Back to Models
PythonPandasProphetSciPyStreamlitPlotly

Pricing Insight Tool

Retail Inventory & Pricing Optimizer

Pricing Insight Tool

Model Intelligence Report

The Client’s state before Us:
A mid-sized consumer electronics DTC brand with 300+ SKUs was bleeding cash on dead stock and simultaneously losing sales from stockouts on their top 20 products. Their pricing strategy was “cost-plus” — they had zero dynamic adjustment. Margins were shrinking and the operations team was burned out from firefighting.

The Diagnosis:
Their inventory forecasting was based on simple averages, ignoring seasonal demand spikes, cannibalization from new product launches, and competitor pricing moves. We found that 40% of their warehouse value was tied up in products with a "slow mover" classification that would take 9+ months to sell through, while their bestsellers went out of stock within 72 hours of a competitor running a promotion.

The Intervention:
We deployed the Inventory Optimization and Dynamic Pricing Insight Tool, which ingested their two years of sales data, competitor price feeds, and seasonal trends. The model generated a weekly replenishment order list, flagged SKUs for markdown or bundling, and suggested price adjustments within a 15% band to maximize margin while staying competitive on key items.

The Hard Numbers:
  • Reduced lost revenue from stockouts by $340,000 in the first 12 months.
  • Decreased excess inventory holding costs by 22% ($85,000 freed up working capital).
  • Increased gross margin on top 50 SKUs by 4.2 percentage points through micro price adjustments that customers didn't perceive.

Core Technology Stack:

PythonPandasProphetSciPyStreamlitPlotly