High-intent conversions
  Contact form submission, request a demo, schedule a meeting, phone call clicks, etc.
• Low-intent conversions
  Subscribe to newsletter, download report, register for webinar, etc.

When you repeat the analysis, segmented by conversion type, the same pattern appears. Compared to other traffic sources, visitors from AI sources are twice as likely to subscribe to a newsletter, download a guide or register for a webinar.

Where do AI visitors land? Which landing pages convert them?

For insights into the pages these visitors are landing on, we categorized the pages into groups, using the same categorization method we used in the AI Crawl vs Traffic Report (home, services/products, articles, etc.).

Now we can see which types of pages attract the most visitors from AI. Also, which types of pages drive conversions and what rates from these visitors.

The landing page indicates where the visitor is in their decision process. So it’s no surprise that the higher-funnel page types have lower conversion rates. Visitors who land on articles may have no buying-intent at all. But those who land on a contact page? They’re ready to go.

The most surprising thing on this chart is that all of the conversion numbers are high.

Why are visitors from AI more likely to become leads?

Conversion is complicated. Many factors affect the actions of a visitor. Some may remember the MECLABS conversion formula from 20 years ago. It has weighted variables: C = probability of conversion, m = visitor motivation, v = clarity of the value proposition, i = incentive to act, f = friction in the process, a = anxiety about taking the action.

C = 4m + 3v + 2(i − f) − 2a

Skip the details for now and consider the premise: some conversion factors are more important than others. And the most important factor is the motivation of the visitor. MECLABS defined motivation as “the magnitude and nature of the customer’s demand for the product” but we’ll just call it intent.

If intent is the key to conversion, why would visitors from AI have stronger intent?

Here are four hypotheses:

1. The AI already did the shortlisting
By the time someone clicks through from ChatGPT, they’ve had a conversation. They asked for recommendations, they compared, they narrowed it down. The consideration stage happened inside the model before they hit your websites. They just came for confirmation.
Image source: Traditional Search vs. AI Search

This may also explain the low levels of traffic. People who are not a fit for your services are filtering out your brand earlier. AI sends fewer unqualified visitors. The internet is becoming more efficient at matching prospects with providers.

---

Joanna Wiebe, Author, The Copyselling System “Chatty-G is the ultimate Connector and Maven. When you add in your own website/presence that acts as the Salesman (persuasive, emotional, trustworthy), you’ve got the pieces in place to move someone to action. It is so wildly uncommon to click any of the source links that ChatGPT produces in its answers… so that, when you do click one, to me it indicates the depth of your desire to resolve the problem you went to AI with. Like, wow, you wanted a solution so badly, you actually clicked through on one of those little truncated grey-button linky things!!! You’ve essentially crossed digital hot coals (invisible grey buttons) to arrive on a brand’s website.”

---

Gaetano Nino DiNardi “Website visitors coming from AI search platforms are generally better informed due to the hyper-personalized and conversational nature of the search journey.”

---

2. The user already knows you. AI just helped them get to you.
Our high- and low-intent conversions are two of the three types of visitor intent. The third type is navigation intent. Some AI traffic is just like some regular search traffic: the visitor already knows you. They’re searching for your brand. They just asked AI to route them to your website. This helps explain the high levels of homepage traffic.

Either way, the visitor showed up late in the journey, which is exactly why they convert at higher rates.

3. AI responses feel like advice, not an ad
The user experience of an AI chatbot and a Google search are very different. Consider:

The visitor from an AI source… just had a personalized experience with personalized recommendations. They talked to an advisor. When the AI names your brand, it feels like word-of-mouth marketing.

The visitor from search… just clicked on a page filled with ads. They came to you through a digital shopping mall with dozens of options. They’re wary and primed to hit the back button.

4. AI users skew serious
Nobody opens ChatGPT to kill time the way they scroll on social media. They use it with purpose. They’re talking to a chatbot to do serious research. They show up with a problem to solve and a decision to make. That intent is baked in before they ever land on your page.

An honest final take…

After seeing this across several accounts for several years, I always suspected that AI traffic was more likely to convert. But the plural of anecdote is not data. So we went looking for evidence and found it. Here’s what the data actually supports, and what it doesn’t.

• AI referrals are rare. Half a percent of all traffic. That’s one visit in every 200, barely enough to be called a channel. But those visitors are much more likely to take action. Three times more likely than direct or organic search. That pattern holds on roughly two thirds of the sites individually. So we are confident in the pattern.
• AI sends genuinely better-qualified traffic. AI helps users qualify options by providing personalized recommendations. They’ve done their consideration-stage thinking within the model. And the sliver of people motivated enough to click a link out of a chatbot are probably decisive to start with.
• The AI traffic numbers are likely higher. Google’s AI Mode and AI Overviews record in GA4 as “Organic Search.” And AI mobile apps often strip the referrer, so those visits show up as Direct. Real AI traffic is likely higher than 0.5%

Check your own data. Build the GA4 exploration for AI traffic to see the pages AI actually sends people to (it’s probably your homepage). Now look at the conversion rates.

Are those pages answering the real questions buyers ask? Are they filled with supportive evidence? Do they name the buyer, process and outcomes? This is easy to check with the prompts in our 8-point AI-readiness checklist. Now go polish those pages to win more recommendations from AI sources.

And if you’d rather have someone else do that work, let’s talk.

Data & Methodology

Data and scope. GA4 data from 97 B2B and lead-generation websites, covering July 1, 2025 through June 30, 2026 and 28.9 million sessions. All sites had a minimum of 100 AI-referred sessions.

Defining AI traffic. AI-referred sessions were identified by pattern-matching session source and medium against a list of AI assistant domains (ChatGPT, Perplexity, Google Gemini, Claude, Microsoft Copilot and others) plus any session carrying the “AI Assistant” medium that GA4 now assigns, which began operating in mid-May 2026. The source-and-medium pattern was applied uniformly across the full historical window and catches referrers that the “AI Assistant” grouping misses.

Defining conversions. GA4 key events are configured differently on every site, so each event that fired during the window was tiered: high-intent (contact forms, demo and quote requests, phone-call clicks), low-intent (newsletter signups, downloads, subscriptions), or excluded (page views, scroll depth, account logins, and misconfigured events firing more often than there were sessions). This tiering is the most judgment-dependent step in the analysis.

Which of my pages does AI visit most? Where does it send visitors?

AI learns about brands by reading websites. And AI sends visitors to webpages through citations in responses. Marketers have adapted their strategies (and expectations) but mostly, we do this blind. We’re missing key information. We haven’t answered these key questions:

• What pages does AI read the most? What pages does AI rarely read? 
• What kinds of pages does AI send visitors to? 
• How deep do AI crawlers go into websites? 

