August 14, 2017
One of the most basic concepts of pricing is that price generally reflects (or should reflect) value. But it’s important to remember that value is not defined from the seller or supplier’s perspective – rather it’s defined by the customer or buyer. A certain product or service is only worth what a buyer is willing to pay for it. And their willingness to pay is based largely on how they perceive the value of the seller’s product, service or solution.
Also important to remember is different customers perceive value differently. Therefore, their willingness to pay is also different which means their pricing could be different too.
Why Price Segmentation?
Why would you go to the trouble of differentiating prices based on customer willingness to pay? Because you stand to gain a LOT of value for your organization. If you have one price for a given market or region, you will be pricing too high for some customers and leaving money on the table for other customers. By pricing according to different customers’ willingness to pay levels, you can capture the maximum amount of revenue (which of course drives operating profit) for each deal or transaction. (See figure 1)
In most cases these additional sources of value (revenue) come with little or no additional incremental costs, so there’s a direct – and large – link from that revenue to your operating profit. Moving to differentiated pricing, based on pricing segments, has been shown to generate anywhere from 1% to 6% of revenue lift (with virtually no incremental costs). That’s typically a 10% to 60% increasing operating profits.
How Do You Use Price Segmentation?
The concept behind price segmentation is pretty clear but how do you go about discerning and then leveraging these pricing segments in the first place? Experience has shown that the best formula is a combination of data science, and business judgement and context. There are well-establish statistical methods for taking your company’s sales history and identifying pricing segments based on attributes of each sale. These attributes tend to fall into three categories:
- Product / Service – attributes about what is being sold (e.g. Product Lifecycle, Unique or Competitive, etc.)
- Customer – attributes related to whom it is being sold (e.g. Customer Industry, Customer Size, New or Existing Customer, the End-use Application, etc.)
- Transaction – attributes related to the historical transaction itself (e.g. Spot Quote or Contract related transaction, Order Size, Competitor, etc.)
Once you leverage the attributes you already have in your sales history, you then need to factor in logical rules or strategies that may or may not be empirically present in the data.
A classic example is that you don’t want to group transactions sold directly with those sold through a distribution channel. (For more information on this topic, join our webcast on August 15 where we discuss price segmentation best practices for distributors.) Other insights are more nuanced and come from human interaction with the data – often your best sales people. This approach not only results in a more accurate segmentation, it also has benefits with regard to change management and adoption.
Finding the right level of segmentation is a bit of a “goldilocks” process: too much granularity and science in your segmentation and you will have way too many segments and they will be difficult to explain or understand. Too little granularity and you are just defining a managerial segmentation model (segments by rule), then you are most likely sub-optimizing your value by pricing too broadly.