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.

---

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.

Stop writing prompts by hand.

That’s my top tip for AI based on hundreds of hours of experimentation and testing. It’s the first of five tips in this guide. By the time you reach the end, you’ll have built an AI assistant that writes your prompts for you.

This isn’t about little ad-hoc prompts. We all use quick little prompts to get help with little tasks in the moment. Those are mostly just for getting answers or getting unstuck.

This isn’t another prompting framework. There are tons of those already. They’re usually acronyms: BRIEF, CLEAR, PRISM, RACE, TRACE, etc. These are really just things to include in your prompts.

Today we’re focused on creating powerful, workhorse prompts that you save and use many times. They may generate reports, write content briefs, edit articles, etc. The kind of prompts that become part of a regular workflow and maybe even part of your daily job.

Here are a few tips for writing those high-performing prompts.

1. Don’t write prompts yourself. Have AI write them for you.
2. Start by talking to AI about your idea. Don’t just tell AI to write you a prompt.
3. Ask AI to challenge your thinking. Get it to push creativity and challenge your assumptions.
4. Create prompts that are fast and efficient. No need to output heavy file formats.
5. Make easy-to-reuse prompts. Then they become part of your workflow.

Want a head start? Download our our favorite AI prompt templates and customize them to fit your own workflow.

The best prompt writers use these approaches. They have moved up the AI proficiency curve, from doing things by hand, to getting help from AI, to finally, building AI tools.

You can move up that curve much faster than I did. You can jump right to the end and make a Prompt-Writing Assistant that makes better prompts every time. It incorporates all of the five tips above.

How to make a “Prompt-Writing Assistant”

If you haven’t created an AI assistant yet, today could be your big day.

An AI assistant (sometimes called an automation or agent) is a reusable little tool that does a specific little job. They’re easy to create and easy to share with your team.

Every paid version of AI has this feature. They’re called Claude Projects, Custom GPTs, Gemini Gems or Copilot Agents. They’re all really the same idea: a set of instructions (like a big prompt) that the AI references every time you use it.

We’ve made assistants that research brands, build personas, audit webpages and edit articles. But one of our most powerful is an AI assistant that helps write high-performing prompts. 

If AI is the master of language, then it’s also the master of the language of prompting.

Here are the instructions for this assistant. We mostly use Claude so it’s a “Project” but this will work as a custom GPT or Gemini Gem just the same.

Just open your AI tool, click the button to create, and copy/paste these instructions into the box. If you’re not sure where to click, you can see our guide (and video) on building custom GPTs. It’s literally five clicks. I counted.

In a hurry? We made this a custom GPT that you use anytime: Orbit’s Prompt-Writing Assistant GPT.

Instructions for a Prompt Collaborator (Claude Project, Custom GPT or Gemini Gem)

You help the user design, refine, and publish AI prompts — primarily for marketing and adjacent business functions. Your job is not just to write prompts. It’s to help the user think more creatively about what a prompt could do, what inputs would make it stronger, and how to share the result.

Scope: This project builds workhorse prompts — reusable, high-performance prompts that become part of a regular workflow. Not ad-hoc questions (“summarize this email”), not generic framework templates (BRIEF, CLEAR, PRISM). Workhorse prompts earn their place in someone’s job by producing consistent, decision-grade output across many runs. If a user’s request is clearly ad-hoc, answer it directly without invoking the full methodology. The principles below apply when the prompt will be reused.

How to start a conversation: When the user opens a new conversation about building a prompt, don’t draft immediately. Briefly frame what you’re about to do, then ask 2–3 targeted discovery questions before writing anything.

A good opening looks like: “Before we draft, I want to understand the idea. A few quick questions: 1. What problem are you solving, or what decision should the output drive? 2. What inputs do you have available (URLs, screenshots, transcripts, exports)? 3. What’s the conventional approach here and what part of it usually feels generic or formulaic?” Adapt the questions to the request. The third question should always probe the framing or the conventional approach.

Skip discovery when the user is iterating on an existing prompt, if they’ve front-loaded enough detail to draft confidently, or if they’re asking a quick question that doesn’t require a full prompt. When skipping, draft directly but still apply the principles below.

Principles: These are moves available to you, not steps to execute. Use the ones that genuinely improve the prompt for the user’s situation. Reaching for clever moves (framing challenges, inversions, cross-discipline borrowing) when the straightforward approach is fine produces worse output, not better. It adds friction and makes the project feel performative rather than useful. Inversion, framing challenges, and cross-discipline borrowing should surface as observations and questions about their idea, not as labeled techniques.

1. Push for creative, unexpected angles when the conventional approach feels stale. Workhorse prompts produce better output when their methodology is non-obvious. If the user’s idea sounds like a standard workflow, look for the move that would make the prompt distinctive: reframing the reader’s role, starting from an unexpected input, inverting the process, asking for output the user didn’t think to request. Not every prompt needs this. Reach for it when the user is searching for novelty or when the conventional approach would produce average results. Start with the quantitative data the user likely has access to: open rates, CTR, conversion data, GA4 exports, win/loss outcomes. This is the input most likely to sharpen the prompt. Then suggest non-obvious inputs they may not have considered: transcripts, support tickets, replies, internal docs. The best prompts triangulate both.

2. Match the input format to the question. A webpage can be given to AI three ways, and each surfaces different things: 1. URL or pasted text → copy and messaging 2. Full-page screenshot → design, layout, and images 3. Uploaded HTML → code, schema, title tags, meta tags …Apply the same thinking to other input types.

3. Push for surprise, in both methodology and output. Workhorse prompts produce better results when they go beyond the average, expected answer. Two places to engineer for that:

• In how the prompt is built: look for non-obvious angles — changing the sequence, reframing the reader’s role, starting from an unexpected input.

• In what the prompt instructs AI to produce: build in moves that push past AI’s defaults. Challenge widely accepted beliefs. Highlight common assertions not supported by data. Surface counter-narratives. Reveal overlooked areas of importance. Suggest counter-intuitive approaches. Connect seemingly unrelated insights. Draw from unconventional sources.

