Algorithmic pricing has quietly become a feature of everyday life, shaping the costs of everything from Uber rides to Amazon purchases. Anyone who has rushed to book a car to the airport on a Friday, only to discover the fare had jumped sharply, or who has checked the same product online twice in a day and found the price altered, has experienced its effects first-hand. At its core, algorithmic pricing uses automated systems to adjust the cost of goods and services in response to variables such as customer demand, stock availability, competitor activity, and even individual consumer data. This can make pricing appear responsive and efficient, but it also introduces risks that businesses must navigate carefully.

While the financial appeal of such systems is clear, boosting profits by squeezing out additional margins, there are pitfalls if implementation is insensitive or opaque. History offers some striking examples: Uber drew widespread condemnation in 2012 for sharply raising prices during Hurricane Sandy, and more recently, ticket platforms have faced consumer fury over surge pricing for popular concerts. Gizem Yalcin Williams, assistant professor of marketing at Texas McCombs, highlights that the backlash is often rooted not just in the prices themselves but in perceptions of fairness. A sudden shift in cost can leave customers feeling cheated, even when the product or service remains unchanged, undermining the trust on which long-term relationships depend.

Williams highlights the psychological aspect of these price shifts. If a shopper sees the price of an item drop after purchase, resentment may arise from the sense of having overpaid, regardless of how satisfactory the product proves to be. Conversely, encountering a higher price later may trigger satisfaction, as buyers believe they acted wisely and secured a bargain. These perceptions reveal how deeply emotions, rather than cold calculations, colour responses to dynamic pricing. The stakes are high because in the age of social media, a sense of unfairness can spread rapidly, damaging a brand’s reputation far beyond a single customer interaction.

The personalisation of pricing intensifies these issues. Algorithms no longer merely reflect supply, demand, or production costs, but increasingly integrate customer-level data drawn from demographics, location, or browsing history. Although the exact formula remains opaque, shoppers are acutely aware of the possibility that their personal information may have influenced the outcome. Two customers paying different amounts for the same product can feel that the system has unfairly singled them out, even if the underlying rationale was commercially sound. This perception of unequal treatment creates new marketing challenges, eroding loyalty and heightening scepticism about corporate motives.

There are also broader implications beyond consumer sentiment.
Regulators and legislators are increasingly paying closer attention to dynamic pricing practices. In the United States, for example, grocery chain Kroger faced questions in Congress over its exploration of algorithmic pricing in stores, a reminder that legal scrutiny may follow if companies misjudge the acceptability of these tactics. Williams and her co-authors, drawing on surveys of pricing managers and interviews with experts, caution that companies must establish clear guardrails to ensure effective pricing strategies. Transparency, oversight, and sensitivity to consumer tolerance are not optional extras but essential strategies for avoiding reputational and legal consequences.

Ultimately, the research emphasises that algorithmic pricing is not a simple plug-and-play tool. Many firms adopt artificial intelligence in the hope of boosting efficiency or cutting costs, but often fail to prepare for the challenges of design, integration, and monitoring. Williams emphasises the importance of deliberate and strategic adoption. Even in contexts where automation drives decisions, human judgment and oversight remain vital. Companies must strike a balance: using the power of algorithms to stay competitive while ensuring that fairness, transparency, and customer trust are not casualties of efficiency. As this field evolves, the real test for businesses will be less about technological capability and more about maintaining credibility in the eyes of the very consumers they seek to serve.

More information: Gizem Yalcin Williams et al, Algorithmic pricing: Implications for marketing strategy and regulation, International Journal of Research in Marketing. DOI: 10.1016/j.ijresmar.2025.05.001

Journal information: International Journal of Research in Marketing Provided by University of Texas at Austin