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Digital Marketing | 2024
Enterprise Data Warehouse Development
- The marketing team at MATW lacked centralized visibility into cross-regional performance data, leading to siloed decision-making and inefficient ad spend allocation.
- Data from multiple platforms (Meta, PPC, SMS) was scattered across disconnected sources, hindering comprehensive performance analytics and automation of reporting processes.
Client
Country
Australia
Section
Enterprise Data Platform
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
Recommendations
- 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
