AI Personalization Engine for Retail Giant

Executive Summary: MarketPro had huge amounts of user data but no way to leverage it. We built a customer Data Lakehouse and a real-time recommendation engine. This personalization increased average order value and conversion rates, driving roughly $1.8M in incremental revenue.

+8%Conversions

+5%Avg Order Value

1.5TBData Processed

The Challenge

Data Silos: Online and offline data were never merged.
Generic UX: Every user saw the same homepage products.
Slow API: Existing search was too slow for real-time suggestions.

Our Solution

We built a custom recommendation engine:

Data Lakehouse: Consolidated data from POS, website, and app into Databricks.
Collaborative Filtering Models: To suggest "Users who bought X also bought Y".
Real-Time API: Served recommendations in <50ms during page loads.

Performance Metrics

Conversion Rate by Segment

1.2

1.8

New Users

3.5

5.2

Returning

8.5

Loyalty Members

Generic

Personalized

Implementation Roadmap

2 Months

Ingestion

Building pipelines from 6 core data sources.

3 Months

Modeling

Training and back-testing ML models.

2 Months

A/B Testing

Testing personalization against generic.

Ongoing

Optimization

Retraining models weekly.

Key Results

Personalized product suggestions drove an 8% increase in conversion rate. The "Recommended for You" section became one of the highest-revenue placements on the site.

"We finally feel like we know our customers. The AI suggests products they want before they even know they want them."

— Robert Vance, CMO