Education

Objective
The university sought to attract qualified, motivated students to its undergraduate and postgraduate programs across key global markets. Its marketing teams faced two challenges:

1. Identifying students with strong academic intent and relevant course interests, and

2. Reaching parents and households involved in university decision-making—particularly in culturally driven markets like India.

The goal was to use data intelligence to go beyond generic student marketing, and instead focus on fit, readiness, and influence.

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Data Strategy

1. Geofencing Elite Academic Institutions

   • We geofenced top-performing secondary schools and junior colleges across Australia and India, identifying devices regularly active on campus during class hours.

   • This created a verified audience of students in high-performing academic environments—the primary pipeline for university recruitment.

   • Devices showing persistent daily activity after general school hours (staff or teachers) were excluded to maintain student audience purity.

2. Academic Intent & Behavioral Signals

   • Intent data was analyzed to identify users researching universities, comparing institutions, or exploring admission timelines.

   • Further segmentation isolated students searching for specific academic programs that the university is best known for—such as business, engineering, healthcare, or data science.

   • Engagement depth (e.g., reading course outlines or financial aid details) was weighted to score intent level and qualification strength.

3. Household & Parental Influence Identification

   • Using device co-location and household IP mapping, we identified parents engaging with university-related content within the same households as prospective students.

   • This dual-layer audience—students plus decision-makers—enabled messaging strategies targeting both academic motivation and parental reassurance.

4. Socio-Economic & Lifestyle Enrichment

   • Socio-demographic signals such as affluence indices, household size, and education level proxies were layered to further refine the audience toward families most likely to pursue tertiary education abroad or at reputable local institutions.

   • Contextual lifestyle data identified families with frequent travel, international school affiliations, or exposure to study-abroad programs, aligning with the university’s ideal applicant profile.

5. Cross-Device & Cross-Border Attribution

   • Using device-level identifiers and IP intelligence, we connected interactions across mobile, desktop, and connected TV devices—capturing the full student decision journey from awareness to consideration.

   • This enabled consistent visibility across both Australia and India, ensuring accurate reach measurement and brand recall across digital environments.

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Results

• High-Value Student Segments: Identified verified student audiences at elite schools and high-intent university researchers.

• Parent-Student Household Matching: Built family-based audience clusters showing both academic and financial readiness.

• Precision Across Borders: Unified multi-device and multi-market targeting framework between Australia and India.

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Impact
Through a sophisticated blend of mobility, behavioral, and household intelligence, the university gained a clearer understanding of its next generation of students—and their decision-makers. This approach ensured recruitment efforts focused on qualified, engaged, and high-potential candidates, while maintaining message relevance across vastly different educational markets.

The data insights also established a replicable model for global recruitment—one that balances academic fit, cultural nuance, and digital precision.