Pick the moves that fit the prompt. A trends piece benefits from counter-narratives. A strategy doc benefits from counter-intuitive approaches. A research prompt benefits from connecting unrelated insights. Not every prompt needs surprise. Operational and categorization prompts often shouldn’t have it. Reach for it when the user wants novelty or when conventional output would be too easy to ignore.

4. Challenge the framing only when there’s a materially better one. If the user’s framing is workable, build within it. Only challenge the framing when there’s a clearly better alternative that would meaningfully change the output — not when there’s a defensible-but-different way to slice the problem. Polished pedantry is worse than a polished answer to the question as asked.

5. Look for methods from adjacent disciplines. Frameworks from content strategy, SEO, UX research, paid media, positioning, strategy, or sales methodology can sometimes be repurposed in novel ways. Schema.org as a content brainstorming tool is one example. Worth surfacing when the user is searching for a new angle.

6. When describing what “good” looks like, show an example instead. Examples beat adjectives. A single before/after pair teaches AI more than three paragraphs describing the standard. If the user is describing desired output in adjectives, ask whether they have an example to include instead.

7. When assigning AI a role, include a constraint and say what to avoid. “Act as a world-class copywriter” does little. What works: role expertise plus audience plus constraint. “You are reviewing this page on behalf of a CFO who has 90 seconds, is skeptical of marketing claims, and needs to justify a purchase to a board.” The constraint is where the leverage is. Also include what to avoid, not just what to aim for Example: “allergic to the word ‘unlock’” is more useful than “professional tone.”

8. When a prompt evaluates or compares, require a committed judgment plus rationale. A 0–5 score on specificity, a ranking by impact, a pass/fail on message match, paired with a short rationale. Use a table format. The judgment forces AI to take a position the user has to engage with. The rationale is where the actionable insight lives. Avoid vague feedback.

9. For high-stakes outputs, build in a self-critique step. “After generating, rate this against the criteria above. What’s the weakest part? Rewrite that part.” AI is often better at evaluating its work than producing it on the first try. Make recommendations from any field that may provide insights. Not necessary for every prompt — most useful when the output will be published or acted on directly.

10. When asked to generate a prompt that outputs files, suggest lighter output formats when possible. When a prompt’s output is going to DOCX or PDF, suggest generating a lightweight format like Markdown or HTML first instead. Reserve heavy formats for when the formatting itself is the deliverable.

Of course, I used my own prompt assistant to create these instructions. We worked hard on it together. If you read it closely, you’ll see how it works. It creates a Prompt-Writing Assistant that does a few very clever things:

• It starts by asking questions (so goals are clear and opportunities are discovered)
• It challenges assumptions, crosses disciplines and inverts thinking (so the user gets better responses)
• It thinks about smart inputs for prompts (so the user gives it everything it needs)
• It builds prompts that include little self-evaluations and rating systems and rationale (so the user thinks through the response)
• It builds prompts that generate efficient output formats (so the user doesn’t use more credits or carbon than necessary)

You’ve pasted in those instructions to your Claude Project (or Custom GPT or Gemini Gem) and given it a name. A new member of your team is born.

Run a test prompt to check the quality

We’ve tested this carefully across many use cases and AI models for hours. But you’ll want to test it yourself.

Ready for your first chat with your new Prompt-Writing Assistant? Here are a few conversation starters you can try as test prompts. The use cases are infinite.

• “Write me a prompt that writes email subject lines and body text when given a draft article.”
• “Write me a prompt that generates content briefs when given a topic.”
• “Write me a prompt that edits articles for brand voice and tone.”
• “Write me a prompt that helps me improve underperforming landing pages.”
• “Write me a prompt that writes case study intros.”
• “Write me a prompt that creates internal linking strategies for an article.”
• “Write me a prompt that audits a competitor’s homepage for messaging weaknesses.”

Try one of these. The assistant will draft you a fresh prompt. Review it first or copy it straight into a new conversation to test the output. Here’s what a test looks like.

I used the last example from the list above (audit a competitor’s homepage) and generated two prompts: one with the Prompt-Writing Assistant, one with a plain unguided request to the same AI. Then I ran both side by side. Compare:

In this test, the difference is dramatic. The Prompt-Writing Assistant generated a prompt that had much more detail and insights.

• It started by asking me questions (“What decision are you driving toward?”)
• It suggested inputs (“Can you provide a screenshot?”)
• It created a scoring system (“Ten-second test results: 2.5 out of 5”)
• It critiqued its own output (“The weakest judgment in this audit was scoring the hero based on a single screenshot of a rotating carousel.”)

Of course, they are both better than a one-shot prompt written by hand “Audit this homepage for messaging weaknesses.” I tried that too. It was a list of generic recommendations. Useless.

What do you do next?

1. The output is good…
   Save your new prompt! (more on this below)
2. The output is not great…
   Fix the issues in the new prompt. Edit it then fire the test prompt again.
3. The output is way off…
   There’s something structurally wrong, useless or irrelevant. Training time. Go back and tell your prompt assistant what’s not right. Ask it to create a revision to its own instructions. Then copy the revised instructions, paste them in, start a new conversation and fire the test prompt again.

This more diligent approach to dialing in your Prompt-Writing Assistant involves checking it in several stages.

It’s a strange and abstract way to work. You’re using an AI assistant to generate new prompts that you’re testing with test prompts in another conversation, sometimes with the assistant auditing its own output. Layers of language. It’s mind bending work with multiple levels of editing. It’s like that Leonardo DiCaprio movie ‘Inception’. Great movie.

Keep iterating until you have an assistant you trust and a final prompt worth sharing. This approach is how we’ve made many of our most effective prompts.

Use your Prompt-Writing Assistant to improve your old prompts

You may already have a little repository of prompts you love. They’re very good. But could they be better? Could the analysis prompts provide even deeper insights? Could the content prompts generate even more complete articles?

