AXIA AIMarketing Visibility · HS3
█████████████████ · run ██████ · silo hs3 · 2026-06-15

You're visibility-rich — but invisible where the next student starts the search.

A composite of 4.4 — On track conceals the decision that matters: ███████ appears in only 12% of AI-engine answers, and AI cites the school 59 times — zero from pages it owns. The work is redirecting assets you already hold toward where enrollment decisions are now made.

HS3 · Marketing Visibility Composite 4.4 / 10 run ██████ UNITID ██████
Composite · 8 dimensions
4.4
/ 10.0
On track
Scale (0–10): Material gaps 0–3.9 · On track 4.0–6.9 · Strong 7.0–10.0
Dimension radar · all 8 scores
00

The headline for the president

The composite is healthy. The single most consequential gap is not.

The institution's single most consequential gap is AI answer-engine visibility, scored 2.9 — material gaps. When prospective students ask AI engines about affordable, career-focused ███ degrees, ███████ appears in only 12% of relevant answers — collapsing to roughly 1.4% when the searcher omits "███". The leverage is not spending more: you already out-keyword every peer at the 100th percentile and hold tuition 51.7% below the peer median.

composite
4.4 / 10
On track across eight dimensions
AI visibility
2.9
D4 — material gaps. 12% of relevant answers; ~1.4% without "███"
citations
59 · 0 owned
AI cites ███████ 59 times — zero from pages it owns
keywords
30,831
More organic keywords than any peer — 100th-percentile footprint
price moat
51.7%
Tuition below the peer median — $6,660/yr vs Competitor 1 $13,920
domestic CPL
$99.39
vs $4.06 international — efficient blended figure hides a fragile engine
01

Two truths

What the public scan sees — and what the first-party data reveals.

Truth 1 · the public scan

A big keyword count that doesn't move students.

30,831 keywords
more than any peer — yet 43,083 visits/mo, 79% below Competitor 1's 205,080

A 4.4★ rating on 148 reviews and a paid footprint of $9,238/mo against Competitor 1's $643,398. AI engines cite the institution 59 times — and zero come from pages ███████ owns. NCES, Niche, Wikipedia and Day1CPT directories author the narrative.

cold / public-data read
Truth 2 · the warm data

An efficient blended CPL hiding a broken domestic engine.

Intl$4.06 CPL
$4.06
Blended$10.74 CPL
$10.74
Domestic$99.39 CPL
$99.39

35% of 18-month paid spend is international — the structural reason the blended figure looks efficient. The real domestic engine is expensive and thin, and the public scan cannot see the split. Without it, the D6 paid score of 6.5 overstates domestic enrollment health.

first-party CRM · 18-month spend
02

The scorecard

Composite 4.4/10 — On track. Two dimensions sit in material gaps; the rest are on track.

DimDimensionScoreBand
D7Technical Foundation1Material gaps
D5Organic Search Authority4.9On track
D1Brand & Reputation3.6Material gaps
D8Structured-Data & Schema5.7On track
D2Conversion & Pricing Clarity5.2On track
D3Content & Messaging5.7On track
D6Paid Acquisition6.5On track
D4AI Answer-Engine Visibility2.9Material gaps
⚠️
D4 — AI Answer-Engine Visibility (2.9) is the number that most directly shapes ███████'s enrollment future. Every other dimension either feeds that gap or is positioned to close it.
03

The spine — AI visibility & the citation gap

D4 · 2.9 — material gaps. Whether ███████ exists in the information layer where the next generation begins its search.

The claim

AI cites ███████ 59 times — and not once from a page ███████ owns.

The geo-dependence is the tell

The same prospective-student questions, asked with and without geography. Students searching without naming ███ — the highest-intent national and international queries — rarely see the institution at all.

Discovery
4/20 · 0/20
Comparison
3/20 · 1/16
Decision
3/20 · 0/20
Trust
3/20 · 0/16

bar = "with ███" share · labels: with ███ · without ███

Engine split

The visibility that exists is concentrated in two engines and absent from a third.

Anthropic
22.2%
Perplexity
22.2%
OpenAI
3.7%
Gemini
0%

Top third-party voices for the school: nces.ed.gov (4) · niche.com (4) · en.wikipedia.org (3) · Competitor 1.edu (3).

📡
So what. As traditional search shifts toward AI-mediated discovery, a 12% presence rate (geo-dependent) becomes the ceiling on organic discovery — unless the citation infrastructure is rebuilt. This is whether ███████ exists in the information layer where students begin their search.
04

The sentiment room — where the narrative is written

AI share-of-voice, twelve months of mention composition, and what students actually say.

AI share-of-voice · shared-query panel

The smallest peer in the room commands the largest AI share.

Competitor 3smallest peer
57.4%
Competitor 1
24.1%
Competitor 2
17.6%
███████you
12%
🔎
Proof that AI visibility is won by citation infrastructure, not enrollment size — Competitor 3 has the largest AI share and the smallest enrollment.
Sentiment evolution · last 12 months

The composition trend is the signal.

