Customer Profitability Analysis and Pricing Optimization
“Our wholesale distribution business, National Paper & Packaging, started working on Customer Profitability Analysis (CPA) all the way back in the 1980s. CPA contributed to our industry-leading profitability. Ahead of its time, National Paper was featured in an Arthur Andersen & Co. book, an IBM White Paper and a NAW videocast about CPA. The first customer profit ‘whale curve’ I ever saw was an exhibit about National Paper in the Arthur Andersen book nearly 30 years ago.”Brent Grover, Former CEO, National Paper & Packaging Co.
Evergreen Consulting’s top-down approach to CPA for distributors, SMART CPA, is a systematic and consistent methodology for allocating and assigning costs. The algorithms we use are based on cost behaviors and our experience working with distributors of all types and sizes. Our principles call for a customized approach for each project. We account for all significant variables (sales cost, freight, vending machines, customer inventory, cost of getting paid) for each customer. Different methods are used for each order type (warehouse, direct, will call, counter sale, service).
SMART CPA is uniquely integrated into SMART Pricing. Our holistic approach to margin optimization and customer profitability requires that we use CPA data to determine our pricing recommendations. Evergreen Consulting’s philosophy is that customer profitability is a “three-legged stool” balancing margin, order size and cost to serve.
SMART CPA reporting is diagnostic of root causes of profitability for each customer family, segment, branch and sales territory. It is also therapeutic including specific recommended actions for each customer in each profit quadrant.
After undertaking pricing optimization and customer profitability projects for hundreds of distributor operations, our firm has a distinct perspective about the cause-and-effect relationship between customer profitability and pricing. Our viewpoint is unique because Evergreen is the lone consulting firm that performs both of these analyses for distributors.
There are several reasons why customer profitability data is required to achieve success with pricing optimization. For example:
Many customers are low-profit or lose money for the distributor because of below-market pricing. In other instances the culprit is small order size, high cost to serve, or both. If pricing is already at market levels the correct therapy is not raising margins.
A small number of customers generate a large proportion of total distributor profits. The root cause of their profit contribution is typically a combination of profit margin, order size and cost to serve. Fixing margin outliers for these customers isn’t a priority if the improvement wouldn’t “move the needle” and could jeopardize the distributor’s relationship with a high-profit account.
The “speed to revenue” temptation when implementing pricing recommendations may be hard to resist. Customer profitability insights make it clear which pricing outliers can be put into effect quickly and others better implemented over a period of time.
Customer Profitability Analysis (CPA) for Distributors
Now that “big data” analytics are becoming widely available to distributors, CPA is no longer an extravagance.
The purpose of the “customer profit” metric is to identify the profitability of individual customers. Companies commonly look at their performance in aggregate. A common phrase within a company is something like: “We had a good year, and the business units delivered $400,000 in profits.” When customers are considered, it is often using an average such as “We made a profit of $2.50 a customer.” Although these can be useful metrics, they sometimes disguise an important fact that not all customers are equal and, worse yet, some are unprofitable. Simply put, rather than measuring the “average customer,” we can learn a lot by finding out what each customer contributes to our bottom line.
Quite often a very small percentage of the firm’s best customers will account for a large portion of firm profit. Although this is a natural consequence of variability in profitability across customers, firms benefit from knowing exactly who the best customers are and how much they contribute to firm profit.
At the other end of the distribution, firms sometimes find that their worst customers actually cost more to serve than the revenue they deliver. These unprofitable customers actually detract from overall firm profitability. The firm would be better off if they had never acquired these customers in the first place.
The whale curve, so-called because of its shape, plots the cumulative profit from a distributor’s customers (vertical axis) against the number of customers (horizontal axis). For example, the profit earned from the highest-profit account is plotted, then the cumulative profit from the two most profitable accounts, and so on. The curve heads almost straight at first, then reaches a peak and flattens out as customers generating small losses are added to the total. Finally, at the far right, customers resulting in large losses cause the plotted curve to drop precipitously.
The whale illustrates that distributors reach a peak profit potential which is usually much greater than the actual profit total. Tactical steps can be taken, including fixing pricing outliers, to help distributors raise profits closer to potential.
CPA starts with a study of the distributor’s P&L statement. Analysis of the expenses in the chart of accounts allocates expenses and certain revenues to order type and customers based on behavioral relationships. Certain items are assigned to specific customers.
The outcome is a profit and loss statement for each customer and group of customers, including sales territories, branches and regions.
Ranking reports and further analysis leads to tactical advice about pricing, order size and cost to serve improvements.
Dividing customers into profit quadrants helps management develop strategies for groups of customers, and specific action steps for larger accounts. Pricing optimization implementation recommendations are based in part by profit quadrant information.