Retail
Retail Intelligence Engine
Real-time retail intelligence platform unifying demand forecasting and personalization across 2,000+ store locations.
94%
Forecast Accuracy
18%
Revenue Uplift
2,000+
Store Locations
35%
Inventory Cost Cut
94%
Forecast Accuracy
18%
Revenue Uplift
2,000+
Store Locations
35%
Inventory Cost Cut
Measurable Results
Business Impact
Context
The Challenge
A national retailer needed unified demand forecasting and personalization across 2,000+ stores with siloed inventory, pricing, and customer data systems.
“Our inventory decisions were based on last month's data. We needed to predict demand before the shelves emptied.”
Siloed Systems
Inventory, pricing, and customer data spread across disconnected legacy platforms.
Manual Forecasting
Spreadsheet-based demand planning unable to account for real-time market signals.
Channel Fragmentation
Online and in-store experiences operated as separate businesses with no unified customer view.
Our Approach
The Solution
Built a real-time customer data platform with ML-driven demand forecasting, dynamic pricing models, and personalized recommendation engines at point of sale.
Demand Forecasting Engine
ML-driven forecasting achieving 94% accuracy across 2,000+ omnichannel store locations.
Personalization AI
Real-time recommendation models driving 18% revenue uplift at point of sale.
Retail Operations Hub
Unified dashboard for inventory, pricing, and customer analytics across all channels.
Customer Data Platform
Snowflake-powered CDP consolidating siloed inventory, pricing, and loyalty data.
Cloud Infrastructure
AWS-native platform with Kubernetes serving and real-time event streaming.
System Design
Solution Architecture
Store Associates
Retail Operations Hub
Forecasting Engine
Demand Models
Customer Data Platform
Revenue Analytics
Delivery
Implementation Timeline
Discovery
Stakeholder alignment, data audit, and success criteria definition.
Architecture
Reference architecture, security model, and integration blueprint.
Model Training
Feature engineering, model development, and validation framework.
Deployment
Production rollout, observability, and enterprise integration.
Go Live
Full production launch, monitoring, and optimization handoff.
Engineering
Technology Stack
Python
Snowflake
React
Prophet
AWS
Docker
Kubernetes
Transformation
Business Outcomes
Before
- Spreadsheet forecasting
- Siloed channel data
- Reactive inventory management
- Generic customer experiences
After
- ML demand forecasting
- Unified customer platform
- Predictive inventory optimization
- Personalized recommendations
“Forecast accuracy jumped to 94% and we finally have one view of the customer across every channel. Revenue uplift speaks for itself.”