Monthly brand mentions split by connotation — latest month is partial.

16%12-mo median negative share · ███████ (you)
12.4%Competitor 1 — the best credible target in the room
40.7% → 34.7%Oct–Nov spike: volatility with no earned-media counterweight
Sentiment vs peers · same room, head to head
InstitutionGoogle reviewsMentionsNeg share %12-mo median neg %Forum share %
█████████████████ (you)4.4 ★ · 148146,74423%16%2.7%
Competitor 13.5 ★ · 22158,68019.7%12.4%6.8%
████████ University of Science & Technology4.6 ★ · 144654,25617.1%15.3%1.8%
Competitor 23.8 ★ · 170759,90437.2%22.9%2.7%
So what. ███████ holds the strongest Google rating among its for-profit peer (4.4★ vs Competitor 1 3.5★) but the highest current negative-mention share at 23%. The review signal is positive; the wider web narrative is not yet aligned — because the institution does not own the surfaces where it forms.
What students actually say
"I had a very easy transition to the university and all thanks to Kamal Kablan for supporting me right through my transfer process."
Google · 2026-06 — admissions praise (Kamal Kablan, Adam Pavlakovich, Ravena Guedes drawn repeated mention)
"████████████████████████████████████████"
Google · 2024-12 — communication failures
Legitimacy questions on Reddit — where the school surfaces mainly in Day 1 CPT conversations, with critics questioning accreditation value and visa pathways.
Reddit · r/Day1CPTuniversities
Google 4.4★ (148)owner-response 62.2%RateMyProfessors 0/5 (5)
05

The reputation pressure

D1 · 3.6 — material gaps. A brand under narrative pressure from two directions at once.

Day1CPT brand-SERP contamination

high regulatory risk

Searches for "█████████████████ Day1CPT accreditation" return at least 8 dedicated Day1CPT directory sites in the top 20, alongside a Reddit thread in r/Day1CPTuniversities. ███████'s own CPT page ranks #3 organically — confirming active promotion. This conflates the institution with visa-mill operators in AI descriptions before a domestic prospect reaches ███████'s own content.

Zero mainstream press in 6 months

NewsData.io (0 articles) and GDELT (null) found no coverage in the trailing window. There is no earned-media counterweight — the only external narrative is the one ███████ did not author. 3 active reputation risks flagged.

  • One operational positive: no OPM dependency detected — the institution controls its own brand presentation and enrollment infrastructure, no white-label dilution or revenue-share encumbrance.
06

The assets — large, and pointed the wrong way

Schema, organic engine, an unadvertised price moat, an under-served market, and a paid program teaching the market about competitors.

D8 · Structured-Data & Schema — 5.7 on track

The fastest fix in this report — and it unblocks the AI gap.

The homepage JSON-LD declares an Organization + Person hybrid that neither Google's Knowledge Panel nor AI crawlers map to a university. Course, FAQPage, EducationalProgram and accreditedBy schema are all absent. The schema audit scored 2.5/10.

2.5/10schema audit score
~642/moWikipedia pageviews — well below the 5,000–50,000 of high-AI-recall institutions
Niche > ███████.edudoes not own top-3 on its own reviews SERP
So what. Correcting schema to CollegeOrUniversity with accreditedBy and Course entities is the single fastest-ROI move for AI-search visibility — and it directly unblocks the D4 story.
D5 · Organic Search Authority — 4.9 on track

30,000+ rankings that don't move prospective students.

███████30,831 kw
43,083 visits/mo
Competitor 229,506 kw
89,630 visits/mo
Competitor 123,232 kw
205,080 visits/mo

More keywords than any peer — yet traffic 79% below Competitor 1 and 52% below Competitor 2. The footprint is dominated by informational queries ("what does PhD stand for," "capstone meaning") rather than enrollment intent.

104winnable keywords ranking positions 11–50 — in striking distance
$12.13 · $10.76 · $11.33high-CPC enrollment terms: "bachelor's degree", "bba", "health information management"
2,668msmobile LCP — fails Core Web Vitals by 168ms; Lighthouse mobile lab 40/100
D2 · Conversion & Pricing Clarity — 5.2 on track

A real price advantage, surfaced almost nowhere.

51.7%

Published tuition of $6,660/year is 51.7% below the peer median — undercutting Competitor 1 ($13,920) and Competitor 3 ($13,785) by more than 2×.

$6,660peers ~$13,800+

But communicated almost nowhere

  • No net price calculator detectable in crawlable HTML — discoverability score 2/10
  • Only 2 named discounts identified
  • Financial aid page is fully JavaScript-rendered — invisible to crawlers & AI indexers
⚖️
Compliance flag: College Scorecard returns null for average net price (UNITID ██████). Prospective students cannot compare true cost; the federally-required NPC is missing or JS-inaccessible.
D3 · Content & Messaging — 5.7 on track

A demand-dense market, an under-built local surface.

