Quantifying demand sensitivity across product categories and customer segments to unlock revenue hidden inside a static pricing strategy.
An analytics consultancy client managing a large product catalogue was operating with a static pricing strategy — prices set periodically by category managers without systematic analysis of how demand responds to price changes across different products, segments, or competitive conditions.
Static pricing was leaving significant revenue on the table — some products were underpriced relative to demand willingness, while others were priced above the elasticity threshold, suppressing volume. Without visibility into demand curves, pricing decisions were based on intuition rather than evidence. The organisation had no systematic way to identify where price adjustments would increase revenue versus where they would reduce volume.
We built a price elasticity model that analysed demand sensitivity across product categories, customer segments, and competitive context simultaneously. The model estimates price-demand curves at granular levels, identifies where elasticity is low (price can be raised without volume loss) and where it is high (price cuts would drive disproportionate volume). Model outputs were integrated directly into pricing workflows, enabling category managers to make evidence-based decisions with quantified revenue impact projections.
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