Analytical Frameworks: The Path to Identifying Pricing Improvement Opportunities

At NextLevel Pricing we believe there are three main activities an organization needs to perfect to maximize the financial potential that pricing analytics can bring to their bottom line:
1. Producing price/margin waterfall data that is timely, accurate, and structured appropriately to allow for various pricing analyses
2. Employing the proper analytical frameworks to analyze this price/margin waterfall data in a manner that brings opportunities to light
3. Successfully integrating analysis activities into everyday business practices by providing business resources with a process to identify, validate, and act on top potential opportunities exposed by each framework

An analytical framework in pricing analytics basically answers the questions What do we want to look for? and How do we want to find it? Frameworks are generated by manipulating price, cost to serve, and margin data along with the appropriate qualitative data (customer name, product name, etc) required to make the data realistic and actionable.

A very basic and popular framework used by almost every organization is a framework that identifies customers with pricing or margins that are outside the mean for a given product. By graphing customer price or margin on the Y-axis and volume on the X-axis, we can identify low-volume customers who might be receiving a price that is lower than someone with their expected buying buyer should receive.

Analytical Framework: Pricing Outliers
What we want to find: Cases were we might not be charging as much as we should at a customer
How we find it: Identifying customers with below-average pricing and volumes for a product

Graphical Example

As one of the less complex analysis frameworks, running a pricing outlier analysis a great starting point for organizations just beginning their pricing journey to ensure they’re capturing appropriate value for their products in the marketplace. There are a number of ways to take this analysis further – one option is to group customers by overall volume then give certain groups allowances from average pricing levels before they are considered an outlier. For example, we might conclude that if a customer is one of our Top 10 customers by volume we’d expect them to receive lower prices – so we’ll ignore any pricing outliers at these customers unless they’re at least 20% below average.

For more information on how to setup this analysis in a simple format or use more detailed parameters to take this analysis to the next level, please see our White Papers section for information. In the future we’ll break down more complicated pricing frameworks and discuss methods to run this analysis for an entire Fortune 500 company in one day – but for now if you have any questions feel free to contact us at info@nextlevelpricing.com.