There are two ways to use your new Prompt-Writing Assistant to audit and improve them

1. Give your original prompt directly to the Prompt-Writing Assistant
   “Here’s a prompt I’ve been running regularly. Audit it against the principles in your instructions. Suggest specific improvements that would make it sharper, more reusable, or more decision-driving. Don’t change its purpose or the shape of its output. Tell me which principles each suggested change is grounded in.”
2. Have the Prompt Writing-Assistant make a new prompt, then give both prompts back to the assistant
   “I’m giving you two prompts that solve the same problem: one you generated, one I’ve been using. Compare them across the principles in your instructions. Where does each one outperform the other? Then write a third prompt that combines the strongest elements of both.”
3. Run both prompts and compare the actual outputs.
   “I ran both prompts on the same input. Here’s what each one produced. Which output is stronger, and why? What does the weaker prompt need to match or beat the stronger one?”

The same approach works to audit the instructions behind Custom GPTs, Claude Projects, Gemini Gems, and Claude Skills. Paste in the instructions, ask the Prompt-Writing Assistant to audit them against its principles, and you may spot gaps that have been quietly limiting every conversation you’ve had with that assistant.

---

Andrej Karpathy, OpenAI Cofounder “The hottest new programming language is English.”

---

Give your new reusable prompt a home

A workhorse prompt without a home gets rewritten from scratch by the next person who needs it. Or by you, three weeks from now, when you can’t remember where you saved it. Put your workhorse prompt in the stable. This could be several different places:

• Save it in a shared prompt library that your team can easily access
  This could be Google Drive, Sharepoint, Notion or whatever else you use to share docs. Then link to it from your team training docs and SOPs (“standard operating procedures”)
• Make the new prompt its own custom GPT, Claude Project or Gemini Gem
  If it’s used all the time, no need to copy and paste it every time. Just make it another new assistant. Give it a name “Orbit Email Editor” or “Cletus the Copywriting Helper.” As long as you have a team AI account, everyone can access it.
• Claude Users Only: Make it a “Skill”
  Tell your Prompt-Writing Assistant to adapt it and make it a Claude Skill. Skills get invoked automatically when the request matches the request. The user doesn’t need to go to the assistant.

Make to your Assistant ever-smarter (upload knowledge sources)

What would make your assistant smarter? What could you upload to it that it could reference when generative prompts?

That depends on its purpose. If it’s generating prompts that do editing, upload some writing standards that it can incorporate. If it’s generating prompts that make reports for clients, upload your company style guides or a past report. If it’s generating prompts that do audits, upload your current methodologies.

Our page auditor has several interesting knowledge sources that it can reference.

Anytime, anywhere. If you spot something that might make any of your AI assistants smarter, save it, convert it to a lightweight markdown (.md) format, and upload it to the knowledge sources section of your assistant. Your assistant may be a bit smarter forever after.

I have many marketer friends who bristle at this kind of AI delegation. They have valid reasons, from labor market impact to environmental costs. But one of the strongest objections is just about the future of work and its intrinsic value to human purpose. What about the simple joy of making things? What about that?

The joy of creativity is alive and well

In December of 1999, I quit my recruiting job to become a web designer. I wanted to build websites more than anything else in the world. By 2001, my cofounder and I had started Orbit Media as a web development company and we never looked back. I haven’t personally designed a website in many years, but I still remember the joy of making things.

And I still feel that creative joy, but it’s moved. I feel another kind of joy, The joy of building little tools and seeing them work. It’s still creative, but it’s a different kind of creativity. Maybe it’s more like the joy that a programmer feels when their code does a good job.

And of course, the greatest joy is performance. Knowing that you made a difference for a brand.

Marketing is important work. It fills the pipeline that drives deals that drive the revenue. That revenue creates job security for the humans that work at those brands.

With this singular purpose, we must consider any tool that drives results. AI is one of them. But the point was never AI or any other tool. The point is the impact that the work has on the business.

And now that we can make our own little tools, we can make one shift that secures the future for all of us.

Focus on outcomes, not outputs.

We are marketers, not artists. We take pride in our work, but we must also take pride in the performance of our work. Because marketing matters.

Every AI prompt has two prices.

One shows up on your invoice. The other gets paid in carbon, electricity and water. Both are driven by token usage and what you upload to and export from the AI. And the file formats you choose can change the bill by 10x.

Why do some files require more processing?
There are three factors that combine to determine how hard the AI has to work with any file.

1. Does the AI need to use a tool to open the file or generate the output?
2. How much structural noise (tags, formatting, styles) does the file format require?
3. Is anything being processed as an image instead of text?

Some file types that have lots of formatting and visuals. These require much more processing power. Here’s a simple way to remember this:

• If you need software to open it, it’s expensive (DOCX, PPTX, XLSX, PDF)
• If you can open it in Notepad, it’s efficient (TXT, MD, CSV, JSON, HTML)

If you need some desktop software to open the file, then the AI needs libraries and tools to parse the file. These libraries and tools add tokens and carbon cost. It may be obvious. You can sit back and watch the AI work harder (and slower) on these files.

Imagine you’re working on a big report and getting help from AI… 

You can generate all kinds of file formats using AI. Which do you choose?

To get a sense for which file formats are the most popular with marketers, we put a little poll on LinkedIn. We had about 500 responses to the question “When you ask AI to make a big deliverable, what format do you ask for?” The top answer was DOCX by a wide margin.

But those are all very different types of files, each requiring very different levels of effort from the AI to generate. They have different costs in terms of tokens (processing power), electricity (carbon output) and credit usage (money).

Which file formats have the highest costs?

To measure that difference, I took a recent article and gave it to Claude in six different file formats, then analyzed the token count using the “v1/messages/count_tokens” API endpoint. I measured in both directions:

• Input Measurement (added to prompts): I asked the AI to “summarize” the piece as provided in the six formats, including a nicely designed PowerPoint version of the article that I’ve used in presentations.
• Output Measurement (generated for downloading): I then reversed the process and asked AI to “generate” the content in those same file formats.

Here is the relative cost in tokens, which translates directly into energy consumption, environmental impact and financial cost. You can see that mileage varies.

The difference is huge. HTML and PDF are expensive to read but cheap to write. DOCX is expensive in both directions. PPTX is by far the heaviest, slowest and most expensive in the bunch.

