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Industry Report

B2B Ad Agencies

Content Phrases
Strong · Homepage Keywords ρ=0.271

AI recommendation signal analysis across 5,601 domains and 11 buyer personas in B2B Ad Agencies, led by directiveconsulting.com as the most LLM-recommended domain.

5,601Domains Tracked
11Buyer Personas
149.8MReddit Posts
168KWikipedia Articles
20.2MOpen Web Matches
content_phrases.report
TermScore
b2b advertising agency
26.4
b2b ad agency
13.4
b2b marketing agency
10.6
This is a shortened preview of the B2B Ad Agencies 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 B2B Ad Agencies 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.