Boosting Online Jewelry Sales with Hyper-Personalized Interventions & Data-Driven Insights

Traditional personalization methods lacked context and real-time responsiveness, resulting in generic recommendations and missed opportunities.

Customer data was scattered across various systems, hindering a complete customer view and targeted marketing campaigns.

Existing infrastructure couldn't handle real-time data processing and analysis, leading to delayed personalized interventions.

Integrated data from diverse sources (website interactions, purchase history, etc.) to create a unified customer profile for personalized recommendations.

Utilized real-time data processing and analytics on AWS infrastructure to offer personalized interventions and recommendations with minimal delay.

Built a central data lake on AWS using Glue, S3, and Athena to store, manage, and integrate data from various sources.

Leveraged EMR for big data processing and PySpark for advanced data analysis to personalize interventions based on individual customer behavior and preferences.

Used EC2 instances for running application code and delivering personalized interventions at scale.

Employed Cloudfront for content delivery network (CDN) functionality to ensure fast and reliable delivery of personalized content

Personalized interventions led to a significant increase in conversion rates and click-through rates, directly impacting revenue growth.

Optimized operations and improved targeting reduced overall costs and increased profit margins.

Personalized recommendations drove cross-sell and upsell opportunities, leading to a rise in order volume.