A large company in the food sector was facing a critical challenge: it didn’t know precisely how its prices were affecting consumer purchasing behavior across different regions and sales channels.
Without understanding how much sales volume reacted to price changes, adjustments were made with a high degree of uncertainty—leading to volume losses, margin erosion, and risks to brand competitiveness at the point of sale.
Moreover, the scenario was complex: hundreds of SKUs with different formats, weights, and market positions, and no structured criteria to compare them to direct competitors. This made it even harder to build a strong, agile pricing strategy responsive to market dynamics.
What we did:
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We used actual point-of-sale data, complemented with competitive intelligence, to calculate price elasticity by SKU, channel, and region.
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We created a detailed product categorization, grouping SKUs based on attributes like format, size, and market positioning.
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We built highly accurate statistical models (with an average precision of 91%) capable of reliably predicting how each SKU’s market share varies in response to pricing.
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We integrated these elasticities directly into the company’s platform, enabling dynamic volume and revenue simulations across various pricing scenarios.
Results so far:
The validated elasticities were integrated directly into the margin use case across the chain with a clear logic:
What was the business impact?
More than just predictability: the company gained the ability to simulate before acting, optimizing margins, protecting volumes, and making pricing decisions strategically and in line with competitive realities. This translates into greater control over outcomes and faster responsiveness to the market.
Want to turn your pricing decisions into a competitive advantage?
Talk to us and let’s explore your company’s pricing challenges together.
