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Industry Report

Colleges Universities

Content Phrases
Dominant · SE Outbound Links ρ=0.367

AI recommendation signal analysis across 2,080 domains and 11 buyer personas in Colleges Universities, led by wgu.edu as the most LLM-recommended domain.

2,080Domains Tracked
11Buyer Personas
94.7MReddit Posts
1.1MWikipedia Articles
108.6MOpen Web Matches
content_phrases.report
TermScore
college admissions
22.7
undergraduate admissions
22.3
university admissions
9.5
This is a shortened preview of the Colleges Universities report

Many tables and charts on this page show only the top few results; the full data behind them runs far deeper. The complete report unlocks every row, chart, and download for this industry.

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About This Report

How to use this page

Industry view: this page aggregates every persona's data for the whole industry, plus industry-wide queries. Pick a persona from the sidebar to narrow the lens to one segment.
Use Case

Speak the language LLMs expect

These are the phrases AI models associate with this market. Work them into your pages and positioning so you describe your offer in the same vocabulary the models use when matching brands to buyer questions.

How It's Calculated

Where the numbers come from

We ask multiple LLMs which content phrases they associate with this industry or persona, then aggregate across models and runs. Score is a rank-weighted sum; count and % are plain tallies.

Overview

What's on this page

Phrase rankings with per-model filtering and a side-by-side comparison of any two models.

Keyword Data

Content Phrases

Phrases and terms LLMs associate with Colleges Universities across all personas, ranked by recommendation strength. Score is a rank-weighted sum across every model (higher-ranked appearances count for more); count and % are plain appearance tallies.

Filter By Model

Content Phrases by model

Same ranking as above, scoped to whichever models you select below: pick one, several, or none. Updates automatically as you check/uncheck.

Model Comparison

Compare two models

Pick a model for each column to compare their recommendation patterns side by side.