In today's competitive higher education landscape, Australian universities face unique challenges in maintaining consistent student registrations throughout the year. Seasonal fluctuations, changing student preferences, and the evolving digital landscape create a complex environment that demands data-driven solutions. This case study explores how a leading Australian university partnered with More Than Data to implement Marketing Mix Modelling (MMM), resulting in significant improvements in student registration rates and marketing efficiency.
The Challenge: Registration Volatility and Marketing ROI
The university faced significant challenges with student registration patterns, experiencing up to 25% fluctuation in registration rates across different seasons. Despite maintaining a consistent marketing budget of $2.8 million annually, the marketing team struggled to achieve predictable registration numbers and justify their marketing investments across various channels.
"Before implementing Marketing Mix Modelling, we were essentially making educated guesses about which marketing activities were truly driving student registrations. We needed a more scientific approach to understand the real impact of our marketing efforts."
— Marketing Manager of The University
Key challenges included:
- Distinguishing between marketing effectiveness and seasonal enrollment patterns
- Optimizing spend across digital (search, social, display) and traditional channels (radio, print, events)
- Understanding the unique impact of marketing on domestic versus international student enrollment
- Measuring long-term brand building activities against short-term enrollment tactics
The Approach: Implementing Marketing Mix Modelling
The university partnered with a data analytics firm to implement a comprehensive Marketing Mix Modelling approach. This statistical analysis method would help decompose enrollment data to identify the true impact of each marketing channel while controlling for external factors.
Seasonal Enrollment Patterns
The MMM implementation process involved:
- Data Collection and Preparation: Gathering 3 years of historical data across marketing spend, enrollment metrics, and external factors
- Variable Identification: Mapping marketing activities, seasonal factors, competitive actions, and macroeconomic indicators
- Model Development: Creating statistical models to isolate the impact of each variable on enrollment outcomes
- Validation and Testing: Back-testing the model against known outcomes to ensure accuracy
- Scenario Planning: Developing optimization scenarios to maximize enrollment ROI
Key Metrics Analyzed
Marketing Spend
Enrollment Rate
Seasonal Factors
ROI by Channel
The Findings: Uncovering Marketing Impact
The Marketing Mix Model revealed several critical insights that challenged previous assumptions about marketing effectiveness:
Marketing Channel Effectiveness
Digital Channels
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Paid search delivered 3.2x higher ROI than previously measured
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Social media impact varied significantly by platform, with LinkedIn outperforming Facebook by 2.1x for graduate programs
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Display advertising showed 40% lower contribution than previously attributed
Traditional Channels
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Campus events generated 28% more enrollments than digital-only campaigns for certain programs
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Print advertising showed minimal direct impact on enrollments
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Radio campaigns were 2.3x more effective during morning commute hours than other dayparts
Seasonal Insights
The model identified distinct seasonal patterns that significantly impacted marketing effectiveness:
- Marketing efforts in February-March showed 2.7x higher enrollment impact than the same activities in October-November
- International student marketing required a 4-month lead time versus 2 months for domestic students
- Certain programs showed counter-seasonal trends, with winter enrollment outpacing traditional autumn peaks
The Implementation: Strategic Shifts
Based on the MMM insights, the university implemented several strategic changes:
Budget Reallocation Results
- Channel Reallocation: Shifted 35% of display advertising budget to paid search and LinkedIn campaigns
- Seasonal Optimization: Developed program-specific marketing calendars aligned with identified enrollment windows
- Audience Segmentation: Created separate marketing approaches for domestic and international students based on different response patterns
- Creative Optimization: Refined messaging based on program-specific MMM insights about effective value propositions
- Budget Smoothing: Implemented a more consistent year-round marketing approach rather than concentrated "campaign bursts"
The Results: Transformative Outcomes
"The Marketing Mix Model transformed our approach from intuition-based to data-driven. We now have a clear understanding of not just what works, but why and when it works."
— Marketing Manager of The University
Within 12 months of implementing the MMM-informed strategy, the university achieved:
Overall enrollment increase year-over-year
Reduction in total marketing spend
Improvement in marketing ROI
Additional benefits included:
- Reduced enrollment volatility between terms (standard deviation decreased by 42%)
- More predictable enrollment forecasting (accuracy improved from ±15% to ±4%)
- Improved marketing team alignment with enrollment management goals
- Enhanced ability to respond to competitive actions with targeted counter-measures
Key Learnings and Best Practices
This case study highlights several best practices for universities considering Marketing Mix Modelling:
Implementation Recommendations
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1
Data Integration
Ensure marketing, enrollment, and external data sources are properly integrated and normalized before modeling
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2
Model Granularity
Develop separate models for undergraduate, postgraduate, and international segments
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3
Continuous Refinement
Update models quarterly to incorporate new data and evolving market conditions
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4
Cross-Functional Alignment
Ensure marketing, enrollment management, and finance teams share a common understanding of the model
Conclusion: The Future of Data-Driven Enrollment Marketing
This Australian university's experience demonstrates the transformative potential of Marketing Mix Modelling in higher education. By moving beyond simplistic attribution models to sophisticated statistical analysis, institutions can overcome seasonal challenges, optimize marketing investments, and achieve more predictable enrollment outcomes.
As competition for students intensifies and marketing budgets face increased scrutiny, MMM provides a powerful framework for universities to make evidence-based decisions that drive sustainable enrollment growth.