Artificial intelligence is rapidly transforming how retailers set prices, sparking public debate and drawing regulatory attention. The Federal Trade Commission’s recent announcement of an inquiry into “surveillance pricing”—the use of personal shopper data for individualized pricing—has intensified scrutiny of AI-driven pricing practices. This development follows efforts by major brands like Wendy’s and Walmart to implement real-time pricing technologies, raising questions about AI and consumer protection, privacy, and fair practices.
The Rise of AI in Retail Pricing
The adoption of AI in retail pricing is accelerating. A recent study by Coresight Research and Competera found that 92% of surveyed U.S. retailers currently use AI in their strategies, with 97% planning to increase investments in 2024. Of those leveraging AI-based solutions, 56% already use real-time pricing capabilities. According to one Gartner Market Guide: “By 2025, the top 10 global retailers by revenue will leverage contextualized real-time pricing…to manage and adjust in-store prices for customers.”
The overall market outlook for AI pricing solutions indicates continued growth, with Coresight estimating the market will reach $1.6 billion in 2024 and grow at a compound annual growth rate (CAGR) of 16.5% through 2028.
Rapid adoption of AI pricing underscores the urgency to understand its applications and develop frameworks for responsible implementation. Failing to do so may invite heavy-handed regulation, hindering innovation and compromising retailers’ ability to optimize prices for the benefit of businesses and consumers.
Understanding AI Pricing Strategies
Put simply, AI pricing uses algorithms to set optimal prices for products by analyzing vast amounts of data, identifying trends among the various factors impacting prices and basket composition. It then recommends prices aimed at achieving set performance metrics, such as balancing customer satisfaction, profitability and sales across product portfolios. AI solutions can also continuously adjust prices based on real-time market dynamics, allowing retailers to automate time-consuming tasks and focus on strategic decision-making.
AI pricing doesn’t require personal customer data to generate optimal prices — despite the FTC’s concerns about surveillance pricing. For example, “contextual pricing” is one method that can analyze market- and store-level trends without relying on individual consumer information, offering precise, location-specific recommendations by analyzing hyperlocal datasets across various factors such as seasonality, inflation, competitor pricing and basket composition.
To illustrate how this works, let’s consider the following example of a grocery chain utilizing a contextual strategy. Its pricing algorithm might detect that when peanut butter prices drop in one of its suburban stores, not only do sales of complementary items like jelly and bread increase, but so do purchases of seemingly unrelated products such as milk and pretzels. It might also factor in seasonality, noting that during the back-to-school season, peanut butter sales spike regardless of price and impact the overall basket value.
How AI Pricing Looks in Real World Applications
One grocery chain with over 300 locations demonstrated impressive results using AI pricing software during a 90-day pilot. The brand utilized a model that analyzed 20 factors to recommend prices aimed at increasing profits by driving higher sales volumes. The retailer accepted 98% of the repricing recommendations, resulting in an 8.49% increase in sales and a 3.63% rise in average basket value compared to the control group. Notably, the average shelf price remained stable as the algorithms rebalanced prices, increasing sales without resorting to across-the-board price hikes.
Another compelling example comes from a leading department store that implemented AI pricing across its physical and digital channels. In the test group of brick-and-mortar stores, the AI solution led to a 13% increase in new and existing customers while boosting basket-level revenue by 4%.
The impact was even more pronounced in the digital store, where sales surged 40.1% and the number of products sold rose 59.1%. The digital strategy also drove significant customer growth, with new customers increasing by 37%, reactivated customers by 58% and renewed customers by 68%.
The results in both cases demonstrate the potential benefits of AI-driven pricing for customers and retailers alike. This aligns with findings in the previously cited study, in which retailers using AI pricing reported enhanced customer loyalty, attributed to perceived pricing fairness and transparency.
Benefits of AI Pricing for Retailers and Consumers
For retailers, AI-driven strategies provide several benefits. They enable more competitive pricing and better-targeted promotions, enhance inventory management and reduce waste, lowering operational costs. During sudden market shifts, like during the COVID-19 pandemic, AI pricing allows retailers to swiftly adjust prices based on supply chain disruptions and changes in consumer demand, potentially mitigating price gouging and panic buying behaviors. Finally, it levels the playing field, allowing smaller retailers to implement sophisticated strategies once accessible only to big brands.
For consumers, AI pricing can lead to more stable, predictable and lower prices overall. As retailers adjust prices more efficiently across their products, consumers often benefit from more competitive pricing. Smarter promotional strategies can lead to better deals for shoppers. Moreover, AI pricing can help maintain product availability by optimizing inventory, reducing the likelihood of stockouts on popular items.
Balancing Personalization and Privacy
For businesses wanting to implement personalized pricing without running afoul of FTC policy, opt-in customer loyalty programs or exclusive membership clubs offer an ethical avenue. These programs allow customers to voluntarily share data in exchange for premium benefits such as personalized offers, curated product recommendations, and early access to new releases.
Key to an ethical implementation of these programs is transparent communication about data usage and strong assurances of customer control, including the option to opt out and delete personal data at any time. This approach balances the benefits of AI-driven personalization with a respect for consumer privacy.
Charting the Path Forward
While AI pricing offers significant benefits, we must acknowledge the legitimate concerns surrounding its implementation. The FTC’s inquiry into “surveillance pricing” highlights valid worries about consumer privacy and potential price discrimination. Poorly implemented AI could lead to unfair pricing practices, eroded consumer trust and reduced market competition. The complexity of these systems can make it difficult to identify and correct biases or errors. There’s also the potential for AI systems to enable collusion, harming competition.
To address these concerns and harness AI’s full potential, retailers should implement several key strategies:
Clearly communicate their use of AI in pricing decisions to consumers;
Provide regular reports on AI pricing’s impact on price levels and product availability;
Engage in ongoing dialogue with regulators to develop industry best practices;
Implement internal auditing processes to prevent discriminatory outcomes.
The FTC’s inquiry presents an opportunity to develop frameworks for responsible AI use in retail. As the technology evolves, retailers should prioritize transparency in their pricing methodologies, while regulators focus on safeguarding consumer privacy and fair competition.
The future of retail pricing is undoubtedly intertwined with AI. To mitigate risks and maximize benefits, retailers, tech companies, regulators and consumer advocates must work together. By prioritizing transparency and ethical implementation, we can create a pricing landscape that drives innovation, fosters fair competition and ultimately benefits consumers. AI pricing done thoughtfully has the potential to create a more efficient, responsive and equitable marketplace for all.
Aleksandr (Alex) Galkin is Co-founder and CEO of Competera, an AI-powered retail pricing software company. He is a former Deloitte consultant and seasoned entrepreneur with more than two decades of retail expertise and an extensive practical knowledge of machine learning technology. Combining his expertise developing enterprise applications for Fortune 500 companies with his deep industry knowledge, Galkin is now focused on delivering powerful AI-driven pricing technology, empowering global brands with applied AI. His vision: transforming retail with intelligent, customer-centric pricing.
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{URL}https://www.retailtouchpoints.com/features/executive-viewpoints/the-ai-pricing-debate-balancing-retail-innovation-and-consumer-trust{/URL}
{Author}Aleksandr Galkin, Competera{/Author}
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{Keywords}AI & Machine Learning,Data & Analytics,Data Security,Executive ViewPoints,Loyalty,Personalization,Pricing,Security,aleksandr galkin,competera,coresight research,ftc,Gartner,Walmart,Wendy’s{/Keywords}
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