Let’s look closer and consider more file formats. This table explains how AI sees the files and handles the processing, in and out.

Let’s look closer at the rankings, share tips and consider lighter-weight alternatives to the heavy, popular options.

The formats, use cases and alternatives

1. Text Files (TXT)
The smallest, lightest file possible. But contains no formatting. Good for large amounts of text where formatting isn’t important, such as podcast transcripts.

Lighter alternative: None. 

2. Markdown Files (MD)
Markdown is the language AI agents speak best. It’s also readable by humans and not much heavier than a TXT file. Formatting and tables are added through characters, not tags. It’s the fast, efficient champion of AI inputs. It’s also great as a draft that you can edit within the AI, getting all of the content ready before publishing to a heavier format.

Lighter alternative: None. 

3. Comma Separated Files (CSV)
Lightweight, structured and easy for AI to parse. Great for simple tables, lists, exports, keyword research, analytics data and CRM records. No formatting, formulas or multiple sheets. Just rows and columns.

Lighter alternative: If it’s just a short list or simple table, paste it directly into the prompt.

4. Web Pages (HTML)
HTML was  our least common answer in the LinkedIn Poll above, but they have many advantages. They’re not too heavy to generate and easy to share (everyone has a browser) Maybe the ideal for reports. Then can even include interactivity, but this adds a lot of weight.

HTML files don’t play nice with Google Docs. You can’t paste HTML into a Google Doc and if you upload them to Google Drive, they don’t render the HTML when you open them.

Lighter alternative: If you don’t need print-ready formatting, MD carries the same content at a fraction of the cost.

5. Word Docs (DOCX)
This is the most common file format for outputs, according to our little LinkedIn Poll above. No surprise. It’s still a standard for word processing. But it’s a heavy file format with lots of tags and formatting. It wasn’t built for AI.

Like all archive formats, these need parsing on the way in and libraries on the way out.

• Instead of importing a DOCX file to AI, convert it into an MD file and then upload (use Google Docs or save to TCT then change the file extension)
• Instead of exporting a DOCX file from AI, make an MD file then paste it into a fresh DOCX file (fast, easy, but may require a little manual styling)

Lighter alternative: MD for drafts, HTML for polished-looking documents. 

6. Excel Files (XLSX)
Useful when you need multiple sheets, formulas, filters, formatting or charts. But all of that structure adds weight. For AI inputs, XLSX files are overkill unless you need formulas and logic.

Lighter alternative: If you don’t need formulas or formatting, CSV does the same job at a fraction of the cost.

7. PowerPoint Files (.PPTX)
These have a very high cost output. They require a ton of processing. Every layout decision in every slide is all code that the AI has to read. And images require a separate, slower model. Those visuals drive up the bill.

Better yet, just don’t mix AI and PPTX files if you’re concerned about costs. If you need to make a PPTX in AI, produce the output just once, at the very end of the process.

And don’t use AI for small edits to decks. It’s faster and cheaper to make little changes manually in PowerPoint. If you must edit within the AI, be explicit: “Just regenerate slide 3, keep everything else the same.”

Lighter alternative: If the slide format isn’t the deliverable, an HTML file carries the same content far more cheaply.

8. PDF Files
If you’re building a Custom GPT, Claude Project or Gemini Gem, you may be tempted to upload some relevant PDFs. But before you do, start with this prompt: “I’m planning to add this PDF document to your knowledge sources, so let’s convert it to an MD file first. But before we do, what should be trimmed? Or consolidated”

The AI will suggest edits that will reduce costs without sacrificing quality. And the smaller file (MD, not PDF) will make all future conversations with this project cheaper and lower-emissions. It’s a double-win.

Lighter alternative: Use MD for text-heavy documents, HTML for designed reports

9. Images (PNG / JPG)
AI image generation is not done with an LLM. It’s a separate model that uses “visual tokens.” If you’ve had AI make pictures, you’ve seen how slow it is. That’s because it requires a lot of processing power.

If you’re only worried about the financial costs and not the environment, you can generate images in a cheaper model (Ideogram is the best) and save the credits in your daily workhorse AI, where you sometimes hit credit limits.

Lighter alternative: For screenshots of text, just paste the text instead, if possible. 

Where costs multiply: editing files in AI

Here’s where things get really expensive. It isn’t obvious, but all marketers should know this:

Every iteration on a heavy file format costs as much as making it from scratch. 

That’s right. If you ask for even a small change to that DOCX file, the AI may start over and create the document all over again, with all of that token cost, energy use and carbon impact.

Smart AI users know to work on files in a simple format, such as an .md file, where the costs are much lower. Iterate in text, then generate the final format once everything is final. Here are a few tips for efficient AI prompting:

1. Brainstorm first, no file generation yet. Tell the AI what you want to make, but instruct it not to make the final format yet… “Don’t generate the DOCX output until I ask for it. Let’s refine the idea first…”
2. Ask for lightweight previews. While working on the draft, ask it to show a simple markdown format you can preview… “Show a preview in a lightweight markdown format so I can review it efficiently.”
3. Iterate in the text. Have the conversation about content in MD or right in the chat. Get the structure right, get the wording right, get the order right, all in plain text, where iterations are cheap.
4. Batch your changes. If you have five edits, give all five in one prompt. Each prompt that edits a heavy file pays the full regeneration cost. Five separate change prompts is 5x the cost. One consolidated change prompt that lists five edits is 1x the cost. “Change X to Y and move it up in the doc. Remove Z completely.”
5. Ask for the final format once, in the end. Don’t ask for the output until you’re ready for showtime. This is the single biggest efficiency win. All set? Content locked in? Now have it make the big file once and once only. “Changes are final. Now generate the DOCX file. Use these styles…”
6. One more final tweak? Do it outside the AI. Export the big file and make final final changes (there’s always one more, right?) after it’s landed on your computer. Download it → Review it → Tweak it → Ship it

Having AI make tweaks to a DOCX or PPTX file is slow, processor intensive and uses more energy than necessary. It’s like driving a giant truck a half block to pick up a few groceries.

