Dynamic Pricing Challenges

Delta Air Lines is piloting AI-driven dynamic pricing on a portion of its fares, with plans to expand the program substantially by year's end. Framed as a modernization of pricing strategy, this shift warrants a deeper examination of how algorithmic systems are shaping access and at what cost. Dynamic pricing is often described as demand responsive. But in execution, it frequently introduces volatility that obscures fairness. Similar approaches in retail have led to disproportionate price increases in lower income communities, raising concern that these systems are less responsive to human need than to data correlations detached from context. Several issues demand scrutiny: - Bias and disparity: Pricing algorithms can reproduce regional, racial, and economic inequities, particularly when data reflects underlying structural imbalances. - Loss of predictability: Consumers face fluctuating costs without the tools to understand or anticipate those changes, making budgeting and planning increasingly difficult. - Opaque logic: There is little transparency around how these models are developed, what inputs are prioritized, or what safeguards exist to ensure equitable outcomes. Delta reports that early results are "amazingly favorable." Without clarity on who benefits, how outcomes are defined, or which metrics are being used, these claims raise more questions than they resolve. This initiative signals a broader transformation in how corporations deploy AI across consumer-facing systems. These models are increasingly designed to maximize extraction without transparency or accountability. The consequences are rarely confined to the checkout screen. They affect who has access, who carries the burden, and who is excluded from the benefits of technological progress. This also applies to healthcare. As AI becomes more embedded in clinical decision making, triage, and resource allocation, the same concerns apply. We acknowledge that a lot of policies and stands in the field have been adopted from aviation. Hello, Universal Protocol! An algorithm that controls pricing today could soon influence how risk is scored or how treatment urgency is determined. Without safeguards, these systems risk distorting clinical judgment and widening disparities in care. What begins in commerce often finds its way into health systems, especially when the underlying logic is left unchallenged. In addition to technical efficiency and "optimization," we need governance frameworks that prioritize equity, transparency, and accountability across every domain touched by AI. "It's not about what it is, it's about what it can become."- Dr. Seuss #healthcareonlinkedin #aiethics #consumerrights #aiinaviation

Most conventional cost-effectiveness models in pharmacy assume drug prices never change – they are static. That’s a problem. In the real world, prices fall dramatically after patent loss. --- New #research from Center for the Evaluation of Value and Risk in Health (CEVR) and National Pharmaceutical Council shows just how far off conventional cost-effectiveness analyses (CEAs) can be when they ignore real-world #DrugPricing dynamics. The study compared static CEAs to dynamic models that incorporated drug price changes over time, which is particularly crucial to account for price changes after loss of exclusivity. --- The result: static CEAs consistently overstated #DrugCosts, skewing the cost-effectiveness ratio by 27% to 82%, depending on the treatment type. Key takeaways: -Chronically administered treatments are most affected. Their price drops after losing exclusivity were the single biggest factor in shifting cost-effectiveness. -One-time treatments are less sensitive to price changes, but they are highly influenced by baseline patient age and discount rates. -Dynamic models offer a more realistic view of opportunity costs and better reflect the #pharmacoeconomics of patented drugs over time. --- Why does this matter? Conventional CEAs may currently be penalizing certain #pharmacy therapies, especially those treating chronic conditions, by ignoring the competitive market forces that eventually drive prices down. This may distort resource allocation, payer negotiations, and long-term pricing strategy. Models should reflect how prices behave in the real world, and not assume a fixed price indefinitely. --- How are you accounting for future price shifts in your trends and projections? Most drug prices act pretty similarly after patent loss. It may be worth creating some patent loss assumptions to incorporate into your models if you aren't already.

One of the least-discussed challenges in AI adoption today is pricing. Everyone talks about model performance, benchmarks, or features. But for enterprises, the real sticking point often shows up when the bill discussion starts. The problem: current pricing models don’t align with how enterprises budget and buy. Usage-based pricing makes perfect sense for vendors, but it feels like a blank cheque for buyers. If adoption succeeds, the bill grows in unpredictable ways. No CFO wants to be surprised by a doubling in costs because usage spiked. Flat subscriptions feel safer for buyers, but they put vendors at risk. The underlying compute costs fluctuate, and a heavy customer can easily push margins underwater. Hybrid models try to balance the two, to put in predictability for buyers’ forecast, and vendors try to to defend and improve profitability. This mismatch slows progress. Solution: a new generation of pricing models. Simple enough to understand, predictable enough to budget for, but still sustainable for vendors. It could also mean having periodic reviews instead of fixed term pricing for multi year deals. That could mean outcome-based contracts, tiered usage bands with hard caps, or bundled services that absorb variability in spikes. Until AI economics are solved, adoption will remain slower than the technology itself.

