Advances in big data, artificial intelligence, and sophisticated pricing algorithms have made it easier than ever for firms to fine-tune prices at a highly detailed level, aligning closely with the specific costs and perceived value of individual products. The dominant assumption has been straightforward: better data, more powerful algorithms, and sharper segmentation should naturally translate into stronger profits. However, new research suggests that increasingly granular pricing is not always the optimal approach. In many cases, firms may achieve better financial outcomes by limiting the number of price points rather than expanding them.

This idea is explored in the study “Consumer-Driven Class Pricing” by Zuhui Xiao from the University of Wisconsin-Milwaukee. The research focuses on class pricing, a common yet often overlooked strategy in everyday markets. Class pricing involves assigning a relatively small set of prices to a much larger assortment of related products. For example, a bar may offer a wide selection of draft beers but only a few pricing tiers, while a supermarket might stock hundreds of products but display a limited number of shelf prices. Similar patterns appear across industries, including fast-moving consumer goods, restaurants, discount retail, convenience stores, travel, books, and car rentals.

The logic behind class pricing extends beyond convenience or operational simplicity. It is rooted in how consumers interpret and evaluate prices. Rather than assessing each price independently, shoppers tend to form expectations based on the range of products presented to them. They compare prices across similar items and judge whether what they are paying feels fair relative to nearby alternatives. In this context, pricing becomes a comparative experience shaped by expectations rather than a purely objective calculation.

A central concept driving this behaviour is loss aversion. Consumers are generally more sensitive to perceived losses than to equivalent gains. This means that paying more than expected creates a stronger negative reaction than the positive feeling generated by paying less than expected. As a result, when firms introduce more finely differentiated pricing, they may inadvertently intensify these comparisons and heighten negative perceptions, particularly for higher-priced items.

“When firms introduce more granular pricing, it triggers consumers’ direct comparison of prices,” Xiao explains. “Consumers perceive higher-priced items as losses relative to cheaper alternatives and tend to resent higher prices more than they reward lower ones.” This dynamic amplifies the perceived disadvantage of premium products, making them seem less appealing than their underlying value would suggest. Even when these products offer higher quality, better features, or greater prestige, the psychological impact of price comparisons can undermine their attractiveness.

Because of this imbalance, firms often face a difficult trade-off. They may be unable to raise prices on premium products enough to reflect their full value, while still needing to keep lower-priced options sufficiently inexpensive to attract demand. This asymmetry reduces overall profitability, as firms give up more on the lower end than they gain on the higher end. The findings challenge the assumption that more pricing precision is always beneficial. Even with advanced technologies, simpler and more carefully structured pricing systems may ultimately deliver stronger results.

More information: Zuhui Xiao et al, Consumer-Driven Class Pricing, Marketing Science. DOI: 10.1287/mksc.2023.0133

Journal information: Marketing Science Provided by Institute for Operations Research and the Management Sciences