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Renowned University Aims to Increase Applications for Online Learning Program

Renowned university partners with Orange 142 to boost online learning applications, achieving remarkable results with a cost-per-application of $20.39. Learn their strategy secrets.

Renowned University Aims to Increase Applications for Online Learning Program

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Orange 142

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Case Study

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Renowned University Aims to Increase Applications for Online Learning Program
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A well-known university was looking to drive applications for their e-learning program. They came to Orange 142 with a CPA goal of $100.

One week after the campaign launch, we reviewed all the data we had gathered and began making optimizations. We moved the budget toward the strategies driving the most conversions, created new strategies based on our learnings, and turned off strategies that weren't 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 the Orange 142 team additional budgets to run a summer recruitment campaign.

888

Applications Delivered

How Orange 142 Delivered Results

LAUNCH STRATEGIES

» Behavioral Targeting: We targeted online learning and education, interest in colleges and universities, and parents with high school seniors in the household.
» Contextual Targeting: Focused on targeting online and college degrees.
» Lookalike Modeling: We implemented lookalike modeling and targeted the right consumers.
» Re-Marketing: Focused on visitors to the university's site, but did not fill out an application.
» Keyword Targeting: We took universities' search keywords and looked for them in contextual content.
» Premium E-Newsletters: Implemented ads to be shown in premium newsletters.

OPTIMIZATIONS

»  Behavioral changes, including shifting more budget into “online learning”.
»  Implemented a top-scoring users' custom strategy.
»  Lookalike targeting to reach new audiences who resemble existing customers.
»  Re-marketing included increasing bid/budget as retargeting pool grew.
»  Added adaptive Segments to weed out exhausted users from the retargeting pool and adapt to users who were expressing interest by serving them more ads.
»  Utilized a dayparting strategy to analyze when audiences would be engaged on top-converting websites.


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