Dynamic pricing can be a favorite strategy for data scientists in retail, but if the product manager and data scientist don't handle it well, it can turn into a PR disaster. Consumers will always feel cheated if two devices in the same location show different prices. To make dynamic pricing work, be transparent with your customers. For example, tell them, "Since you've ordered from us 1,000 times, you get an extra 5% off!" This essentially creates a VIP program. But sneakily showing different prices on devices connected to the same Wi-Fi? That's a big no-no. The best approach to dynamic pricing is regional, geo-based price variation. This method is fairer. All you need to ensure is that no two devices together should have different pricing. Uber is the smartest at this, with the best dynamic pricing algorithm that takes into account consumer ratings and historical bookings. From Day 1, they have been transparent about showing dynamic pricing, so when two friends book a cab together, both check the pricing on their devices. Giants like DoorDash and Instacart use geo-based pricing and test its success with a difference-in-difference technique. By comparing two similar regions at the same distance from the pickup store but with different pricing, they can figure out if the rollout will be a hit or a miss. Remember, folks, dynamic pricing is like seasoning your food—too much or too little can ruin the dish. Be smart, be transparent, and most importantly, don’t make your customers feel cheated.

Pricing isn’t just about supply and demand. Coca-Cola learned that the hard way. Decades ago, they experimented with dynamic pricing in vending machines that changed based on the outside temperature👇👇 Higher temp 🌡️ ➡️ Higher Demand 🙏 ➡️ Higher Price 💰 𝗟𝗼𝗴𝗶𝗰𝗮𝗹? Yup. 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲? Nope. Users felt it was exploitative. But wait a second, what about Uber and Lyft? They use dynamic pricing every day, and users accept it—even expect it. 🤯 So what's the difference? Ride-sharing services offer varied experiences each time—different distances, car types, and ride conditions. Dynamic pricing feels justified because it reflects this variability. On the contrary, Coca-Cola’s product is the same everywhere, every time. Variable pricing for a standardized product feels unfair to users. So when forming your pricing model consider: 1️⃣ 𝗧𝗵𝗲 𝗡𝗮𝘁𝘂𝗿𝗲 𝗼𝗳 𝗬𝗼𝘂𝗿 𝗣𝗿𝗼𝗱𝘂𝗰𝘁: Is it standardized or variable? 2️⃣ 𝗨𝘀𝗲𝗿 𝗣𝗲𝗿𝗰𝗲𝗽𝘁𝗶𝗼𝗻: Will users see dynamic pricing as fair or exploitative? 3️⃣ 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝗰𝘆: Clearly communicate why prices fluctuate. Have more tips? share them in the comments below! #pricing #pricingmodel #CX #userbehavior

 Learning from McDonald's: Strategic Insights for Pricing Professionals 🍔📉 In a surprising turn, McDonald’s has reported its first global sales slump since 2020 (details in the comment 🔗). This decline, driven by inflation-weary consumers and increased competition, offers critical lessons for pricing professionals and C-level managers. Here’s what we can learn: 1. Understand Your Value Proposition 💡 McDonald’s has struggled to maintain its value perception, as rising costs forced price hikes. When your value leadership shrinks, as McDonald's CEO Chris Kempczinski noted, customers look elsewhere. Ensure your pricing strategy continuously reflects your value proposition, adjusting to both market conditions and consumer perceptions. 2. Coordinated Marketing and Promotions 🎯 While competitors like Burger King and The Wendy's Company swiftly rolled out attractive value deals, McDonald’s lagged, playing catch-up. Coordination across franchises and a unified marketing approach are vital. Implement promotions that are timely, well-communicated, and consistent across all locations to reinforce value. 3. Monitor Competitor and Consumer Behavior 🔍 McDonald’s found itself defending against not just other fast-food chains but also grocery stores offering better value. Regularly analyze where your customers are spending and why. This insight can guide proactive adjustments to pricing and product offerings to stay competitive. 4. Flexibility and Responsiveness 🚀 Economic conditions and consumer preferences are fluid. McDonald’s current $5 meal deal is a step in the right direction but came late. Develop a dynamic pricing strategy that allows for rapid response to market changes, ensuring you can implement necessary adjustments swiftly. Over the past few years, McDonald’s has been hailed as a pricing mastermind, consistently raising prices while seeing sales soar. This time, the challenge is different. However, having seen firsthand how McDonald's navigates complex market conditions and pricing challenges, I’m confident they will be the first to take the most appropriate action to turn the situation around💪💪💪 Actionable Takeaways: ℹ️ Reassess and Align Value Perceptions: Ensure your pricing reflects the value your customers perceive, and adjust marketing messages accordingly. ℹ️ Streamline Promotions: Implement cohesive and timely promotions that reinforce value across all customer touchpoints. ℹ️ Stay Informed: Regularly monitor competitor actions and consumer spending trends to stay ahead of shifts in the market. ℹ️ Be Agile: Maintain flexibility in your pricing strategy to quickly adapt to economic changes and consumer behavior. Remember, every challenge is an opportunity to learn and grow. How do you think businesses can better align their pricing strategies with consumer expectations? Share your thoughts and experiences in the comments below! 💬👇

