Case Studies/
Enterprise Data Warehouse Development - MATW
Enterprise Data Warehouse Development - MATW
Digital Marketing | 2024
The marketing team at MATW lacked centralized visibility into cross-regional performance data, resulting in siloed decision-making and inefficient ad spend allocation. Regional data from multiple platforms (Meta, PPC, SMS)was scattered across disconnected sources, making it impossible to generate comprehensive performance analytics or automate reporting processes.
Client
Country
Australia
Section
Data Engineering
Approach & Methodology
- Conducted extensive stakeholder interviews with marketing leaders to document business requirements and establish key performance indicators.
- Designed a centralized data architecture to ingest and transform data from multiple regional sources.
- Implemented automated ETL pipelines using Airflow and dbt on Redshift for consistent, scheduled data processing.
- Developed a standardized data model to normalize regional marketing data for cross-regional analysis.
- Created a comprehensive marketing performance dashboard with visualizations for conversion values and ad spend metrics.
Data Visualizations & Analysis
Key Data:
- Total conversion value peaked in August-September across all regions.
- Ad spend was highest in March-April before optimization occurred.
- Canada showed dramatically improved performance after July when new targeting parameters were implemented.
- US market required consistently higher ad spend to maintain conversion rates.
Key Data:
- Daily ETL pipelines process approximately 2.3 million marketing events
- Region-specific transformation pipelines ensure data compliance with local regulations
- Automated alert system identifies pipeline failures within 15 minutes of occurrence
- Redshift transformation processes optimize query performance by 67%
Results & Impact
28%
Increase in marketing ROI
73%
Reduction in reporting time
$1.2M
Annual savings in ad spend
Implementation & Challenges
- Legacy system integration required custom connectors for regional platforms
- Data quality inconsistencies between regions required extensive transformation logic
- Initial pipeline performance issues required optimization of dbt models
Reccomendations
- Implement machine learning models to predict optimal ad spend allocation by region based on historical performance.
- Expand dashboard capabilities to include predictive analytics for campaign planning.
- Develop API-based connections to allow real-time optimization of ad platforms based on performance data.
- Standardize meta-tagging across all regional campaigns to improve data granularity and comparability
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Delivered & Finessed