Let’s ask the experts: “Which are the best file formats for AI?”

Marketing veterans and AI experts make thoughtful cases about AI efficiency and file formats.

---

Chris Penn, Trust Insights Choose the smallest model that will get the job done. “In tools like Gemini, ChatGPT, and Copilot, there are a range of model choices but they all tend to fall in two categories: fast and dumb (instant, fast, Flash, etc.), slow and smart (pro/thinking/etc.) The smaller and faster (and dumber) the model is, the less energy it uses. My rule of thumb: if you’re doing a task that requires less than two rounds of revision, use a fast model. Example: summarizing an email, etc. If you’re doing a task that requires more than two rounds of revision, use a slow model, but know it imposes a much heavier carbon cost.”

---

John Jantsch, Duct Tape Marketing HTML wins on both ends. Cheaper to make, cheaper to change. “I’ve been making slide decks for small business clients for 20 years. Most get opened once and never looked at again. So why am I burning an hour designing in PowerPoint when an HTML page does the same job and renders on any device the client owns? Same logic with SOPs. Nobody wants to crack open a 14-page Word doc to figure out how to post on LinkedIn. They want a link they can scroll on their phone. The reason we didn’t already ship HTML is simple. You had to know HTML, or fight with a website builder. PowerPoint and Word had visual editors that a non-coder could use. AI flipped that. You describe what you want in plain English, and you get a self-contained file you can open locally or host for free. Need changes? Just ask. It rewrites in seconds. It’s also lighter on the planet. HTML is plain text that the AI types directly. DOCX and PPTX are zip archives of XML that the AI has to assemble with separate code libraries. That burns far more processing every time you generate a file. And every time you ask for a small tweak to a DOCX, the AI usually rebuilds the whole document from scratch. The same edit on an HTML file costs almost nothing. Multiply that across a year of client work, and the gap is enormous.”

---

John Nugent, Magnolia Slides are becoming obsolete “The advancements in AI tools in the last 4-5 months have made slide/presentation tools almost obsolete. You still need to deliver in a client-friendly format, but if you’re doing custom HTML right, they won’t want to receive anything else.”

---

Chris Penn, Trust Insights XLSX files are a nightmare “For tabular data, LLMs struggle natively with anything in a table format. Want to represent your data in a machine-readable format that’s also eternally compatible? Ask for JSON or even better, YAML. It converts tables into lists, and LLMs can use them perfectly – AND modify them easily. When you’re done with the LLM, use a free program like jq or yq to convert them back into a tabular format of your choosing, no AI needed.”

---

John Jantsch, Duct Tape Marketing Markdown is the next move. “Drop a free tool like MD Reader into your browser, and any .md file renders cleanly the second you open it. (This is what Wikipedia runs on.) Your team’s playbooks and SOPs look like a real page without anyone fussing over fonts. You write in plain text, and the browser handles the rest. All while using the most token-friendly file that exists. Perhaps today is a new beginning for you: a new format for your big deliverables (HTML) and a new file format for everyday AI collaboration (MD).”

---

So far, we’ve covered things you can control. Now we’ll pan out and see how your prompts fit into a much bigger picture…

The AI cost you can’t control: training the models

Prompting isn’t the biggest cost of AI. It’s training the models. Building a model and keeping it trained requires huge amounts of energy. Every big tech firm with a frontier AI model spends at least $100M per year on training, emitting tens of thousands of tons of carbon (and methane, nitrous oxide, etc.) not to mention water (evaporative cooling to keep servers from overheating).

This is not something users like you and I can control. No matter how much or little you use AI, these companies will continue to invest, incurring financial costs for themselves and environmental costs for us all. Compare the cost for AI model training and user AI prompting:

When asked about this, Sam Altman recently brushed off concerns about AI’s energy use, comparing it to the food humans eat.

“People talk about how much energy it takes to train an AI model, but it also takes a lot of energy to train a human. It takes about 20 years of life, and all the food you consume during that time, before you become smart.”

Sounds dismissive. But he has a point about the high carbon cost for human consumption. So let’s look at that…

If you’re really concerned about your carbon footprint…

There are two main inputs that drive the carbon footprint for you, me and everyone else on the planet: air travel and meat eating. AI aside, any of us can reduce our carbon footprint by cutting back a bit on these activities. This puts AI use in the proper context of each of our true individual impacts.

Shrink your AI carbon footprint

My friends who are the most proficient with AI talk about “token efficiency.” They work hard to reduce credit expenditures. They run the cheapest model possible for each specific job. They put data in tables to avoid prompting completely when possible. They even install AIs locally on their laptops for simpler tasks. The best AI users obsess over efficiency.

But you don’t need to be an AI pro to be efficient. Smart decisions about file formats saves processing power, carbon emissions, water use and yes, money.

Minimizing the impact of AI on the planet is all of our jobs.

Great webpages are great in different ways; bad webpages are bad in the same way. They lack specificity.

A page with details and specifics works hard at all three of its main jobs:

• Provides the key info that converts human visitors (clarity)
• Uses phrases that drive search rankings  (relevance)
• Trains the AIs on the details of your offer (accuracy)

A vague page is less likely to do any of those things well. And it’s less likely to perform in every important way.

Of all the little AI audits you can run on a webpage, this one may be the best. It checks for specificity in five places where vague pages tend to fail:

1. Headers
2. Hero image
3. Subheads
4. Navigation labels
5. Calls to action

We’ll start with the prompt, then we’ll explain the logic behind it in each of the five areas. At the end of this article, I’ll show you a mini-report that you can generate from the prompt’s response.

The Specificity Audit Prompt

This prompt has a special feature. Not only does it check the specifics of the page, but it also checks whether the call to action mismatches the text on the page. This is a type of path analysis, which is easy to miss when you check pages individually.

But to do that, it needs to run in an AI (Claude, ChatGPT, or Gemini) with web access enabled so that the tool can follow the CTA link to the destination page.

The inputs are the full-page screenshot of the page and the URL. That’s all it needs. No need to even upload your persona for this one.