Back to the grid…. Most people who use electricity don’t fully appreciate where it comes from or how the cost is calculated for them as a consumer. Energy spot price auctions are a mechanism used in electricity markets to price electricity according to real-time, market based supply and demand conditions at a particular moment. The goal of spot price auctions is to create a competitive market for electricity and facilitate efficient allocation of resources. However, the volatility in spot prices has created significant challenges for all stakeholders. The symptoms include price volatility and instability in electricity markets. To enhance grid reliability and stability, alternative approaches must be considered. While spot pricing may seem like a straightforward way to determine prices, it fails to account for the unique dynamics of the power grid and the long-term investments required to maintain a reliable and resilient energy ecosystem. The primary issue with spot price auctions is that they treat electricity as a commodity, subject to the whims of the market. Dramatic price fluctuations have severe consequences for both consumers and energy providers. When prices spike during periods of high demand or supply disruptions, it imposes enormous financial burdens on households and businesses, threatening economic stability. From the perspective of energy suppliers, the unpredictability of spot prices makes it challenging to plan for long-term capital investments in new generation capacity, transmission infrastructure, and grid modernization. These investments are crucial for ensuring the continued reliability and sustainability of the power grid, but they require a level of price certainty that spot markets simply cannot provide. It is time for policymakers and industry leaders to explore alternative pricing mechanisms that prioritize stability and long-term planning. One alternative is the use of capacity markets, where energy providers are compensated not only for the #electricity they generate but also for the availability of their generation assets. This model would provide a more reliable revenue stream for suppliers, enabling them to make the necessary investments in the grid's future. Another alternative is the implementation of forward contracts and hedging strategies. By locking in prices for electricity over longer time horizons, these mechanisms can help smooth out price volatility and provide the predictability that energy providers and consumers require. Transitioning to a more resilient energy system will not be easy, but it is a necessary step to ensure the long-term prosperity and security of our communities. By moving beyond the limitations of #energy spot price auctions, we can build a power grid that is truly fit for the future. The #grid of the future should reward a diverse portfolio of generation sources. It is time to move past demonizing reasonable energy sources even if they don’t fit certain ideologies.

From Airline Seats to AI Tokens: The Revenue Management & Dynamic Pricing principles remain timeless. The “capacity” may have shifted from aircraft seats to GPU cycles, but the core challenge is unchanged: price intelligently, or risk leaving value on the table. Pricing is never just about revenue extraction. It’s about aligning value delivered, costs incurred, and demand patterns — whether for an airline seat, a hotel room, or an AI token. In streaming, flat subscriptions work because marginal costs per user are almost zero. In AI, however, serving each request consumes expensive GPU cycles and electricity. Here, token-based pricing aligns usage with cost: ~$5 per million input tokens, ~$15 per million output tokens. Why this matters: Fairness: Heavy users pay proportionally more. Cost recovery: Output-heavy tasks (costlier to run) are priced higher than simple inputs. Efficiency: Firms are incentivized to optimize prompts, reduce waste, and choose model tiers wisely. Looking ahead, hybrid models will likely dominate — much like telecom plans: a base subscription plus overage fee. This balances customer predictability with provider sustainability. Indian Institute of Management, Lucknow

I don’t usually comment on competition or antitrust law anymore (since my days at the CJEU), but this paper by Maxime Cohen caught my attention. It doesn’t argue that competition law itself has already been reshaped. Rather, it shows how AI-driven pricing is reshaping pricing conduct — and amplifying risks in ways existing doctrine wasn’t built to detect. We’ve moved well beyond a world where collusion is about cartel communications. Pricing is now dynamic, adaptive, and often delegated to learning systems. Coordination can emerge without any human agreement — yet most detection rules still assume that older paradigm. Another tension: when prices change constantly and differ across users, it becomes genuinely hard to say what counts as a “spike,” “gouging,” or even parallel conduct. The most interesting part, to me, is the GenAI angle. Generative AI dramatically lowers the barrier to pricing experimentation. Non-technical teams can now interact with pricing logic through prompts and scenarios, at scale. That doesn’t imply misconduct — but it amplifies risk by increasing speed, diffusion of responsibility, and opacity. In other words, teams may drift into problematic outcomes without intent, simply because underlying models learn to align or react to one another. That’s a serious challenge for competition authorities. The paper’s quieter implication is that antitrust may need to shift focus: less on outcomes alone, more on processes — data inputs, model behavior, prompts, and guardrails. Curious how others see this. Are competition rules adapting quickly enough to algorithmic and GenAI-driven pricing — or are we still applying 20th-century concepts to 21st-century systems? Tagging Aymeric de Moncuit and Daniel Francis, who think about this far more than I do. https://lnkd.in/gK8htW3R

When Delta’s CEO confirmed they’re exploring AI-powered personalized pricing, the backlash was swift. And familiar. Remember when Wendy’s floated dynamic pricing last year? Same story. I ran a study with a nationally representative sample of 284 consumers comparing personalized airline pricing to traditional “rate-fenced” pricing. The results were clear—and a bit brutal. Consumers said they trusted the company less, felt the pricing was less fair, and sensed they had little control over what they were being charged. Not surprisingly, they were also less likely to say they’d book again. One respondent put it this way: “At least with regular airfare, I know the rules. Here, I’m just stuck with whatever they feed me.” It wasn’t so much the price that bothered them—it was the sense that they’d been cut out of the conversation. #RevenueManagement #PricingStrategy #AirlineIndustry #Hospitality #CustomerExperience