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The client is a European multinational brewer with a presence across multiple continents and more than 140 brands in its portfolio.
The client was not new to Price Elasticity models. However, its key concern was about the relevance and accuracy of its existing price elasticity calculations for genuinely optimizing the market demand.
The client also had profitability concerns and believed it was incurring more costs than required due to an outdated method of calculating Price Elasticities.
The client wanted to use an innovative approach to ensure accuracy in the calculation of price elasticities at a granular level along with essential correlations with SKUs, channels, and geographies.
Datamatics implemented an innovative methodology by truncating time series and giving more weightage to ‘recent past’ over ‘remote past’ for calculating price elasticity.
The approach ensured higher relevance to more contemporary data elements, resulting in sharper actionable business decisions to drive the dual objectives of higher product uptake and profitability.
AI/ML models were built and implemented to ensure the highest level of precision and accuracy in calculating price elasticities across the client’s portfolio.
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