The answers to these questions are in the reports from bot-tracking services like Cloudflare. Cloudflare tracks AI requests in the “Most crawled paths” report and visits in the “AI referral traffic” report. We looked at these reports across 74 accounts to discover what AI is doing with marketing websites.

After many hours of research and analysis, we’re ready to share the insights. Here is The AI Crawl vs. Traffic Report.

1. AIs crawl homepages 15x more than other types of pages

Here’s the first insight: AIs visit homepages a lot. Homepages get roughly 15 times more AI attention than other types of pages. Nothing else comes close.

If AI requests were evenly distributed, a page type that makes up 5% of a site’s total URLs would get roughly 5% of AI crawls. This is definitely not the case. Adjusting for the fact that the homepage is just one page competing against dozens or hundreds of others, the homepage requests are literally off the chart.

The homepage completely shatters the baseline. We carefully categorized the other URLs for comparison: service and product pages, articles, about pages, contact pages, pricing, and case studies. And even when combined, they get much less proportional attention from AI. AI crawlers don’t disproportionately favor any particular type of page. AIs disproportionately favor the homepage itself.

Marketers everywhere are asking “what kind of content does AI prefer?”  But the data shows that ‘where to publish’ may be more important than ‘what to publish.’ If you really want AI to know something about your brand, put it on your homepage.

---

Cyrus Shepard, Founder at Zyppy “Incredible. This data suggests that many publishers may be sleeping on the potential of their homepages. Makes sense, but you have to wonder how many brands really pay attention to what their homepage says about them, or if they are offloading this valuable information space to about, support, and article pages.”

---

Takeaway for marketers

If you only fix one page, fix this one. To train AI to be a sales rep for your brand, confirm that your homepage includes all of the following:

• A short, sharp sentence that summarizes your core positioning
• Details about what you do, how you do it, whom you do it for and your key differentiators
• Answers to the most common sales questions
• Strong evidence that supports your specific claims (data, awards, testimonials, etc.)
• Add case studies to the homepage, but don’t just link to them. Add the most impactful statement from each case study to the homepage.

That chart above controlled for website size (number of URLs) because the dataset includes sites large and small. Next we’ll look at how website size correlates with AI attention.

2. Larger websites command more attention from AIs

The size of a site (total number of URLs) has a strong correlation with the amount of attention.

Bigger sites pull in more total AI attention. The relationship is almost perfectly proportional (0.86 correlation). AI requests grow proportionally to page count. There isn’t a drop-off or compounding advantage with scale.

As with traditional search, sites with more URLs generally attract more bot requests than sites with fewer URLs. More pages mean more entry points to match against more queries and prompts. More surface area means more requests.

But it’s not a perfect correlation. Some small sites in the dataset attract much more attention from AI than expected. Look closely at the dots on that chart. Notice the outliers. Sites with 50 pages may get as many requests at sites with 1000 pages.

This could be for many reasons. They may have better content on better optimized pages. They may have stronger brands that are more prominent in AI’s background training. Or they may just be better at marketing.

Takeaways for marketers

• Build out your service page sections. You should have a URL for each combination of service, target industry, geography, etc.
• Add pages that frame your brand within AI models: comparison pages, case studies, team pages, an awards page, service-specific FAQ pages and a dedicated pricing page if you don’t already have one.
• Keep your content program turned on. Even if it doesn’t drive traffic from search or from AI, it’s likely still feeding what AI models know (and say) about your brand’s expertise

Now let’s look closer at the types of pages, not just how many, and we’ll discover what kinds of pages AI sends traffic to.

3. Which types of pages drive the most AI referral traffic?

Next we look at which pages attract referral traffic from AI sources.

These are not just citations. These are actual visits. A citation is just a link in an AI response. Here we’re measuring actual clicks on those links. These are real humans visiting websites from AI sources. Most citations are “AI visibility” without traffic. And because Cloudflare sits upstream, the data is more accurate than Google Analytics.

When we categorized the pages into groups, we put all of the “decision-shaping” pages into one category called “service/product.” These pages directly promote the offerings of a brand. Other special page types (home, articles, about, case studies) all got their own categories.

As a ratio of crawls-to-referrals, a few page types outperform others by a mile.

Note: The dataset here is smaller. Only 27 of the websites have pro-tier Cloudflare accounts. But you can easily see this pattern on your own website. Look at your traffic from AI sources in GA4 and you’ll notice the homepage is your most visited from AI sources by a large margin.

While the home and service pages that attract the most visitors from AI sources, articles underperform homepages by nearly 20%. They get read by AI, but it doesn’t send them a lot of traffic. We call this the “Dark Library Effect.” Articles get summarized and may be cited, but not clicked.

This phenomenon is familiar to SEO-focused content marketers. AI systems are simply absorbing article content and answering users’ questions directly. It’s the same zero-click dynamic that’s reshaped traditional search for many years.

• For information-intent users, AI does a wonderful job of satisfying without sending them to websites. Got your answer? No need to click.
• For commercial-intent users, who are making an actual decision, AI may provide the short list, but the users still want to visit the website (especially the homepage and service pages) before making a final decision. So they click.

We see this pattern again in the data showing total visits from AI sources, removing the AI crawls per page data and just exposes median total referral visits. Service and product pages earn roughly three times more total AI-referral traffic per page than a typical article.

When we look at the rate at which AI sends visitors to various types of pages (totals divided by how many pages that exist in that category), the picture sharpens dramatically.

You have just one homepage, but many service pages. So in the rate-per-page-type data, we see the relative importance of page types for AI referrals.

Takeaway for marketers

• To drive AI-referral traffic that may convert into leads, audit your homepage and service/product pages before investing in more top-of-funnel content.
• To shape how AIs understand your brand’s expertise, publish highly relevant and differentiated content (reports like this are an example) but don’t expect much traffic from AI sources.

Content marketing is valuable even if there is no referral traffic. Zero search referrals doesn’t mean zero brand value. We shouldn’t apply search traffic goals to articles that were never going to win those clicks in the first place.

And remember the many and powerful benefits of content marketing beyond clicks from search and AI: sales support, word of mouth, email list growth, social engagement, thought leadership, etc.

4. Page depth and URL structure

Our Cloudflare “most crawled paths” report shows the URL structure of every page that the AIs visited, giving us an opportunity to look at how deep the AI crawlers go. We can answer a few more interesting questions:

• Do AI bots go deep into the site structure?
• Are pages in sub-sub-directories visited less often?
• How does page depth correlate with AI crawl intensity and AI referral traffic?

The data shows that AIs crawl pretty much everything. AI bots are perfectly willing to find a page three or four folders down. But AIs are much less likely to send visitors to the pages buried deep within a folder structure. And the drop-off is steep…

There’s a well-understood technical reason to expect this kind of pattern: crawl budget. All bots operate with finite resources per site and allocate them accordingly. The willingness to go deeper trails off. We checked to see if this was related to site size or types of pages. It’s not. The pattern holds for big and small websites, for service pages and articles.

