E-commerce Case Study
Copperline Retail Group
A feature store implementation that eliminated training-serving data mismatches.
+20%Conversion Rate
The Challenge
Where things stood before
When Copperline Retail Group first reached out, manual processes that couldn't scale with a growing catalog and customer base was the issue sitting at the top of their list.
It was the kind of issue that showed up in day-to-day frustration long before it showed up in a report.
The Solution
What we built
Rather than jumping straight to a model, we spent time understanding the specific shape of the problem first.
The result was straightforward: a feature store implementation that eliminated training-serving data mismatches, built specifically around what Copperline Retail Group needed.
- Designed around Copperline Retail Group's actual data and workflow, not a generic template
- Built using MLflow and Kubernetes for a stable, maintainable foundation
- Structured so Copperline Retail Group's own team could monitor and maintain it going forward
- Delivered within a 4-week timeline without cutting scope
Results
The impact, by the numbers
+20%
Conversion Rate
+39%
Forecast Accuracy
-24%
Manual Review Time
+58%
Revenue Lift
Tech Stack
Built with
MLflowKubernetesDockerAWS SageMaker
Dawnfield Industries
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