priceQED is a tool that allows you to estimate the price and promotional sensitivity of all items within a category. It is specifically designed to analyze an entire Consumer Packed Goods category. It uses historical data and advanced modeling software to provide marketers with greater visibility and a better understanding of price sensitivity, promotional effectiveness and SKU incrementality. priceQED allows you to:
See how price changes affect own and competitor sales and profit across the entire category Understand the effects of price on substitution and cross-product cannibalisation Forecast sales volume and profitability over hundreds of price scenarios Help you understand category-wide impact from individual product promotions
How does it work?
priceQED autonomously estimates regression models for each item within a category as well as for the category total. This provides an estimate of direct elasticity as well as item to category elasticity.
Who is it for?
priceQED is for any packaged goods company that wants to:
- Identify the optimal price for each item within a competitive set, as well as for the health of the category
- Estimate the impact of future SKU delisting, with respect to the remaining items and the total category
- Time series data for volume sales and price, by SKU (or PPG), for own brand and competitors
- Optional factors: distribution, promotional metrics, and seasonality
- Estimation of the incremental sales and profit from a promotion or price change
- Net of any cannibalisation from other products
- Financial results from both the manufacturers and the retailer’s perspective
- Forecasted impact of promotional plans
- Identification of the best promotional plans
- The order in which SKUs should be delisted to minimise the impact on the rest of the category
Case Study Example
A global cereal company had been running marketing mix modeling across several brands for a number of years. They found that they couldn’t capture all the price interactions with brand and pack-size-level models and category-level models weren’t feasible given the complexity of the arena in which they operated.
Using priceQED they were able to gain a full understanding of the price interactions, both within their portfolio and the rest of the category. This was used to determine the best pricing strategy to maximise profitability. The insights gained about the impact of delisting SKUs enabled them to minimise losses.
Sound interesting? Book a demo and get accurate ROI projections now. Contact us today and we’ll be happy to discuss options with you.