Python

Pricing Elasticity Simulator

An interactive simulator estimating revenue impact of price changes by segment.

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Pricing Elasticity Simulator preview
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.