Retail Case Study
Solantis Commerce
A recommendation engine that noticeably lifted average order value.
+36%Conversion Rate
The Challenge
Where things stood before
When Solantis Commerce 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
We started by getting a clear picture of exactly where things were breaking down before proposing anything.
What we ultimately delivered was a recommendation engine that noticeably lifted average order value, built specifically around what Solantis Commerce needed.
- Structured so Solantis Commerce's own team could monitor and maintain it going forward
- Designed around Solantis Commerce's actual data and workflow, not a generic template
- Built using Python and PyTorch for a stable, maintainable foundation
- Delivered within a 10-week timeline without cutting scope
Results
The impact, by the numbers
+36%
Conversion Rate
+34%
Forecast Accuracy
-17%
Manual Review Time
+27%
Revenue Lift
Tech Stack
Built with
PythonPyTorchBigQueryRedis
Meridian Technologies
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