AnalyticsRetail
RetailEye
Outcome
40% revenue lift from insights
PythonPlotlySQL
The problem
A 30-store retail chain had rich POS data but no way to act on it - pricing and promo decisions were made on instinct, and margin leaks went unnoticed for weeks.
What we built
Jora built a nightly analytics pipeline that clusters SKUs by elasticity, flags margin outliers, and recommends price adjustments. Store managers get a weekly, prioritized action list.
The outcome
Targeted repricing lifted revenue 40% on promoted SKUs within two quarters, and the ops team caught a recurring pricing error that had been bleeding margin for months.
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