524 media placements
AI prompts naming the client
56 branded, 264 non-branded
The Business
The client is an educational publisher of high-interest, low-readability books, known in schools as hi-lo books, for striving readers in upper elementary, middle and high school. Its buyers range from reading interventionists and special education teachers to school librarians, ELL coordinators and district curriculum directors. The client competes on books that teach early reading skills without looking young: decodable phonics books written at beginning reading levels for tweens and teens, and a wider hi-lo catalog for students to read next.
A reading interventionist with a ninth grader who missed foundational skills needs books at a first-grade reading level with a teenager’s interests. Many teachers in that spot now ask an AI engine where to find them. The client’s position is simple: an older student who is behind in reading needs practice books written for their age, at their reading level.
The Objective
The AI visibility optimization pilot set out to make the client a source that AI engines cite when buyers ask where to find phonics books for older students. It used two published assets and measured the results once after launch. Specifically:
- Focus on buyer questions about decodable books for tweens and teens, reading levels and age-appropriate intervention materials, where the buyer is close to a decision
- Publish one Anchor Article on the client’s domain to build its authority on phonics for older students
- Publish one AI Notice on the wire to carry the same age-respectful phonics case into third-party sources
- Track the client’s AI visibility on five AI engines, with a fixed set of buyer prompts for each asset
- Measure share of voice and prompt coverage against competing literacy publishers
Core Challenges
Competing With Literacy Publishers Already Established in AI Answers
During the pilot, nine competing brands were each named more than 45 times in the AI Notice prompt set. The answers cited 2,024 different websites. Social media platforms, literacy nonprofits, online retailers and rival publishers’ own websites were among the most-cited. Engines tend to recommend the brands these sources already discuss, so the client needed current sources of its own on the questions buyers ask.
Buyers Ask AI for Phonics Books That Fit an Older Student
Buyers in this category ask exact questions: which publishers make decodable books for teenagers, what reading level a set starts at, how a phonics sequence moves from short words to multisyllabic text and what a student reads after the last decodable book. To answer them, the client needed an authoritative source that gives buyers and AI engines every one of these details.
Tracing New AI Visibility Back to Its Source
With two assets in market, both telling the same age-respectful phonics story, a total citation count would not have shown which one the engines were citing. Only per-citation attribution could separate them, so the pilot traced every appearance back to the page behind it.
The Strategy
Prompt Selection Around the Buying Moment
The AI Notice prompts were written in the voices of 78 buyer personas, from Reading Interventionist and Special Education Teacher to School Librarian and District Procurement Manager. Some read like a parent or teacher thinking out loud. One parent asked for decodable books for a 14-year-old who “won’t pick up anything that looks like it belongs in an elementary classroom.”
One Anchor Article
A long-form guide on the client’s site walks educators through phonics for older students, from why early-elementary books feel too young to what to look for in a program. Because the client owns the page, AI engines can keep crawling and citing it after the pilot ends.
One AI Notice
The AI Notice went out on the wire and made the case for age-respectful phonics books on a third-party domain.
Day-Zero Launch, Day-7 Measurement
The AI Notice went live on day 0. The program waits seven days before it measures. After that wait, the pilot ran every buyer prompt on five AI engines and counted every citation that pointed back to the AI Notice’s placements and to the Anchor Article. The figures on this page are a single snapshot taken after that wait.
AI Visibility Optimization Results
After launch, the client was the most-named brand in the AI Notice prompt set, with 545 mentions on the five engines.
545 mentions
AI Notice as a source
the client
never mentioned it
The four tiles measure the 200 buyer prompts. The campaign report shows how far the AI Notice traveled in the media and how often AI engines cited it.
Share of Voice Among Educational Publishers
With 545 mentions, the client was named more than twice as often as the 2nd-ranked brand, at 243. The next two brands were named 196 and 178 times. Against its three most frequent rivals, the client ranked higher in 76% to 87% of the answers that named both brands.
Prompt Coverage and First-Named Finishes
The client was named in 175 of 200 buyer prompts. Its top-3 hit rate was 84%, and it finished first 468 times across the five engines. Among the prompts naming the client, the largest group was buyer-intent questions, followed by category and terminology questions.
Unprompted Discovery
Of the 175 prompts that named the client, 40 asked about the company or one of its product lines by name. The other 135 did not mention the brand, so the engines brought up the client on their own. Their questions covered materials for students with learning disabilities, books for English learners and newcomers, and how to move a student from decodable books to independent reading.
