Leaning into the Curve: How Volume Curves Improve Pricing Performance

By Israel Rodrigo
November 17, 2016

What are volume curves and how do you optimize them for your business needs?

Volume curves form the basis of discounting the terms of business for many companies across various vertical industries like semiconductors, high-tech distribution, OEM manufacturing,  and software—to name a few.

The idea is a simple one: if a customer buys more volume, we can discount the product or services further and incentivize the purchase of more units. Most likely, your company also has a standard set of curves with “volume break-points,” which may have some resemblance to Figure 1.

Figure 1

Figure 1

The two curves serve as examples for a) different product lines or b) for the same product line under normal or competitive/aggressive business scenarios. The table below represents an internal set of policies for various volume break points.

Table 1

Table 1

In Table 1 above, the first three set of break points (1-25, 26-99, 100-499) can serve the low volume customers from a company’s website, where customers directly go to the website to research the product line-card, product features, compare specifications, and download datasheets. In this environment, the companies operate more like a B2C where they can directly reach the customer.

The next set of break points (500-999, 1K-4999, 5K-9999) entices and rewards customers for purchasing additional volume. In this case, the customer enters a quote through a partner portal which can be evaluated by a “deal desk” or “quote center.”

The last set (10K+ and above) works like a negotiated B2B environment where many additional factors can be taken into account (customer purchase history, volume compliance, etc.) to give a negotiated discount on top of the volume policy discount.

In my experience in working with customers across various vertical industries, I have seen different forms of the curve. Some customers keep it very simple and have just a few volume curves for their entire line of products or business. Some choose to have a unique curve for each of the product lines or product parent families, thereby creating numerous curves that need to be maintained over time. And there are some companies that are in between and walk the fine line between simplicity and complexity of business terms.

There are various factors to consider when creating and implementing volume curves. In particular, there are two important aspects to keep in mind:

1.) Evaluate your volume curves periodically to make sure the intended purpose is served:

Things change, so you must be prepared for any scenario. Let’s look at two simplified examples where the volume curves may need to be adjusted periodically.


Data: Actual recognized revenue against each volume tier and corresponding discount from the list price or some common reference price.

Current Policy: Current volume discount policy based on volume breaks.

Approved Policy: The result of user reviewed and implemented discount (can include volume discount and negotiated discount in some cases).

Proposed Policy: The result of optimization algorithm (statistical analysis) recommending where the volume discount policy should reside.

Example 1. Decreased Discounts from the Current Policy in Place

Let’s take a look at Example 1: the current policy is in place and expected to give volume discounts at a much steeper rate, but the approved policy based on the transaction data suggests that the volume discounts should be decreased from the current policy in place. By performing a statistical analysis on the data set, we can recommend the right slope at which the proposed policy curve should reside. This can be pertinent to certain sub-sets of products or could be a result of legacy and outdated policies that need to be evaluated and further refined as the market and business conditions change.

Example 2. Increased Discounts from the Current Policy in Place

Figure 3

Let’s take a look at the Example 2: for a different product family, the current policy is in place and expected to give volume discounts at a certain standard rate, but the approved policy based on the transaction data suggests that the volume discounts should be increased from the current policy in place to meet the competitive market conditions. By performing statistical analysis on the data-set, we can recommend the right slope at which the proposed policy curve should reside, thus leaving ample room to apply the negotiated discount for each customer deal.

2.) Distinguishing between the volume curve discount (list price at volume) and negotiated discount:

“Negotiated Price = List Price @ Volume + Negotiated Discount”

Volume curves have a special role in pricing and should be managed separately for consistency and clarity of pricing. The volume curve policy should form the basis of an intelligent decision support system giving the user the ability to apply more conservative or realistic negotiated discounts. It is important to clearly differentiate between the list price at volume (which should be driven from a policy) and the negotiated discount.

This becomes even more important for companies considering a smart pricing (price optimization) project, as the policy based volume discount can be separated from the actual negotiated discount, thus giving users the true sense of what is being negotiated in each quote or deal. Once the true negotiated discount is calculated, the smart price guidance will be able to provide the right target price guidance.

By properly utilizing and monitoring volume curves, businesses with an array of product lines and customers buying at various levels can tune their pricing to better fit their situation.

  • B2B Pricing , discounts , rebates , sales , volume curves

    Israel Rodrigo

    Israel is a Business Consultant at Vendavo with more than 15 years of extensive international experience in logistics, wholesale distribution and software industries. Prior to Vendavo, he worked at Deutsche Post DHL and McKesson in several strategic positions, such as controlling, customer finance, sales development, and leading profit optimization and pricing transformation. He holds a BS degree in Statistics and Economics from Universidad Carlos III de Madrid (Spain) and lives in Seattle, Washington.