Improved forecasting and pricing for a major food manufacturer
What We Did: Enabled Pricing Managers better evaluate the impact of pricing changes on volume sales
The Impact We Made: An improved volume forecasting and pricing decision process helped realize a 2% revenue increase
Summary – Impact of pricing on sales
Mu Sigma helped a leading food manufacturer proactively identify the impact of pricing tactics adopted by its retail channel partners on the volume sales. The solution framework enabled the Pricing Managers make more informed decisions by evaluating multiple scenarios and planning volume sales better by each of its retail channel partners, translating to a 2% increase in revenues. The solution comprised of price elasticity, threshold and gap models, which taken together helped the Pricing Managers better estimate the impact of retailer led pricing and promotion decisions on volume sales.
About The Client – Major food manufacturer
The client is a Fortune 500 food manufacturer with a wide portfolio of products that can be found in a majority of US households. Their products are primarily sold through retail channel partners, which happen to be some of the largest retailers in the world.
The Challenge – Legacy algorithms
The retailer partners set their own prices for the manufacturer and competitor products, including offering discounts on the manufacturer’s products in addition to changing prices across different markets. Since the manufacturer had no visibility to these price changes, it was very difficult to plan for the impact on volume sales through the retail channels, resulting in a significant loss of revenue opportunities. The existing pricing algorithms did not incorporate the cross-price elasticity with respect to price changes implemented by the retail partners for their products as well as competitor products.
The Approach – A new pricing framework
Mu Sigma helped the Pricing Managers develop the capability to generate improved volume forecasts by better understanding the impact of retailer pricing decisions. The solution had three components which taken together, helped quantify the impact of retailer pricing decisions on volume forecasts:
- Cross Price Elasticity Models at a product-market level which quantified the impact of price changes implemented by the retail partners on its own brands as well as competitor brands
- Price Threshold Models which identified the price bands beyond which the sales showed a significant drop in volumes. This was an insight that was not part of the original problem statement but was discovered over the course of the analysis
- Price Gap Models that factored the price difference between their products and competitor products to determine the optimal price gaps with respect to competitor products
These models were developed for each of the major retail channel partners. They helped the Pricing Managers react better to retailer led price changes and promotions and incorporate the impact of these events into the forecasting process.
These models were codified in an easy-to-use dashboard which is currently being used by the Pricing Managers to evaluate alternative scenarios and develop better volume forecasts by retail channel partners on an ongoing basis.
The Outcome – Improved forecasting and planning
- With the enhanced ability to understand the pricing strategies adopted by retailers both with respect to its own products as well as competitor products, the manufacturer has the ability to develop better volume forecasts and improve the planning and execution of trade promotions by retail partner.
- Mu Sigma helped the manufacturer identify a group of stores (56 % for a specific retailer) that had the potential to increase price of a product up to a difference of $ 0.25 from its closest competitor without impacting sales. This had a potential revenue upside ranging from 2% to 3%.