Audit this webpage for vague language in the five elements that most affect clarity: H1 header, hero image, subheads, navigation labels, and CTAs. Provide the URL (and a screenshot if helpful).

For each element, return a markdown table with four columns: Element | Score | Current | Rewrite.

Use this scoring format, where 1 = generic (could apply to any company) and 5 = specific to this business: 1 · 2 · 3 · 4 · 5

The Current column shows what’s on the page now. The Rewrite column shows the exact replacement — not advice about what to change. Flag any word that could apply to any company in any industry (“solutions,” “services,” “learn more,” “get started”). For the hero image, describe what a more specific image would show.

Then follow the primary CTA link to its destination page. Compare the promise made by the CTA to what the visitor actually lands on. Is there a message match, or a gap? If there’s a gap, name it in one sentence.

Prioritize the single highest-impact fix at the end.

[Provide a URL and a full-page screenshot of the page]

The analysis is both enlightening and practical. It includes the problems and the remedies. We’ll show an example report below. But first, let’s look at the five areas we audited, side-by-side with examples…

1. Write Descriptive <h1> Headers

Every page (in fact, every scroll depth on every page) has a visual hierarchy. One element is always the most visually prominent. It attracts the visitor’s attention first. A second element comes next and so their eyes move there next, and so on down the page. That’s your visual hierarchy and it is the key to web design and usability.

We’ll start with the <h1> header because it’s at the top of the visual hierarchy. If it’s generic, vague and boring, that’s bad. If it’s specific and descriptive, that’s good. You’ll pass the 5-second test (more on that below).

The first question of every visitor to every webpage is “Am I in the right place?” The header should answer that question. Compare these two and ask which does a better job of answering that question…

The difference is huge. And it’s an easy opportunity to improve. Making your header more descriptive may take just a few minutes. If internal stakeholders push back, tell them it’s just a test (all changes are, really) and that you’ll change it back if it doesn’t perform well.

Clear is more important than clever in headers. The most helpful, compelling thing should also be the most visually prominent thing. But on websites everywhere, the header is vague but the small text below it is specific. Just flip that and you’ll have a better page for visitors, search engines and AIs.

2. Use a relevant hero image

Also at the top of the visual hierarchy is the featured area image. If the header is specific, then the image doesn’t have to work as hard. The visitor may already know they’re in the right place.

But a relevant image can show what you actually do. But a generic image (often a stock photo) is a missed opportunity to be specific (and authentic). A stock photo doesn’t hurt, but it doesn’t help either. It tastes like water.

Take a look at that image at the top of your page. Ask yourself: “Is this specific to my business, or generic to millions of businesses?”

The hero image doesn’t need to be a picture at all. Sometimes a texture or looping background video. These are fine options that reduce the prominence of that element. The visitor gets clarity from other elements.

Other times, the hero area is the perfect place to show the product (example), show the product in context (example), show the team (example) or show a video where you explain the value of the offer (example).

3. Rewrite (or remove) generic subheads

Once they know they’re in the right place, the visitor starts scrolling and scanning for answers to their questions. That’s why you have <h2> subheads. They are typically the most visually prominent element at every scroll depth. So they should be compelling, specific and descriptive.

Generic subheads just take up space without adding any value at all. They simply push down the rest of the page. Look at your subheads and ask yourself: “Could this be more specific? Would this page be just as helpful if I removed this completely?”

Subheads are an opportunity to put something relevant and helpful into view for the visitor. The best subheads are specific to your business. They communicate quickly, adding value to the experience of the visitor.

Ironically, “What we do” doesn’t actually say what you do. It’s not helpful. “Our solutions” is just as bad. If you ran the prompt above, you already have ideas for more descriptive subheads.

Testimonial subheads are another good example. The subhead “What clients say about us” doesn’t actually show what clients say. Far better to use the subhead to showcase a compelling part of a testimonial.

In the first example above, the visitor isn’t likely to read those super compelling words in the actual testimonial. Again, the most compelling thing is the least visually prominent thing. In the second example, the visitor is very likely to read that part of the testimonial, because the visual hierarchy aligns with the messaging priorities.

---

Talia Wolf, GetUplift “Often, brands stay on the surface level instead of actually going deeper. Social proof is a prime example. We all know it matters, so everyone uses it, but usually that just means slapping a few logos on the page and adding a generic testimonial like ‘they’re the best.’ But no one is sitting there thinking, “I just need to know they’re the best”. They’re thinking: “Will this work for me? What if I get blamed for this? Am I about going to lose my job?” Social proof is your best chance to reduce that risk, so it has to be specific.”

---

4. Use descriptive navigation labels

The navigation labels in your menu are another opportunity to be specific (or super generic and vague). On a homepage, the navigation bar is always near the top of the visual hierarchy. Eye tracking studies always reveal this. Most visitors scan the main menu first, even before scrolling at all.

The goal of your homepage is to get your visitor off the homepage. And of course, the copy is absolutely critical for keyphrase relevance (search rankings) and training LLMs on what you do (AI recommendations).

Specific, descriptive nav labels are part of website navigation best practices. They both tell the visitor what you do and guide them toward deeper pages, help the visitor segment themselves, finding pages more specific to their needs.

Labels such as solutions, services, products and industries are generic, vague and unhelpful. Literally any business could use these.

Scan through your website navigation and ask yourself: Are these labels specific to our business? Or are they common to millions of businesses?

Of course, businesses with many offers may have a good reason to use generic labels at the top level of the navigation. It makes sense. But even huge companies can be very specific in the sub-navigation labels within their mega menus.

5. Craft calls-to-action with compelling verbs

Finally, we get down to the CTA. It’s in your header navigation, partway down your page, and probably, in its own page block above the footer. You’ve got several. But how specific are these CTAs? Are they relevant to your business, or generic to all businesses?

Compare the difference in these two examples. The first is generic and common to every website on the internet. The second is specific to the visitor. It gives them a reason to click.

Look closely at the verbs on your page. Verbs are key. Some calls to action have common verbs like contact, read, learn and click. Great CTAs have specific verbs like schedule, chat, check, download and watch.

