Improved pricing strategy by understanding price elasticity of demand for a leading food manufacturer
What We Did: We used manufacturing analytics to develop a pricing analysis framework to identify the impact of change in price of a product on its demand to improve promotion effectiveness for a leading food manufacturer.
The Impact We Made: With the pricing analysis framework, the client was able to design multiple trade promotions for retailers, which led to 8% increment in sales volume.
Summary – Price elasticity of demand model
Being a food manufacturer, it is imperative for the client to optimally price their products within or outside promotions to sustain demand. The client used to purchase pricing analysis from a third party vendor. This was proving to be an expensive affair and the client saw a need to have an in-house team to be able to do it accurately and in a scalable fashion. Mu sigma team enabled the client with the price elasticity exercise at brand / consumer segment level to achieve the required outcome.
About The Client – Large CPG company
The client is a leading food manufacturer in the US. With over 30 brands across different food product categories, the client generates more than $60 Ban in revenue. It has good presence in Non-Food consumer goods category as well.
The Challenge – Ineffective pricing models
The Client team has not worked on pricing elasticity of demand in their organization. Third Party models did not provide accurate input for building a good pricing strategy. Due to this, various pricing decisions such as setting up of base pricing, setting up capping parameters, examining the impact of pricing scenarios on product portfolio were not measured.
The Approach – Robust scalable price elasticity models
The client wanted a robust and scalable pricing analysis framework to be built in-house. The framework should be able to predict the change in demand of a particular product upon change in its price. Mu Sigma team followed below methodology to develop the framework:
- Various factors affecting the pricing of a product and its demand were studied. Price elasticity models were built to find out customer response in terms of acceptance or rejection of a price offering. The predictors used were both price and non-price related.
- A combination of customer segmentation and regression models resulted in:
- A strong correlation was identified between socio-economic condition of a consumer and change in demand of a product upon price change using big data analytics.
- Hence, for the same product, different segment of consumers reacted differently to the price change. That is, a $1 increase in price might not be affecting to a high income consumer as compared to a cash strapped one.
The Outcome – Incremental sales growth across multiple product categories
With the pricing analysis framework, aggressive trade promotions were designed on selected product categories, which led to 8% increment in overall revenue. The entire exercise enabled understanding of factors driving sales for better business/ pricing decisions. The product-category level price sensitivity analysis improved demand forecasts’ accuracy as well.
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