AI Makes Granular Pricing Easier, But Consumer Psychology May Make It Less Profitable

Research finds how consumers’ comparison across products can erode total profit from more differentiated pricing

BALTIMORE, April 15, 2026 – Big data, artificial intelligence and advanced pricing algorithms make it easier than ever for companies to fine-tune prices for individual products to closely reflect their unique value and cost. The conventional wisdom is straightforward: better data, better algorithms and sharper segmentation should produce better profits. But new research suggests that the most profitable answer isn’t always more fine-grained pricing across a product line. In fact, it is fewer, better-chosen price points.

The study, titled “Consumer-Driven Class Pricing,” is by Zuhui Xiao from the University of Wisconsin-Milwaukee. Class pricing is a surprisingly widespread feature of everyday markets: the practice of assigning a small number of price points to a much larger assortment of related products. Think of a bar menu with many draft beers but only three price points, or a supermarket aisle with hundreds of SKUs but a dozen distinct shelf prices. Similar patterns extend to fast-moving consumer goods, restaurants, toys, discount stores, convenience retail, budget travel, books and car rentals.

The rationale for class pricing is not just operational simplicity; it is consumer psychology. Consumers do not evaluate prices in isolation. Rather, they form price expectations across the products in front of them and compare what they pay with what they expected to pay for nearby alternatives. Paying more than expected is perceived as a psychological loss, while paying less than expected is perceived as a psychological gain.

Xiao finds that the key driver of class pricing is “loss aversion,” the well-established tendency for people to be more sensitive to perceived losses than to equivalent gains. In this context, consumers feel the pain of paying more than expected more intensely than they appreciate the pleasure of paying less than expected.

 “When firms introduce more granular pricing, it triggers consumers’ direct comparison of prices,” said Xiao. “Consumers perceive higher-priced items as losses relative to cheaper alternatives and tend to resent higher prices more than they reward lower ones. As a result, the price disadvantage of higher-priced items is psychologically amplified, making them look worse than the underlying price difference alone would suggest.”

Because of this amplified price disadvantage, even when higher-priced products carry greater prestige, better taste or higher quality, firms cannot fully translate that stronger appeal into sufficiently higher willingness to pay. At the same time, they must keep lower-priced products cheap enough to attract additional demand. The result is an asymmetry: firms give up more on the lower-priced products than they can recover on the higher-priced ones, reducing total profit.

“This asymmetry can reduce consumers’ total willingness to pay across the assortment and outweigh the benefits of differentiating prices based on cost or value,” added Xiao. “That is why adding more price points can actually backfire.”

As a result, expanding the number of price points may reduce total profitability. The findings challenge the assumption that more data and better algorithms should always lead to more precise pricing.

“Even with advanced technologies, firms should be cautious,” Xiao explained. “More pricing flexibility does not necessarily translate into higher profits. In many cases, simpler pricing structures are more effective.”

Read the full study here.

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