E-Commerce Intelligence Platform
Overview
A Fortune 500 retailer needed to understand why customers were abandoning carts and how to personalize recommendations at scale. We built a real-time ML pipeline that ingests clickstream data, trains behavioral models, and serves personalized product recommendations via API.
The Challenge
The client had siloed data across 3 platforms with no unified view of customer behavior, and their existing recommendation engine was rule-based and static.
Our Solution
We unified data sources into a real-time feature store, trained a collaborative filtering model, and deployed it behind a low-latency API serving 10M+ requests/day.
Tech Stack
Client
Major US Retailer
Duration
14 weeks
Key Outcomes
40% increase in revenue within 6 months
Cart abandonment reduced by 22%
Average order value up 18%
Model serving latency under 50ms at p99
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