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Development of AI-Driven Business Intelligence Platform with Computer Vision for Fashion Retail Personalization

itransition.com

Retail

eCommerce

Consumer products & services

Challenges in Data Management and Conversion Optimization

The retailer struggled with processing 10TB+ of daily data from multiple sources (web, mobile, email) to derive actionable insights. They needed to automate predictive analytics for buyer conversion forecasting, improve personalization accuracy, and reduce infrastructure costs while managing 200K+ daily active users and 9M SKUs.

About the Client

Established global ecommerce company offering clothing, accessories, and home goods to 20M+ registered customers with 8M daily website/app users

Key Objectives for BI Platform Development

Centralize data collection and analysis from web, mobile, and backend systems
Implement AI-powered predictive analytics for conversion rate forecasting
Develop computer vision capabilities for automated product attribute recognition
Reduce infrastructure management costs by 50%
Improve buyer conversion rate by 8% through personalized experiences

Core System Functionalities

Multi-source data ingestion (clickstream, mobile, server events)
Real-time data processing and normalization
AI-powered recommendation engine using collaborative filtering (ALS algorithm)
Computer vision for product attribute detection (color, patterns, clothing type)
Image similarity search with embedding layer
Custom reporting dashboard with 100+ report types
Ad-hoc query builder for marketing team
Personalized website/email content delivery

Technology Stack Requirements

Apache Spark with MLlib

TensorFlow/Keras

ResNet50 CNN

AWS (serverless architecture)

Apache Kafka

Hortonworks Data Platform

Amazon DynamoDB (on-demand)

EC2 Spot Instances

System Integration Needs

CRM systems
Ecommerce platforms
Email marketing tools
Third-party data sources via ETL connectors
Mobile/web analytics platforms

Operational Requirements

Auto-scaling infrastructure for variable traffic
Real-time processing with <1s latency
99.9% system availability
Data security compliance (GDPR, PCI)
Cost-optimized cloud resource utilization

Anticipated Business Impact

The platform is expected to increase buyer conversion rates by 8% through personalized recommendations, reduce infrastructure costs by 50% via optimized cloud architecture, and enable faster data-driven decisions through 100+ automated reports while maintaining scalability for 20M+ users and 9M SKUs.