A page three folders deep earns about a quarter of the AI traffic its footprint predicts. At four folders deep, that number is close to zero. The deeper you bury a page in a folder, the less likely AI is to send it visitors. AI systems will find your deep pages, but they don’t often recommend them.

This suggests an “architecture tax” for site structure decisions. But this is correlation, not causation. Moving a page to a shallower location won’t necessarily improve its AI traffic performance. We don’t recommend moving pages around just for this reason, but if you’re planning a new website or reorganizing your pages, this data may help guide your decisions…

Takeaways for marketers

• Flatten the architecture if possible. This is good UX and it may help drive AI traffic to key pages.
• Put key services pages within a click or two of the homepage.
• Redesign your website. Be honest. When it’s time, it’s time.

Got your own Cloudflare data? Run this diagnostic prompt…

If your site is on Cloudflare, you can run a lightweight version of this analysis yourself. Go to AI Crawl Control → Metrics → Paths and download the “Most Crawled Paths” CSV. If you have a paid account, download the Referrals report too. The screen will look something like this:

Then upload to your favorite AI along with the following prompt…

Cloudflare “AI Crawl Control” Report Analysis Prompt

I run a B2B site and want to know if AI bots are wasting attention on the wrong pages, or if my best pages are getting seen. Analyze the attached Cloudflare AI crawl data. Check the actual columns first — don’t assume a format.

1. Categorize: Group pages into homepage, service/product, blog/articles, case studies, contact/about, everything else. Show each category’s share of crawl requests, and referrals per page (not just raw totals — a category with 300 pages will look big even if each one is barely read).

2. Benchmark: Try fetching this report for context: https://www.orbitmedia.com/blog/ai-crawl-vs-traffic-report/ — it studied 74 B2B/services sites and found homepages get ~15x more attention than their page count predicts, service/product pages earn ~3-4x more referral traffic per page than articles, and roughly 1 in 5 pages get crawled but never sent anyone a visit. If you can’t fetch the link, use those numbers anyway and say so. Tell me how my site compares.

3. Flag data problems: duplicate homepage rows (www/non-www, port variants, secondary subdomains), non-content files inflating the count (CSS/JS/images, cache-busting assets, login pages), any single page eating an outsized share of requests.

4. If I gave you referral data too: which page types get crawled a lot but rarely convert to a visit? Flag anything like a case-studies or portfolio section that’s heavily crawled but almost never referred.

5. Give me a one-paragraph verdict: is my site structured to turn AI attention into leads, or is my best content buried where AI can’t easily point to it? Say plainly what’s solid versus a rough guess from limited data.

This won’t have the rigor of our 74-site study (no cross-site comparison, just one snapshot of your site) but it references this study. This will quickly show you the same kinds of problems and patterns on your own data. It’s a diagnostic and benchmark analysis.

Give it your brand styles and it will make a nice little report for you.

Train the AIs to recommend your brand

AI reads a huge percentage of the public internet. And it may learn about your brand from many sources. But your website is the corner of the internet that you control. Your site is your best (and most direct) hope of training an AI to recommend your brand.

AI bots are likely visiting your website today. And now that we have a clear picture of what those bots visit, you can look at those pages and see what you’re teaching them. We also learned where AI tends to send visitors, which shows the same pattern.

47% of all pages in this dataset generated zero referrals. That’s fine. There are actually two very different reasons an AI bot visits your site: to fill in background knowledge (training bots) and to answer someone’s question right now (search agent bots). Either way, we now know where that attention actually goes.

Have a conversation with your favorite AI about your brand. Share this article with it and ask it for insights. Few marketers have had a serious conversation with an AI about why and when it would (and wouldn’t) recommend their brand.

Now go fill those gaps. Train the AIs on your difference, your offers and the impact of your work. AI needs us marketers to keep improving the web, so it can efficiently connect our best-fit prospects with the value we bring to the world.

About the dataset

This analysis covers a wide range of marketing websites, covering professional services, B2B tech, industrial, nonprofit, education and even a few ecommerce websites. Every page was classified by type and by how many folders deep it sits, then measured against its own site’s baseline so no single large site could skew the results.

• 74 sites, ranging from 45 to 95,522 crawl requests each
• 560,695 AI crawl requests from all 74 websites
• 446,267 AI referrals from the 27 sites with Cloudflare Pro-tier referral tracking
• 21,212 individual paths

The analysis was tricky because URL structures vary. Websites have all kinds of pages in all kinds of places. Some sites have all of their blog posts in a common directory, some do not. We had to carefully review the sites to categorize pages accurately. Data was processed and analyzed using Claude Opus with a careful review of all major judgement calls.

PDFs were handled separately. Accurate classification of PDFs is tricky. Some are newsletters, some are product manuals, some are case studies. In the end, we just gave them their own category.

AI bots are weird. They repeatedly crawl pages and files that have no practical value at all. AI is perfectly happy to crawl a useless backend file (such as /login/ajax.php) thousands of times. There were so many of these zero-value requests that we had to carefully exclude them using a pre-processing data cleanup step.

Is your website blocking AI?

Is your website blocking AI bots? How many? Which ones? There’s an easy way to answer this question. Just plug your domain into our AI Bot Access Checker, and you’ll instantly know.

As a marketer, you probably don’t want to block bots from training on your content. AI discovery is a goal, after all. How can AI cite and recommend you if it can’t get in the front door?

When concepts get tough, content gets visual. Marketing diagrams are a fast way to explain complicated topics and tricky ideas. Nothing makes a muddy post clear like a good visual.

So for this post, we’re stacking up the best charts and diagrams from 15+ years of publishing digital marketing articles.

Here are 59 diagrams and charts that explain tough marketing concepts fast. We’ve organized them into four big concepts:

• Content Marketing Diagrams
• SEO and AI Search Optimization Diagrams
• Social Media and Influencer Marketing Diagrams
• Email Marketing Diagrams
• Web Design and UX Diagrams
• Data-Drive Marketing and Google Analytics Diagrams

As you scan through, think about your own content and keep this in mind.

You may use any of these diagrams in your marketing

Would any of these diagrams improve an article you’re working on?

Feel free to use them. Please just cite the source and link to the original location of the image.

Need a quote to support the image?

I am happy to provide a quick contribution. Just drop us a line with a quick request.

---

Content Marketing Diagrams

The diagrams and charts in this section make content marketing concepts visual. Some are research, some are flow charts, others are simple tables that compare and contrast ideas.

1. Formats and the Funnel

If you consider those formats and their abilities to attract visitors and/or build trust, you begin to see the strengths and weaknesses of each format. With all of this in front of you in one diagram, you can strategically add formats to your content mix to drive specific results. When it works well, it’s like magic.

