This Power BI solution analyses marketing effectiveness across academic programs and evaluates how marketing investments influence student admissions.
The dashboard helps decision-makers:
- Identify high-performing programs
- Measure marketing ROI
- Analyse lead-to-admission conversion efficiency
- Optimise budget allocation
- Improve enrollment outcomes
Educational institutions invest significant budgets in marketing campaigns, but determining which programs generate the best admissions outcomes remains challenging.
This project provides a data-driven framework to evaluate:
- Marketing Spend
- Leads Generated
- Admissions Achieved
- Cost Per Lead (CPL)
- Cost Per Acquisition (CPA)
- Conversion Efficiency
- Program Performance Trends
- Power BI
- DAX
- Power Query
- Data Modeling
- Star Schema Design
- Business Intelligence
The solution follows a Star Schema design with a centralized fact table and supporting dimension tables.
- Fact_ProgramAnalysis
- Dim_Program
- Dim_Group
- Dim_Drive
- Dim_AcademicYear
- Total Spend
- Cost Per Lead (CPL)
- Cost Per Acquisition (CPA)
- Total Admissions
- Admissions Growth %
- Conversion Rate %
- Total Leads
- Lead Growth %
- Lead-to-Admission Conversion %
The dashboard leverages DAX measures to calculate business KPIs, evaluate marketing efficiency, and identify program performance trends.
Total Admissions =
SUM(Fact_ProgramAnalysis[B2C Admissions])
Conversion % =
DIVIDE(
[Total Admissions],
[Total Leads],
0
)
Cost Per Lead =
DIVIDE(
[Total Spend],
[Total Leads],
0
)
Cost Per Acquisition =
DIVIDE(
SUM(Fact_ProgramAnalysis[Spends]),
SUM(Fact_ProgramAnalysis[B2C Admissions]),
0
)
Admissions Growth % =
DIVIDE(
[2025 Admissions] - [2024 Admissions],
[2024 Admissions],
0
)
Performance Gap =
SWITCH(
TRUE(),
[Admissions Change %] >= 0.50, "Exceeding Market Expectations",
[Admissions Change %] >= 0.10, "Strong Performance",
[Admissions Change %] > -0.10, "Stable Performance",
[Admissions Change %] > -0.40, "Moderate Underperformance",
"Significant Underperformance"
)
Provides a high-level overview of admissions performance, marketing spend, leads, and conversion metrics.
Compares 2024 and 2025 performance across admissions, leads, and marketing spend.
Evaluates individual program performance and identifies growth opportunities.
Analyzes return on marketing investment and identifies programs requiring optimization.
Measures lead-to-admission conversion effectiveness across programs.
- M.Tech AI & ML generated the highest admissions volume.
- Several B.Tech programs delivered strong admissions at lower acquisition costs.
- Marketing spend alone does not guarantee admissions success.
- Conversion efficiency is a major driver of enrollment performance.
- Certain programs present strong growth opportunities with optimised spending.
Programs with high acquisition costs and lower admission outcomes should undergo campaign review and budget optimisation to improve ROI.
Programs demonstrating strong admissions growth and efficient acquisition costs should receive increased marketing investment.
Programs generating high lead volumes but low admission conversions should focus on lead nurturing strategies and admissions counseling improvements.
Future marketing budgets should be allocated using program performance metrics rather than equal distribution across all programs.
Monitor admissions, conversion rates, CPL, and CPA regularly to support proactive decision-making.
- Reduced student acquisition costs
- Improved marketing ROI
- Higher lead-to-admission conversion rates
- Better budget allocation
- Increased admissions growth across programs
Assets/
│
├── Marketing_ROI_Optimization.pbix
Dashboard_Screenshots/
│
├── Executive_Dashboard.png
├── YoY_Trends.png
├── Program_Analysis.png
├── Marketing_Efficiency.png
└── Lead_Conversion_Funnel.png
Documentation/
│
├── Data_Model.png
└── Project_Architecture.png
Gourav Dutta
Power BI | Data Analytics | Business Intelligence