Optimized inventory
Predictive analytics for customized shopping
Business growth
The challenge
Need for a platform for ML and predictive growth
A leading footwear retailer needed a modern data platform for machine learning and predictive use cases to boost revenue. They aimed to analyze customer data for personalized experiences, optimized inventory, and trend prediction, crucial for growth and loyalty. The absence of this platform was a major hurdle.
Key challenges
Fragmented customer insights
Need for modern platform for ML and predictive analytics
Need for personalized and optimized sales and customer loyalty
The solution
AWS-powered "Customer Genomics" accelerator
Data management
Lake Formation for structured data
AWS S3 data lake for raw storage
SFTP via AWS Transfer Family
Processing and transformation
AWS Glue for data transformation
DBT for efficient data processing
Streamlined ETL workflows
Warehousing and modeling
Snowflake for scalable data warehouse
High-performance data storage
DBT for easy data modeling
Security and governance
AWS IAM and KMS for secure access
VPC and Security Groups for network
Snowflake and Glue for data catalog
Implementation approach
1
Data foundation
Configured Lake Formation for structure
Implemented robust data ingestion
Established secure AWS S3 data lake
2
Transformation pipelines
Deployed AWS Glue for ETL jobs
Integrated DBT for data modeling
Ensured data quality and flow
3
Analytics and ML environment
Utilized Snowflake for fast analytics
Leveraged EMR & SageMaker for ML
Scalable data science workflows
4
Automation and operations
Airflow for workflow automation
CI/CD via CodeCommit and Pipeline
CloudWatch and CloudTrail for logs
The impact
Improved targeting, cost savings and faster deployment
Enhanced campaign effectiveness
Achieved better audience targeting achieved
Personalized product recommendations
Improved marketing campaign ROI
Significant cost reduction
Lower AWS infrastructure spending
Optimized resource utilization seen
Reduced overall operational costs
Accelerated deployment
Reduced deployment time
Faster feature release cycles
Increased operational efficiency
Looking ahead
Advanced analytics
Further refine risk prediction for deeper insights
Expanded data
Incorporate more data sources for holistic customer views
Personalized growth
Deepen tailored experiences to boost customer loyalty