(source)

2. How long does it take to write a blog post?

This is one of many charts from our annual blogger survey. It shows the average investment of time in a single piece of content:

(source)

3. Which AI use cases are driving content marketing results?

Some marketers use AI for everything. Others use it for nothing. These groups are the least likely to drive results. The answer is somewhere in the middle. Not too much AI, not too little.

(source)

4. AI efficiency: input and output formats

What you upload to an AI model and what you ask it to create have widely different token (and carbon) impacts.

(source)

5. Time required to create a long-form search optimized article

This is a more specific look at the same question. It shows how much time goes into which categories for a single piece of content for this blog.

(source)

6. Content promotion channels and the funnel

Ultimately, content marketers do three things: create content, promote it and then measure results. Here is a top-view look at the funnel, showing how the three different promotion channels affect the funnel in different ways.

Search = Awareness
Google is a discovery engine. High rankings for a relevant phrase can have a dramatic impact on awareness.

Social media = Awareness and consideration
Social media also increases awareness, since it makes you visible to friends of friends. When they interact with the content, it indicates stronger connection and consideration.

Email marketing = Consideration
Email signup requires a higher level of trust. But when they subscribe, you can reach them consistently. You’ve moved beyond simple awareness and toward greater consideration and action.

7. Website traffic sources

Traffic flows into websites through channels. Some require money (paid advertising) while others require time (content marketing). Some require ongoing activity (email and social) but SEO becomes a durable source of traffic over time. Together, they are the oars, sails and motors.

And of course, famous brands drive tons of direct traffic.

(source)

8. How content aligns with website pages

Funnel stages can also be aligned with pages on a website. Here in one diagram, you can see which pages on a site align with the psychology of visitors on their path toward conversion.

(source)

9. The virtuous cycle of blog traffic …and eventual demand

Regardless of the traffic source, some visitors may subscribe, follow and link, which then helps power the next visit through email, social and search.

Eventually, a small percentage of visitors move through to the rest of the pages and a lead is born.

(source)

10. The Content Performance Matrix

This diagram shows what to do with your content after you’ve published and measured results.

(source)

11. Two types of offerings: Transactional and consultative

They require two different types of marketing. Understanding the difference unlocks quick insights into the types of content and calls to action that will work for any brand.

(source)

12. Brand marketing vs. Performance marketing

Brand marketing and performance marketing have different goals, timelines and metrics. One builds awareness. The other drives action.

But every marketing activity fits into two simple frameworks. It’s either brand or performance. And it’s either paid, earned or owned media.

(source)

13. The Periodic Table of Content

There are so many different content formats in digital marketing. Repurposing content is about moving the material from one format into another. This little chart shows all of the possible formats and triggers repurposing ideas.

(source)

14. The three types of marketing videos

Speaking of formats, video is one of the most powerful and versatile. Videos have different outcomes, depending on where they are and who views them. There are really three types of videos, each aligned with a different stage in the funnel. This little chart explains.

(source)

15. The content marketing promotion cycle

Regardless of the format, success of a piece of content depends on how well it’s promoted. The best content doesn’t win, the best promoted content wins.

This diagram shows the cycle of promotion, which starts with research before the content goes live.

(source)

16. What happens when a potential visitor sees your headline?

With a great headline, promotion is easy. With a bad (or boring) headline, promotion is almost impossible. The key is to understand that the reader of the headline does a split-second cost benefit analysis before they decide to click. This chart explains.

(source)

17. What and where is a headline in digital marketing?

Actually, a headline isn’t one thing. It’s different things in different places. In email it’s a subject line. In search it’s a title tag. On the page is an H1 header.

The “headline” can be aligned with the opportunities of the location, depending on where it appears. This chart shows the various locations and success factors of each.

(source)

18. Topics and focal distance

Zoom in or pan out. Cover the topic in detail with a long-form search-friendly post. Or stay high-level and cover a range of related items. Every topic in the universe can be organized on a fractal. You can find great ideas at any focal distance.

19. Content quality and longevity

Some content makes a strong impact. Other content lasts a long time. Unfortunately, they’re usually not the same piece.

• Content quality
  Face-to-face interaction is ideal, forming the strongest connections and building the greatest trust. Text is sadly the weakest format at conveying tone and nuance.
• Content longevity
  Some last just for that moment, passing in real-time. Social posts are visible for hours or days. Search optimized articles may be visible for months or years.

This chart shows the quality and longevity of various types of content:

20. Solo, lonely blogger vs collaborative marketer

Before we called it “organic influencer marketing” we just called it content collaboration. The idea is to include others in your content, through contributor quotes, roundups and interviews. It improves the quality of the content, but also the traffic.

(source)

21. Post and pray vs Zero waste marketing

Even better than including influencers is including your audience. When you involve your actual prospects in the process of content creation (interviews, contributor quotes), the post is guaranteed to reach its target. This is known as zero-waste marketing.

(source)

---

SEO and AI Search Optimization Diagrams

This set of visuals all relate to SEO. There are a lot of deep concepts related to search. It would be impossible to teach this topic without visuals. These are some of the most useful we’ve created:

22. Traditional search vs. AI search

Search is about getting ranked and clicked. AI is about getting recommended.

That’s the simplest way to understand the biggest shift happening in digital marketing.

Traditional search helps users discover options. AI evaluates those options, compares them and recommends the best fit based on the user’s context. That moves AI deeper into the lead generation funnel and changes how marketers think about visibility, trust and optimization.

This chart compares traditional search and AI search through three lenses: the technology, the user experience and the marketing implications. It ends with a look at what’s coming next.

(source)

23. What is AI Search Optimization called?

Within the SEO community, there is one area where there is no consensus at all: the name of this type of marketing. I’ve been calling it AI search optimization, but it goes by many names, as we can see in our analysis of the 150 SEO articles.

(source)

24. Organic lead generation in the era of AI

The user journey has changed.

In search, people clicked around the web looking for answers. In AI, they describe their problems and goals and get recommendations. Search gave users options. AI gives them recommendations.

(source)

25. Are people switching from search to AI chat tools??

Some have. Some haven’t. But the shift is real. More than half of respondents now begin their research in an AI tool, where answers and recommendations come before clicks. Search isn’t disappearing, but AI has become a new starting point for discovery.

(source)

26. When do people prefer to ask an AI chatbot?

AI is becoming more popular for all types of queries, even the simplest ones. But the exception is local search, which makes sense because AIs don’t integrate well with maps yet. Local SEOs are the least disrupted by AI for now.

(source)

27. The three types of keyphrases

Every keyphrase indicates intent. And the strength of that intent varies widely. As searchers we are problem aware, some are solution aware, some are brand aware. In these moments we search for different phrases: KNOW, DO and GO.

