Pricing and value creation in the age of AI: what marketers need to get right
In today’s rapidly evolving marketplace, setting prices that resonate with customers has never been more complex—or more important. In the age of AI, marketers and insights professionals now have unprecedented analytical power to better understand pricing sensitivity. But the challenge goes beyond precision: marketers must decide how dynamic and personalized pricing should be, while balancing the science of pricing with the art of value creation.
Understanding price sensitivity
For years, marketers have tried to understand the disconnect between what customers say about price and how they actually behave. Surveys and traditional research methods, such as the Van Westendorp Price Sensitivity Meter and the Gabor-Granger technique, can provide useful insight. More advanced approaches, such as conjoint analysis, can also help measure willingness to pay and assess trade-offs in more complex scenarios. However, these methods are still limited by hypothetical bias: stated preferences do not always align with real-world behaviour.
Adding to the complexity, customers may compare your product or service not only with direct competitors, but with entirely different categories. For example, price-sensitive diners may compare restaurant menu prices not just with other restaurants in the neighbourhood, but with lower-cost prepared or ready-to-cook meal options increasingly available on grocery store shelves.
Behavioural data analysis—such as transaction data and A/B testing—offers a more accurate view of true price sensitivity. Even so, data alone cannot always predict how consumers will behave until they are actively making purchase decisions. This underscores the importance of continuous testing and learning, rather than relying solely on static research.
The promise and risk of AI-driven pricing
The good news for marketers is that AI-powered research and analytics tools make it easier than ever to interpret evolving consumer behaviour and generate actionable insights quickly enough to keep pace with changing customer preferences. More importantly, marketers now have access to real-time and dynamic pricing capabilities, including forms of personalization that must be implemented carefully and transparently.
These tools can drive higher conversion rates and fuel profitable growth. While this opens up opportunities for precision and margin optimization, it also introduces new risks—particularly around transparency and brand trust.
Customers have become increasingly aware of pricing variability and may adjust their purchasing behaviour in response. In some categories, consumers accept fluctuating prices as normal; in others, they do not. When pricing changes go beyond what a brand can clearly and credibly explain, customers may feel taken advantage of. Recent backlash against “surge pricing” in industries from ride-share to hospitality illustrates the dangers of unpredictability and perceived unfairness.
Transparency, trust and ethical guardrails
Algorithmic pricing also brings legal and ethical questions to the forefront. Personalized pricing can quickly cross into “price discrimination” if not carefully managed. Regulators are paying close attention, and the reputational risks of eroding trust are significant.
However, as Marco Bertini and Oded Koenigsberg argue in “Dynamic Pricing Doesn’t Have to Alienate Your Customers,” the key is to use algorithmic or AI-driven pricing as a tool for customer centricity, not just margin maximization. Practical tips include:
- Transparency: Clearly communicate when and why prices change.
- Empowerment: Help customers find better value by guiding them to lower-priced periods or loyalty programs.
- Value framing: When prices rise, emphasize the added value or improved experience rather than focusing solely on the price increase.
Another important consideration is that AI doesn’t just automate pricing—it shapes the very narratives that appear in large language models (LLMs) and online search results. Marketers must ensure that their brand’s value proposition is accurately and positively reflected in these digital channels. AI pricing tools should operate within clearly defined brand and governance guardrails. That said, just because you can personalize or dynamically adjust prices doesn’t mean you always should. Prioritize fairness, transparency and explainability in all pricing strategies. Organizations should also assess whether pricing practices could produce unintended discriminatory outcomes or disproportionately disadvantage certain groups of consumers.
Why value creation still matters most
After focusing so much on pricing, marketers should ask a critical question: are we overemphasizing price at the expense of value?
Customers buy based on perceived value—which is shaped by a complex mix of functional, emotional and social attributes—not just price. Companies that deliberately add new forms of value to their offerings (even incrementally) tend to see higher loyalty and revenue growth.
The internal pricing conversation should shift from “How much can we charge?” to “What additional value are we providing that matters most to our customers?” Marketers should identify which elements of value their brand can credibly deliver, then communicate them clearly while consistently reinforcing their brand promise. The marketer’s role in pricing is evolving. In the AI age, the winners will be those who combine data-driven precision with a relentless focus on customer value and trust. By reframing the conversation around value—not just price—we can build stronger brands and more resilient customer relationships for the future.
For more information, check out this 2025 blog authored by the CMA’s Director, Public and Regulatory Affairs, which includes perspectives from the Deputy Commissioner, Deceptive Marketing Practices Directorate, Competition Bureau, on what marketers need to know about pricing claims and deceptive marketing practices to avoid putting businesses at risk.