For ideas and a helpful prompt, here’s our guide on creating high click-through rate CTAs.

Remember, no one clicks anything until they’ve done a split-second cost/benefit analysis. You can increase the clickthrough rate of anything by making the perceived cost smaller or the benefit bigger. That’s why low-commitment CTAs often work well.

When they click the CTA, the words on the contact page should match the words they just clicked on. Otherwise, the mismatch may cause a bit of friction at exactly the wrong moment. This is one of our best practices for contact pages.

---

Pam Didner, B2B marketer and Copilot Trainer “Good writing is in the details. In a novel, you need specific moments, tension, and turning points to keep people reading. The same applies to business content if you want storytelling to land. Too many blog posts and eBooks stay at the surface. They sound right, but they don’t help. They rely on clean frameworks and tidy steps that look polished but don’t solve real problems. If you want storytelling to work, go deeper. Be specific about features and benefits. Show exactly how something works and why it matters. Walk your audience through the logic, and give them a reason to care – your emotional hook.  Start with why it matters. Then show how it works. Then make it clear what to do next.”

---

The Output: A Specificity Audit Report

Once I got the response from AI, I used this simple prompt to convert the response into a mini-report.

Make this into a simple HTML report.

Why HTML? Because it’s lightweight, portable, easy to edit and universally viewable. You could make a DOCX file or a PPT, but why burn the credits? These are heavy file formats requiring a lot of processing for the AI to generate and edit. Besides, why use PowerPoint unless you absolutely have to?

The report is more visual and interesting than the AI response. Now you’ve got something you can bring to a meeting.

When I create reports, I do it in a little automation (Claude Project, custom GPT, etc.) that knows the Orbit Media styles, so everything aligns with our brand. Here’s part of what it looks like…

Ouch! AI doesn’t like my subheads. It suggests more detailed and specific alternatives. And it has a recommendation for each deficiency it identifies.

As always with AI audits, we should always feel free to dismiss any advice that we find off-target or irrelevant. Better yet, talk to the AI about the report. The audit is really just a point of view, not prescriptive instructions. You, the human marketer, decide what to do next.

The 5 Second Test: Check your revisions with actual humans

You’ve made some changes! The draft looks good. You’re ready to publish …but if it’s a high-stakes page, you may want one final check to confirm that your page communicates quickly and clearly.

The “5 Second Test” is a clever and simple way to test. Upload a screenshot to Lyssna and enter one question: “What does this company do?” The tool then shows the screenshot for five seconds to real humans who then answer the question. You pay $1 per response.

Spend $25 and in a few hours, you’ll see 25 answers.

If the answers look like these, you have a problem…

When we show the results of these tests to clients, everyone suddenly feels urgency to fix the problem. And the fixes add specificity and clarity. Sometimes the fixes are to remove distractions.

So why do marketers use vague language?

If using specific, descriptive language is good for converting humans, ranking in search engines and training the AI models, why is vague language so common on websites?

The main reason is fear.

Marketers are often afraid of excluding one of their audiences. They want to address many different markets. To do so, they water down their language. Ironically, they are so worried they’ll exclude someone that they end up excluding everyone.

Let’s ask Doug. If you don’t know Doug, here’s my standard introduction: Doug Kessler is your favorite writer’s favorite writer. He’s that good. And he has an explanation for vague copy:

---

Doug Kessler, Velocity Partners “The urge to try to be all things to all people is so strong it’s almost the default setting for marketing teams. Excluding a potential buyer (no matter how unlikely it is that they actually do buy) just feels wrong to marketers. And it can be hard to convince stakeholders (especially voracious sales carnivores) that laser-focusing only on ideal prospects is actually a better strategy. The all things to all people mindset leads to fuzzy, flabby, lowest common denominator copy that just doesn’t resonate with anyone.”

---

You invested weeks into that original research piece. You devoted days refining a thought leadership essay, one that encapsulates your brand’s philosophy so eloquently. And yet, you click “publish,” share it on LinkedIn, and watch your traffic soar, only to return to normal 48 hours later.

That’s content decay in a nutshell. And marketers are stuck in this cycle. You create content, you promote content, you abandon. And you’re left with burnout and decreased ROI.

Your brilliant ideas shouldn’t have an expiration date. If your audience is too busy to sit down and consume a 2,000-word whitepaper, they’re not ignoring you. They’re just multi-tasking. They’re commuting, exercising, or walking their dog.

To reach them, you need to transition from screen to earbuds. Audio content isn’t just a new channel.

It’s a new life for your content. Audio content bridges the space between a lead and a listener, a level of intimacy that written content can’t match.

Why Audio is the “Unlock” for Modern Content Strategy

Before we go into the tactics, we must recognize the change in consumer behaviors. Screen fatigue is a very real thing. According to recent industry data, the average professional is looking at a screen for more than seven hours a day.

Source

When a consumer closes their laptop, they’re not closing off their desire for information consumption. Instead, they’re engaging in passive consumption. This is the “eyes-busy, ears-open” consumer market.

• Multitasking capability: Audio is the only media form that doesn’t demand 100% visual attention from the consumer.
• Emotional connection: Tone, sarcasm, empathy, and authority are much easier to convey with the human voice than any font choice.
• Accessibility: Making audio versions of your information increases your brand’s accessibility to consumers with visual impairments or learning differences.

11 Strategic Ways to Repurpose Your Content for the Ear

Repurposing is the ultimate hedge against content decay. It allows you to take the intellectual property you’ve already invested in and deploy it across a channel with significantly higher engagement rates. Here is how to build your audio ecosystem.

1. Convert High-Performing Blogs into Narrated Articles

The easiest way to begin is to offer your visitors the option to listen to your content. Long blog posts are like a goldmine for audio content.

Many people prefer “listening” to a blog post while they’re doing chores instead of scrolling through a mobile device.

You can use tools like ElevenLabs or Play.ht to create high-quality, AI-generated narrations that sound incredibly human.

You can embed this audio player at the top of your blog post. This increases “Time on Page,” which is a crucial metric for SEO.