(source)

28. The two main search ranking factors

If you need to explain SEO in ten seconds with one visual, this might be it. The two most important search ranking factors (and types of SEO) are authority and relevance. Of course, there’s more to it, but this is 90% of the game.

(source)

29. Link reclamation flowchart

You don’t have to be a full-time link builder to capture those quick wins. It’s simply a matter of listening and outreach. The listening involves brand alert tools. The outreach is just a simple request to turn that mention into a link.

All the best SEOs follow this basic flowchart. It’s called link reclamation and it’s a close cousin to PR.

(source)

30. Three criteria for selecting keyphrases

It’s not helpful to target a phrase if no one is searching for it. It’s also not helpful to target a phrase that you have no chance of ranking for. There are really three criteria for selecting keyphrases. This Venn diagram makes that clear at a glance.

(source)

31. Domain Authority and keyphrase length

There’s a shortcut in keyphrase research that can help anyone focus on the realistic phrases quickly. It’s to first understand that longer, more specific phrases are less popular, but also far less competitive. That means that you can often win with a lower domain authority just by targeting longer keyphrases.

(source)

32. What’s that link worth?

Not all links are created equal. Smart SEOs and link building agencies understand link equity. They can estimate the value of a link (actual or potential) very quickly. It’s a skill that requires a complete view of both the source and the target.

Here are seven factors that determine the SEO value of every link on the internet.

(source)

33. User interaction signals and rankings: Short vs. long clicks

Beyond authority and relevance, there is another important search ranking factor: what visitors do after they click on a search result.

• If they quickly hit the back button, Google may see that as a signal of low quality content.
• If they stay for several long minutes before hitting the back button, that suggests to the search engine that the page is good.

This is often referred to as the short or long click, also known as low or high dwell time. This chart makes that concept visual:

(source)

---

Social Media and Influencer Marketing Diagrams

The charts in this section explain some of the biggest (and trickiest) concepts in social media. Some are obvious, others might seem new.

34. Which social media networks have the most spam?

Every social network is competing for attention. Some are also competing with spam.

The differences are striking. On some platforms, users are far more likely to encounter low-quality content and suspicious accounts. That affects trust, engagement, and ultimately, marketing performance.

(source)

35. The social media “Rule of Thirds”

There are definitely three kinds of social media posts. They are never equal thirds in a social stream, but all great social accounts include all three:

• Creation: Posts with links promoting articles created by the brand
• Curation: Posts with links to news and articles from friends
• Conversation: Personal messages of thanks, hello, questions, answers and congratulations.

This chart shows the goals and the tools for each.

(source)

36. The five types of influencers

This chart is taken from a page in Mark Schaefer’s Marketing Rebellion.

Mark didn’t use the term “Nano Influencers.” There’s some debate on the use of that term. The idea is that you can find influencers at all levels, and that anyone can be an advocate within their own networks.

(source)

37. Influencer marketing strategy: Formats and outcomes

Which influencers and which channels determine the outcome of the program. Some collaborations lead to traffic, others do not. Some can drive registrations and leads, most don’t.

This chart shows at a glance how different channels and formats support different marketing goals.

(source)

38. How influencers affect search engine rankings

The “Links & authority” column in the chart above is an SEO thing. The ranking potential of web pages is partly a function of the links to that website and webpage. This is often called Domain Authority (more on that topic here).

Influencers are critical to growing Domain Authority. Bloggers and editors create content, content often includes links. This chart shows how influencers, when combined with link-worthy content, lead to rankings.

(source)

39. Profile picture best practices

Do a friend a favor and share this post and diagram. It’s a simple tip for the composition in headshot photography that will help them look more professional. A quick tour through LinkedIn shows just how many accounts seem to miss this basic idea.

(source)

---

Email Marketing Diagrams

Email is such a powerful and important channel. Once a visitor subscribes, they’ve given you permission to connect with them directly. No more giant tech company (Facebook, Google, Twitter) in the middle.

But email marketers often miss the little things that make a big difference.

Here are a few diagrams that explain these quick hits fast.

40. The three main factors in email open rates

There’s the sender name, the subject line and the preheader text. These are the main factors in the open rates for email, aside from timing and deliverability.

Improving these three elements costs no money and takes very little time. Still, lots of email marketers fail at the basics. Compare the difference:

(source)

41. Optimizing email subject lines for the mobile inbox

Here’s another simple trick. The mobile inbox may truncate subject lines after as few as 45 characters. With this in mind, make sure to include the most compelling or emotional words at the beginning of the subject line, in the first five words.

If the recipient doesn’t see the benefits, they’re unlikely to tap to open.

(source)

42. Email signup calls to action

The battle to win or lose your next subscriber is fought in the sign up box. If your email signup box includes the three P’s, you’re likely to see much higher conversion rates and faster list growth.

• Prominence
  The signup form is clearly visible (top of the blog, sticky footer, popup window)
• Promise
  It indicates the topic of the newsletter and gives them a reason to subscribe. It says more than “sign up for updates”
• Proof
  There’s evidence that others have taken this action before. That could mean the size of a list or a mini-testimonial of a happy subscriber.

The diagram shows a signup box with all three P’s.

(source)

43. Email engagement vs website engagement

The analytics in your ESP (email service provider) only go so far. To connect the dots to website traffic, you’ll need to add campaign tracking code using a URL builder.

Once the tracking codes are in place, you’ll be able to see the post-click data (traffic, engagement and conversion metrics) from that email. Very handy.

(source)

---

Web Design and UX Diagrams

These diagrams explain some of the most important web design and usability concepts, including conversion, which is the ultimate goal in digital marketing.

44. The psychology of conversion

The percentage of visitors who take action on a website is called the conversion rate. There are a lot of factors. Ultimately, the visitor’s motivation must be greater than the friction.

This chart shows those opposing forces, clarity and confusion, and the goal of the website.

(source)

45. Adding evidence to webpages

Most websites make similar claims.

What separates one company from another is the evidence behind those claims. The strongest pages support their messaging with proof: testimonials, data, awards, certifications and other trust signals.

(source)

46. Contact page best practices

Some contact pages help visitors take action. Others create friction.

The difference comes down to clarity, trust and usability. Compare these two examples and you’ll see the elements that separate a high-performing contact page from an average one.

(source)

47. About us page best practices

Every visitor has questions. Your About page should answer them.

Who are you? Why do you exist? What makes you different? The best About pages tell a story, answer important questions and help visitors connect with the people behind the brand.

(source)

48. Homepage design: Quick communication with descriptive labels

Every visit to every page starts with the same question: am I in the right place?

Some home pages answer that question fast. Others do not.

This little comparison chart shows some of the ways in which homepage design can be clear or confusing. Spot the differences? They impact both search rankings and visitor engagement.

