Pricing and Promotion Decision Engine

A live, end-to-end applied decision tool built on 6.6 million rows of Dominick’s Finer Foods scanner data (cleaned panel of 486 UPCs, 93 stores, 366 weeks). Translates academic counterfactual-simulation workflow into firm-level pricing decisions.

🚀 Launch the Live App

💻 View Source Code on GitHub

What it does

• Estimate behavioral response: log-log fixed-effects demand model with Duan smearing retransformation to reduce log-to-level retransformation bias.
• Simulate counterfactuals: price and promotion counterfactual simulations over the estimated demand system.
• Optimize under constraints: constrained profit optimization under cost, inventory, and margin limits; A/B validation plans with power analysis before any candidate action ships.
• Ship: deployed as an interactive Streamlit application backed by a modular Python codebase, a pytest test suite, reproducible requirements, and auto-deploy from GitHub.

Stack

Python (pandas, statsmodels, scikit-learn), Streamlit, pytest, Git/GitHub.

Narrative

The workflow — estimate → simulate → optimize — mirrors the research-to-decision pipeline I use in my regional trade and market-integration research. The unit of analysis shifts from regions to firms, but the underlying logic is identical.