Python
Pricing Elasticity Simulator
An interactive simulator estimating revenue impact of price changes by segment.
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PythonpandasstatsmodelsStreamlitMatplotlib
Business problem
Commercial teams changed prices on instinct with no way to test the revenue impact before rollout.
Dataset
18 months of transaction-level pricing and volume data across 5 customer segments.
Methodology
- Estimated own- and cross-price elasticities per segment.
- Controlled for promotions and seasonality with fixed effects.
- Wrapped the model in a scenario simulator for commercial users.
Key findings
- Enterprise demand was almost inelastic below a 7% increase.
- SMB volume dropped sharply beyond a 4% increase.
Recommendations
- Apply differentiated price increases by segment.
- Re-estimate elasticities every two quarters.