price-predictor-h

Price Predictor: Price Elasticity Analytics Reimagined



Pricing is a crucial lever for CPG manufacturers, providing a huge opportunity to increase revenue. Before pulling the trigger on any price change, it’s important to first consider how alternative pricing will impact consumer demand and the result of competitive response. Price elasticity, which measures the increase or decrease in demand for a product when its price is manipulated, can be a useful tool when it comes to understanding the market, specifically consumer behavior.

However, due to the pandemic, traditional price elasticity analysis is no longer adequate for analyzing CPG sales data collected over the past year. The crisis caused an erratic shift in spending behaviors, rendering the data on the books useless for determining price elasticity. And while traditional price elasticity analysis can be beneficial in certain contexts, the approach has shortcomings beyond inadequate data for past-year sales, such as the lack of pricing variations in historical scanner data that is needed for projections.

Our new approach, Price Predictor™, combines the predictive benefits of virtual shopping with machine learning to achieve the best price. This solution gets quick and dynamic results with the ability to isolate variables and easily respond to any changes.

The virtual aisle is ideal for testing pricing because it puts shoppers in the context of an actual purchasing decision with a realistic set of competitive alternatives. Machine learning then runs tens of thousands of simulations to explore all possible scenarios. Results are forward-looking and can be updated regularly. 

Case Study Example: Salty Snacks

Price-Predictor-Case-Study-3

Using internal research conducted in Mexico with the Salty Snacks category, a category where prices can change very quickly, alternative prices were tested for a line of products along with simulated competitor response. As an example, when the price of “Cheese Poofs” is increased, it’s likely that a competitive cheese snack will increase their price as well. In this framework, results illustrate how demand for all products in the category (including within own portfolio) are impacted at various levels, including tortilla and potato chips.

While common belief is that cross elasticity happens on a one-to-one basis, this example demonstrates that a price change for one product can impact demand for a lot of different products, even products that one wouldn’t consider initially. This service calculates the cross-elasticity against all competitors. This is important because as price goes up on one product, the demand will likely change on ALL items in the category for your brands as well as your competitors’ products.

This is a powerful approach, especially with the volatility from COVID, and allows manufacturers to test with reliability. In addition, it provides an understanding of the impact of potential new pricing tactics and competitive response.

The research will answer questions like:

  • What base prices maximize incidence, unit sales and dollar sales?
  • How do competitor price changes impact incidence, unit sales, and dollar sales for your products?
  • What is the impact of pricing changes on the category overall?

For more information, please download our new white paper, Reimagined Price Elasticity

Price-Predictor-Download2

Please contact Leslie to learn more about how we can help optimize price quickly and with reduced risk.