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Case Studies

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Next best action for a leading footwear retailer

Next best action for a leading footwear retailer

Next best action for a leading footwear retailer

AWS-powered customer data platform for personalized experiences

AWS-powered customer data platform for personalized experiences

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