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Driving Online Program Applications at CSU

Cleveland State University was looking to drive applications for their e-learning program with a CPA target of $100.

Exterior building shot of Cleveland State University

Published on

Jan 17, 2024

Written by

Huddled Masses

Category

Case Study

Cleveland State University was looking to drive applications for their e-learning program

One week after the campaign launch, we looked at all the data we had gathered and began making optimizations and moving budget towards the strategies that were driving the most conversions, creating new strategies based upon our learnings and turning off strategies that were not working.

At the end of the 60 day campaign we delivered 888 applications and a CPA of $20.39. The client was blown away by the results and quickly gave Huddled Masses team additonal budgets to run a Summer Recruitment campaign.

How Huddled Masses delivered results

LAUNCH STRATEGIES
Behavioral Targeting: We targeted people who had shown interest in online learning and online education. We also looked at audiences who had interest in Colleges & Universities as well parents with high school seniors in the HH.

CONTEXTUAL TARGETING
We targeted content related to online degrees, and college degrees.

KEYWORD TARGETING
We targeted content related to online degrees, and college degrees.

LOOKALIKE MODELING
Our proprietary algorithm created a seed audience from conversion data and targeted consumers who are similar to ones that are converting.

ENEWSLETTER
We layered the behavioral targeting above and ran across our network of premium publisher e-Newsletters. Once a newsletter is opened we dynamically served an ad into the content of the page.

Optimizations

  • Behavioral: We removed the general colleges & universities tactics and shifted more budget into “online learning” interest because it was performing better.
  • Top scoring users: Once we started seeing conversions come in, we were able to use our audience scoring to create a custom strategy targeting the best users.
  • Lookalike Targeting: Was the best performing strategy so we increased budget allocation and decrease budget on lesser performing strategies.
  • REM: Increased bid and budget as the Re-targeting pool grew.
  • Added: Adaptive Segments: These segments ·weed out exhausted users from the re-targeting pool and adapt to users who are expressing interest by serving them more ads.
  • Dayparting: We added in a top converting website strategy and layered top performing daypart for maximum efficiency.

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