From Siloed Data to Profitable Insights: BluePi Transforms Real Estate Search Portal with KPI Consolidation and Data Democratization
12 December 2023
BluePi Empowers New-Age NBFCs with AI-Driven Risk Profiling, Personalization & Cross-Selling
12 December 2023
Boosting Online Jewelry Sales with Hyper-Personalized Interventions & Data-Driven Insights
Executive Summary:
This case study examines how BluePi empowered online jewelry retailers to achieve a significant boost in customer experience and revenue through personalized user interventions, real-time targeting, and consolidated data lake infrastructure.
Background:
Online jewelry retailers face the challenge of creating engaging customer experiences while effectively targeting marketing campaigns. Siloed data and limited personalization capabilities hindered these retailers’ ability to deliver relevant interventions and recommendations.
Challenges and Objectives:
- Limited personalization: Traditional personalization methods lacked context and real-time responsiveness, resulting in generic recommendations and missed opportunities.
- Disparate data sources: Customer data was scattered across various systems, hindering a complete customer view and targeted marketing campaigns.
- Latency issues: Existing infrastructure couldn't handle real-time data processing and analysis, leading to delayed personalized interventions.
Solution:
BluePi implemented a comprehensive solution leveraging the following technologies:
- Single and consolidated user profile: Integrated data from diverse sources (website interactions, purchase history, etc.) to create a unified customer profile for personalized recommendations.
- Personalized user interventions with milliseconds latencies: Utilized real-time data processing and analytics on AWS infrastructure to offer personalized interventions and recommendations with minimal delay.
- Consolidated Data Lake: Built a central data lake on AWS using Glue, S3, and Athena to store, manage, and integrate data from various sources.
- EMR + PySpark: Leveraged EMR for big data processing and PySpark for advanced data analysis to personalize interventions based on individual customer behavior and preferences.
- EC2: Used EC2 instances for running application code and delivering personalized interventions at scale.
- Cloudfront: Employed Cloudfront for content delivery network (CDN) functionality to ensure fast and reliable delivery of personalized content.
Business Value Delivered:
The Impact: Measurable Success and Enhanced Relationships
- Increased Conversion Rates and Click-Through Rates: Personalized interventions led to a significant increase in conversion rates and click-through rates, directly impacting revenue growth.
- Reduced Costs and Higher Margins: Optimized operations and improved targeting reduced overall costs and increased profit margins.
- Increased Orders through Cross-Sell and Upsell: Personalized recommendations drove cross-sell and upsell opportunities, leading to a rise in order volume.
BluePi’s solution delivered tangible benefits to the DTH company:
Technology Deployed:
- AWS Glue: Data cataloging and preparation service
- AWS Data Migration Service: Data migration tool
- AWS S3: Object storage service
- AWS Athena: Interactive query service for serverless data analysis
- AWS EMR + PySpark: Big data processing framework and programming language
- AWS EC2: Cloud-based virtual machines
- AWS Cloudfront: Content delivery network service
- Boosted revenue and profitability.
- Enhanced marketing effectiveness and reach.
- Data-driven strategic decision-making.
- Fortified relationships with channel partners and consumers.
Conclusion:
By implementing a personalized intervention strategy and consolidating data lakes, BluePi empowered online jewelry retailers to deliver exceptional customer experiences and drive significant revenue growth. This case study demonstrates the effectiveness of data-driven personalization and consolidated data platforms in enhancing customer engagement and optimizing marketing efforts, leading to competitive advantage in the retail industry.