(source)

49. Service page design: The 13-point checklist

Once they’ve clicked deeper and landed on a service page, it’s time to sell. The best service pages emulate a sales conversation. They use answers, evidence and calls to action, but there’s more to it. Does the page show the people? Is it optimized to rank? Are the visuals supportive or stocky?

Here’s an entire checklist for service pages in one diagram.

(source)

50. The anatomy of a great testimonial

A testimonial is highly effective supporting evidence for any marketing message. But there are a lot of ways to use them, some good, some not as good.

After 20 years of building websites, here’s what we’ve learned. Testimonials that include logos, headlines and pictures are more compelling. As a bonus, look for testimonials that include keywords. Good for SEO!

(source)

51. Color and visual prominence

Every page has a visual hierarchy, whether it was intentionally designed or not. And color is one powerful way to guide the eyes through a page. Visual contrast is visual prominence. Colors on the opposite sides of the color wheel (known as complementary colors) have the strongest contrast.

Designers can leverage this contrast to draw attention to key messages or calls to action.

52. The laws of visual hierarchy

Beyond color, there are other factors that determine where the visitor looks. Position, size, whitespace and movement are all opportunities to align the prominence of elements with the priority of messages.

(source)

53. Web design standards

Which features are standard on websites? Which are just common conventions? A little research revealed the elements that are most and least common, from search boxes to slide shows.

(source)

54. Making calls to action personal

Faces do two things: they are magnets for visitors’ attention and they humanize the website. When placed near a call to action, these forces combine in a way that can improve the click through rate to the contact form.

This diagram makes that difference obvious:

(source)

---

Data-Driven Marketing and Google Analytics Diagrams

Our final collection of marketing diagrams are all about measurement. Analytics can be a daunting topic for readers. Visuals help the tips flow right into the brains and skills of the reader. The complex becomes simple.

55. How to make marketing decisions using data

Reporting vs. analysis. There’s a big difference. Reports don’t affect your marketing. Only actions do.

So the best marketers use Analytics to find insights and test hypotheses. Analytics is a decision support tool, not just a set of pretty charts. If you aren’t using it to make decisions, you’re doing it wrong.

(source)

56. Hypothesis formation

Ideas are great, but a hypothesis can be tested and confirmed. The difference is data.

This approach shows the difference between opinion-driven decisions and data-driven decisions.

(source)

57. Sources (and quality) of marketing insights

Analytics isn’t the only source of insights.

• A/B Testing is the ideal source of truth, because it’s your visitors, in the context of your content and design. And time has been removed as a variable.
• Marketing analytics (including GA) is the next best option. It’s not a test, but it’s still showing the behavior of your visitors on your website.
• Industry best practices are useful if you have no data of your own. They can inform hypotheses but aren’t as likely to be effective as decisions made using your own data.
• Personal opinion is the riskiest. Definitely allow for inspiration, but make sure to use data to validate.

(source)

58. The 12 types of marketing research methods

There’s more to life than Google Analytics. It’s actually just one of many possible research methods. Here are a few examples:

• Interviews gather detailed qualitative data. Follow up questions can gather deep insights.
• Eye-tracking studies show where the eyes move, going far beyond clicking and scrolling.
• Surveys can include those who are non-users and non-customers to learn the needs of a potential audience.

Here is a chart of user research methods, showing a dozen of the options.

(source)

---

The Power of Visuals

I love writing. So many marketers love writing. A lot of content marketers are trained journalists.

But we all have to admit, visuals are amazingly powerful. They have some big advantages over text.

• Images are more visually prominent than text on any page
• Visuals are processed up to 600x faster than text in the brain
• Diagrams are uniquely able to simplify complex topics

This is why we include an original diagram in almost every article.
This is why our book is The Illustrated Handbook for Content Marketing.

59. How to use visuals in your marketing

….so we’ll end this with one final diagram from our guide on how images affect your rankings in search engines.

Who made all these diagrams?

Almost every diagram you see here was created by Jantzen Loza, web designer, photographer and Orbiteer. He is a key part of the Orbit marketing process. Huge thanks goes to Jantz the Snapper!

“Mind if I use some of these in my blog?”

Sure! Go right ahead. Please just link back to the source.

AI is a magical typewriter for content marketers …or is it?

How and when to use AI in content marketing remains a huge question. The debate rages on. My friends who are purists see no place for AI in content (where’s the authentic voice?), and my friends who are big on AI adoption see no place without AI (why not use this helpful new tool?).

Personally, I don’t use AI to write. This sentence is being written by hand. That’s my preference, and it’s what works best for me.

In fact, most marketers don’t write entire articles with AI. According to our annual survey, just 11% of content marketers draft articles using AI. Most use it for ideas and editing.

Beyond writing, these are wonderful ways to use AI for content strategy, including ideation, audits and analysis. Today we’ll review five of these and share the prompts that power them. Here’s what we’ll cover:

1. AI for auditing your blog’s calls to action (maximize list growth)
2. AI for topic research (focus on the three best formats for content)
3. AI for initial edits (alignment with content best practices)
4. AI for finding partners and places to publish (PR and outreach ideas)
5. AI for performance analysis (find topic/channel fit)

Beyond CTAs, topics, editing and analysis, there are many other aspects of a content strategy. We’ll touch on those at the end.

Pro tip! Looking for more ways to use AI in your marketing? Download our free collection of 22 AI prompt templates for digital marketing, including prompts for personas, SEO, content strategy, conversion optimization and analytics.

1. Blog main page quick audit

Before hitting publish on your next article, take a look at the place where you’re publishing. This is the home of your content marketing program. Does it include all of the key success factors?

• Specific, descriptive email signup call to action
• Categories are aligned with topics (“content strategy”) rather than formats (“videos”)
• Proof points that build the credibility of the content program

The email signup CTA is the most important element on a blog’s main page. And you’ll find bad examples all over the web. Ideally, your signup box is high on the page and aligns with email signup form best practices. Compare:

With this perspective, you may see huge, immediate improvement opportunities. If the issues aren’t obvious, try this audit prompt. The inputs are a link to the page (or full-page screenshot) and a few details about the visitor and your proof points.

Blog Homepage Subscription Audit Prompt

You are an email growth strategist auditing this blog homepage. A good blog homepage does one thing above all else: converts interested visitors into subscribers. Your job is to assess how well this page does that — and make a clear recommendation.

Topic scan — Extract the 3–5 main topics this blog covers from the screenshot and URL. Then answer:

• Are those topics reflected in the CTA copy, or does the signup feel generic?
• Is the blog organized around topics (what you’ll learn) or formats (videos, webinars, guides)? Format-based organization weakens conversion — it tells visitors nothing about whether the content is relevant to them.

