An AI-First Campaign Structure for Efficient Application Scale
A Strong Program Portfolio Buried by Campaign Sprawl
This case study focuses on a private, multi-campus institution committed to accessible, career-aligned education. With programs spanning health sciences, nursing, business, education, and technology, the institution serves a diverse student population including first-time undergraduates, working adults, and transfer students across multiple degree levels.
The institution had partnered with EducationDynamics to manage its paid media strategy, with goals spanning inquiry generation, application growth, and enrolled starts. Over time, as the program portfolio expanded, so did the campaign structure. By mid-2025, the account had grown to 48 active search campaigns, each built around an individual program, making efficient budget increasingly difficult to sustain.
Meanwhile, the search landscape was shifting as well. AI-powered search platforms were reorganizing how prospective students discover and evaluate educational options. A campaign structure built to win at keyword-level specificity was now working against the algorithms designed to serve them.
AI-Powered Bidding Strategies Need Robust Performance Signals
A review of the institution’s digital media ecosystem revealed a structural challenge: 48 program-specific campaigns were competing for budget and fragmenting the conversion signals Google’s machine learning depends on to optimize.
In an AI-mediated search environment, signal quality is everything. Spread across a high number of campaigns, each with limited conversion data, the algorithm had too little to effectively optimize.
Without strong aggregated signals, the algorithm couldn’t confidently match ads to high-intent queries. Budget was being diluted rather than directed, and cost efficiency was eroding across the account.
The opportunity was a full structural reset: fewer campaigns, organized around search intent, rather than how an internal program catalog was organized.
Fewer Campaigns, Clearer Objectives
The Case for Building Smaller and Smarter
- Build a campaign architecture that works with Google’s optimization systems, not against them.
- Grow application volume and starts without requiring a significant increase in overall spend.
- Concentrate investment in proven, high-performing campaigns and eliminate structural waste.
- Improve nonbrand search performance, which had consistently seen an unprofitably high CPA.
A Multi-Phase Restructuring Built Around Signals, Not Granularity
Campaign Consolidation
The account was restructured from 48 individual program campaigns into 14 campaigns organized by Area of Study and Program Level, mirroring how prospective students search and how Google’s AI categorizes educational intent. The consolidation immediately strengthened optimization signals across the account: more conversion data per campaign, more efficient budget allocation, and a clearer feedback loop for Google’s bidding algorithms.
Performance Optimization
A full start analysis was conducted at the campaign and ad group level, identifying underperforming campaigns that were consuming budget without delivering down-funnel results. Investment shifted entirely to the 14 highest-performing campaigns, with additional ad groups layered in to expand keyword coverage across program categories. The result was a leaner, sharper account that delivered more volume at lower cost.
Microsite Optimization
Ongoing microsite enhancements, including mobile touch target improvements, site speed optimization, and A/B testing of calls to action. supported sustained conversion performance even as industry-wide paid search costs increased.
Strategic Insight
Modern search is AI-mediated. Google surfaces results based on topical relevance and aggregated conversion signals, not just keyword lists.
An overly-granular campaign structure was sending fragmented, low-confidence signals into algorithms that reward breadth and data volume. Reorganizing around Areas of Study didn’t just reduce complexity; it made the account speak the same language as the platforms it was running on.
Better Campaign Structure Drove Double-Digit YoY Performance Improvements
+36%
Total Applications YOY
+68%
Nonbrand Inquiry Volume
-29%
Overall Cost per Application
+21%
Nonbrand App Volume
Structure Is the Strategy in an AI Search Landscape
The most important decision in this engagement wasn’t identifying which keywords to add or remove. It was the willingness to question the entire account structure, and to rebuild it around a simple insight: search engines no longer reward the most granular campaigns. They reward the clearest signals.
In an environment where AI systems mediate the relationship between an institution and a prospective student, the strategy has to be built for the algorithm. Institutions that build their paid search architecture around intent clusters and topical relevance, rather than internal program catalogs, are the ones that get seen first, trusted earlier, and chosen more often.
EducationDynamics helps institutions build enrollment marketing strategies for how the Modern Learners actually searches, developing future-proof approaches that support engagement with prospective students across all search surfaces.