███████ operates in a 72nd-percentile student-age market███ carries a 31.6% share of 18–34-year-olds, among the highest in the nation. Yet the content is not built to its scale.

  • Only 1 city-level landing page across 3 campuses; the Falls Church location 404s at its own URL
  • Online enrollment sits at roughly 20% of total — under-using the channel that widens catchment without capital
  • Health Information Management sits in a +23% BLS growth corridor anchored by HHS, CMS, NIH in the immediate catchment — with no employer-partnership pages to capture it
D6 · Paid Acquisition — 6.5 on track

A paid program teaching the market about competitors.

48.1%

of analyzed paid budget (≈$4,443/month) flows to competitor brand keywords — bidding on "a national-brand university computer science phd", "a national-brand university apply" and similar. Brand bidding teaches the market about competitors with your budget.

  • 0 of 50 live ads mention the tuition that undercuts Competitor 1 by more than half
  • Absent from competitive paid SERPs for its own highest-completion program queries

The warm read on efficiency

The blended $10.74 CPL is carried by international acquisition at $4.06; domestic leads cost $99.39 each. The efficiency is real but fragile — it depends on international volume.

⏸️
So what. Pausing competitor-brand bids recovers ≈$4,443/month. Rewriting top ad copy to lead with the price moat teaches the market the advantage exists — at zero incremental media cost.
07

The money — what the assets are worth

Revenue base ≈ $6.4M/yr (966 students × $7K). A 5% enrollment move ≈ $322K/yr; one marginal student ≈ $7K/yr.

LinkageValueWhat it says
Organic paid-equivalent value vs paid$2.2M/yrYour organic engine returns $2.2M/yr of paid-equivalent value. The category leader pays $7.7M/yr for what you earn largely unpaid.
Striking-distance keywords → organic value$480K/yr$131K–$1.1M104 winnable keywords ranking 11–50 → page-1 CTR uplift → incremental paid-equivalent organic value.
AI-Overview traffic-at-risk$202K/yr$54K–$410KOrganic value on informational queries now subject to AI-Overview CTR erosion — measured value × cited erosion.
Schema / rich-result gap → CTR uplift$134K/yr$45K–$314KAdding Course/Program/FAQ schema lifts organic CTR on eligible pages — conditional on rich-result grants.

Visibility-rich / enrollment-poor

You are not invisible — 100th-percentile keywords, #3 of 4 on traffic, $2.2M/yr organic value. The bottleneck is conversion, AI-presence and price-surfacing — not reach.

The winnable battlefield

You cannot outspend the leader on paid (1:1). But you out-keyword them on organic (30,831 vs 29,506). The battlefield where ███████ can win is organic + AI, not paid.

🔓
What sharper data unlocks. The differentiator-moat linkage (L15) is blocked cold — no differentiator seed survived volume validation. A first-party data overlay is what establishes the moat the paid program should be leading with.
08

The risks if nothing changes

What compounds while the assets stay pointed the wrong way.

🤖

AI discovery shift

On panels tested, ███████ appears in 12% of relevant answers. Gartner forecasts traditional search volume dropping ~25% by 2026. Pew (Jul-2025): users click a result only ~8% of the time when an AI summary appears, vs ~15% without. As discovery shifts, today's presence leaves a widening share of future discovery uncaptured.

📰

Reputation exposure

3 active reputation risks, 0 earned-media items in 6 months. The Day1CPT SERP contamination carries SEVP/ICE scrutiny risk and conflation with visa-mill operators in AI descriptions — a risk flag, not a dollar figure.

🏛️

Federal-aid dependence

Title-IV aid funds ≈$1.5M/yr ($1.3M–$1.7M) of undergraduate revenue — a floor. Estimated dependence ≈28.2% of published tuition. Worth watching against the 90/10 cap.

⛓️

Single-channel fragility

Paid spend is negligible against organic; the LCP failure and performance gaps make the organic-reliant posture more exposed than the keyword count suggests.

09

The ask — what Axia does next

One structural logic: genuine assets, not visible to the channels where enrollment decisions are made.

Where Axia creates value
~$1.2M/yr addressable

overlapping levers — not additive

Closing the AI visibility gap (D4 · 2.9) is the highest-leverage move.

It cannot be closed without also fixing the schema infrastructure (D8) AI engines use to contextualize the institution, and the content posture (D5) that determines which pages get cited. None of this requires new programs, new campuses, or spending at Competitor 1's scale.

What first-party data would upgrade the picture:

  • A full warm-data overlay for D7 (Technical Foundation), which could not be scored this run
  • A per-engine AI visibility breakdown to confirm which platforms drive the 12% aggregate and which sit at zero
  • A data-room review of actual net price (versus the published $6,660 gross upper bound) — sharpening every enrollment-value and pricing linkage

The Health Score opened the door. Phase 1 is where the assets get pointed at the channels that convert.