Label anything uncertain as an assumption. Then score the page across these eight criteria. The first four are directly on the conversion path and carry the most weight. Present results as a table with four columns: Criterion | Score ( 1–2 / 3 / 4–5) | What you observed | Specific fix.

Conversion-critical:

• CTA clarity and specificity — does the signup feel like a named, valuable product? Do specific topics appear in the headline, description, or button copy?
• CTA proof and trust — is there a subscriber count, testimonial, or credibility signal adjacent to the form?
• Topic-to-CTA alignment — does the CTA promise content that matches what the blog visibly covers?
• Topic-first organization — are categories and featured content organized around subjects, not delivery formats?

Supporting:

• Top-of-page clarity — is it immediately clear what the blog covers, who it’s for, and what to do next?
• Audience alignment — do the topics, tone, and CTA feel relevant to the target visitor?
• Skimmability — can a fast visitor grasp the value of subscribing without reading every word?
• Visual support — does the design make the CTA stand out, or does it disappear into the page?

Strategic gaps — Identify one or two issues the table can’t capture: structural or strategic problems that row-by-row fixes wouldn’t solve.

Verdict

• Overall strength: High / Moderate / Low
• Recommended action: Republish as-is / Light update / Significant rewrite / Rebuild
• Single highest-leverage edit: the one change most likely to improve subscription rate
• Conversion read: would a first-time visitor who cares about these topics feel compelled to subscribe — or leave without a reason to return?

[Upload a full-page screenshot. Optional: note the target audience job title and any proof points: subscriber count, testimonials, or trust signals]

The report rates the extent to which your blog main page aligns with content marketing best practices and email list growth. 

I ran the Orbit Media blog main page through this mini-audit and it definitely had some ideas. It called the way we show our posts a “chronological tile dump” and I can’t disagree.

2. AI for topic research

There are three types of content that tend to outperform all others:

1. Original research (new data)
2. Strong opinion (thought leadership)
3. Topics that aren’t yet covered (new angles)

The lowest performing content is often general, how-to articles that offer no unique methods, data or perspectives. If it could have been written by any brand (or an AI) then it isn’t going to be memorable, be bookmarked, or start a conversation. Maybe don’t write it.

AI can absolutely help you uncover relevant content ideas for strong opinions, original research and hidden gaps. Here are three prompts that do exactly that:

The “Provocative-But-Mundane Topics” Prompt:

What are some relatively mundane, almost trivial topics that [job title] has very strong opinions about?

Everyone should try this prompt at least once. It elicits topics that are triggering without being overly controversial. Here I put in “content strategist” as the job title…

As you scan the response, you’ll feel an almost physical reaction to the topics. You’ll laugh. You’ll groan. You’ll wonder if you could dip your toes into the pool of opinion-based content. Or perhaps dive directly into the depths of true thought leadership.

The magic here is that these are topics that AI can’t touch. No LLM can write strong opinion content. That’s because AI has no opinions. It has no point of view. But you do. Thought leadership is a human-only format.

The challenge is courage: Are you willing to take a stand? Is everyone on the team ok with this? Does it fit within your brand guidelines? Remember, it’s not thought leadership unless someone can disagree with it.

The “Original Research Topics” Prompt

Target audience job title: [job title]

Suggest original research topics relevant to the industry of the role above. The goal is to identify frequently asserted but rarely supported claims — statements everyone repeats but nobody has actually measured. The best topics, once researched, would be cited by journalists, editors, and bloggers looking for fresh data.

Answer these three questions:

1. Where are the gaps? Which topics in this industry are widely discussed but lack solid supporting data?
2. What’s worth measuring? Which unverified claims, if proven or disproven, would be most useful to journalists looking for fresh statistics to cite?
3. What’s the angle? For the two or three strongest opportunities, suggest a research approach — survey, analysis, experiment — and explain what would make the finding credible and linkable.

New research gets cited in other people’s content. It’s catnip for journalists and editors. This leads to links that drive search rankings for both your content and your key conversion pages. These rankings drive traffic through traditional SEO, but also inform AI responses, because of course, AI searches using search engines.

So if you can find a “missing stat” or a common assertion that is rarely supported with data, and then produce that data with a new report, you’ll have plugged a hole in the internet. Go beyond the typical industry trends. Promote it well with other content creators (ideally, by including them in the report as contributors) and you’ll attract links.

I ran this prompt using the “Content Strategist” job title. It had several interesting ideas, along with possible methodologies and angles. None of these would produce serious peer-review level research papers. Any of these would produce interesting, link-worthy content.

The challenge is time: Can you design a study that you can produce without a huge investment? Do you have any data within your company you can use? Or partner with another content program?

The “Topics-Rarely-Covered” Prompt

What are the most important topics in the [job title]’s industry that are the least likely to be covered by the popular blogs?

It’s not possible for you, a human, to read every article in an industry and spot the topics that no one is writing about. But AI does this well. Drop that prompt into any of the AI tools and you’ll see a wide-open ocean of highly differentiated content opportunities. Anything you could cover?

I also ran this one using “content strategy,” and it found some off the less common topics on content marketing blogs. I can easily imagine high clickthrough rates for these…

The challenge is alignment: Which of these rarely-covered topics aligns with your buyer and the rest of the buying committee? Just because it’s new doesn’t mean it fits. 

It’s also possible to combine these methods with good brainstorming, human creativity and subsequent prompts. Imagine if you published a research piece that gave you an opportunity to express a strong point of view. Plant your flag on a solid ground of new data. It’ll be your strongest piece of the year.

3. Article audit prompt

The email signup box is polished. The topics are set. The article is written and it’s ready to go live.

Again, before you hit publish, take a minute to check the new piece against content best practices. In our experience, these are some of the most important elements in any article:

• Strong opening hook: Does the first sentence make them want to read on?
• Evidence and examples: Are the claims backed with data, quotes or specifics?
• Expert or contributor input: Does any voice other than the brand’s appear?
• Formatting and scannability: Headers, bullets, bold, paragraph length
• Visual support: Is there something visually interesting at every scroll depth?
• Internal links: Do they guide readers to related content? Does it link to a service page?
• Personal angle or opinion: Does the article take a clear position, or does it just cover the topic?
• Call to action/next step: Is there a logical next move for the reader?

No, you don’t need to include them all. But you should consider them all. Scan through the final draft and look for content gaps. Better yet, give AI the draft along with this Article Audit Prompt:

Article Audit Prompt

You are a content strategist auditing this article on behalf of an editor who needs to decide whether to update, rewrite, or remove it. Your job is to make a clear recommendation, not hedge.

Score the article across these eight criteria. Present results as a table with four columns: Criterion | Score ( 1–2 / 3 / 4–5) | What you observed | Specific fix.

