Pocket Price Waterfall

Pricing has always been an important lever for managers to shore up profitability. Simple math will tell you that in a business that has a 5% net margin, a 1% price increase, all else being equal, will bring this net margin to 6%, a whopping 20% spike. One element of any pricing project will typically include the strategic aspects of pricing. Those are issues largely focused around how to set the list price: How do the features of our products/service match up to those of competitors? In which markets do we over/under-price? Should we be price leaders or followers? Etc.
But tactical elements in pricing are often just as important as the strategic questions, and here is where the pocket price waterfall comes in. It’s an analysis of all the elements that affect what the company takes in “net net,” after all the discounts, rebates, allowances, costs to serve a customer, etc.

The graph above shows an example of a pocket price waterfall. It includes typical elements, such as distributor and end-user discounts, promotions, cash discounts, the financing costs of having receivables on your books for 60 days, etc. There are other potential elements not listed above (e.g. promotional advertising, merchandising costs, etc.).
Putting together a pocket price waterfall is usually quite an analytical exercise. It starts with downloading a set of data from a company’s ERP system. The trick is to get enough data points to make sure the analysis is relevant (if there is strong seasonality, or strong business cycles, make sure you cover a period that is long enough to be representative). The whole analysis really has to be done on a “line item” basis: If a customer buys 6 products on an invoice, this really needs to be broken out into six line items (essentially representing 6 lines in your XLS spreadsheet). Some of the data will be available on a line item basis. But much of it will not, which is where the fun starts. You will need to gather that data (e.g. what was the volume discount we gave to customer X at the end of the year), and then proportionately allocate that amount to all the all the line items of customer X. Similarly, if we ran a promotion for product Y, you will have to allocate the costs of that promotion to all the line items of this product Y. It’s this allocation process that usually is quite tricky. If you have a large data set, it becomes quite difficult to do this in XLS. There is specialized pricing software out there, that allows you to do this.
But once you have it all allocated, it actually becomes quite easy to slice and dice the data according to a number of dimensions (by product or product segment, by customer or customer segment, by sales rep or region, by quarter, etc.). The analysis will usually show a number of outliers, which often generates interesting discussions and material to suggest process changes that have a quick positive bottom line impact.