This project analyzes marketing channel efficiency, customer acquisition costs, conversion performance, and ROI contribution across multiple acquisition channels using Marketing Mix Modeling (MMM) and scenario-based revenue forecasting.
The system combines:
- channel performance diagnostics
- budget optimization
- revenue forecasting
- marginal ROI simulation
- spend reallocation analysis
to support data-driven marketing investment decisions.
Marketing teams often struggle to determine:
- which acquisition channels drive the highest ROI
- where marketing spend is inefficient
- how budget reallocations impact revenue
- which channels should be scaled or reduced
This project was designed to simulate marketing budget optimization scenarios and identify high-efficiency growth opportunities using data-driven analysis.
The objective of this project is to evaluate marketing acquisition efficiency, identify underperforming channels, optimize budget allocation, and simulate revenue forecasting scenarios using Marketing Mix Modeling (MMM) principles and business intelligence analytics.
The system helps answer critical business questions such as:
- Which marketing channels generate the highest ROI?
- Where is acquisition spend inefficient?
- How should budget be reallocated to maximize revenue efficiency?
- Which channels should be scaled, reduced, or optimized?
Tracks:
- Revenue trends
- Marketing ROI
- CAC performance
- Revenue seasonality
- Rolling revenue performance
- Channel efficiency ranking
Key objective:
Evaluate portfolio-level marketing efficiency and revenue performance trends.
Analyzes:
- CAC efficiency
- Revenue contribution
- Conversion rates
- Spend allocation
- Channel-level ROI
Key objective: Identify high-performing acquisition channels and inefficient spend areas.
Simulates:
- spend reallocation strategies
- marginal ROI impact
- projected revenue lift
- optimized budget distribution
Key objective: Support marketing investment decisions using scenario-based forecasting.
This project combines multiple analytical approaches:
- Marketing Mix Modeling (MMM)
- Revenue forecasting
- Marginal ROI simulation
- Budget optimization analysis
- Customer acquisition cost analysis
- Conversion efficiency diagnostics
- Scenario-based forecasting
The framework evaluates both current marketing efficiency and projected revenue impact under different budget allocation strategies.
The primary dataset contains:
| Field | Description |
|---|---|
| date | Transaction/reporting date |
| channel | Marketing acquisition channel |
| spend | Marketing spend |
| revenue | Revenue generated |
| customers | Acquired customers |
| impressions | Campaign impressions |
| clicks | Campaign clicks |
| leads | Generated leads |
| conversion_rate | Channel conversion rate |
| cac | Customer acquisition cost |
| campaign_type | Campaign classification |
| device | Device segmentation |
The dashboard follows a star-schema-inspired analytical model.
| Table | Purpose |
|---|---|
| marketing_performance | Primary fact table |
| Calendar | Time intelligence dimension |
| Measure | Centralized DAX measure table |
| Parameter Tables | What-if scenario modeling |
The Calendar table supports:
- Year-Month analysis
- Quarterly analysis
- Weekly reporting
- Rolling calculations
- MoM calculations
- WoW calculations
- PM calculations
- Customer Acquisition Cost (CAC)
- Marketing ROI
- Conversion Rate
- Revenue Contribution
- Marginal ROI
- Revenue Lift
- Optimized Spend Allocation
- Revenue Forecasting
- Monthly Growth Rate
- Power BI
- Power Query
- DAX
- Python
- Scenario Modeling
- Marketing Mix Modeling Concepts
- Time Intelligence
- Data Visualization
- KPI Analytics
- Executive Dashboard Design
- Gen AI - Dataset generator
Email generated the strongest ROI and lowest CAC across all acquisition channels, making it the most scalable revenue driver.
LinkedIn demonstrated the highest customer acquisition cost with weak revenue contribution and lower conversion efficiency, reducing marginal return effectiveness.
Referral channels consistently maintained strong ROI performance while requiring lower relative spend allocation.
Current spend distribution over-invests in lower-performing channels while underfunding high-efficiency channels.
Budget reallocation simulations projected improved ROI performance and incremental revenue lift through optimized channel investment strategies.
This system enables:
- smarter marketing budget allocation
- improved ROI forecasting
- identification of inefficient acquisition spend
- optimization of customer acquisition efficiency
- data-driven GTM investment decisions
- executive-level marketing performance visibility
-
Increase investment in Email and Referral campaigns due to stronger marginal ROI efficiency and lower acquisition costs.
-
Reduce exposure to channels with elevated CAC and weaker conversion performance, particularly LinkedIn campaigns.
-
Continuously monitor marginal ROI before scaling acquisition spend to avoid diminishing return inefficiencies.
-
Combine MMM with cohort and retention analysis to improve lifecycle visibility and long-term revenue forecasting.
-
Use scenario analysis to guide future GTM investment and budget allocation decisions.
Potential future improvements include:
- Econometric marketing mix modeling
- Machine learning forecasting
- Campaign-level attribution modeling
- Predictive customer lifetime value analysis
- Automated optimization engines
- AI-assisted budget allocation recommendations
- Real-time data integration
Marketing performance should not be evaluated solely on spend volume or top-line revenue.
Sustainable growth depends on:
- acquisition efficiency
- marginal ROI performance
- conversion quality
- optimized budget allocation
- data-driven forecasting
This project demonstrates how marketing analytics can support smarter revenue investment decisions through integrated business intelligence and scenario-based optimization.
Abodunrin (Richard) Oketade
Data Analytics | Business Intelligence | Marketing Analytics | Power BI | SQL | Python
“Turning data into business decisions.”