Before You Offer Volume Discounts, Crunch the Numbers: Here’s How
Yale School of Management Associate Professor of Marketing Soheil Ghili's research explores optimal pricing strategy when it comes to offering volume discounts.
Consumers are increasingly faced with decisions about not just what to buy, but how much. This is especially true in their online and digital worlds; the choice is not just which cell phone carrier to choose, but how much data to purchase for a monthly plan.
This type of how-much question introduces additional complexity not only for the consumer — it also raises complicated questions for sellers about optimal pricing. Does it make sense to apply a linear pricing model, where each unit of a product (like phone data, or online storage space) is priced consistently, or would it be more profitable to offer discounts for large-volume purchases?
This question becomes especially pertinent in the business-to-business (B2B) space, where potential clients are often wielding large budgets, and sellers typically invest significant time and resources into converting leads into a sale. What’s more, a growing number of profitable B2B products demand this how-much question: think SaaS (software-as-a-service) products, cloud services like Azure, Amazon Web Services, and Google Cloud, and B2B online payment systems like PayPal and Amazon Pay.
A new paper from Yale SOM’s Soheil Ghili, coauthored with MIT’s Russ Yoon, presents a practical, straightforward model for determining whether a seller in such a setting should apply large-volume discounts.
In making this decision, the central aim, Ghili says, should be identifying which customers need a large number of units of your product, and which need just a few — and then determining whether the large-need or small-need customers are more price-sensitive. “This will have direct implications for optimal pricing strategy,” Ghili explains.
Ghili and Yoon simulated this decision-making process using a model that called for the type of data typically available in a B2B seller-customer scenario: the number of units the customer intended to buy— whether or not that purchase actually materialized.
These data points not only have the advantage of typically being available in B2B customer conversations, they also reveal whether customers fit into the “large needs” or “small needs” category, and can help reveal which category of customers is more price-sensitive. Ghili and Yoon measure this price-sensitivity by looking at deal success rates across customer segments. As a simplified example, if a seller running this calculation were to find that only 20% of large customers ultimately purchased, while 40% of small customers converted to a sale, then the large-customer set is more price-sensitive.
What’s the upshot of this realization? It reveals the optimal pricing strategy.
On one hand, say the larger customers is more price-sensitive, in this case Ghili recommends applying volume discounts: it’ll mean that large customers will be attracted to buy without having to make the concession of lowering the price for smaller customers that are willing to pay more.
On the other hand, it’s also conceivable that smaller customers are the group more sensitive to price, in which case Ghili recommends not applying volume discounts. Doing so, the seller would lose money discounting a product that larger, less-price-sensitive customers would have purchased anyway.
“When you think about the real world, it’s not clear, without rigorous work and data analysis, which of these two scenarios we’re in,” Ghili says. Maybe larger customers have more information about alternative products and prices across the market. It’s possible the smaller customer has a less compelling need for the product than larger customers do. This ambiguity on which segment is more price-sensitive is the reason why he recommends resolving this through designing a careful data analysis approach. At the core of this approach, he suggests, should be the comparison between the deal success rate for larger need customers and that for smaller ones.
Ghili emphasizes that often, formulating the right price strategy on volume discounts entails complex analytical modeling work that would go above and beyond just comparing the aforementioned deal success rates. The paper develops and applies such a model and showcases its ability to predict the right pricing strategy.
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