Source

But don’t just read the text aloud. Create a short, informal introduction to make it sound like a personal message to the reader.

2. Transform Research Data into “Audio Snacks”

If you’ve done original research, then you’ve got a wealth of statistics at your disposal that are great for social media. Instead of doing a static infographic, consider turning your statistics into 60-second audio clips.

You can use a tool like Headliner to create audiograms, which are static images with a moving waveform and text.

These do much better than static images on LinkedIn and X.

• Highlight one unexpected statistic in the clip.
• Share the “why” behind the statistic.
• Share your full report in the show notes or comments.

3. Take Advantage of Captive Audiences with Podcast Advertising

Podcasts have become one of the most trusted media environments for audiences. Listeners often spend 30 minutes or more with a single episode, creating a level of attention that’s difficult to replicate with other digital channels.

For marketers looking to extend the reach of their existing content, podcast advertising offers a powerful entry point into this ecosystem. Instead of launching your own show, you can promote your ideas, reports, or thought leadership within podcasts your audience already listens to.

Audio ads work particularly well for amplifying existing content. For example, a short ad can highlight a key insight from a report, invite listeners to explore a resource, or introduce a new perspective on an industry issue.

Platforms like AudioGo allow marketers to place targeted audio ads across podcasts and streaming platforms, helping brands reach listeners based on demographics, interests, and listening behavior.

By meeting audiences inside the shows they already trust, audio advertising helps extend your content strategy beyond the screen and into the moments when people are commuting, exercising, or multitasking.

Source

4. Turn Case Studies into Audio Testimonials

Case studies are often dry. They follow the “Challenge, Solution, Result” format. Audio changes that by adding emotion.

Ask your client to jump on a 10-minute call on Zoom. Record the audio as they talk about the relief they feel when your solution solved the problem.

• Cut this into small pieces.
• Embed the audio next to the text quote in your case study.
• The human voice creates a trust factor that text does not.

5. Create an Audio Version of Your Newsletter

If you have a weekly newsletter, you’re already a content creator. However, inbox fatigue is real. An audio version of your newsletter, often called a private podcast, allows your subscribers to stay up to date without adding to their screen time.

For this to work best, it has to feel exclusive. It’s not a podcast. It’s a briefing. It should be under five minutes and focus on the “big idea” of the week.

6. Use Audio for Thought Leadership Distribution

Thought leadership is all about perspective. When you’re writing an opinion piece, you want to shift the reader’s mindset. And you can do that more quickly when they can hear the passion in your voice.

Take your most provocative or insightful LinkedIn posts and expand on them in short audio essays. Platforms like Swell are designed for these types of “micro-audio” conversations.

Source

Tip: Do these while the thought is fresh in your mind. Authenticity is more important than production quality in this format.

7. Build an Audio Resource Library

Think of it as an “Audible for Your Brand.” Eventually, you’ll build up your collection of narrated blog posts, interview excerpts, and essays. You can create a library section of your site.
It’s nice for a busy marketer to be able to “binge-listen” to something they’re interested in. If they’re a marketer who cares about SEO, it should be possible for them to click a button and start listening to all of your content related to search strategy.

8. Create Audio “How-To” Walkthroughs

If you have technical “how-to” guides or documentation, repurpose the steps into an audio walkthrough.

Instead of making a user tab back and forth between a help document and your software, give them an audio guide they can listen to while they perform the task.

This reduces friction and improves the user experience. It shows that you’ve thought about the practical reality of how people actually use your products.

9. Develop “Flash Briefing” Style Skills

For brands with a high volume of news-driven content, repurposing your “Top News” or “Daily Tip” section into a smart speaker skill, like Alexa Flash Briefings, is a powerful way to become part of a user’s morning routine.

Source

Once a listener subscribes, your 2-minute repurposed tip plays automatically alongside the morning news and weather.

It builds brand recall through daily, low-friction interaction.

10. Record Personable Q&A Responses

Static “Frequently Asked Questions” pages are usually where user engagement goes to die. They are functional but rarely memorable or persuasive. Audio responses turn a dry FAQ into a conversation.

Source

Take the top five questions your sales team hears every week and record short, personable audio responses.

Instead of just reading a paragraph, the user can hear the expertise and confidence in your team’s voice. This humanizes the problem-solving phase of the buyer’s journey and addresses objections with a personal touch.

11. Curate Audio “Roundups” of Industry News

Most industries move at a breakneck pace, and your audience likely struggles to keep up with the weekly firehose of updates. If you already write a “Weekly Wrap-up” or “Industry News” blog post, you have the perfect script for a high-value audio asset.

Instead of just linking to external articles, record a 5-minute summary that provides your brand’s unique take on why these news items matter. This shifts you from being a mere aggregator to a trusted curator.

Busy professionals can get their weekly industry briefing during their morning coffee or while checking emails. It positions your brand as the filter that helps them separate signal from noise.

Making Audio Work Without the Overhead

The biggest barrier to audio is the perceived effort. Marketers assume they need a soundproof room and an expensive microphone. In reality, the barrier to entry has never been lower.

The democratization of these tools means that small agencies and mid-sized businesses can now compete with global brands.

Whether you’re using Descript to edit audio as easily as a Word doc, or leveraging self-serve platforms like AudioGo to handle your podcast advertising, the technology is finally catching up to our creative needs.

Summary Checklist for Audio Repurposing:

• Audit: Identify your top 10 most-read blog posts from the last year.
• Narrate: Use AI or a voice actor to create audio versions of those posts.
• Clip: Break down long audio into 60-second “social snacks.”
• Promote: Use targeted audio ads to reach listeners on streaming platforms.
• Analyze: Track whether audio listeners stay on your site longer than traditional readers.

Your Turn to Speak Up

Audio isn’t replacing the written word; it’s amplifying it. By giving your content a voice, you meet your audience where they are—in their cars, at their desks, and on the move.

The real opportunity is in converting your best content into audio experiences that can be consumed on the go. This could mean narrated blog posts, audio insights, or audio ads to reach new audiences with your ideas.

Which of your current “hero” content pieces could translate well into audio?