A Guide to Rate-Mix Modeling to Accelerate Margin Performance

Understand the granular impact of your pricing actions or sales execution with robust Revenue and Gross Profit Growth Drivers Analysis, also known as Price-Cost-Volume-Mix analysis. 

What is PCVM?

Companies are in the business of making money, and most often, they care about maximizing their Revenues, Gross profit, or Operating income (roughly the same as EBIT). One of the biggest challenges Finance and Revenue Management teams face is the ability to properly and systematically decompose and diagnose the drivers of fundamental business performance changes.

Price-cost-volume-mix analysis, or PCVM analysis, helps you understand the relevant components that drove your revenue, gross profit, or other critical financial metric changes from one period to the next (or for actuals vs. budget). More specifically, it enables us to dynamically drill down into important Customer or Product level drivers to understand:

1. Pricing Impact: What is our Net Price Realization, and what Business Segments, Customers, Brands, Products, etc. are the winners/losers of our pricing execution?

2. Cost Impact: What areas in our business are driving the highest Cost Inflation? Where do we need to focus on rightsizing our List Prices or Sales Discounting efforts because our Price Realization has not kept pace with rising costs?

3. Volume Impact: Which Customer Segments or Products drove substantial volume losses despite lower prices (or a negative Pricing Impact)? Which Customers or Products increased volume despite higher prices? What are the learnings we can get from this?

4. Mix Impact: What are the areas (Customers, Products, or both) where we have offset a negative Price Realization with positive Mix impacts? In other words, despite lower prices, we have sold a higher portion (mix) of highly profitable products this year than last year (or some prior period).

Measuring the impact of sales mix can be tricky and is often misused in corporate finance teams. When used in a dynamic, drill-down fashion for root cause analysis, we can separate the impact of sales mix into several hierarchical categories, such as margin impacts from channel, geography, customer, brand, or product mix shifts.

Let's understand our example below, which has been built from detailed transactional data using a fictional manufacturer:

1. Our US & Canada Region drove the lowest Gross Profit $ improvement YTD vs. YTD Prior Year @ $0.2M. Despite favorable Cost impacts (+ $34.8M), the region managed to drive a -$38M Price Realization. Costs declined heavily, and instead of capitalizing on it, the region lowered Net Prices by -$38M (either through List Price decreases or Discounts & Rebates). Volume impact was -$0.7M (sold fewer units), while it had a positive Mix Impact.

2. Once we drill down into US & Canada, we see that all of the negative Price Realization is driven by one large Customer Segment, "Buying Groups."

3. Double-clicking on "Buying Groups," we see that two Products are responsible for all the negative Pricing Realization. We should understand if this behavior is concentrated with a handful of Customers or Sales Reps before formulating a plan to course correct.

Benefits of robust PCVM capabilities

Much of the PCVM analysis I've seen in corporate finance environments has been relegated to high-level Excel work that's descriptive and nice to have at best but does nothing to identify and create actionable insights and specific Customer Segment - Product level course corrections. Most often, PCVM analyses in organizations are limited to executive decks or Excel workbooks during monthly finance reviews, usually done at the Channel or Brand level, with actual pricing and cost impacts heavily masked by underlying changes in customer or product mix shifts.

You gain a substantial competitive advantage by performing the proper PCVM analysis from customer-product level transactional data (sales, operations, and financial data). Identifying and separating the effects of your pricing actions, cost levers, or changing sales trends on your company's margin performance enables you to identify specific pain points in your revenue management or commercial strategies and address them in a surgical way for increased profit.

Beyond Excel…

Sorry MS Excel aficionados, I'm not discounting the usefulness of Excel. I use it quite a bit, but it has its obvious limitations, particularly if you want to build a robust analytics solution that's both descriptive and diagnostic, allowing for dynamic performance drill-downs (e.g., "peeling back the onion").

For organizations to get better and more intentional with their pricing and margin performance, their Finance and Revenue Management teams need to deploy robust PCVM capabilities democratized across vital functional areas. 

A handful of software vendors (mostly B2B pricing vendors) specialize in this type of enterprise-grade rate-mix variance or PCVM capability. Still, it's not that complicated. Basic Python or R skills or self-serve BI tools like Tableau or Power BI can enable you to ingest relevant financial and operational data and build a robust and actionable PCVM tool that can be dynamic and deployed company-wide.

I included an Excel workbook that illustrates two popular methods for Revenue and Gross Profit performance decomposition to get you started:

• Method 1: Top-down, sequential PCVM decomposition, great for drill-downs and further isolating the impact of sales mix into channel, customer, brand, product, etc. mix impacts. This approach allows you to peel back the onion on your company's margin performance and dig deeper into problem areas. The downside is that pricing and costs impacts include sales mix effects from hierarchical layers underneath.

• Method 2: Bottoms-up PCVM decomp, recommended if you want to slice and dice your analysis and truly isolate the impact of your pricing actions or cost trends. The downside is that your mix impact encapsulates the sum of all mix elements, and we cannot further drill down (e.g., into customer, product, etc.). Nevertheless, this is an excellent approach if you want to quickly find drivers of margin performance for your company or a business unit.

For those who are more hands-on with coding and want to build a robust PCVM capability for your organizations, I included the R code for Method 1 to get you started on a production-level buildout (it's hard to beat tidyverse for fast and efficient business analysis).

As always, remember, building an analytics tool is the easy part: change management and stakeholder alignment to understand and leverage PCVM capabilities for valuable insights, and pricing or commercial model adjustments will be much more complex.