• Opening hook — does the first screen earn a scroll?
• Evidence and examples — are claims backed with data, quotes, or specifics?
• Expert or contributor input — does any voice other than the brand’s appear?
• Formatting and scannability — headers, bullets, bold, paragraph length
• Visual support — do images or charts add meaning, or just break up text?
• Internal links — are there contextual links to related content? Does at least one link point to a company service page?
• Personal angle or opinion — does the article take a clear position, or does it just cover the topic? A distinct point of view is one of the clearest signals that this content wasn’t written by AI.
• Call to action / next step — is there a logical next move for the reader?

Strategic gaps — After scoring, identify one or two things the table can’t capture: positioning weaknesses, missing angles, or structural issues that no amount of polishing would fix.

Verdict

• Overall strength: High / Moderate / Low
• Recommended action: Republish as-is / Light update / Significant rewrite / Remove
• Single highest-leverage edit: one specific change that would move the needle most

[Upload a full-page screenshot of the article — include top, mid-page, and bottom if the article is long]

The audit pushes you to challenge yourself, to strengthen your assertions, back up your claims and pull in the voice of experts. I ran this on a draft of this article, and it made some strong suggestions. Looks like I have some work to do before this meets the standards of our own content strategy.

4. Discover where your audience gets their content

It’s a mistake to only publish content on your own website. The best content strategists will write for any platform where their audience is paying attention. They network with editors, reach out with little pitches and collaborate with influencers.

If we know where our audience spends time, we know where to listen and where to engage. With the right audience insights, we’ll know the publications to pitch to and the events we can attend. We can meet them where they are.

Can AI help you find these people and places? Yes.

For this, I’ll use a simplified version of Liza Adams “Watering Holes” prompt.

A Simplified “Watering Holes” Prompt

You are a content strategist familiar with [job title and industry]. Identify the “watering holes” where this audience learns, engages, and connects.

For each category, name specific real examples — actual communities, publications, associations, events, podcasts, YouTube channels, forums, newsletters, and influencers.

Create a table with watering hole categories as rows and specific named examples as cells. For each, note the opportunity type: places to publish or pitch guest content, podcasts or channels to pitch yourself as a guest, events to attend or sponsor, communities to listen and engage in, or partnerships to pursue.

Finally, add a short section recommending the three highest-priority watering holes for someone just starting to build presence in this market, with a one-sentence rationale for each.

ProTip! Sign up for the audience intelligence tool, Sparktoro. The free version will show you relevant social networks and websites. The paid version also lists podcasts, YouTube channels and SubReddits. Either way, download the report as a PDF and upload it with the same prompt. 

Here I ran the prompt for “Marketing Directors” in “Healthcare Tech.” I’ve trimmed the results because the original response was a huge list of opportunities.

Scan through and your head will fill with ideas. Here are just a few to get you started:

• Which topics would get traction in which places?
• Look at the past sessions at those conferences. Have we covered those?
• Check out the editors of those publications. Do we know them yet?
• Who sponsors those tradeshows. Anyone we can partner with?
• Listen to a few of the shows. Which influencers does our audience trust?

Even if you’re starting from zero, with no knowledge of this vertical whatsoever, you can quickly see where to start listening. Within an hour of AI-supported research, you may have some great digital marketing ideas for PR, event participation and influencer marketing.

---

Liza Adams, AI Advisor & GTM Strategist, GrowthPath Partners “Most of us use AI to create more content. But it can also help us find where our audience already pays attention. That’s what the watering holes prompt does. It shows you the communities, publications, and events your buyers already trust, so you can go meet them there instead of waiting for them to find you.”

---

5. Give AI your GA4 reports… and align topics with channels

Every content strategist knows how important it is to align topics with marketing channels. Some topics work better in some channels.

Just think about search vs. social. Users of search engines are busy; users of social media are bored. That’s why some topics have opportunities in SEO (detailed long-form content that answers questions) and others do great in social media networks (surprising and visual).

Here we’ll give AI data from our Google Analytics account, showing which articles (title tags) performed how well (traffic, engagement, and conversion rates) across which channels (session source). The analysis is all done by the AI of your choice.

First, we’ll get the GA4 report. This is really just one of many ways you can have AI help with data analysis. First, a screenshot that shows all of the settings.

1. Go to the “Page path + query string” report with a nice long date range.
2. Set the drop-down above the first column to “Page title and screen class”
3. Click the blue plus (+) which is also at the top of that column to add another dimension: “Session source”
4. Set a filter to show only blog posts (“Page path and screen class” contains “blog”) and to remove direct traffic (session source does not contain “direct”) because that isn’t helpful here.
5. Export to CSV.
6. Finally, open the file and remove rows with super low traffic, irrelevant data such as translated titles (non-English) or anything else that looks weird.
7. Save it then upload that to your favorite AI with this analysis prompt

The “Top Channel Alignment” Prompt:

You are a content strategist skilled at using data to identify which topics perform well in which channels. I’m giving you a GA4 report showing blog content performance by traffic source, for a blog targeting [job title]. Suggest 10 new article topics matched to the channels where they’re likely to perform well. For each, note the visitor mindset (e.g., answer-seeking, scroll-stopping), explain the channel fit, and suggest one promotion tactic.

[upload GA4 report]

Like many AI methods, the input is the secret sauce. This one analyzes the performance of your own content with your own audience based on the GA4 report. This isn’t based on best practices. It’s based on what’s worked for you in the past.

Here’s what it looks like from our Analytics. I trimmed it a bit because it was long…

The topics are closely related to your existing content, they’re aligned with channels. For some, you can optimize the content for search. For others, the social media posts write themselves.

The AI even gives recommendations for content promotion. This doesn’t mean that one piece can’t be promoted in all channels. But it helps the content strategist think ahead to distribution, even before the content creation.

AI gave us a boost, but there’s a lot more to content strategy

We’ve reviewed five use cases for AI content strategy, but the ideas here were just the foundation. The target and topics are the beginning, but there are many other aspects to content planning and your content creation process.

• Alignment with business objectives (sales support content)
• Content formats: visual content (videos, infographics), written content (articles, interviews, guides)
• Promotion channels
• Publishing frequency
• Keyword research and SEO strategy
• Technology and tools (content personalization and predictive analytics)
• Team, partners and workflow
• Success metrics

Imagine how ChatGPT or Claude could help with these other aspects of content marketing strategy.
Imagine how the related tasks can get easier, faster and more comprehensive.
Imagine the prompts that would provide the most valuable insights and support your content marketing efforts.

But even with the cleverest prompts and the most interesting AI responses, you’ll need to stay involved. Watch it closely. Stay critical. None of those prompts will let you abdicate your job defending quality, filtering out the noise, adding your own perspective, connecting with the partners and ultimately, the prospects.

We’ve made the case that AI-driven content strategy is better than AI generated content. Generative AI can support the content creator with content ideas.

Then you, the expert, provides the human oversight and decides how to apply the insights.