The report’s awareness funnel follows the client’s AI visibility from a buyer’s first question to a brand search. Coverage started at 46% among Unaware buyers and rose to 83% at the Problem-Aware stage. From the Solution-Aware stage on, it stayed at 96% or higher.
| Buyer awareness stage | Coverage | Named the client | Prompts in stage | Avg position |
|---|---|---|---|---|
| Unawarehas not identified the problem | 46% | 6 | 13 | #1.4 |
| Problem-Awareknows the problem, still scoping solutions | 83% | 25 | 30 | #1.3 |
| Solution-Awareevaluating solution categories | 97% | 29 | 30 | #1.2 |
| Product-Awarecomparing specific products | 96% | 45 | 47 | #1.4 |
| Most-Awaresearching by brand name | 100% | 24 | 24 | #1.1 |
AI Visibility by Engine
The client’s AI visibility held on all five engines, and each one read it positively. Claude, Gemini, Perplexity and Grok rated it a top pick, and ChatGPT rated it recommended. The report logged no negative mention. Grok named the client in 145 of 200 prompts, followed by Claude at 143, Gemini at 136, Perplexity at 97 and ChatGPT at 89. On average, each engine named the client in 61% of the prompts. Average positions ran from #1.2 on Perplexity and Grok to #1.4 on Claude, and first-place finishes ranged from 117 on Grok to 63 on ChatGPT.
Where the AI Visibility Came From
When AI engines answered these buyer questions, they drew on 2,024 different websites. The client’s own website was the most-cited source, with 2,410 citations, more than twice the 960 of a social media platform in second place. The wire release itself was the most-cited copy of the AI Notice, with 166 citations, and a financial news site’s copy followed with 110. The AI Notice earned 320 placement citations on its own, more than four times the 75 the program guarantees. Engines used it as a source in 156 of 200 buyer prompts. In a separate prompt set of 200 prompts, four of the five engines cited the Anchor Article 73 times across 51 prompts. ChatGPT cited it most often, 31 times, followed by Grok with 28.
Distribution Reach
The AI Notice reached 524 media placements with a combined audience of 108.6M monthly unique visitors. Seven placements carried the citations: the wire release, three financial news sites and three other third-party news sites. These account for 38.0M of the reach, 35% of the total. Gemini, Grok and Perplexity cited them.
Google AI Overview Visibility
Google tracking covered 200 keywords drawn from the AI Notice, 28 branded and 172 non-branded. Copies of the AI Notice ranked on Google for 30 of them, 28 in the top 10 and 11 in the top 3. Google AI Overviews cited the AI Notice on 11 keywords and quoted it as the first source on two. All 11 were searches for the client by name or domain.
Google’s AI Overviews also named the client on 54 keywords, and on 24 of them the search itself carried no brand term. The Anchor Article had its own set of 200 keywords. It ranked first on Google for one non-branded search about decodable readers for older students, and the AI Overview for that search cited it as the first source.
Examples From AI Answers
The examples below show what the client’s AI visibility looked like inside real answers to buyer questions about phonics books for older students, and which sources the engines cited.
“Which publishers make decodable readers designed specifically so teenagers won’t feel infantilized by the format?” (asked as High School English Teacher)
Both engines ranked the client first and rated it a top pick. Perplexity described the client’s phonics books for high school students as designed for older readers. Grok presented its decodable series as built to avoid a babyish look.
“How do I find phonics materials for a 16-year-old who is reading at a beginning reader level but needs content that reflects teen-centric subject matter?” (asked as High School Special Education Teacher)
Gemini listed the client first among the publishers it recommended. It described the client’s teen phonics libraries as written for high school students, with content that respects their age. Perplexity and Grok also ranked the client first.
“What are the top Hi-Lo book publishers for ELL and Newcomer students in middle and high school?” (asked as ELL Coordinator)
For a coordinator choosing books for English learners, all five engines ranked the client first and rated it a top pick. Perplexity presented the client as the strongest option for secondary English learners and newcomers.
None of the three prompts named the client, yet the engines ranked it first each time.
How We Measured
The pilot measured AI visibility with two separate sets of 200 buyer prompts, one for the AI Notice and one for the Anchor Article. Both ran on Claude, ChatGPT, Gemini, Perplexity and Grok. The AI Notice set covered eight question types: buyer intent, category, terminology, brand-direct, comparison, best practices, education and problem solving. Five of them also made up the Anchor Article set: category, terminology, buyer intent, brand-direct and education. Both reports recorded every brand each engine named. Share of voice counts those mentions on all five engines. The AI Notice report logged cited sources on Gemini, Grok and Perplexity, and the Anchor Article report logged them on all five engines. A separate Google check ran 200 keywords drawn from each asset against live search results and AI Overviews. Citations were matched by exact URL, so every citation credited to an asset on this page points at that asset’s page.
About the measurement
All results come from a pilot built around one Anchor Article and one AI Notice. Each asset was measured once after launch. The two prompt sets differ, so their figures are reported separately and never combined.
Why This Matters
The client was the most-named brand in AI answers about phonics books for older students. Of the 175 prompts that named it, 135 came from questions that did not mention the brand. Those buyers found the client through the answer itself.
Coverage was lowest at the start of a buyer’s research, before the buyer had named a problem. More Anchor Articles and AI Notices aimed at those early questions can extend this AI visibility optimization work to them.
Zen Media has used the same approach to build AI visibility in financial advisor training and modular furniture. Want to know what AI engines tell your buyers about your brand? Contact Zen Media for an AI visibility strategy built around the questions your buyers ask.
